"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
- "coverage.write_agg_area_cat()\n",
- "\n",
- "#Saved as cov_aggregate_health_tracts_comm_cat.csv"
+ "coverage.plot_choropleth(column = \"percap_spending\",\n",
+ " shapefile='/Users/whlu/spatial_access/data/chicago_boundaries/chi_comm_boundaries.shp',\n",
+ " title = 'Per Capita Spending (USD), Chicago Community Areas',\n",
+ " include_destinations = False,\n",
+ " filename = '/Users/whlu/spatial_access/data/coverage score/coverage_choropleth.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "See all results in chicago_boundaries.shp"
- ]
+ "source": []
},
{
"cell_type": "code",
diff --git a/docs/notebooks/notes.ipynb b/docs/notebooks/0_master.ipynb
similarity index 75%
rename from docs/notebooks/notes.ipynb
rename to docs/notebooks/0_master.ipynb
index c50a48b..c83eb26 100755
--- a/docs/notebooks/notes.ipynb
+++ b/docs/notebooks/0_master.ipynb
@@ -5,14 +5,12 @@
"metadata": {},
"source": [
"# Estimating Travel Time Distance Matrices, Access, and Coverage Metrics \n",
- "## Chicago's Health Facilities \n",
"\n",
" \n",
- "Irene Farah \n",
- "irenef@uchicago.edu \n",
+ "[Irene Farah](https://www.linkedin.com/in/imfarah/), [Richard Lu](https://www.linkedin.com/in/richard-lu-576874155/), [Caitlyn Tien ](https://www.linkedin.com/in/caitlyn-tien-0b784b161/) \n",
"Center for Spatial Data Science \n",
"University of Chicago \n",
- "July 31, 2018 \n",
+ "July 30, 2019\n",
"
"
]
},
@@ -21,7 +19,7 @@
"metadata": {},
"source": [
"---\n",
- "\n",
+ "This notebook describes the reasoning of the Python modules p2p, ScoreModel, and CommunityAnalytics which compose the calculation of the algorithm of travel time **matrices** and the estimation of the **metrics**. Please note that this report stipulates the **_parameters_** specified within the estimations so the users can reproduce the calculations.\n",
"\n",
"The purpose of this report is threefold: present the comprehensive methodology for efficiently estimating travel time network distances, generating useful metrics, and illustrating its usage. Specifically, we create an open-source backend infrastructure for estimating travel time network distances (walking and driving), providing the framework for calculating **access** and **coverage** metrics. These two metrics address both the demand and supply of services and goods. On the one hand, the access score focuses on the attributes of the **origins** while on the other, the coverage score focuses on the attributes from the **destinations** of interest. Moreover, this framework allows subsetting of the data according to the user's specific questions of interest. These analyses create a powerful tool for supporting stakeholders and researchers in improving transparent decision-making processes and in understanding the issues of unequal spatial access.\n",
"\n",
@@ -39,10 +37,6 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "\n",
- " \n",
- " \n",
- " \n",
"Note: \n",
"More seasoned users can edit the Python scripts directly. \n",
"The package is written in Python 3.6, C++ 11, and Cython by [Logan Noel](https://www.linkedin.com/in/lmnoel/). (Minimum Python version 3.5) \n",
@@ -69,7 +63,7 @@
"Specifically, there are two routes the user can take for getting the travel distances: Creating **asymmetric** or **symmetric** matrices. Symmetric matrices are estimated origin to origin, while asymmetric matrices calculate origin to destination. The user can generate a symmetric distance travel matrix and snap the points of interest to the matrix or create an asymmetric distance matrix that already incorporates origin and destination. The symmetric approach is more appropriate when you need to calculate several scores (across years or different types of scores) for the same area and at the same level of analysis. \n",
"\n",
"**Metrics** \n",
- "After obtaining the distance travel times from origins to destinations (in this case, from the centroids of tracts to the health facilities), the user can calculate the **access score** from each tract areal unit to health facilities, the time to **closest destinations** (nearest neighbor), the **count of nearby destinations** within a thirty minute buffer, and the destination's **coverage score**. \n"
+ "After obtaining the distance travel times from origins to destinations (in this case, from the centroids of tracts to the health facilities), the user can calculate the **access score** from each tract areal unit to health facilities, the time to **closest destinations** (nearest neighbor), the **count of nearby destinations** within a buffer, the **sum of an attribute** of nearby destinations (wthin a community area), the destination's **coverage score** (per capita spending within a buffer), and the amount of per capita spending each tract areal unit has access to using a **Two Stage Floating Catchment Area** method.\n"
]
},
{
@@ -83,14 +77,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- " **(PENDING)** (Show table in which the calibration was tested for different cities, suggesting values for each.) It can be specified within the **p2p_parameters.json file**. \n",
+ "* Also for walking and driving, a **node penalty** of X seconds can also be specified by the user for the number of intersections within the area of analysis. The logic is that having more intersections will increase the travel time due to crossings. However, by doing a time travel calibration between the p2p algorithm and GoogleMaps, there was no need for adding penalities for the city of Chicago. The calibration can be assessed for different cities by using a random set of latitude and longitude points and comparing both estimated travel times for p2p (using the default average speeds and speed limits) and the GoogleMaps API. **(PENDING)** (Show table in which the calibration was tested for different cities, suggesting values for each.) It can be specified within the **Configs.py** file.\n",
" \n",
"* The network is **directed**, meaning that one way streets are respected and A->B and B->A can have different edge traversal speeds. \n",
"\n",
- "* **Threshold constants**: the walk or drive average speed can be specified within the **p2p_parameters.json file**. \n",
+ "* **Threshold constants**: the walk or drive average speed can be specified within the **Configs.py** file.\n",
"\n",
"* **Epsilon**: Controls how large to make the network bounding box beyond your dataset. Larger epsilons result in longer computation times, but smaller epsilons result in slightly reduced accuracy at the very edges of the bounding box, especially for driving networks. The default is currently set at 0.05, which seems to balance the two reasonably well. (+/-) 0.02 will result in a large increase/decrease in computation time and accuracy. If too many values are defined as -1, it means that the epsilon is too small. Refer to the epsilon calibration to assess if this value must change and the matrix contains too many -1. The value of -1 is hardcoded in the tmat.h file and is considered as an NaN value of the origins when estimating the metrics. \n",
"\n",
@@ -197,9 +191,9 @@
"source": [
"**_Specifications_** \n",
"\n",
- "In the **demo prompt**, the user can specify: **network type, epsilon, primary input data (origin file), secondary input data (destination file if running an asymmetric matrix), and if the user wants to save the matrix to a csv file.** \n",
+ "In the **demo prompt**, the user can specify: **network type, primary input data (origin file), secondary input data (destination file if running an asymmetric matrix), and if the user wants to save the matrix to a csv file.** \n",
"\n",
- "In the **scripts**, the user can specify: **Average speeds, last mile corrections, node penalty, threshold constants, and output results** (meters instead of seconds - lines 580 - 599 of p2p.py). \n",
+ "In the **Configs**, the user can specify: **Average speeds, last mile corrections, node penalty, epsilon, threshold constants, and output results** (meters instead of seconds - lines 580 - 599 of p2p.py). \n",
"\n",
"In order to construct the matrices, the input table should contain **ID, latitude, longitude** for the origins and destinations.\n"
]
@@ -220,7 +214,7 @@
"(Disregard this section if you only care about the travel-time matrix.)\n",
"\n",
"**Scripts** \n",
- "CommunityAnalytics.py creates the classes: **AccessModel** and **CoverageModel**. As mentioned above, the access score, time to closest destination, and count of nearby destinations metrics are attributes of the origins while the coverage score is an attribute of the destinations in a catchment area. The access, coverage, and count of nearby destionation metrics are all estimated within a travel time network distance buffer. \n"
+ "Models.py creates the classes: **AccessModel** and **Coverage**. As mentioned above, the access score, time to closest destination, sum of nearby attributes, TSFCA (access to per capita spending), and count of nearby destinations metrics are attributes of the origins while the coverage score is an attribute of the destinations in a catchment area. The access to faciilities, acccess to per capita spending, coverage, and count of nearby destionation metrics are all estimated within a travel time network distance buffer. \n"
]
},
{
@@ -300,22 +294,20 @@
"In the **demo prompt**, the user can specify parameters in two different commands: \n",
"\n",
"**name.AccessModel( ):** \n",
- "- network_type ('drive' or 'walk')\n",
- "- source_filename (primary input data) \n",
- "- dest_filename (secondary input data) \n",
- "- sp_matrix_filename (origin-destination travel time matrix) \n",
- "- decay_function ('linear', 'root', 'logit') \n",
- "- limit_categories (only estimate model for specific categories)\n",
- "\n",
+ "- network_type ('walk', 'bike', 'drive', 'otp') \n",
+ "- sources_filename (primary input data)\n",
+ "- destinations_filename (secondary input data)\n",
+ "- source_column_names (dictionary that map column names to expected values)\n",
+ "- dest_column_names (dictionary that map column names to expected values)\n",
+ "- transit_matrix_filename (sources-destination travel time matrix)\n",
+ "- decay_function ('linear', 'root', 'logit', default is 'linear')\n",
"**name.calculate():**\n",
- "- custom_weight_dict (Specifies the weights of each facility/establishment defined as a previously generated dictionary. If custom_weight_dict=None, the dictionary will contain [1,1,1,1,1,1,1,1,1,1] weights.)\n",
- "- largest_weights_first=True sorts the weight array such that the largest values will be used first. If False, it will do the opposite.\n",
- "- limit_categories (None is defined as default, but user can delimit on particular categories of interest to calculate the access score.) \n",
- "- upper (is the time (in minutes) in which the origin and destinations are considered to be out of range of each other. As a default, if the travel time distance exceeds 30 minutes, the score will be zero.)\n",
- "- custom_threshold=40 (change binning threshold specifying it in calculate() function. Example: model_name.calculate(custom_threshold=40) for changing the percent of population above a certain threshold.)\n",
- "- normalize (Default is True and shows both normalized and non-normalized values. False shows only non-normalized results.) . \n",
+ "- upper_threshold (maximum time considered out of range in seconds, default is 30 minutes, when out of range, score will be zero)\n",
+ "- category_weight_dict (specifies the weights of each facility/establishment defined as dictionary, default dictionary will contain [1,1,1,1,1,1,1,1,1,1] weights.)\n",
+ "- normalize (accepts boolean, default is False and shows only non-normalized results, true shows normalized values.)\n",
+ "- normalize_type ('z_score' or 'minmax', default is 'minmax')\n",
"\n",
- "In the **scripts**, the user can specify: The default weights set before as [1,1,1,1,1,1,1,1,1,1] weights, different standarizations, and other modifications to the access score.\n",
+ "In the **Models**, the user can specify: The default weights set before as [1,1,1,1,1,1,1,1,1,1] weights, different standarizations, and other modifications to the access score.\n",
"\n",
"GO TO [ACCESS SCORE DEMO](./access_score.ipynb) "
]
@@ -357,21 +349,20 @@
"source": [
"---\n",
"**Destination-based** \n",
- "The **Coverage** model generates a coverage access which defines the reach of services by a specific targeted population. The score focuses on the coverage of the destination, scrutinizing how many people are within a catchment area. Specifically, the per capita spending score is the ratio of the coverage score by population. Specifically, it takes the magnitude of the facility/establishment and divides it by the total population it serves within a bufffer (in this case, 30 minutes). In the specifications, the magnitude of the destination is denominated as target. \n",
+ "The **Coverage** model generates a coverage access which shows the per capita spending available to a specific targeted population. The model focuses on the coverage of the destination, scrutinizing how many people are within a catchment area. Specifically, it takes the total spending of the facility/establishment and divides it by the total population it serves within a buffer (in this case, 30 minutes). In the specifications, the magnitude of the destination is denominated as target. \n",
"\n",
"**_Specifications_** \n",
"\n",
- "**name.CoverageModel( ):** \n",
- "- network_type ('drive' or 'walk')\n",
- "- source_filename (primary input data) \n",
- "- dest_filename (secondary input data) \n",
- "- upper (is the time (in minutes) in which the origin and destinations are considered to be out of range of each other. As a default, if the travel time distance exceeds 30 minutes, the score will be zero.)\n",
- "- The prompt will ask for the target which should be specified in the destination file, according to the user's needs. \n",
+ "**name.Coverage( ):** \n",
+ "- network_type ('walk', 'bike', 'drive', or 'otp')\n",
+ "- sources_filename (primary input data)\n",
+ "- destinations_filename (secondary input data)\n",
+ "- transit_matrix_filename (origin-destination transit matrix)\n",
" \n",
"**name.calculate():**\n",
- "- Just processes the model specified above. \n",
+ "- upper_threshold (the time (in seconds) in which the origin and destinations are considered to be out of range of each other)\n",
"\n",
- "In the **scripts**, the user can specify further changed to the coverage score.\n",
+ "In the **Models**, the user can specify further changed to the coverage score.\n",
"\n",
"GO TO [COVERAGE SCORE DEMO](./coverage_score.ipynb) "
]
diff --git a/docs/notebooks/0_reqs_install.ipynb b/docs/notebooks/0_reqs_install.ipynb
new file mode 100644
index 0000000..83a176d
--- /dev/null
+++ b/docs/notebooks/0_reqs_install.ipynb
@@ -0,0 +1,200 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installation Setup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "--- \n",
+ "The package is written in Python 3.6, C++ 11 and Cython by [Logan Noel](https://www.linkedin.com/in/lmnoel/). (Minimum Python version 3.5) \n",
+ "Currently, the only supported operating systems are MacOS and Ubuntu (if you don't have either, a guide for installing Ubuntu 16.04 LTS is in README.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Note: More seasoned users can download installation requirements directly on the terminal.**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**For MacOS:**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install Python3\n",
+ "! brew install python3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install homebrew\n",
+ "! /usr/bin/ruby -e \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install pip3\n",
+ "! brew install pip3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install jupyter, jupyterlab, and jupyter hub\n",
+ "! brew install jupyter\n",
+ "! brew install jupyterlab"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Clone the repository:\n",
+ "! git clone https://github.com/jupyterhub/jupyterhub"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install spatial index package\n",
+ "! brew install spatialindex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install spatial access package\n",
+ "! pip3 install spatial_access"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install scipy package\n",
+ "! brew install scipy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install geopy package\n",
+ "! pip install geopy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install geopandas package\n",
+ "! conda install geopandas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run setup.py to install all the packages\n",
+ "! sudo python setup.py install"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**For Ubuntu:**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "! sudo apt-get install libspatialindex-dev"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "! sudo apt-get install python-tk"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "! pip3 install spatial_access"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/1_matrix.ipynb b/docs/notebooks/1_matrix.ipynb
new file mode 100755
index 0000000..d91fd09
--- /dev/null
+++ b/docs/notebooks/1_matrix.ipynb
@@ -0,0 +1,913 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Transit Matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys, os\n",
+ "os.chdir('/Users/whlu/spatial_access/spatial_access')\n",
+ "from p2p import *\n",
+ "\n",
+ "import geopandas as gpd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "--- \n",
+ "DEMO \n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " geoid10 \n",
+ " lon \n",
+ " lat \n",
+ " Pop2014 \n",
+ " Pov14 \n",
+ " community \n",
+ " ID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 17031842400 \n",
+ " -87.630040 \n",
+ " 41.742475 \n",
+ " 5157 \n",
+ " 769 \n",
+ " 44 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 17031840300 \n",
+ " -87.681882 \n",
+ " 41.832094 \n",
+ " 5881 \n",
+ " 1021 \n",
+ " 59 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 17031841100 \n",
+ " -87.635098 \n",
+ " 41.851006 \n",
+ " 3363 \n",
+ " 2742 \n",
+ " 34 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 17031841200 \n",
+ " -87.683342 \n",
+ " 41.855562 \n",
+ " 3710 \n",
+ " 1819 \n",
+ " 31 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 17031838200 \n",
+ " -87.675079 \n",
+ " 41.870416 \n",
+ " 3296 \n",
+ " 361 \n",
+ " 28 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
**Specifications for the asymmetric and symmetric distance matrices:** \n",
+ "\n",
+ "- network_type (drive or walk)\n",
+ "- epsilon=0.05 (can change default) \n",
+ "- primary_input \n",
+ "- secondary_input \n",
+ "- output_type='csv' \n",
+ "- n_best_matches=4 (for simulations)\n",
+ "- read_from_file=None \n",
+ "- write_to_file (set as True if user wants to save results) \n",
+ "- load_to_mem=True (True is default but can set it to False if the user is running a computational intensive process >>>.)\n",
+ "\n",
+ "**Please make sure latitude and longitude are correct if using X and Y.**\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Model 1: Asymmetric Matrix \n",
+ "---\n",
+ "The first model directly creates an asymmetric matrix from destination points to the centroids of the area of analysis (also takes ~ 20 min). This approach is most effective when you are only calculating the distance matrix or a particular distance score once."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "code_folding": [],
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The variables in your data set are:\n",
+ "> geoid10\n",
+ "> lon\n",
+ "> lat\n",
+ "> Pop2014\n",
+ "> Pov14\n",
+ "> community\n",
+ "> ID\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the longitude coordinate: lon\n",
+ "Enter the latitude coordinate: lat\n",
+ "Enter the index name: ID\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The variables in your data set are:\n",
+ "> ID\n",
+ "> Facility\n",
+ "> lat\n",
+ "> lon\n",
+ "> Type\n",
+ "> target\n",
+ "> category\n",
+ "> community\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the longitude coordinate: lon\n",
+ "Enter the latitude coordinate: lat\n",
+ "Enter the index name: ID\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:p2p:Approx area of bounding box: 2,445.05 sq. km\n",
+ "INFO:p2p:All operations completed in 15.88 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Calculate asymmetric distance matrix for walking (takes ~5 minutes to run) \n",
+ "\n",
+ "w_asym_mat = TransitMatrix('walk', \n",
+ " primary_input='/Users/whlu/spatial_access/data/input_data/tracts2010.csv', \n",
+ " secondary_input='/Users/whlu/spatial_access/data/input_data/health_chicago.csv')\n",
+ "w_asym_mat.process()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:p2p:Wrote to /Users/whlu/spatial_access/spatial_access/data/matrices/walk_asym_health_tracts.csv in 0.04 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "w_asym_mat.write_csv(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/walk_asym_health_tracts.csv\")\n",
+ "\n",
+ "#Saved as walk_asym_health_tracts.csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:p2p:Wrote to /Users/whlu/spatial_access/spatial_access/data/matrices/walk_asym_health_tracts.tmx in 0.00 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "w_asym_mat.write_tmx(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/walk_asym_health_tracts.tmx\")\n",
+ "\n",
+ "# Saved as walk_asym_health_tracts.tmx"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "code_folding": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The variables in your data set are:\n",
+ "> geoid10\n",
+ "> lon\n",
+ "> lat\n",
+ "> Pop2014\n",
+ "> Pov14\n",
+ "> community\n",
+ "> ID\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the longitude coordinate: lon\n",
+ "Enter the latitude coordinate: lat\n",
+ "Enter the index name: ID\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The variables in your data set are:\n",
+ "> ID\n",
+ "> Facility\n",
+ "> lat\n",
+ "> lon\n",
+ "> Type\n",
+ "> target\n",
+ "> category\n",
+ "> community\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the longitude coordinate: lon\n",
+ "Enter the latitude coordinate: lat\n",
+ "Enter the index name: ID\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:p2p:Approx area of bounding box: 2,445.05 sq. km\n",
+ "INFO:p2p:All operations completed in 7.94 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Calculate asymmetric distance matrix for driving (takes ~1.5 minutes to run) \n",
+ "\n",
+ "d_asym_mat = TransitMatrix('drive', \n",
+ " primary_input='/Users/whlu/spatial_access/data/input_data/tracts2010.csv', \n",
+ " secondary_input='/Users/whlu/spatial_access/data/input_data/health_chicago.csv')\n",
+ "\n",
+ "d_asym_mat.process()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:p2p:Wrote to /Users/whlu/spatial_access/spatial_access/data/matrices/drive_asym_health_tracts.csv in 0.04 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "d_asym_mat.write_csv(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/drive_asym_health_tracts.csv\")\n",
+ "\n",
+ "#Saved as drive_asym_health_tracts.csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "d_asym_mat.write_tmx(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/drive_asym_health_tracts.tmx\")\n",
+ "\n",
+ "# Saved as drive_asym_health_tracts.tmx"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Model 2: Symmetric Matrix\n",
+ "\n",
+ "--\n",
+ "\n",
+ "The second model creates a symmetric distance travel matrix from block to block (801 x 801 matrix). Then, we snap the destination points to the area of analysis (blocks), getting a matrix that calculates the distance between the destinations and every block in the dataset.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The variables in your data set are:\n",
+ "> geoid10\n",
+ "> lon\n",
+ "> lat\n",
+ "> Pop2014\n",
+ "> Pov14\n",
+ "> community\n",
+ "> ID\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the longitude coordinate: lon\n",
+ "Enter the latitude coordinate: lat\n",
+ "Enter the index name: ID\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:p2p:Approx area of bounding box: 2,067.39 sq. km\n",
+ "INFO:p2p:All operations completed in 9.09 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Specify walking distance matrix (takes ~3 min to run) \n",
+ "w_sym_mat = TransitMatrix('walk', \n",
+ " primary_input='/Users/whlu/spatial_access/data/input_data/tracts2010.csv')\n",
+ "\n",
+ "# Run process\n",
+ "w_sym_mat.process()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:p2p:Wrote to /Users/whlu/spatial_access/spatial_access/data/matrices/walk_sym_health_tracts.csv in 0.14 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "w_sym_mat.write_csv(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/walk_sym_health_tracts.csv\")\n",
+ "\n",
+ "# Saved as walk_sym_health_tracts.csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "w_sym_mat.write_tmx(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/walk_sym_health_tracts.tmx\")\n",
+ "\n",
+ "# Saved as walk_sym_health_tracts.tmx"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The variables in your data set are:\n",
+ "> geoid10\n",
+ "> lon\n",
+ "> lat\n",
+ "> Pop2014\n",
+ "> Pov14\n",
+ "> community\n",
+ "> ID\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the longitude coordinate: lopn\n",
+ "Enter the longitude coordinate: lon\n",
+ "Enter the latitude coordinate: lat\n",
+ "Enter the index name: ID\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:p2p:Approx area of bounding box: 2,067.39 sq. km\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requesting network data within bounding box from Overpass API in 1 request(s)\n",
+ "Posting to http://www.overpass-api.de/api/interpreter with timeout=180, \"{'data': '[out:json][timeout:180];(way[\"highway\"][\"highway\"!~\"cycleway|footway|path|pedestrian|steps|track|proposed|construction|bridleway|abandoned|platform|raceway|service\"][\"motor_vehicle\"!~\"no\"][\"motorcar\"!~\"no\"][\"service\"!~\"parking|parking_aisle|driveway|emergency_access\"](41.60021990,-87.95448850,42.07126140,-87.48049640);>;);out;'}\"\n",
+ "Downloaded 48,348.1KB from www.overpass-api.de in 4.67 seconds\n",
+ "Downloaded OSM network data within bounding box from Overpass API in 1 request(s) and 5.56 seconds\n",
+ "Returning OSM data with 249,806 nodes and 55,393 ways...\n",
+ "Edge node pairs completed. Took 79.76 seconds\n",
+ "Returning processed graph with 77,575 nodes and 117,269 edges...\n",
+ "Completed OSM data download and Pandana node and edge table creation in 89.67 seconds\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py:2531: PerformanceWarning: \n",
+ "your performance may suffer as PyTables will pickle object types that it cannot\n",
+ "map directly to c-types [inferred_type->mixed,key->block2_values] [items->['name', 'ref', 'highway', 'service', 'bridge', 'oneway', 'toll', 'area', 'junction']]\n",
+ "\n",
+ " pytables.to_hdf(path_or_buf, key, self, **kwargs)\n",
+ "INFO:p2p:Finished querying osm\n",
+ "INFO:p2p:All operations completed in 95.81 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Specify driving distance matrix (takes ~1.5 minute to run) \n",
+ "d_sym_mat = TransitMatrix('drive', \n",
+ " primary_input='/Users/whlu/spatial_access/data/input_data/tracts2010.csv')\n",
+ "\n",
+ "# Run process. For driving, p2p queries OSM to fetch the street network and then output the shortest path transit matrix\n",
+ "d_sym_mat.process()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:p2p:Wrote to /Users/whlu/spatial_access/spatial_access/data/matrices/drive_sym_health_tracts.csv in 0.13 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "d_sym_mat.write_csv(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/drive_sym_health_tracts.csv\")\n",
+ "\n",
+ "# Saved as drive_sym_health_tracts.csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "d_sym_mat.write_tmx(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/drive_sym_health_tracts.tmx\")\n",
+ "\n",
+ "# Saved as drive_sym_health_tracts.tmx"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, snap the points to the units of analysis. However, snapping the destination points is not always so straightforward. Deciding which points (laying on the network) are assigned to each area of analysis may be arbitrary; therefore, it is important to scrutinize the structure of the data before doing any further processing. If the destinations fall within the unit of analysis, the best option is to run a within function that incorporates the destinations to the unit of analysis and then doing a join with the area IDs.\n",
+ "The following image shows that in this case, we can safely run a function that assigns each point to the area of analysis of interest. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
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\n",
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801 rows × 6 columns
\n",
+ "
\n",
+ "\n",
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\n",
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\n",
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\n",
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\n",
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+ " 3.046944 \n",
+ " 8.741944 \n",
+ " 0.826667 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " OHARE \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " PORTAGE PARK \n",
+ " 6.010079 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 2.499762 \n",
+ " 0.000000 \n",
+ " 3.510317 \n",
+ " \n",
+ " \n",
+ " PULLMAN \n",
+ " 1.640370 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.882963 \n",
+ " 0.000000 \n",
+ " 0.757407 \n",
+ " \n",
+ " \n",
+ " RIVERDALE \n",
+ " 0.083333 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.083333 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " ROGERS PARK \n",
+ " 19.547424 \n",
+ " 0.000000 \n",
+ " 3.511667 \n",
+ " 13.243131 \n",
+ " 1.292121 \n",
+ " 1.500505 \n",
+ " \n",
+ " \n",
+ " ROSELAND \n",
+ " 6.157540 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 3.767460 \n",
+ " 0.000000 \n",
+ " 2.390079 \n",
+ " \n",
+ " \n",
+ " SOUTH CHICAGO \n",
+ " 8.496500 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 5.608167 \n",
+ " 0.000000 \n",
+ " 2.888333 \n",
+ " \n",
+ " \n",
+ " SOUTH DEERING \n",
+ " 0.882639 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.453472 \n",
+ " 0.000000 \n",
+ " 0.429167 \n",
+ " \n",
+ " \n",
+ " SOUTH LAWNDALE \n",
+ " 21.876327 \n",
+ " 0.000000 \n",
+ " 1.998241 \n",
+ " 14.159259 \n",
+ " 1.361111 \n",
+ " 4.357716 \n",
+ " \n",
+ " \n",
+ " SOUTH SHORE \n",
+ " 11.883750 \n",
+ " 0.000000 \n",
+ " 0.838380 \n",
+ " 3.794167 \n",
+ " 0.145185 \n",
+ " 7.106019 \n",
+ " \n",
+ " \n",
+ " UPTOWN \n",
+ " 35.728935 \n",
+ " 0.000000 \n",
+ " 5.613657 \n",
+ " 14.766481 \n",
+ " 1.348333 \n",
+ " 14.000463 \n",
+ " \n",
+ " \n",
+ " WASHINGTON HEIGHTS \n",
+ " 3.001597 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 3.001597 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " WASHINGTON PARK \n",
+ " 19.053333 \n",
+ " 3.075926 \n",
+ " 1.269722 \n",
+ " 6.574352 \n",
+ " 1.262963 \n",
+ " 6.870370 \n",
+ " \n",
+ " \n",
+ " WEST ELSDON \n",
+ " 8.303472 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 8.303472 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " WEST ENGLEWOOD \n",
+ " 5.347602 \n",
+ " 1.731579 \n",
+ " 0.046667 \n",
+ " 3.265263 \n",
+ " 0.000000 \n",
+ " 0.304094 \n",
+ " \n",
+ " \n",
+ " WEST GARFIELD PARK \n",
+ " 10.749389 \n",
+ " 0.109722 \n",
+ " 1.005667 \n",
+ " 5.979556 \n",
+ " 0.000000 \n",
+ " 3.654444 \n",
+ " \n",
+ " \n",
+ " WEST LAWN \n",
+ " 2.975926 \n",
+ " 0.005556 \n",
+ " 0.324722 \n",
+ " 2.645648 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " WEST PULLMAN \n",
+ " 3.101319 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 2.386736 \n",
+ " 0.000000 \n",
+ " 0.714583 \n",
+ " \n",
+ " \n",
+ " WEST RIDGE \n",
+ " 11.524484 \n",
+ " 3.396429 \n",
+ " 0.619444 \n",
+ " 6.026786 \n",
+ " 0.363175 \n",
+ " 1.118651 \n",
+ " \n",
+ " \n",
+ " WEST TOWN \n",
+ " 26.040574 \n",
+ " 1.385556 \n",
+ " 3.318056 \n",
+ " 11.777111 \n",
+ " 0.133926 \n",
+ " 9.425926 \n",
+ " \n",
+ " \n",
+ " WOODLAWN \n",
+ " 8.177222 \n",
+ " 0.068182 \n",
+ " 0.715909 \n",
+ " 1.160404 \n",
+ " 1.804444 \n",
+ " 4.428283 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
77 rows × 6 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " all_categories_score \n",
+ " All Free Health Clinics_score \n",
+ " School-Based Health Centers_score \n",
+ " Federally Qualified Health Centers_score \n",
+ " Other Health Providers_score \n",
+ " Hospitals_score \n",
+ " \n",
+ " \n",
+ " spatial_index \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " ALBANY PARK \n",
+ " 15.033384 \n",
+ " 0.000000 \n",
+ " 6.700202 \n",
+ " 6.021566 \n",
+ " 0.000 \n",
+ " 2.311616 \n",
+ " \n",
+ " \n",
+ " ARCHER HEIGHTS \n",
+ " 6.844778 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 6.844778 \n",
+ " 0.000 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " ARMOUR SQUARE \n",
+ " 14.017778 \n",
+ " 0.014444 \n",
+ " 4.840333 \n",
+ " 3.961000 \n",
+ " 3.122 \n",
+ " 2.080000 \n",
+ " \n",
+ " \n",
+ " ASHBURN \n",
+ " 0.126389 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000 \n",
+ " 0.126389 \n",
+ " \n",
+ " \n",
+ " AUBURN GRESHAM \n",
+ " 5.466593 \n",
+ " 0.000000 \n",
+ " 2.484741 \n",
+ " 2.981852 \n",
+ " 0.000 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "accessM.plot_cdf(filename = \"data/output_data/accessModel_CDFplot\", \n",
+ " xlabel = \"Access Score\", ylabel = \"% population\", title = \"Access Model CDF Plot\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:spatial_access.BaseModel:Figure was saved to: data/output_data/accessModel_choropleth_map\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "accessM.plot_choropleth(column = \"all_categories_score\", title = \"Access Model Scores in Chicago\", filename = \"data/output_data/accessModel_choropleth\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### **Subset the Data to Calculate the Access Score**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:spatial_access.p2p:You should use tmx instead of csv for significantly better performance\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The variables in your data set are:\n",
+ "> geoid10\n",
+ "> lon\n",
+ "> lat\n",
+ "> Pop2014\n",
+ "> Pov14\n",
+ "> community\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the unique index variable: geoid10\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "If you have no population variable, write \"skip\" (no quotations)\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the population variable: Pop2014\n",
+ "Enter the latitude variable: lat\n",
+ "Enter the longitude variable: lon\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The variables in your data set are:\n",
+ "> ID\n",
+ "> Facility\n",
+ "> lat\n",
+ "> lon\n",
+ "> Type\n",
+ "> target\n",
+ "> category\n",
+ "> community\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the unique index variable: ID\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "If you have no capacity variable, write \"skip\" (no quotations)\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the capacity variable: target\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "If you have no category variable, write \"skip\" (no quotations)\n"
+ ]
+ },
+ {
+ "name": "stdin",
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+ "text": [
+ "Enter the category variable: category\n",
+ "Enter the latitude variable: lat\n",
+ "Enter the longitude variable: lon\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Subset for Federally Qualified Health Centers (walking)\n",
+ "accessM.set_focus_categories(['Federally Qualified Health Centers'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Example of density and variety weights:\n",
+ "dict = {\n",
+ "\"Federally Qualified Health Centers\": [10,10,10,10,10]\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:spatial_access.BaseModel:Using weights: {'Federally Qualified Health Centers': [10, 10, 10, 10, 10], 'Other Health Providers': 'No decay', 'School-Based Health Centers': 'No decay', 'All Free Health Clinics': 'No decay', 'Hospitals': 'No decay'}\n"
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+ " \n",
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\n",
+ "
801 rows × 6 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " time_to_nearest_All Free Health Clinics \n",
+ " time_to_nearest_School-Based Health Centers \n",
+ " time_to_nearest_Federally Qualified Health Centers \n",
+ " time_to_nearest_Other Health Providers \n",
+ " time_to_nearest_Hospitals \n",
+ " time_to_nearest_all_categories \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 17031842400 \n",
+ " 3580 \n",
+ " 1718 \n",
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+ " 2760 \n",
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+ " \n",
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+ " 17031841200 \n",
+ " 2864 \n",
+ " 536 \n",
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+ " 536 \n",
+ " \n",
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+ " 1437 \n",
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+ " 246 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " time_to_nearest_All Free Health Clinics \n",
+ " time_to_nearest_School-Based Health Centers \n",
+ " time_to_nearest_Federally Qualified Health Centers \n",
+ " time_to_nearest_Other Health Providers \n",
+ " time_to_nearest_Hospitals \n",
+ " time_to_nearest_all_categories \n",
+ " \n",
+ " \n",
+ " spatial_index \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " ALBANY PARK \n",
+ " 3160.909091 \n",
+ " 759.545455 \n",
+ " 1020.545455 \n",
+ " 3787.818182 \n",
+ " 1666.727273 \n",
+ " 734.272727 \n",
+ " \n",
+ " \n",
+ " ARCHER HEIGHTS \n",
+ " 4226.800000 \n",
+ " 2647.200000 \n",
+ " 886.800000 \n",
+ " 3563.200000 \n",
+ " 4692.200000 \n",
+ " 886.800000 \n",
+ " \n",
+ " \n",
+ " ARMOUR SQUARE \n",
+ " 2600.600000 \n",
+ " 1041.600000 \n",
+ " 1108.800000 \n",
+ " 826.600000 \n",
+ " 1509.200000 \n",
+ " 765.400000 \n",
+ " \n",
+ " \n",
+ " ASHBURN \n",
+ " 3996.875000 \n",
+ " 3354.375000 \n",
+ " 2955.875000 \n",
+ " 8258.750000 \n",
+ " 2287.000000 \n",
+ " 2236.375000 \n",
+ " \n",
+ " \n",
+ " AUBURN GRESHAM \n",
+ " 3638.533333 \n",
+ " 1179.400000 \n",
+ " 1132.466667 \n",
+ " 5745.866667 \n",
+ " 3260.133333 \n",
+ " 911.333333 \n",
+ " \n",
+ " \n",
+ " AUSTIN \n",
+ " 4301.333333 \n",
+ " 1670.291667 \n",
+ " 1232.125000 \n",
+ " 4345.375000 \n",
+ " 1662.041667 \n",
+ " 1129.208333 \n",
+ " \n",
+ " \n",
+ " AVALON PARK \n",
+ " 5176.000000 \n",
+ " 2090.000000 \n",
+ " 2328.500000 \n",
+ " 3363.000000 \n",
+ " 1235.000000 \n",
+ " 1235.000000 \n",
+ " \n",
+ " \n",
+ " AVONDALE \n",
+ " 4087.600000 \n",
+ " 2226.100000 \n",
+ " 1714.700000 \n",
+ " 1264.700000 \n",
+ " 1303.100000 \n",
+ " 1004.500000 \n",
+ " \n",
+ " \n",
+ " BELMONT CRAGIN \n",
+ " 5651.647059 \n",
+ " 3257.470588 \n",
+ " 1140.058824 \n",
+ " 3214.000000 \n",
+ " 1726.176471 \n",
+ " 1028.411765 \n",
+ " \n",
+ " \n",
+ " BEVERLY \n",
+ " 6459.428571 \n",
+ " 4404.142857 \n",
+ " 2527.714286 \n",
+ " 8925.000000 \n",
+ " 1922.285714 \n",
+ " 1720.142857 \n",
+ " \n",
+ " \n",
+ " BRIDGEPORT \n",
+ " 2600.100000 \n",
+ " 1706.100000 \n",
+ " 1592.200000 \n",
+ " 1551.800000 \n",
+ " 2442.100000 \n",
+ " 1376.200000 \n",
+ " \n",
+ " \n",
+ " BRIGHTON PARK \n",
+ " 3112.600000 \n",
+ " 1112.900000 \n",
+ " 1266.400000 \n",
+ " 3974.100000 \n",
+ " 3558.600000 \n",
+ " 942.300000 \n",
+ " \n",
+ " \n",
+ " BURNSIDE \n",
+ " 6355.000000 \n",
+ " 3520.000000 \n",
+ " 3581.000000 \n",
+ " 4864.000000 \n",
+ " 2526.000000 \n",
+ " 2526.000000 \n",
+ " \n",
+ " \n",
+ " CALUMET HEIGHTS \n",
+ " 6412.600000 \n",
+ " 3326.600000 \n",
+ " 1867.800000 \n",
+ " 4613.000000 \n",
+ " 1240.000000 \n",
+ " 1240.000000 \n",
+ " \n",
+ " \n",
+ " CHATHAM \n",
+ " 4742.400000 \n",
+ " 2419.300000 \n",
+ " 3154.500000 \n",
+ " 4175.300000 \n",
+ " 2767.700000 \n",
+ " 2326.800000 \n",
+ " \n",
+ " \n",
+ " CHICAGO LAWN \n",
+ " 1027.818182 \n",
+ " 989.727273 \n",
+ " 712.909091 \n",
+ " 6282.727273 \n",
+ " 1111.090909 \n",
+ " 673.090909 \n",
+ " \n",
+ " \n",
+ " CLEARING \n",
+ " 4596.428571 \n",
+ " 4560.428571 \n",
+ " 2030.142857 \n",
+ " 7603.285714 \n",
+ " 4720.000000 \n",
+ " 2030.142857 \n",
+ " \n",
+ " \n",
+ " DOUGLAS \n",
+ " 2642.700000 \n",
+ " 750.200000 \n",
+ " 790.800000 \n",
+ " 769.600000 \n",
+ " 1456.200000 \n",
+ " 537.800000 \n",
+ " \n",
+ " \n",
+ " DUNNING \n",
+ " 8892.545455 \n",
+ " 6337.181818 \n",
+ " 2604.636364 \n",
+ " 6534.454545 \n",
+ " 1603.272727 \n",
+ " 1576.909091 \n",
+ " \n",
+ " \n",
+ " EAST GARFIELD PARK \n",
+ " 797.083333 \n",
+ " 1293.666667 \n",
+ " 811.250000 \n",
+ " 2266.166667 \n",
+ " 802.416667 \n",
+ " 486.333333 \n",
+ " \n",
+ " \n",
+ " EAST SIDE \n",
+ " 10074.833333 \n",
+ " 6127.333333 \n",
+ " 911.666667 \n",
+ " 8261.833333 \n",
+ " 3717.166667 \n",
+ " 911.666667 \n",
+ " \n",
+ " \n",
+ " EDGEWATER \n",
+ " 2536.647059 \n",
+ " 936.294118 \n",
+ " 1008.176471 \n",
+ " 1420.411765 \n",
+ " 759.941176 \n",
+ " 570.058824 \n",
+ " \n",
+ " \n",
+ " EDISON PARK \n",
+ " 7595.333333 \n",
+ " 7669.333333 \n",
+ " 6531.333333 \n",
+ " 9291.333333 \n",
+ " 1849.000000 \n",
+ " 1849.000000 \n",
+ " \n",
+ " \n",
+ " ENGLEWOOD \n",
+ " 1132.090909 \n",
+ " 2518.363636 \n",
+ " 842.636364 \n",
+ " 3306.909091 \n",
+ " 1469.272727 \n",
+ " 833.727273 \n",
+ " \n",
+ " \n",
+ " FOREST GLEN \n",
+ " 3856.250000 \n",
+ " 4081.750000 \n",
+ " 3882.500000 \n",
+ " 5413.000000 \n",
+ " 3832.000000 \n",
+ " 3410.000000 \n",
+ " \n",
+ " \n",
+ " FULLER PARK \n",
+ " 1781.500000 \n",
+ " 976.500000 \n",
+ " 1062.500000 \n",
+ " 1439.500000 \n",
+ " 1719.500000 \n",
+ " 905.500000 \n",
+ " \n",
+ " \n",
+ " GAGE PARK \n",
+ " 1650.375000 \n",
+ " 2181.250000 \n",
+ " 1081.000000 \n",
+ " 5058.375000 \n",
+ " 2559.750000 \n",
+ " 1078.375000 \n",
+ " \n",
+ " \n",
+ " GARFIELD RIDGE \n",
+ " 5589.818182 \n",
+ " 4817.909091 \n",
+ " 954.909091 \n",
+ " 5754.090909 \n",
+ " 4971.727273 \n",
+ " 954.909091 \n",
+ " \n",
+ " \n",
+ " GRAND BOULEVARD \n",
+ " 1364.133333 \n",
+ " 668.800000 \n",
+ " 920.133333 \n",
+ " 1083.933333 \n",
+ " 1270.600000 \n",
+ " 536.333333 \n",
+ " \n",
+ " \n",
+ " GREATER GRAND CROSSING \n",
+ " 2676.083333 \n",
+ " 1371.166667 \n",
+ " 1990.833333 \n",
+ " 2622.333333 \n",
+ " 1580.000000 \n",
+ " 1172.000000 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " NEAR NORTH SIDE \n",
+ " 3213.421053 \n",
+ " 3059.578947 \n",
+ " 770.684211 \n",
+ " 3814.736842 \n",
+ " 1042.947368 \n",
+ " 683.315789 \n",
+ " \n",
+ " \n",
+ " NEAR SOUTH SIDE \n",
+ " 2418.666667 \n",
+ " 909.333333 \n",
+ " 1326.333333 \n",
+ " 1283.000000 \n",
+ " 1326.333333 \n",
+ " 909.333333 \n",
+ " \n",
+ " \n",
+ " NEAR WEST SIDE \n",
+ " 1087.000000 \n",
+ " 907.450000 \n",
+ " 1036.150000 \n",
+ " 1370.950000 \n",
+ " 1078.350000 \n",
+ " 665.900000 \n",
+ " \n",
+ " \n",
+ " NEW CITY \n",
+ " 985.411765 \n",
+ " 2650.000000 \n",
+ " 698.000000 \n",
+ " 3187.941176 \n",
+ " 3152.529412 \n",
+ " 666.117647 \n",
+ " \n",
+ " \n",
+ " NORTH CENTER \n",
+ " 3715.428571 \n",
+ " 1524.071429 \n",
+ " 1115.928571 \n",
+ " 2292.071429 \n",
+ " 1535.785714 \n",
+ " 1097.071429 \n",
+ " \n",
+ " \n",
+ " NORTH LAWNDALE \n",
+ " 2315.933333 \n",
+ " 1081.400000 \n",
+ " 673.466667 \n",
+ " 1505.200000 \n",
+ " 1278.333333 \n",
+ " 561.200000 \n",
+ " \n",
+ " \n",
+ " NORTH PARK \n",
+ " 2010.250000 \n",
+ " 1971.000000 \n",
+ " 1895.750000 \n",
+ " 4057.250000 \n",
+ " 1963.000000 \n",
+ " 1675.250000 \n",
+ " \n",
+ " \n",
+ " NORWOOD PARK \n",
+ " 7005.857143 \n",
+ " 6031.857143 \n",
+ " 4598.714286 \n",
+ " 7564.142857 \n",
+ " 1440.571429 \n",
+ " 1440.571429 \n",
+ " \n",
+ " \n",
+ " OAKLAND \n",
+ " 1644.000000 \n",
+ " 1176.500000 \n",
+ " 580.500000 \n",
+ " 1616.500000 \n",
+ " 2335.500000 \n",
+ " 580.500000 \n",
+ " \n",
+ " \n",
+ " OHARE \n",
+ " 12224.250000 \n",
+ " 10171.000000 \n",
+ " 6689.000000 \n",
+ " 11194.500000 \n",
+ " 4729.500000 \n",
+ " 4629.250000 \n",
+ " \n",
+ " \n",
+ " PORTAGE PARK \n",
+ " 6476.642857 \n",
+ " 3810.214286 \n",
+ " 1324.785714 \n",
+ " 4122.071429 \n",
+ " 1225.000000 \n",
+ " 1170.357143 \n",
+ " \n",
+ " \n",
+ " PULLMAN \n",
+ " 8116.333333 \n",
+ " 5916.666667 \n",
+ " 2493.000000 \n",
+ " 7410.666667 \n",
+ " 2086.333333 \n",
+ " 2086.333333 \n",
+ " \n",
+ " \n",
+ " RIVERDALE \n",
+ " 16149.500000 \n",
+ " 8701.000000 \n",
+ " 2082.500000 \n",
+ " 15124.000000 \n",
+ " 5051.000000 \n",
+ " 2082.500000 \n",
+ " \n",
+ " \n",
+ " ROGERS PARK \n",
+ " 2541.909091 \n",
+ " 1013.181818 \n",
+ " 512.363636 \n",
+ " 1252.090909 \n",
+ " 1542.000000 \n",
+ " 460.272727 \n",
+ " \n",
+ " \n",
+ " ROSELAND \n",
+ " 6843.928571 \n",
+ " 4767.642857 \n",
+ " 1365.428571 \n",
+ " 7198.642857 \n",
+ " 1778.285714 \n",
+ " 1350.000000 \n",
+ " \n",
+ " \n",
+ " SOUTH CHICAGO \n",
+ " 6827.900000 \n",
+ " 3760.400000 \n",
+ " 823.500000 \n",
+ " 4967.400000 \n",
+ " 1417.100000 \n",
+ " 692.500000 \n",
+ " \n",
+ " \n",
+ " SOUTH DEERING \n",
+ " 8841.000000 \n",
+ " 5755.000000 \n",
+ " 2530.500000 \n",
+ " 7028.000000 \n",
+ " 2514.500000 \n",
+ " 2303.500000 \n",
+ " \n",
+ " \n",
+ " SOUTH LAWNDALE \n",
+ " 3321.611111 \n",
+ " 1320.944444 \n",
+ " 651.333333 \n",
+ " 1274.666667 \n",
+ " 1764.555556 \n",
+ " 510.777778 \n",
+ " \n",
+ " \n",
+ " SOUTH SHORE \n",
+ " 4730.500000 \n",
+ " 1845.666667 \n",
+ " 958.416667 \n",
+ " 2517.666667 \n",
+ " 891.583333 \n",
+ " 671.916667 \n",
+ " \n",
+ " \n",
+ " UPTOWN \n",
+ " 3736.750000 \n",
+ " 798.083333 \n",
+ " 526.666667 \n",
+ " 1210.083333 \n",
+ " 602.166667 \n",
+ " 444.250000 \n",
+ " \n",
+ " \n",
+ " WASHINGTON HEIGHTS \n",
+ " 5369.875000 \n",
+ " 2707.750000 \n",
+ " 1135.250000 \n",
+ " 7017.125000 \n",
+ " 2642.500000 \n",
+ " 1135.250000 \n",
+ " \n",
+ " \n",
+ " WASHINGTON PARK \n",
+ " 768.333333 \n",
+ " 1609.666667 \n",
+ " 951.500000 \n",
+ " 1231.666667 \n",
+ " 1059.333333 \n",
+ " 703.500000 \n",
+ " \n",
+ " \n",
+ " WEST ELSDON \n",
+ " 2973.250000 \n",
+ " 2856.000000 \n",
+ " 623.500000 \n",
+ " 4651.500000 \n",
+ " 3653.250000 \n",
+ " 623.500000 \n",
+ " \n",
+ " \n",
+ " WEST ENGLEWOOD \n",
+ " 1516.315789 \n",
+ " 2491.157895 \n",
+ " 1273.736842 \n",
+ " 4499.736842 \n",
+ " 2184.105263 \n",
+ " 1217.263158 \n",
+ " \n",
+ " \n",
+ " WEST GARFIELD PARK \n",
+ " 2122.600000 \n",
+ " 1755.700000 \n",
+ " 888.200000 \n",
+ " 2886.500000 \n",
+ " 1282.600000 \n",
+ " 888.200000 \n",
+ " \n",
+ " \n",
+ " WEST LAWN \n",
+ " 2470.666667 \n",
+ " 2109.333333 \n",
+ " 1575.000000 \n",
+ " 6172.500000 \n",
+ " 2566.833333 \n",
+ " 1573.500000 \n",
+ " \n",
+ " \n",
+ " WEST PULLMAN \n",
+ " 9250.500000 \n",
+ " 6897.875000 \n",
+ " 1924.000000 \n",
+ " 10293.500000 \n",
+ " 2119.250000 \n",
+ " 1853.375000 \n",
+ " \n",
+ " \n",
+ " WEST RIDGE \n",
+ " 960.285714 \n",
+ " 1981.214286 \n",
+ " 914.214286 \n",
+ " 2020.214286 \n",
+ " 1768.214286 \n",
+ " 647.714286 \n",
+ " \n",
+ " \n",
+ " WEST TOWN \n",
+ " 1405.133333 \n",
+ " 1042.666667 \n",
+ " 652.833333 \n",
+ " 2120.433333 \n",
+ " 936.033333 \n",
+ " 605.666667 \n",
+ " \n",
+ " \n",
+ " WOODLAWN \n",
+ " 2403.818182 \n",
+ " 1840.090909 \n",
+ " 1592.181818 \n",
+ " 988.000000 \n",
+ " 1029.454545 \n",
+ " 742.909091 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
77 rows × 6 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " time_to_nearest_All Free Health Clinics \n",
+ " time_to_nearest_School-Based Health Centers \n",
+ " time_to_nearest_Federally Qualified Health Centers \n",
+ " time_to_nearest_Other Health Providers \n",
+ " time_to_nearest_Hospitals \n",
+ " time_to_nearest_all_categories \n",
+ " \n",
+ " \n",
+ " spatial_index \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " ALBANY PARK \n",
+ " 3160.909091 \n",
+ " 759.545455 \n",
+ " 1020.545455 \n",
+ " 3787.818182 \n",
+ " 1666.727273 \n",
+ " 734.272727 \n",
+ " \n",
+ " \n",
+ " ARCHER HEIGHTS \n",
+ " 4226.800000 \n",
+ " 2647.200000 \n",
+ " 886.800000 \n",
+ " 3563.200000 \n",
+ " 4692.200000 \n",
+ " 886.800000 \n",
+ " \n",
+ " \n",
+ " ARMOUR SQUARE \n",
+ " 2600.600000 \n",
+ " 1041.600000 \n",
+ " 1108.800000 \n",
+ " 826.600000 \n",
+ " 1509.200000 \n",
+ " 765.400000 \n",
+ " \n",
+ " \n",
+ " ASHBURN \n",
+ " 3996.875000 \n",
+ " 3354.375000 \n",
+ " 2955.875000 \n",
+ " 8258.750000 \n",
+ " 2287.000000 \n",
+ " 2236.375000 \n",
+ " \n",
+ " \n",
+ " AUBURN GRESHAM \n",
+ " 3638.533333 \n",
+ " 1179.400000 \n",
+ " 1132.466667 \n",
+ " 5745.866667 \n",
+ " 3260.133333 \n",
+ " 911.333333 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "accessT.plot_cdf(plot_type = 'time_to_nearest_all_categories', \n",
+ " title = 'Time to Nearest', xlabel = 'time (s)', ylabel = 'population',\n",
+ " filename = 'data/output_data/accessTime_CDFplot_allcat'\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:spatial_access.BaseModel:Figure was saved to: data/output_data/accessTime_choropleth\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "accessT.plot_choropleth(column = 'time_to_nearest_all_categories', title = 'Time to Nearest Facility', filename = 'data/output_data/accessTime_choropleth')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### **Subset Data to Focus Categories**\n",
+ "If the user only needs to focus on one category, they can subset their data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#sets focus category to FQHC, can have multiple focus categories separated by commas \n",
+ "accessT.set_focus_categories(['Federally Qualified Health Centers'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "
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+ "\n",
+ "
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+ " \n",
+ " \n",
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801 rows × 6 columns
\n",
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+ " \n",
+ " \n",
+ " NEAR NORTH SIDE \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 1.736842 \n",
+ " 0.000000 \n",
+ " 2.578947 \n",
+ " 4.315789 \n",
+ " \n",
+ " \n",
+ " NEAR SOUTH SIDE \n",
+ " 0.333333 \n",
+ " 2.000000 \n",
+ " 1.000000 \n",
+ " 1.333333 \n",
+ " 0.666667 \n",
+ " 5.333333 \n",
+ " \n",
+ " \n",
+ " NEAR WEST SIDE \n",
+ " 1.500000 \n",
+ " 2.500000 \n",
+ " 2.500000 \n",
+ " 0.800000 \n",
+ " 3.650000 \n",
+ " 10.950000 \n",
+ " \n",
+ " \n",
+ " NEW CITY \n",
+ " 1.705882 \n",
+ " 0.117647 \n",
+ " 3.588235 \n",
+ " 0.058824 \n",
+ " 0.058824 \n",
+ " 5.529412 \n",
+ " \n",
+ " \n",
+ " NORTH CENTER \n",
+ " 0.000000 \n",
+ " 0.857143 \n",
+ " 1.714286 \n",
+ " 0.000000 \n",
+ " 0.928571 \n",
+ " 3.500000 \n",
+ " \n",
+ " \n",
+ " NORTH LAWNDALE \n",
+ " 0.266667 \n",
+ " 1.133333 \n",
+ " 5.933333 \n",
+ " 0.666667 \n",
+ " 2.800000 \n",
+ " 10.800000 \n",
+ " \n",
+ " \n",
+ " NORTH PARK \n",
+ " 0.750000 \n",
+ " 0.750000 \n",
+ " 2.000000 \n",
+ " 0.000000 \n",
+ " 0.500000 \n",
+ " 4.000000 \n",
+ " \n",
+ " \n",
+ " NORWOOD PARK \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.857143 \n",
+ " 0.857143 \n",
+ " \n",
+ " \n",
+ " OAKLAND \n",
+ " 1.000000 \n",
+ " 1.500000 \n",
+ " 3.500000 \n",
+ " 0.500000 \n",
+ " 0.000000 \n",
+ " 6.500000 \n",
+ " \n",
+ " \n",
+ " OHARE \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " \n",
+ " \n",
+ " PORTAGE PARK \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.857143 \n",
+ " 0.000000 \n",
+ " 1.000000 \n",
+ " 1.857143 \n",
+ " \n",
+ " \n",
+ " PULLMAN \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.666667 \n",
+ " 0.000000 \n",
+ " 0.333333 \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " RIVERDALE \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 1.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 1.000000 \n",
+ " \n",
+ " \n",
+ " ROGERS PARK \n",
+ " 0.000000 \n",
+ " 1.363636 \n",
+ " 4.363636 \n",
+ " 0.818182 \n",
+ " 0.818182 \n",
+ " 7.363636 \n",
+ " \n",
+ " \n",
+ " ROSELAND \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 1.500000 \n",
+ " 0.000000 \n",
+ " 0.571429 \n",
+ " 2.071429 \n",
+ " \n",
+ " \n",
+ " SOUTH CHICAGO \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 1.800000 \n",
+ " 0.000000 \n",
+ " 1.200000 \n",
+ " 3.000000 \n",
+ " \n",
+ " \n",
+ " SOUTH DEERING \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.500000 \n",
+ " 0.000000 \n",
+ " 0.250000 \n",
+ " 0.750000 \n",
+ " \n",
+ " \n",
+ " SOUTH LAWNDALE \n",
+ " 0.000000 \n",
+ " 1.000000 \n",
+ " 6.333333 \n",
+ " 0.833333 \n",
+ " 1.277778 \n",
+ " 9.444444 \n",
+ " \n",
+ " \n",
+ " SOUTH SHORE \n",
+ " 0.000000 \n",
+ " 0.500000 \n",
+ " 1.083333 \n",
+ " 0.250000 \n",
+ " 2.000000 \n",
+ " 3.833333 \n",
+ " \n",
+ " \n",
+ " UPTOWN \n",
+ " 0.000000 \n",
+ " 2.416667 \n",
+ " 5.333333 \n",
+ " 0.916667 \n",
+ " 3.833333 \n",
+ " 12.500000 \n",
+ " \n",
+ " \n",
+ " WASHINGTON HEIGHTS \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 1.125000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 1.125000 \n",
+ " \n",
+ " \n",
+ " WASHINGTON PARK \n",
+ " 1.333333 \n",
+ " 0.833333 \n",
+ " 3.166667 \n",
+ " 1.000000 \n",
+ " 2.333333 \n",
+ " 8.666667 \n",
+ " \n",
+ " \n",
+ " WEST ELSDON \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 2.500000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 2.500000 \n",
+ " \n",
+ " \n",
+ " WEST ENGLEWOOD \n",
+ " 1.368421 \n",
+ " 0.052632 \n",
+ " 2.157895 \n",
+ " 0.000000 \n",
+ " 0.157895 \n",
+ " 3.736842 \n",
+ " \n",
+ " \n",
+ " WEST GARFIELD PARK \n",
+ " 0.200000 \n",
+ " 0.800000 \n",
+ " 2.600000 \n",
+ " 0.000000 \n",
+ " 1.400000 \n",
+ " 5.000000 \n",
+ " \n",
+ " \n",
+ " WEST LAWN \n",
+ " 0.333333 \n",
+ " 0.166667 \n",
+ " 1.166667 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 1.666667 \n",
+ " \n",
+ " \n",
+ " WEST PULLMAN \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.875000 \n",
+ " 0.000000 \n",
+ " 0.250000 \n",
+ " 1.125000 \n",
+ " \n",
+ " \n",
+ " WEST RIDGE \n",
+ " 1.642857 \n",
+ " 0.571429 \n",
+ " 2.785714 \n",
+ " 0.357143 \n",
+ " 0.500000 \n",
+ " 5.857143 \n",
+ " \n",
+ " \n",
+ " WEST TOWN \n",
+ " 0.900000 \n",
+ " 1.366667 \n",
+ " 5.666667 \n",
+ " 0.200000 \n",
+ " 2.433333 \n",
+ " 10.566667 \n",
+ " \n",
+ " \n",
+ " WOODLAWN \n",
+ " 0.272727 \n",
+ " 0.454545 \n",
+ " 0.818182 \n",
+ " 1.000000 \n",
+ " 1.181818 \n",
+ " 3.727273 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
77 rows × 6 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " count_in_range_All Free Health Clinics \n",
+ " count_in_range_School-Based Health Centers \n",
+ " count_in_range_Federally Qualified Health Centers \n",
+ " count_in_range_Other Health Providers \n",
+ " count_in_range_Hospitals \n",
+ " count_in_range_all_categories \n",
+ " \n",
+ " \n",
+ " spatial_index \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " ALBANY PARK \n",
+ " 0.0 \n",
+ " 1.818182 \n",
+ " 2.272727 \n",
+ " 0.0 \n",
+ " 0.909091 \n",
+ " 5.000000 \n",
+ " \n",
+ " \n",
+ " ARCHER HEIGHTS \n",
+ " 0.0 \n",
+ " 0.000000 \n",
+ " 2.600000 \n",
+ " 0.0 \n",
+ " 0.000000 \n",
+ " 2.600000 \n",
+ " \n",
+ " \n",
+ " ARMOUR SQUARE \n",
+ " 0.2 \n",
+ " 2.600000 \n",
+ " 1.600000 \n",
+ " 2.6 \n",
+ " 0.800000 \n",
+ " 7.800000 \n",
+ " \n",
+ " \n",
+ " ASHBURN \n",
+ " 0.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.0 \n",
+ " 0.125000 \n",
+ " 0.125000 \n",
+ " \n",
+ " \n",
+ " AUBURN GRESHAM \n",
+ " 0.0 \n",
+ " 0.866667 \n",
+ " 1.066667 \n",
+ " 0.0 \n",
+ " 0.000000 \n",
+ " 1.933333 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "accessC.plot_cdf(filename = 'data/output_data/accessCount_CDFplot', xlabel = 'Access Count', ylabel = 'Population', title = 'Access Count by Population')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:spatial_access.BaseModel:Figure was saved to: data/output_data/accessCount_choropleth\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "accessC.plot_choropleth(column='count_in_range_all_categories', filename = 'data/output_data/accessCount_choropleth', title= 'Access Count in Chicago')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### **Subset Data for Focus Categories**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Limit catgeory to for FQHC\n",
+ "accessC.set_focus_categories(['Federally Qualified Health Centers'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "metadata": {},
+ "outputs": [
+ {
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\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " count_in_range_Federally Qualified Health Centers \n",
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\n",
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\n",
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+ "
\n",
+ " \n",
+ " \n",
+ " sum_in_range_All Free Health Clinics \n",
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\n",
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801 rows × 6 columns
\n",
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\n",
+ "\n",
+ "
\n",
+ " \n",
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\n",
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\n",
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\n",
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+ " GARFIELD RIDGE \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 179000.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 1.790000e+05 \n",
+ " \n",
+ " \n",
+ " GRAND BOULEVARD \n",
+ " 168666.666667 \n",
+ " 297933.333333 \n",
+ " 607666.666667 \n",
+ " 183866.666667 \n",
+ " 133333.333333 \n",
+ " 1.391467e+06 \n",
+ " \n",
+ " \n",
+ " GREATER GRAND CROSSING \n",
+ " 21833.333333 \n",
+ " 116750.000000 \n",
+ " 73666.666667 \n",
+ " 0.000000 \n",
+ " 130000.000000 \n",
+ " 3.422500e+05 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " NEAR NORTH SIDE \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 246210.526316 \n",
+ " 0.000000 \n",
+ " 415157.894737 \n",
+ " 6.613684e+05 \n",
+ " \n",
+ " \n",
+ " NEAR SOUTH SIDE \n",
+ " 47666.666667 \n",
+ " 268666.666667 \n",
+ " 195000.000000 \n",
+ " 228333.333333 \n",
+ " 87333.333333 \n",
+ " 8.270000e+05 \n",
+ " \n",
+ " \n",
+ " NEAR WEST SIDE \n",
+ " 250800.000000 \n",
+ " 467450.000000 \n",
+ " 337850.000000 \n",
+ " 153600.000000 \n",
+ " 561450.000000 \n",
+ " 1.771150e+06 \n",
+ " \n",
+ " \n",
+ " NEW CITY \n",
+ " 222882.352941 \n",
+ " 11882.352941 \n",
+ " 483000.000000 \n",
+ " 6588.235294 \n",
+ " 7882.352941 \n",
+ " 7.322353e+05 \n",
+ " \n",
+ " \n",
+ " NORTH CENTER \n",
+ " 0.000000 \n",
+ " 96285.714286 \n",
+ " 223642.857143 \n",
+ " 0.000000 \n",
+ " 156357.142857 \n",
+ " 4.762857e+05 \n",
+ " \n",
+ " \n",
+ " NORTH LAWNDALE \n",
+ " 34133.333333 \n",
+ " 166000.000000 \n",
+ " 822866.666667 \n",
+ " 112400.000000 \n",
+ " 390200.000000 \n",
+ " 1.525600e+06 \n",
+ " \n",
+ " \n",
+ " NORTH PARK \n",
+ " 116750.000000 \n",
+ " 132000.000000 \n",
+ " 279250.000000 \n",
+ " 0.000000 \n",
+ " 58500.000000 \n",
+ " 5.865000e+05 \n",
+ " \n",
+ " \n",
+ " NORWOOD PARK \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 139142.857143 \n",
+ " 1.391429e+05 \n",
+ " \n",
+ " \n",
+ " OAKLAND \n",
+ " 109000.000000 \n",
+ " 177500.000000 \n",
+ " 570500.000000 \n",
+ " 93000.000000 \n",
+ " 0.000000 \n",
+ " 9.500000e+05 \n",
+ " \n",
+ " \n",
+ " OHARE \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000e+00 \n",
+ " \n",
+ " \n",
+ " PORTAGE PARK \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 118000.000000 \n",
+ " 0.000000 \n",
+ " 184500.000000 \n",
+ " 3.025000e+05 \n",
+ " \n",
+ " \n",
+ " PULLMAN \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 93666.666667 \n",
+ " 0.000000 \n",
+ " 60333.333333 \n",
+ " 1.540000e+05 \n",
+ " \n",
+ " \n",
+ " RIVERDALE \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 114000.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 1.140000e+05 \n",
+ " \n",
+ " \n",
+ " ROGERS PARK \n",
+ " 0.000000 \n",
+ " 245727.272727 \n",
+ " 597181.818182 \n",
+ " 155454.545455 \n",
+ " 126090.909091 \n",
+ " 1.124455e+06 \n",
+ " \n",
+ " \n",
+ " ROSELAND \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 217357.142857 \n",
+ " 0.000000 \n",
+ " 103428.571429 \n",
+ " 3.207857e+05 \n",
+ " \n",
+ " \n",
+ " SOUTH CHICAGO \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 274400.000000 \n",
+ " 0.000000 \n",
+ " 155700.000000 \n",
+ " 4.301000e+05 \n",
+ " \n",
+ " \n",
+ " SOUTH DEERING \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 78250.000000 \n",
+ " 0.000000 \n",
+ " 28000.000000 \n",
+ " 1.062500e+05 \n",
+ " \n",
+ " \n",
+ " SOUTH LAWNDALE \n",
+ " 0.000000 \n",
+ " 140222.222222 \n",
+ " 871222.222222 \n",
+ " 138333.333333 \n",
+ " 184555.555556 \n",
+ " 1.334333e+06 \n",
+ " \n",
+ " \n",
+ " SOUTH SHORE \n",
+ " 0.000000 \n",
+ " 91500.000000 \n",
+ " 118000.000000 \n",
+ " 41250.000000 \n",
+ " 322250.000000 \n",
+ " 5.730000e+05 \n",
+ " \n",
+ " \n",
+ " UPTOWN \n",
+ " 0.000000 \n",
+ " 337333.333333 \n",
+ " 798083.333333 \n",
+ " 116416.666667 \n",
+ " 616583.333333 \n",
+ " 1.868417e+06 \n",
+ " \n",
+ " \n",
+ " WASHINGTON HEIGHTS \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 187500.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 1.875000e+05 \n",
+ " \n",
+ " \n",
+ " WASHINGTON PARK \n",
+ " 203666.666667 \n",
+ " 111166.666667 \n",
+ " 476833.333333 \n",
+ " 120833.333333 \n",
+ " 338166.666667 \n",
+ " 1.250667e+06 \n",
+ " \n",
+ " \n",
+ " WEST ELSDON \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 307250.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 3.072500e+05 \n",
+ " \n",
+ " \n",
+ " WEST ENGLEWOOD \n",
+ " 189263.157895 \n",
+ " 9473.684211 \n",
+ " 263578.947368 \n",
+ " 0.000000 \n",
+ " 31263.157895 \n",
+ " 4.935789e+05 \n",
+ " \n",
+ " \n",
+ " WEST GARFIELD PARK \n",
+ " 25600.000000 \n",
+ " 102300.000000 \n",
+ " 324800.000000 \n",
+ " 0.000000 \n",
+ " 173600.000000 \n",
+ " 6.263000e+05 \n",
+ " \n",
+ " \n",
+ " WEST LAWN \n",
+ " 66000.000000 \n",
+ " 30000.000000 \n",
+ " 148000.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 2.440000e+05 \n",
+ " \n",
+ " \n",
+ " WEST PULLMAN \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 112625.000000 \n",
+ " 0.000000 \n",
+ " 45250.000000 \n",
+ " 1.578750e+05 \n",
+ " \n",
+ " \n",
+ " WEST RIDGE \n",
+ " 237071.428571 \n",
+ " 101071.428571 \n",
+ " 364071.428571 \n",
+ " 67857.142857 \n",
+ " 66214.285714 \n",
+ " 8.362857e+05 \n",
+ " \n",
+ " \n",
+ " WEST TOWN \n",
+ " 150433.333333 \n",
+ " 242566.666667 \n",
+ " 865200.000000 \n",
+ " 30900.000000 \n",
+ " 317600.000000 \n",
+ " 1.606700e+06 \n",
+ " \n",
+ " \n",
+ " WOODLAWN \n",
+ " 43636.363636 \n",
+ " 83181.818182 \n",
+ " 115000.000000 \n",
+ " 165000.000000 \n",
+ " 163909.090909 \n",
+ " 5.707273e+05 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
77 rows × 6 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " sum_in_range_All Free Health Clinics \n",
+ " sum_in_range_School-Based Health Centers \n",
+ " sum_in_range_Federally Qualified Health Centers \n",
+ " sum_in_range_Other Health Providers \n",
+ " sum_in_range_Hospitals \n",
+ " sum_in_range_all_categories \n",
+ " \n",
+ " \n",
+ " spatial_index \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " ALBANY PARK \n",
+ " 0.0 \n",
+ " 299090.909091 \n",
+ " 326181.818182 \n",
+ " 0.0 \n",
+ " 133181.818182 \n",
+ " 7.584545e+05 \n",
+ " \n",
+ " \n",
+ " ARCHER HEIGHTS \n",
+ " 0.0 \n",
+ " 0.000000 \n",
+ " 333000.000000 \n",
+ " 0.0 \n",
+ " 0.000000 \n",
+ " 3.330000e+05 \n",
+ " \n",
+ " \n",
+ " ARMOUR SQUARE \n",
+ " 28600.0 \n",
+ " 335400.000000 \n",
+ " 313600.000000 \n",
+ " 443800.0 \n",
+ " 104800.000000 \n",
+ " 1.226200e+06 \n",
+ " \n",
+ " \n",
+ " ASHBURN \n",
+ " 0.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.0 \n",
+ " 14875.000000 \n",
+ " 1.487500e+04 \n",
+ " \n",
+ " \n",
+ " AUBURN GRESHAM \n",
+ " 0.0 \n",
+ " 104000.000000 \n",
+ " 148200.000000 \n",
+ " 0.0 \n",
+ " 0.000000 \n",
+ " 2.522000e+05 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "accessS.plot_cdf(filename= 'data/output_data/accessSum_cdfplot', title = 'Access Sum CDF Plot', xlabel = 'Access Sum', ylabel = 'Population')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:spatial_access.BaseModel:Figure was saved to: data/output_data/accessSum_choropleth\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "accessS.plot_choropleth(column= 'sum_in_range_All Free Health Clinics',\n",
+ " filename= 'data/output_data/accessSum_choropleth',\n",
+ " title = 'Access Sum in Chicago')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### **Subset Data for Focus Categories**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 120,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Limit catgeory to for FQHC\n",
+ "accessS.set_focus_categories(['Federally Qualified Health Centers'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 121,
+ "metadata": {},
+ "outputs": [
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+ " MCKINLEY PARK \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 200000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 200000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 10.800886 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 10.800886 \n",
+ " \n",
+ " \n",
+ " MONTCLARE \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 111000.0 \n",
+ " 111000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 13.034288 \n",
+ " 13.034288 \n",
+ " \n",
+ " \n",
+ " NEAR NORTH SIDE \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 305000.0 \n",
+ " 0.0 \n",
+ " 486000.0 \n",
+ " 791000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 3.334062 \n",
+ " 0.000000 \n",
+ " 5.312637 \n",
+ " 8.646699 \n",
+ " \n",
+ " \n",
+ " NEAR SOUTH SIDE \n",
+ " 0.0 \n",
+ " 145000.0 \n",
+ " 193000.0 \n",
+ " 0.0 \n",
+ " 131000.0 \n",
+ " 469000.0 \n",
+ " 0.000000 \n",
+ " 5.505353 \n",
+ " 7.327815 \n",
+ " 0.000000 \n",
+ " 4.973802 \n",
+ " 17.806971 \n",
+ " \n",
+ " \n",
+ " NEAR WEST SIDE \n",
+ " 339000.0 \n",
+ " 583000.0 \n",
+ " 292000.0 \n",
+ " 192000.0 \n",
+ " 614000.0 \n",
+ " 2020000.0 \n",
+ " 4.721251 \n",
+ " 8.119438 \n",
+ " 4.066682 \n",
+ " 2.673983 \n",
+ " 8.551175 \n",
+ " 28.132529 \n",
+ " \n",
+ " \n",
+ " NEW CITY \n",
+ " 257000.0 \n",
+ " 0.0 \n",
+ " 538000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 795000.0 \n",
+ " 5.056567 \n",
+ " 0.000000 \n",
+ " 10.585342 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 15.641909 \n",
+ " \n",
+ " \n",
+ " NORTH CENTER \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 132000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 132000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 3.461841 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 3.461841 \n",
+ " \n",
+ " \n",
+ " NORTH LAWNDALE \n",
+ " 0.0 \n",
+ " 140000.0 \n",
+ " 556000.0 \n",
+ " 0.0 \n",
+ " 346000.0 \n",
+ " 1042000.0 \n",
+ " 0.000000 \n",
+ " 2.451238 \n",
+ " 9.734916 \n",
+ " 0.000000 \n",
+ " 6.058059 \n",
+ " 18.244213 \n",
+ " \n",
+ " \n",
+ " NORWOOD PARK \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 152000.0 \n",
+ " 152000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 4.165981 \n",
+ " 4.165981 \n",
+ " \n",
+ " \n",
+ " OAKLAND \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 297000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 297000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 91.950464 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 91.950464 \n",
+ " \n",
+ " \n",
+ " PORTAGE PARK \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 134000.0 \n",
+ " 0.0 \n",
+ " 184000.0 \n",
+ " 318000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 1.995116 \n",
+ " 0.000000 \n",
+ " 2.739563 \n",
+ " 4.734679 \n",
+ " \n",
+ " \n",
+ " PULLMAN \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 111000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 111000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 15.925395 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 15.925395 \n",
+ " \n",
+ " \n",
+ " RIVERDALE \n",
+ " 0.0 \n",
+ " 192000.0 \n",
+ " 144000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 336000.0 \n",
+ " 0.000000 \n",
+ " 26.703755 \n",
+ " 20.027816 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 46.731572 \n",
+ " \n",
+ " \n",
+ " ROGERS PARK \n",
+ " 0.0 \n",
+ " 173000.0 \n",
+ " 551000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 724000.0 \n",
+ " 0.000000 \n",
+ " 3.446078 \n",
+ " 10.975658 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 14.421736 \n",
+ " \n",
+ " \n",
+ " ROSELAND \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 281000.0 \n",
+ " 0.0 \n",
+ " 181000.0 \n",
+ " 462000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 6.351574 \n",
+ " 0.000000 \n",
+ " 4.091228 \n",
+ " 10.442802 \n",
+ " \n",
+ " \n",
+ " SOUTH CHICAGO \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 301000.0 \n",
+ " 0.0 \n",
+ " 138000.0 \n",
+ " 439000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 9.208554 \n",
+ " 0.000000 \n",
+ " 4.221862 \n",
+ " 13.430416 \n",
+ " \n",
+ " \n",
+ " SOUTH LAWNDALE \n",
+ " 0.0 \n",
+ " 141000.0 \n",
+ " 859000.0 \n",
+ " 166000.0 \n",
+ " 100000.0 \n",
+ " 1266000.0 \n",
+ " 0.000000 \n",
+ " 1.721065 \n",
+ " 10.485072 \n",
+ " 2.026219 \n",
+ " 1.220614 \n",
+ " 15.452970 \n",
+ " \n",
+ " \n",
+ " SOUTH SHORE \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 104000.0 \n",
+ " 0.0 \n",
+ " 195000.0 \n",
+ " 299000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 2.761038 \n",
+ " 0.000000 \n",
+ " 5.176945 \n",
+ " 7.937983 \n",
+ " \n",
+ " \n",
+ " UPTOWN \n",
+ " 0.0 \n",
+ " 152000.0 \n",
+ " 753000.0 \n",
+ " 127000.0 \n",
+ " 642000.0 \n",
+ " 1674000.0 \n",
+ " 0.000000 \n",
+ " 2.863009 \n",
+ " 14.183195 \n",
+ " 2.392119 \n",
+ " 12.092445 \n",
+ " 31.530768 \n",
+ " \n",
+ " \n",
+ " WASHINGTON HEIGHTS \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 174000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 174000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 6.582183 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 6.582183 \n",
+ " \n",
+ " \n",
+ " WASHINGTON PARK \n",
+ " 160000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 160000.0 \n",
+ " 8.515621 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 8.515621 \n",
+ " \n",
+ " \n",
+ " WEST ELSDON \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 143000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 143000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 7.461518 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 7.461518 \n",
+ " \n",
+ " \n",
+ " WEST GARFIELD PARK \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 102000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 102000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 5.401112 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 5.401112 \n",
+ " \n",
+ " \n",
+ " WEST RIDGE \n",
+ " 295000.0 \n",
+ " 0.0 \n",
+ " 246000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 541000.0 \n",
+ " 4.040044 \n",
+ " 0.000000 \n",
+ " 3.368986 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 7.409031 \n",
+ " \n",
+ " \n",
+ " WEST TOWN \n",
+ " 155000.0 \n",
+ " 171000.0 \n",
+ " 911000.0 \n",
+ " 0.0 \n",
+ " 397000.0 \n",
+ " 1634000.0 \n",
+ " 1.917842 \n",
+ " 2.115813 \n",
+ " 11.271962 \n",
+ " 0.000000 \n",
+ " 4.912150 \n",
+ " 20.217768 \n",
+ " \n",
+ " \n",
+ " WOODLAWN \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 165000.0 \n",
+ " 126000.0 \n",
+ " 291000.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 6.704319 \n",
+ " 5.119662 \n",
+ " 11.823981 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " All Free Health Clinics \n",
+ " School-Based Health Centers \n",
+ " Federally Qualified Health Centers \n",
+ " Other Health Providers \n",
+ " Hospitals \n",
+ " all_categories \n",
+ " All Free Health Clinics_per_capita \n",
+ " School-Based Health Centers_per_capita \n",
+ " Federally Qualified Health Centers_per_capita \n",
+ " Other Health Providers_per_capita \n",
+ " Hospitals_per_capita \n",
+ " all_categories_per_capita \n",
+ " \n",
+ " \n",
+ " spatial_index \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " ALBANY PARK \n",
+ " 0.0 \n",
+ " 329000.0 \n",
+ " 171000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 500000.0 \n",
+ " 0.0 \n",
+ " 6.215757 \n",
+ " 3.230682 \n",
+ " 0.00000 \n",
+ " 0.000000 \n",
+ " 9.446439 \n",
+ " \n",
+ " \n",
+ " ARCHER HEIGHTS \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 106000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 106000.0 \n",
+ " 0.0 \n",
+ " 0.000000 \n",
+ " 7.572510 \n",
+ " 0.00000 \n",
+ " 0.000000 \n",
+ " 7.572510 \n",
+ " \n",
+ " \n",
+ " ARMOUR SQUARE \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 170000.0 \n",
+ " 0.0 \n",
+ " 170000.0 \n",
+ " 0.0 \n",
+ " 0.000000 \n",
+ " 0.000000 \n",
+ " 17.72495 \n",
+ " 0.000000 \n",
+ " 17.724950 \n",
+ " \n",
+ " \n",
+ " AUBURN GRESHAM \n",
+ " 0.0 \n",
+ " 120000.0 \n",
+ " 141000.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 261000.0 \n",
+ " 0.0 \n",
+ " 2.520426 \n",
+ " 2.961500 \n",
+ " 0.00000 \n",
+ " 0.000000 \n",
+ " 5.481926 \n",
+ " \n",
+ " \n",
+ " AUSTIN \n",
+ " 0.0 \n",
+ " 190000.0 \n",
+ " 378000.0 \n",
+ " 0.0 \n",
+ " 125000.0 \n",
+ " 693000.0 \n",
+ " 0.0 \n",
+ " 1.849148 \n",
+ " 3.678832 \n",
+ " 0.00000 \n",
+ " 1.216545 \n",
+ " 6.744526 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md_sum\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_focus_categories\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Federally Qualified Health Centers'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m~/spatial_access/spatial_access/BaseModel.py\u001b[0m in \u001b[0;36mset_focus_categories\u001b[0;34m(self, categories)\u001b[0m\n\u001b[1;32m 639\u001b[0m \u001b[0munrecognized_categories\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcategories\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_categories\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 640\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munrecognized_categories\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mUnrecognizedCategoriesException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcategory\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcategory\u001b[0m \u001b[0;32min\u001b[0m \u001b[0munrecognized_categories\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mUnrecognizedCategoriesException\u001b[0m: a,H,i,n,s,d, ,l,h,u,y,t,C,Q,F,e,f,r"
+ ]
+ }
+ ],
+ "source": [
+ "d_sum.set_focus_categories('Federally Qualified Health Centers')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/3_coverage_score.ipynb b/docs/notebooks/3_coverage_score.ipynb
new file mode 100755
index 0000000..41371b6
--- /dev/null
+++ b/docs/notebooks/3_coverage_score.ipynb
@@ -0,0 +1,768 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Coverage Scores"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Calculating the Coverage Score\n",
+ "---\n",
+ "#### **Column Inputs for Each Model**\n",
+ "Each model takes in inputs from both the sources and destination csv files. Each model takes in the standard inputs indicated below as well as additional inputs specified under each model\n",
+ "\n",
+ "* Source File\n",
+ " * Unique index identifier (geoid10) (integer or real) \n",
+ " * Latitude and longitude coordinates (real)\n",
+ " * To aggregate: larger areal ID\n",
+ " * Population within the areal unit\n",
+ "* Destination File\n",
+ " * Unique index identifier (ID) (integer or real)\n",
+ " * Latitude and longitude coordinates (real)\n",
+ " * Category for each type of facility\n",
+ " * To aggregate: larger areal ID\n",
+ " * Capacity for each facility\n",
+ "\n",
+ " \n",
+ "\n",
+ "**Specifications for Coverage Model:** \n",
+ "\n",
+ "**name.Coverage( ):** \n",
+ "- network_type ('walk', 'bike', 'drive', or 'otp')\n",
+ "- sources_filename (primary input data)\n",
+ "- destinations_filename (secondary input data)\n",
+ "- transit_matrix_filename (origin-destination transit matrix)\n",
+ " \n",
+ "**name.calculate():**\n",
+ "- upper_threshold (the time (in seconds) in which the origin and destinations are considered to be out of range of each other)\n",
+ "\n",
+ "Functions within the Coverage Model class (use as name.function()): \n",
+ "- calculate () \n",
+ "- model_results (results of the TSFCA calculations)\n",
+ "- write_csv (filename='name') \n",
+ "- aggregate ()\n",
+ "- write_aggregated_results()\n",
+ "- plot_cdf()\n",
+ "- plot_choropleth()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " geoid10 \n",
+ " lon \n",
+ " lat \n",
+ " Pop2014 \n",
+ " Pov14 \n",
+ " community \n",
+ " ID \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 17031842400 \n",
+ " -87.630040 \n",
+ " 41.742475 \n",
+ " 5157 \n",
+ " 769 \n",
+ " 44 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 17031840300 \n",
+ " -87.681882 \n",
+ " 41.832094 \n",
+ " 5881 \n",
+ " 1021 \n",
+ " 59 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 17031841100 \n",
+ " -87.635098 \n",
+ " 41.851006 \n",
+ " 3363 \n",
+ " 2742 \n",
+ " 34 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 17031841200 \n",
+ " -87.683342 \n",
+ " 41.855562 \n",
+ " 3710 \n",
+ " 1819 \n",
+ " 31 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 17031838200 \n",
+ " -87.675079 \n",
+ " 41.870416 \n",
+ " 3296 \n",
+ " 361 \n",
+ " 28 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " ID \n",
+ " Facility \n",
+ " lat \n",
+ " lon \n",
+ " Type \n",
+ " capacity \n",
+ " category \n",
+ " community \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " 1 \n",
+ " American Indian Health Service of Chicago, Inc. \n",
+ " 41.956676 \n",
+ " -87.651879 \n",
+ " 5 \n",
+ " 127000 \n",
+ " Other Health Providers \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " 2 \n",
+ " Hamdard Center for Health and Human Services \n",
+ " 41.997852 \n",
+ " -87.669535 \n",
+ " 5 \n",
+ " 190000 \n",
+ " Other Health Providers \n",
+ " 77 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 3 \n",
+ " Infant Welfare Society of Chicago \n",
+ " 41.924904 \n",
+ " -87.717270 \n",
+ " 5 \n",
+ " 137000 \n",
+ " Other Health Providers \n",
+ " 22 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " 4 \n",
+ " Mercy Family - Henry Booth House Family Health... \n",
+ " 41.841694 \n",
+ " -87.624790 \n",
+ " 5 \n",
+ " 159000 \n",
+ " Other Health Providers \n",
+ " 35 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " 6 \n",
+ " Cook County - Dr. Jorge Prieto Health Center \n",
+ " 41.847143 \n",
+ " -87.724975 \n",
+ " 5 \n",
+ " 166000 \n",
+ " Other Health Providers \n",
+ " 30 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " service_pop \n",
+ " percap_spending \n",
+ " category \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 14 \n",
+ " 100892 \n",
+ " 1.001070 \n",
+ " Federally Qualified Health Centers \n",
+ " \n",
+ " \n",
+ " 15 \n",
+ " 49853 \n",
+ " 2.226546 \n",
+ " Federally Qualified Health Centers \n",
+ " \n",
+ " \n",
+ " 16 \n",
+ " 51802 \n",
+ " 2.721903 \n",
+ " Federally Qualified Health Centers \n",
+ " \n",
+ " \n",
+ " 17 \n",
+ " 74269 \n",
+ " 2.113937 \n",
+ " Federally Qualified Health Centers \n",
+ " \n",
+ " \n",
+ " 18 \n",
+ " 41958 \n",
+ " 4.004004 \n",
+ " Federally Qualified Health Centers \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " service_pop \n",
+ " percap_spending \n",
+ " \n",
+ " \n",
+ " spatial_index \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " ALBANY PARK \n",
+ " 305008 \n",
+ " 1.638229 \n",
+ " \n",
+ " \n",
+ " ARCHER HEIGHTS \n",
+ " 59620 \n",
+ " 1.777927 \n",
+ " \n",
+ " \n",
+ " ARMOUR SQUARE \n",
+ " 58671 \n",
+ " 2.897513 \n",
+ " \n",
+ " \n",
+ " AUBURN GRESHAM \n",
+ " 106340 \n",
+ " 2.461102 \n",
+ " \n",
+ " \n",
+ " AUSTIN \n",
+ " 248120 \n",
+ " 3.094732 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "coverage.plot_cdf(filename = '/Users/whlu/spatial_access/data/coverage score/coverage_cdf_plot.png', \n",
+ " plot_type = \"percap\", title = 'CDF of Coverage Score',\n",
+ " xlabel = 'Per Capita Spending (USD)', \n",
+ " ylabel = 'Number of Tracts')\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Choropleth Mapping"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:spatial_access.BaseModel:Figure was saved to: /Users/whlu/spatial_access/data/coverage score/coverage_choropleth.png\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "coverage.plot_choropleth(column = \"percap_spending\", \n",
+ " shapefile='/Users/whlu/spatial_access/data/chicago_boundaries/chi_comm_boundaries.shp',\n",
+ " title = 'Per Capita Spending (USD), Chicago Community Areas',\n",
+ " include_destinations = False,\n",
+ " filename = '/Users/whlu/spatial_access/data/coverage score/coverage_choropleth.png')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/4_tsfca.ipynb b/docs/notebooks/4_tsfca.ipynb
new file mode 100755
index 0000000..9d0e5ea
--- /dev/null
+++ b/docs/notebooks/4_tsfca.ipynb
@@ -0,0 +1,1547 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Two Stage Floating Catchment Area"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the following lines, the access to per capita spending for each category of health services will be calculated using the Two Stage Floating Catchment Area method. \n",
+ "\n",
+ "#### **Column Inputs for the TSFCA Model**\n",
+ "Each model takes in inputs from both the sources and destination csv files. Each model takes in the standard inputs indicated below as well as additional inputs specified under each model\n",
+ "\n",
+ "* Source File\n",
+ " * Unique index identifier (ID) (integer or real) \n",
+ " * Latitude and longitude coordinates (real)\n",
+ " * To aggregate: larger areal ID\n",
+ "* Destination File\n",
+ " * Population within a certain buffer \n",
+ " * Capacity for each facility\n",
+ " * Category for each type of facility\n",
+ " * To aggregate: larger areal ID\n",
+ "\n",
+ "**Specifications for the TSFCA Model:**\n",
+ "\n",
+ "**name.tsfca():**\n",
+ "- network_type ('walk', 'bike', 'drive', or 'otp')\n",
+ "- sources_filename (primary input data)\n",
+ "- destinations_filename (secondary input data)\n",
+ "- source_column_names: (map column names to expected values)\n",
+ "- dest_column_names (map column names to expected values)\n",
+ "- configs: defaults to None, else pass in an instance of Configs to override\n",
+ " default values for p2p.\n",
+ "- debug (Enables one to see more detailed logging output)\n",
+ "- transit_matrix_filename (origin-destination transit matrix)\n",
+ "\n",
+ "**name.calculate():**\n",
+ "- upper_threshold (the time (in seconds) in which the origin and destinations are considered to be out of range of each other)\n",
+ "\n",
+ "Functions within the TSFCA Model class (use as name.function()): \n",
+ "- calculate () \n",
+ "- model_results (results of the TSFCA calculations)\n",
+ "- write_csv (filename='name') \n",
+ "- aggregate ()\n",
+ "- write_aggregated_results()\n",
+ "- plot_cdf()\n",
+ "- plot_choropleth()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from spatial_access.Models import *"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#View sources and destinations for Chicago Health Facilities\n",
+ "sources=pd.read_csv('/Users/whlu/spatial_access/data/input_data/tracts2010.csv')\n",
+ "dest=pd.read_csv('/Users/whlu/spatial_access/data/input_data/health_chicago.csv')\n",
+ "dest= dest.rename(index=str, columns={\"target\": \"capacity\"})\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
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+ " 22 \n",
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+ " 3 \n",
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801 rows × 6 columns
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+ "
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+ "\n",
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+ " \n",
+ " \n",
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+ " percap_spend_All Free Health Clinics \n",
+ " percap_spend_School-Based Health Centers \n",
+ " percap_spend_Federally Qualified Health Centers \n",
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+ " percap_spend_Federally Qualified Health Centers \n",
+ " percap_spend_all_categories \n",
+ " \n",
+ " \n",
+ " spatial_index \n",
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+ " \n",
+ " ALBANY PARK \n",
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+ " 6.985460 \n",
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+ " ARMOUR SQUARE \n",
+ " 6.248138 \n",
+ " 1.382859 \n",
+ " 0.67558 \n",
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+ " 16.908487 \n",
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+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "tsfca.plot_cdf(filename = '/Users/whlu/spatial_access/data/tsfca/tsfca_cdf_plot.png', \n",
+ " plot_type = \"percap\", title = 'CDF of TSFCA',\n",
+ " xlabel = 'Per Capita Spending (USD)', \n",
+ " ylabel = 'Number of Tracts')\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Choropleth Mapping"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:spatial_access.BaseModel:Figure was saved to: /Users/whlu/spatial_access/data/tsfca/tsfca_choropleth.png\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "tsfca.plot_choropleth(column = \"percap_spend_all_categories\", \n",
+ " shapefile='/Users/whlu/spatial_access/data/chicago_boundaries/chi_comm_boundaries.shp',\n",
+ " title = 'Access to Per Capital Spending (Total), Chicago Community Areas',\n",
+ " include_destinations = False,\n",
+ " filename = '/Users/whlu/spatial_access/data/tsfca/tsfca_choropleth.png')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/README.md b/docs/notebooks/README.md
deleted file mode 100755
index 5ff00ec..0000000
--- a/docs/notebooks/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-**The repository is organized as follows:**
-
-Under the **demo** folder, the notebooks run through Chicago's health example:
-* [reqs.ipynb](./reqs.ipynb) : This notebook shows the installation and files requirements in order to run the demos.
-* [matrix.ipynb](./matrix.ipynb) : This notebook shows how to run the travel time distance matrices. It uses the [p2p](./scripts/p2p.py) script.
-* [access_score.ipynb](./access_score.ipynb) : This notebook shows how to run the access score and the specific parameters that might be tweaked depending on the user's interest. It uses the [ScoreModel](./scripts/ScoreModel.py) and [CommunityAnalytics](./scripts/CommunityAnalytics.py) scripts.
-* [coverage_score.ipynb](./coverage_score.ipynb) :This notebook shows how to run the coverage score and the specific parameters that might be tweaked depending on the user's interest. It uses the [ScoreModel](./scripts/ScoreModel.py) and [CommunityAnalytics](./scripts/CommunityAnalytics.py) scripts.
-* [travel_time_metrics.ipynb](./travel_time_metrics.ipynb) :This notebook shows how to run the travel time distance matrices. It uses the [ScoreModel](./scripts/ScoreModel.py) and [CommunityAnalytics](./scripts/CommunityAnalytics.py) scripts.
-* [calibration.ipynb](./calibration.ipynb) : **? Have it as a notebook/within the demos/in this notebook or not at all?** Comparison (validation and calibration) with GoogleMaps.
-
-The **data** folder contains the files needed for the estimation of the metrics under **ORIG** (for origins) and **DEST** (for destinations). The **matrices** folder contains the estimated symmetric and asymmetric matrices. The **access_score**, **coverage_score**, and **travel_time_metrics** folders contain the results of the analyses. Finally, **figures** contain the results of maps and plots calculated during the process.
-
-Further editing of the Python scripts is accessible under the folder **scripts**.
-
-The rest of this notebook describes the reasoning of the Python modules p2p, ScoreModel, and CommunityAnalytics which compose the calculation of the algorithm of travel time **matrices** and the estimation of the **metrics**. Please note that this report stipulates the **_parameters_** specified within the estimations so the users can reproduce the calculations.
diff --git a/docs/notebooks/access_score.ipynb b/docs/notebooks/access_score.ipynb
deleted file mode 100755
index 811473a..0000000
--- a/docs/notebooks/access_score.ipynb
+++ /dev/null
@@ -1,982 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Access Scores"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Calculating the Access Score\n",
- "---\n",
- "In the following lines, first the _total access score_ will be estimated followed by an example of the _aggregation_ of the data. Finally, the score will be calculated for a _subset_ of the data (where one or several categories is selected).\n",
- "\n",
- "**Specifications for the Access Model:** \n",
- "\n",
- "**name.AccessModel( ):** \n",
- "- network_type ('drive' or 'walk')\n",
- "- source_filename (primary input data) \n",
- "- dest_filename (secondary input data) \n",
- "- sp_matrix_filename (origin-destination travel time matrix) \n",
- "- decay_function ('linear', 'root', 'logit') \n",
- "- limit_categories (only estimate model for specific categories)\n",
- "\n",
- "**name.calculate():**\n",
- "- custom_weight_dict (Specifies the weights of each facility/establishment defined as a previously generated dictionary. If custom_weight_dict=None, the dictionary will contain [1,1,1,1,1,1,1,1,1,1] weights.)\n",
- "- largest_weights_first=True sorts the weight array such that the largest values will be used first. If False, it will do the opposite.\n",
- "- limit_categories (None is defined as default, but user can delimit on particular categories of interest to calculate the access score.) \n",
- "- upper (is the time (in minutes) in which the origin and destinations are considered to be out of range of each other. As a default, if the travel time distance exceeds 30 minutes, the score will be zero.)\n",
- "- custom_threshold=40 (change binning threshold specifying it in calculate() function. Example: model_name.calculate(custom_threshold=40) for changing the percent of population above a certain threshold.)\n",
- "- normalize (Default is True and shows both normalized and non-normalized values. False shows only non-normalized results.) \n",
- "\n",
- "\n",
- "Functions within the Access Model class (use as name.function()): \n",
- "- calculate () \n",
- "- get_aggregate (aggregate_type) Aggregation can be by ('access') score for the lower aral unit. If the user wants to modify the level of aggregation, change 'community' with other spatial unit of analysis in code. \n",
- "- write_aggregate (aggregate_type, filename='name')\n",
- "- plot_cdf (title='CDF Access Score')\n",
- "- write_csv (filename='name') \n",
- "\n",
- "In the **scripts**, the user can specify: The default weights set before as [1,1,1,1,1,1,1,1,1,1] weights, different standarizations, and other modifications to the access score. \n",
- "\n",
- "Dictionary weights should be set as the following example: \n",
- "**dict = { \n",
- "\"Hospitals\": [10,10,10,10,10], \n",
- "\"Federally Qualified Health Centers\": [8, 7, 6, 5, 4], \n",
- "\"School-Based Health Centers\": [7, 7, 6, 6, 5], \n",
- "\"All Free Health Clinics\": [5, 5, 5, 4, 4], \n",
- "\"Other Health Providers\": [4,3,2,1,1] \n",
- "}**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import sys, os\n",
- "os.chdir('scripts')\n",
- "\n",
- "from CommunityAnalytics import *\n",
- "import pandas as pd\n",
- "\n",
- "%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "#View sources and destinations for Chicago Health Facilities\n",
- "sources=pd.read_csv('data/ORIG/tracts2010.csv')\n",
- "dest=pd.read_csv('data/DEST/health_chicago.csv')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " geoid10 \n",
- " lon \n",
- " lat \n",
- " Pop2014 \n",
- " Pov14 \n",
- " community \n",
- " \n",
- " \n",
- " \n",
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- " 41.870416 \n",
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- " 28 \n",
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\n",
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\n",
- "\n",
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\n",
- " \n",
- " \n",
- " ID \n",
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- " lon \n",
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- " category \n",
- " community \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 0 \n",
- " 1 \n",
- " American Indian Health Service of Chicago, Inc. \n",
- " 41.956676 \n",
- " -87.651879 \n",
- " 5 \n",
- " 127000 \n",
- " Other Health Providers \n",
- " 3 \n",
- " \n",
- " \n",
- " 1 \n",
- " 2 \n",
- " Hamdard Center for Health and Human Services \n",
- " 41.997852 \n",
- " -87.669535 \n",
- " 5 \n",
- " 190000 \n",
- " Other Health Providers \n",
- " 77 \n",
- " \n",
- " \n",
- " 2 \n",
- " 3 \n",
- " Infant Welfare Society of Chicago \n",
- " 41.924904 \n",
- " -87.717270 \n",
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- " Other Health Providers \n",
- " 22 \n",
- " \n",
- " \n",
- " 3 \n",
- " 4 \n",
- " Mercy Family - Henry Booth House Family Health... \n",
- " 41.841694 \n",
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- " 159000 \n",
- " Other Health Providers \n",
- " 35 \n",
- " \n",
- " \n",
- " 4 \n",
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- " Cook County - Dr. Jorge Prieto Health Center \n",
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- " \n",
- " \n",
- " 17031840300 \n",
- " 41.832094 \n",
- " -87.681882 \n",
- " 5881.0 \n",
- " 59.0 \n",
- " 6.551565 \n",
- " 22.0 \n",
- " 15.211203 \n",
- " \n",
- " \n",
- " 17031841100 \n",
- " 41.851006 \n",
- " -87.635098 \n",
- " 3363.0 \n",
- " 34.0 \n",
- " 9.284239 \n",
- " 44.0 \n",
- " 21.555834 \n",
- " \n",
- " \n",
- " 17031841200 \n",
- " 41.855562 \n",
- " -87.683342 \n",
- " 3710.0 \n",
- " 31.0 \n",
- " 20.034265 \n",
- " 96.0 \n",
- " 46.514881 \n",
- " \n",
- " \n",
- " 17031838200 \n",
- " 41.870416 \n",
- " -87.675079 \n",
- " 3296.0 \n",
- " 28.0 \n",
- " 40.359906 \n",
- " 111.0 \n",
- " 93.706271 \n",
- " \n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " access \n",
- " \n",
- " \n",
- " lower_areal_unit \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 44 \n",
- " 0.033946 \n",
- " \n",
- " \n",
- " 59 \n",
- " 4.633144 \n",
- " \n",
- " \n",
- " 34 \n",
- " 5.627126 \n",
- " \n",
- " \n",
- " 31 \n",
- " 15.149919 \n",
- " \n",
- " \n",
- " 28 \n",
- " 16.716169 \n",
- " \n",
- " \n",
- "
\n",
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:ScoreModel:Plot was saved to: data/figures/fig_5.png\n"
- ]
- }
- ],
- "source": [
- "access.plot_cdf('CDF of Access Score')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Subsetting the data to calculate the Access Score"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The variables in your data set are:\n",
- "> geoid10\n",
- "> lon\n",
- "> lat\n",
- "> Pop2014\n",
- "> Pov14\n",
- "> community\n",
- "If you have no population variable, write \"skip\" (no quotations)\n",
- "If you have no lower areal unit variable, write \"skip\" (no quotations)\n",
- "The variables in your data set are:\n",
- "> ID\n",
- "> Facility\n",
- "> lat\n",
- "> lon\n",
- "> Type\n",
- "> target\n",
- "> category\n",
- "> community\n",
- "If you have no target variable, write \"skip\" (no quotations)\n",
- "If you have no lower areal unit variable, write \"skip\" (no quotations)\n",
- "If you have no category variable, write \"skip\" (no quotations)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:ScoreModel:Loaded sp matrix from file: data/matrices/walk_asym_health_tracts.csv\n",
- "INFO:ScoreModel:Finished loading sp_matrix in 0.14 seconds\n",
- "INFO:ScoreModel:Processing... This could take a while\n",
- "INFO:ScoreModel:Finished processing ModelData in 0.06 seconds\n"
- ]
- }
- ],
- "source": [
- "#For Federally Qualified Health Centers (walking)\n",
- "access_fed = AccessModel(network_type='walk',\n",
- " sp_matrix_filename='data/matrices/walk_asym_health_tracts.csv',\n",
- " source_filename='data/ORIG/tracts2010.csv',\n",
- " dest_filename='data/DEST/health_chicago.csv',\n",
- " decay_function='logit',\n",
- " limit_categories=set(['Federally Qualified Health Centers']))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Example of density and variety weights:\n",
- "dict = {\n",
- "\"Federally Qualified Health Centers\": [10,10,10,10,10]\n",
- "}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:ScoreModel:Finished calculating hssa in 0.02 seconds\n"
- ]
- }
- ],
- "source": [
- "access_fed.calculate(custom_threshold=40,\n",
- " normalize=True,\n",
- " custom_weight_dict=dict,\n",
- " largest_weights_first=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " lat \n",
- " lon \n",
- " population \n",
- " lower_areal_unit \n",
- " access \n",
- " access_cat \n",
- " access_sd \n",
- " \n",
- " \n",
- " geoid10 \n",
- " \n",
- " \n",
- " \n",
- " \n",
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- " \n",
- " \n",
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- " 3363.0 \n",
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- " 1.781996 \n",
- " \n",
- " \n",
- " 17031841200 \n",
- " 41.855562 \n",
- " -87.683342 \n",
- " 3710.0 \n",
- " 31.0 \n",
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- " 50.0 \n",
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- " 19.886291 \n",
- " \n",
- " \n",
- "
\n",
- "
Pop2014 \n",
" Pov14 \n",
" community \n",
+ " ID \n",
" \n",
" \n",
" \n",
@@ -113,6 +125,7 @@
" 5157 \n",
" 769 \n",
" 44 \n",
+ " 1 \n",
" \n",
" \n",
" 1 \n",
@@ -122,6 +135,7 @@
" 5881 \n",
" 1021 \n",
" 59 \n",
+ " 2 \n",
" \n",
" \n",
" 2 \n",
@@ -131,6 +145,7 @@
" 3363 \n",
" 2742 \n",
" 34 \n",
+ " 3 \n",
" \n",
" \n",
" 3 \n",
@@ -140,6 +155,7 @@
" 3710 \n",
" 1819 \n",
" 31 \n",
+ " 4 \n",
" \n",
" \n",
" 4 \n",
@@ -149,18 +165,19 @@
" 3296 \n",
" 361 \n",
" 28 \n",
+ " 5 \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " geoid10 lon lat Pop2014 Pov14 community\n",
- "0 17031842400 -87.630040 41.742475 5157 769 44\n",
- "1 17031840300 -87.681882 41.832094 5881 1021 59\n",
- "2 17031841100 -87.635098 41.851006 3363 2742 34\n",
- "3 17031841200 -87.683342 41.855562 3710 1819 31\n",
- "4 17031838200 -87.675079 41.870416 3296 361 28"
+ " geoid10 lon lat Pop2014 Pov14 community ID\n",
+ "0 17031842400 -87.630040 41.742475 5157 769 44 1\n",
+ "1 17031840300 -87.681882 41.832094 5881 1021 59 2\n",
+ "2 17031841100 -87.635098 41.851006 3363 2742 34 3\n",
+ "3 17031841200 -87.683342 41.855562 3710 1819 31 4\n",
+ "4 17031838200 -87.675079 41.870416 3296 361 28 5"
]
},
"execution_count": 3,
@@ -174,7 +191,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -203,7 +220,7 @@
" lat \n",
" lon \n",
" Type \n",
- " target \n",
+ " capacity \n",
" category \n",
" community \n",
" \n",
@@ -276,15 +293,15 @@
"3 4 Mercy Family - Henry Booth House Family Health... 41.841694 \n",
"4 6 Cook County - Dr. Jorge Prieto Health Center 41.847143 \n",
"\n",
- " lon Type target category community \n",
- "0 -87.651879 5 127000 Other Health Providers 3 \n",
- "1 -87.669535 5 190000 Other Health Providers 77 \n",
- "2 -87.717270 5 137000 Other Health Providers 22 \n",
- "3 -87.624790 5 159000 Other Health Providers 35 \n",
- "4 -87.724975 5 166000 Other Health Providers 30 "
+ " lon Type capacity category community \n",
+ "0 -87.651879 5 127000 Other Health Providers 3 \n",
+ "1 -87.669535 5 190000 Other Health Providers 77 \n",
+ "2 -87.717270 5 137000 Other Health Providers 22 \n",
+ "3 -87.624790 5 159000 Other Health Providers 35 \n",
+ "4 -87.724975 5 166000 Other Health Providers 30 "
]
},
- "execution_count": 4,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -295,9 +312,26 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dest_updated = pd.read_csv('/Users/whlu/spatial_access/data/input_data/health_chicago.csv').rename(index=str, columns={\"target\": \"capacity\"})\n",
+ "dest_updated.to_csv('/Users/whlu/spatial_access/data/input_data/health_chicago_updated.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:spatial_access.p2p:You should use tmx instead of csv for significantly better performance\n"
+ ]
+ },
{
"name": "stdout",
"output_type": "stream",
@@ -309,60 +343,96 @@
"> Pop2014\n",
"> Pov14\n",
"> community\n",
- "If you have no population variable, write \"skip\" (no quotations)\n",
- "If you have no lower areal unit variable, write \"skip\" (no quotations)\n",
+ "> ID\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the unique index variable: geoid10\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "If you have no population variable, write \"skip\" (no quotations)\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the population variable: Pop2014\n",
+ "Enter the latitude variable: lat\n",
+ "Enter the longitude variable: lon\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"The variables in your data set are:\n",
+ "> Unnamed: 0\n",
"> ID\n",
"> Facility\n",
"> lat\n",
"> lon\n",
"> Type\n",
- "> target\n",
+ "> capacity\n",
"> category\n",
- "> community\n",
- "If you have no target variable, write \"skip\" (no quotations)\n",
- "If you have no lower areal unit variable, write \"skip\" (no quotations)\n",
- "If you have no category variable, write \"skip\" (no quotations)\n"
+ "> community\n"
]
},
{
- "name": "stderr",
+ "name": "stdin",
"output_type": "stream",
"text": [
- "INFO:ScoreModel:Loaded sp matrix from file: data/matrices/walk_asym_health_tracts.csv\n",
- "INFO:ScoreModel:Finished loading sp_matrix in 0.14 seconds\n",
- "INFO:ScoreModel:Processing... This could take a while\n",
- "INFO:ScoreModel:Finished processing ModelData in 0.06 seconds\n"
+ "Enter the unique index variable: ID\n"
]
- }
- ],
- "source": [
- "coverage = CoverageModel(network_type='walk',\n",
- " sp_matrix_filename='data/matrices/walk_asym_health_tracts.csv',\n",
- " source_filename='data/ORIG/tracts2010.csv',\n",
- " dest_filename='data/DEST/health_chicago.csv')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
+ },
{
- "name": "stderr",
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "If you have no capacity variable, write \"skip\" (no quotations)\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the capacity variable: capacity\n"
+ ]
+ },
+ {
+ "name": "stdout",
"output_type": "stream",
"text": [
- "INFO:ScoreModel:Finished calculating CoverageModel in 0.03 seconds\n"
+ "If you have no category variable, write \"skip\" (no quotations)\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the category variable: category\n",
+ "Enter the latitude variable: lat\n",
+ "Enter the longitude variable: lon\n"
]
}
],
"source": [
- "coverage.calculate()"
+ "coverage = Coverage(network_type=\"walk\",\n",
+ " transit_matrix_filename='/Users/whlu/spatial_access/data/output_1_walk.csv',\n",
+ " sources_filename='/Users/whlu/spatial_access/data/input_data/tracts2010.csv',\n",
+ " destinations_filename='/Users/whlu/spatial_access/data/input_data/health_chicago_updated.csv')"
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -386,114 +456,73 @@
" \n",
" \n",
" target \n",
+ " service_pop \n",
+ " percap_spending \n",
" category \n",
- " lat \n",
- " lon \n",
- " lower_areal_unit \n",
- " pop_served \n",
- " coverage \n",
- " \n",
- " \n",
- " ID \n",
- " \n",
" \n",
" \n",
" \n",
- " 1 \n",
- " 127000 \n",
- " Other Health Providers \n",
- " 41.956676 \n",
- " -87.651879 \n",
- " 3 \n",
- " 113180 \n",
- " 1.122106 \n",
+ " 14 \n",
+ " 100892 \n",
+ " 1.001070 \n",
+ " Federally Qualified Health Centers \n",
" \n",
" \n",
- " 2 \n",
- " 190000 \n",
- " Other Health Providers \n",
- " 41.997852 \n",
- " -87.669535 \n",
- " 77 \n",
- " 112084 \n",
- " 1.695157 \n",
+ " 15 \n",
+ " 49853 \n",
+ " 2.226546 \n",
+ " Federally Qualified Health Centers \n",
" \n",
" \n",
- " 3 \n",
- " 137000 \n",
- " Other Health Providers \n",
- " 41.924904 \n",
- " -87.717270 \n",
- " 22 \n",
- " 106064 \n",
- " 1.291673 \n",
+ " 16 \n",
+ " 51802 \n",
+ " 2.721903 \n",
+ " Federally Qualified Health Centers \n",
" \n",
" \n",
- " 4 \n",
- " 159000 \n",
- " Other Health Providers \n",
- " 41.841694 \n",
- " -87.624790 \n",
- " 35 \n",
- " 77870 \n",
- " 2.041865 \n",
+ " 17 \n",
+ " 74269 \n",
+ " 2.113937 \n",
+ " Federally Qualified Health Centers \n",
" \n",
" \n",
- " 6 \n",
- " 166000 \n",
- " Other Health Providers \n",
- " 41.847143 \n",
- " -87.724975 \n",
- " 30 \n",
- " 99714 \n",
- " 1.664761 \n",
+ " 18 \n",
+ " 41958 \n",
+ " 4.004004 \n",
+ " Federally Qualified Health Centers \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " target category lat lon lower_areal_unit \\\n",
- "ID \n",
- "1 127000 Other Health Providers 41.956676 -87.651879 3 \n",
- "2 190000 Other Health Providers 41.997852 -87.669535 77 \n",
- "3 137000 Other Health Providers 41.924904 -87.717270 22 \n",
- "4 159000 Other Health Providers 41.841694 -87.624790 35 \n",
- "6 166000 Other Health Providers 41.847143 -87.724975 30 \n",
- "\n",
- " pop_served coverage \n",
- "ID \n",
- "1 113180 1.122106 \n",
- "2 112084 1.695157 \n",
- "3 106064 1.291673 \n",
- "4 77870 2.041865 \n",
- "6 99714 1.664761 "
+ " service_pop percap_spending category\n",
+ "14 100892 1.001070 Federally Qualified Health Centers\n",
+ "15 49853 2.226546 Federally Qualified Health Centers\n",
+ "16 51802 2.721903 Federally Qualified Health Centers\n",
+ "17 74269 2.113937 Federally Qualified Health Centers\n",
+ "18 41958 4.004004 Federally Qualified Health Centers"
]
},
- "execution_count": 15,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "coverage.results.head()"
+ "coverage.calculate(upper_threshold=1800).head()"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
- "coverage.write_csv()"
+ "#Writes output to csv\n",
+ "\n",
+ "coverage.model_results.to_csv('/Users/whlu/spatial_access/data/coverage score/coverage_results.csv')"
]
},
{
@@ -512,7 +541,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -536,95 +565,74 @@
" \n",
" \n",
" target \n",
- " lat \n",
- " lon \n",
- " coverage \n",
+ " service_pop \n",
+ " percap_spending \n",
" \n",
" \n",
- " lower_areal_unit \n",
- " spatial_index \n",
" \n",
" \n",
" \n",
" \n",
- " 44 \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
+ " ALBANY PARK \n",
+ " 305008 \n",
+ " 1.638229 \n",
" \n",
" \n",
- " 59 \n",
- " 200000.0 \n",
- " 41.831274 \n",
- " -87.676580 \n",
- " 3.231279 \n",
+ " ARCHER HEIGHTS \n",
+ " 59620 \n",
+ " 1.777927 \n",
" \n",
" \n",
- " 34 \n",
- " 170000.0 \n",
- " 41.852812 \n",
- " -87.634843 \n",
- " 1.996641 \n",
+ " ARMOUR SQUARE \n",
+ " 58671 \n",
+ " 2.897513 \n",
" \n",
" \n",
- " 31 \n",
- " 691000.0 \n",
- " 209.274216 \n",
- " -438.350068 \n",
- " 10.296501 \n",
+ " AUBURN GRESHAM \n",
+ " 106340 \n",
+ " 2.461102 \n",
" \n",
" \n",
- " 28 \n",
- " 2020000.0 \n",
- " 502.477414 \n",
- " -1052.049515 \n",
- " 28.417641 \n",
+ " AUSTIN \n",
+ " 248120 \n",
+ " 3.094732 \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " target lat lon coverage\n",
- "lower_areal_unit \n",
- "44 NaN NaN NaN NaN\n",
- "59 200000.0 41.831274 -87.676580 3.231279\n",
- "34 170000.0 41.852812 -87.634843 1.996641\n",
- "31 691000.0 209.274216 -438.350068 10.296501\n",
- "28 2020000.0 502.477414 -1052.049515 28.417641"
+ " service_pop percap_spending\n",
+ "spatial_index \n",
+ "ALBANY PARK 305008 1.638229\n",
+ "ARCHER HEIGHTS 59620 1.777927\n",
+ "ARMOUR SQUARE 58671 2.897513\n",
+ "AUBURN GRESHAM 106340 2.461102\n",
+ "AUSTIN 248120 3.094732"
]
},
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "coverage._get_aggregate('coverage').head()"
+ "coverage.aggregate(aggregation_type=None, shapefile='/Users/whlu/spatial_access/data/chicago_boundaries/chi_comm_boundaries.shp', \n",
+ " spatial_index='community', projection='epsg:4326').head()"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:ScoreModel:Wrote aggregate to file: data/coverage_metrics/coverage_aggregate_0.csv\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "coverage.write_aggregate('coverage')\n",
+ "#For community areas write to csv\n",
"\n",
- "#Saved as cov_aggregate_health_tracts_comm.csv"
+ "coverage.write_aggregated_results(filename = \"/Users/whlu/spatial_access/data/coverage score/coverage_aggregated.csv\", output_type = 'csv')"
]
},
{
@@ -634,237 +642,90 @@
"\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " (target, All Free Health Clinics) \n",
- " (target, Federally Qualified Health Centers) \n",
- " (target, Hospitals) \n",
- " (target, Other Health Providers) \n",
- " (target, School-Based Health Centers) \n",
- " (coverage, All Free Health Clinics) \n",
- " (coverage, Federally Qualified Health Centers) \n",
- " (coverage, Hospitals) \n",
- " (coverage, Other Health Providers) \n",
- " (coverage, School-Based Health Centers) \n",
- " \n",
- " \n",
- " lower_areal_unit \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 44 \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " \n",
- " \n",
- " 59 \n",
- " NaN \n",
- " 200000.0 \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " 3.231279 \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " \n",
- " \n",
- " 34 \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " 170000.0 \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " NaN \n",
- " 1.996641 \n",
- " NaN \n",
- " \n",
- " \n",
- " 31 \n",
- " NaN \n",
- " 369000.0 \n",
- " NaN \n",
- " NaN \n",
- " 322000.0 \n",
- " NaN \n",
- " 5.464956 \n",
- " NaN \n",
- " NaN \n",
- " 4.831545 \n",
- " \n",
- " \n",
- " 28 \n",
- " 339000.0 \n",
- " 292000.0 \n",
- " 614000.0 \n",
- " 192000.0 \n",
- " 583000.0 \n",
- " 5.852741 \n",
- " 4.393548 \n",
- " 8.340839 \n",
- " 2.384945 \n",
- " 7.445568 \n",
- " \n",
- " \n",
- "
\n",
- "
"
]
},
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
- "coverage.agg_area_cat().head()"
+ "coverage.plot_cdf(filename = '/Users/whlu/spatial_access/data/coverage score/coverage_cdf_plot.png',\n",
+ " plot_type = \"percap\", title = 'CDF of Coverage Score',\n",
+ " xlabel = 'Per Capita Spending (USD)',\n",
+ " ylabel = 'Number of Tracts')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Choropleth Mapping"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/usr/local/lib/python3.6/site-packages/pandas/core/reshape/merge.py:544: UserWarning: merging between different levels can give an unintended result (1 levels on the left, 2 on the right)\n",
- " warnings.warn(msg, UserWarning)\n",
- "INFO:ScoreModel:Wrote aggregate to file: data/coverage_metrics/walkagg_area_cat_0.csv\n"
+ "INFO:spatial_access.BaseModel:Figure was saved to: /Users/whlu/spatial_access/data/coverage score/coverage_choropleth.png\n"
]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
- "coverage.write_agg_area_cat()\n",
- "\n",
- "#Saved as cov_aggregate_health_tracts_comm_cat.csv"
+ "coverage.plot_choropleth(column = \"percap_spending\",\n",
+ " shapefile='/Users/whlu/spatial_access/data/chicago_boundaries/chi_comm_boundaries.shp',\n",
+ " title = 'Per Capita Spending (USD), Chicago Community Areas',\n",
+ " include_destinations = False,\n",
+ " filename = '/Users/whlu/spatial_access/data/coverage score/coverage_choropleth.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
- "source": [
- "See all results in chicago_boundaries.shp"
- ]
+ "source": []
},
{
"cell_type": "code",
diff --git a/docs/notebooks/figures/infrastructure.png b/docs/notebooks/figures/infrastructure.png
deleted file mode 100755
index 760f957..0000000
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diff --git a/docs/notebooks/figures/infrastructure2.png b/docs/notebooks/figures/infrastructure2.png
deleted file mode 100755
index f6cf263..0000000
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diff --git a/docs/notebooks/figures/lucid.png b/docs/notebooks/figures/lucid.png
deleted file mode 100755
index 74ffb51..0000000
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diff --git a/docs/notebooks/matrix.ipynb b/docs/notebooks/matrix.ipynb
old mode 100755
new mode 100644
index 64458a0..3d10650
--- a/docs/notebooks/matrix.ipynb
+++ b/docs/notebooks/matrix.ipynb
@@ -14,8 +14,10 @@
"outputs": [],
"source": [
"import sys, os\n",
- "os.chdir('scripts')\n",
- "from p2p import *"
+ "os.chdir('/Users/whlu/spatial_access/spatial_access')\n",
+ "from p2p import *\n",
+ "\n",
+ "import geopandas as gpd"
]
},
{
@@ -69,90 +71,77 @@
" \n",
" \n",
" ID \n",
- " Facility \n",
- " lat \n",
+ " geoid10 \n",
" lon \n",
- " Type \n",
- " target \n",
- " category \n",
+ " lat \n",
+ " Pop2014 \n",
+ " Pov14 \n",
" community \n",
+ " ID \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
+ " 17031842400 \n",
+ " -87.630040 \n",
+ " 41.742475 \n",
+ " 5157 \n",
+ " 769 \n",
+ " 44 \n",
" 1 \n",
- " American Indian Health Service of Chicago, Inc. \n",
- " 41.956676 \n",
- " -87.651879 \n",
- " 5 \n",
- " 127000 \n",
- " Other Health Providers \n",
- " 3 \n",
" \n",
" \n",
" 1 \n",
+ " 17031840300 \n",
+ " -87.681882 \n",
+ " 41.832094 \n",
+ " 5881 \n",
+ " 1021 \n",
+ " 59 \n",
" 2 \n",
- " Hamdard Center for Health and Human Services \n",
- " 41.997852 \n",
- " -87.669535 \n",
- " 5 \n",
- " 190000 \n",
- " Other Health Providers \n",
- " 77 \n",
" \n",
" \n",
" 2 \n",
+ " 17031841100 \n",
+ " -87.635098 \n",
+ " 41.851006 \n",
+ " 3363 \n",
+ " 2742 \n",
+ " 34 \n",
" 3 \n",
- " Infant Welfare Society of Chicago \n",
- " 41.924904 \n",
- " -87.717270 \n",
- " 5 \n",
- " 137000 \n",
- " Other Health Providers \n",
- " 22 \n",
" \n",
" \n",
" 3 \n",
+ " 17031841200 \n",
+ " -87.683342 \n",
+ " 41.855562 \n",
+ " 3710 \n",
+ " 1819 \n",
+ " 31 \n",
" 4 \n",
- " Mercy Family - Henry Booth House Family Health... \n",
- " 41.841694 \n",
- " -87.624790 \n",
- " 5 \n",
- " 159000 \n",
- " Other Health Providers \n",
- " 35 \n",
" \n",
" \n",
" 4 \n",
- " 6 \n",
- " Cook County - Dr. Jorge Prieto Health Center \n",
- " 41.847143 \n",
- " -87.724975 \n",
+ " 17031838200 \n",
+ " -87.675079 \n",
+ " 41.870416 \n",
+ " 3296 \n",
+ " 361 \n",
+ " 28 \n",
" 5 \n",
- " 166000 \n",
- " Other Health Providers \n",
- " 30 \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " ID Facility lat \\\n",
- "0 1 American Indian Health Service of Chicago, Inc. 41.956676 \n",
- "1 2 Hamdard Center for Health and Human Services 41.997852 \n",
- "2 3 Infant Welfare Society of Chicago 41.924904 \n",
- "3 4 Mercy Family - Henry Booth House Family Health... 41.841694 \n",
- "4 6 Cook County - Dr. Jorge Prieto Health Center 41.847143 \n",
- "\n",
- " lon Type target category community \n",
- "0 -87.651879 5 127000 Other Health Providers 3 \n",
- "1 -87.669535 5 190000 Other Health Providers 77 \n",
- "2 -87.717270 5 137000 Other Health Providers 22 \n",
- "3 -87.624790 5 159000 Other Health Providers 35 \n",
- "4 -87.724975 5 166000 Other Health Providers 30 "
+ " geoid10 lon lat Pop2014 Pov14 community ID\n",
+ "0 17031842400 -87.630040 41.742475 5157 769 44 1\n",
+ "1 17031840300 -87.681882 41.832094 5881 1021 59 2\n",
+ "2 17031841100 -87.635098 41.851006 3363 2742 34 3\n",
+ "3 17031841200 -87.683342 41.855562 3710 1819 31 4\n",
+ "4 17031838200 -87.675079 41.870416 3296 361 28 5"
]
},
"execution_count": 3,
@@ -161,7 +150,7 @@
}
],
"source": [
- "df = pd.read_csv('data/DEST/health_chicago.csv')\n",
+ "df = pd.read_csv('/Users/whlu/spatial_access/data/input_data/tracts2010.csv')\n",
"df.head()"
]
},
@@ -203,13 +192,6 @@
"collapsed": true
},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:p2p:Processing network (walk) in format: csv with epsilon: 0.05\n"
- ]
- },
{
"name": "stdout",
"output_type": "stream",
@@ -220,16 +202,17 @@
"> lat\n",
"> Pop2014\n",
"> Pov14\n",
- "> community\n"
+ "> community\n",
+ "> ID\n"
]
},
{
- "name": "stderr",
+ "name": "stdin",
"output_type": "stream",
"text": [
- "INFO:p2p:Total number of rows in the dataset: 801\n",
- "INFO:p2p:Complete number of rows for computing the matrix: 801\n",
- "INFO:p2p:Total number of rows dropped due to missing latitude or longitude values: 0\n"
+ "Enter the longitude coordinate: lon\n",
+ "Enter the latitude coordinate: lat\n",
+ "Enter the index name: ID\n"
]
},
{
@@ -248,135 +231,78 @@
]
},
{
- "name": "stderr",
+ "name": "stdin",
"output_type": "stream",
"text": [
- "INFO:p2p:Total number of rows in the dataset: 199\n",
- "INFO:p2p:Complete number of rows for computing the matrix: 199\n",
- "INFO:p2p:Total number of rows dropped due to missing latitude or longitude values: 0\n",
- "INFO:osmnet:Requesting network data within bounding box from Overpass API in 1 request(s)\n",
- "INFO:osmnet:Posting to http://www.overpass-api.de/api/interpreter with timeout=180, \"{'data': '[out:json][timeout:180];(way[\"highway\"][\"highway\"!~\"motor|proposed|construction|abandoned|platform|raceway\"][\"foot\"!~\"no\"][\"pedestrians\"!~\"no\"](41.55758000,-87.95448850,42.11430300,-87.48049640);>;);out;'}\"\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Requesting network data within bounding box from Overpass API in 1 request(s)\n",
- "Posting to http://www.overpass-api.de/api/interpreter with timeout=180, \"{'data': '[out:json][timeout:180];(way[\"highway\"][\"highway\"!~\"motor|proposed|construction|abandoned|platform|raceway\"][\"foot\"!~\"no\"][\"pedestrians\"!~\"no\"](41.55758000,-87.95448850,42.11430300,-87.48049640);>;);out;'}\"\n"
+ "Enter the longitude coordinate: lon\n",
+ "Enter the latitude coordinate: lat\n",
+ "Enter the index name: ID\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:osmnet:Downloaded 105,557.4KB from www.overpass-api.de in 12.78 seconds\n",
- "INFO:osmnet:Downloaded OSM network data within bounding box from Overpass API in 1 request(s) and 14.61 seconds\n"
+ "INFO:p2p:Approx area of bounding box: 2,445.05 sq. km\n",
+ "INFO:p2p:All operations completed in 15.88 seconds\n"
]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Downloaded 105,557.4KB from www.overpass-api.de in 12.78 seconds\n",
- "Downloaded OSM network data within bounding box from Overpass API in 1 request(s) and 14.61 seconds\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:osmnet:Returning OSM data with 644,575 nodes and 161,731 ways...\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Returning OSM data with 644,575 nodes and 161,731 ways...\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:osmnet:Edge node pairs completed. Took 194.62 seconds\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Edge node pairs completed. Took 194.62 seconds\n"
- ]
- },
+ }
+ ],
+ "source": [
+ "# Calculate asymmetric distance matrix for walking (takes ~5 minutes to run) \n",
+ "\n",
+ "w_asym_mat = TransitMatrix('walk', \n",
+ " primary_input='/Users/whlu/spatial_access/data/input_data/tracts2010.csv', \n",
+ " secondary_input='/Users/whlu/spatial_access/data/input_data/health_chicago.csv')\n",
+ "w_asym_mat.process()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:osmnet:Returning processed graph with 264,787 nodes and 401,733 edges...\n",
- "INFO:osmnet:Completed OSM data download and Pandana node and edge table creation in 227.61 seconds\n"
+ "INFO:p2p:Wrote to /Users/whlu/spatial_access/spatial_access/data/matrices/walk_asym_health_tracts.csv in 0.04 seconds\n"
]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Returning processed graph with 264,787 nodes and 401,733 edges...\n",
- "Completed OSM data download and Pandana node and edge table creation in 227.61 seconds\n",
- "Number of islands initially found: 227\n",
- "Number of disconnected nodes removed: 930\n"
- ]
- },
+ }
+ ],
+ "source": [
+ "w_asym_mat.write_csv(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/walk_asym_health_tracts.csv\")\n",
+ "\n",
+ "#Saved as walk_asym_health_tracts.csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:p2p:Prepared raw network in 23.45 seconds and wrote to: data/matrices/raw_network_0.csv\n",
- "/Users/irene/Documents/GitHub/access/travel_times/scripts/p2p.py:892: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
- " node_array = pd.DataFrame.as_matrix(nodes)\n",
- "INFO:p2p:Nearest Neighbor matching completed in 0.29 seconds\n",
- "INFO:p2p:Nearest Neighbor matching completed in 0.12 seconds\n",
- "INFO:p2p:Writing to file: data/matrices/walk_full_results_0.csv\n",
- "INFO:p2p:Shortest path matrix computed in 13.68 seconds\n",
- "INFO:p2p:All operations completed in 271.54 seconds\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Cleaned up calculation artifacts\n"
+ "INFO:p2p:Wrote to /Users/whlu/spatial_access/spatial_access/data/matrices/walk_asym_health_tracts.tmx in 0.00 seconds\n"
]
}
],
"source": [
- "# Calculate asymmetric distance matrix for walking (takes ~5 minutes to run) \n",
+ "w_asym_mat.write_tmx(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/walk_asym_health_tracts.tmx\")\n",
"\n",
- "w_asym_mat = TransitMatrix(network_type='walk',\n",
- " primary_input='data/ORIG/tracts2010.csv',\n",
- " secondary_input='data/DEST/health_chicago.csv', \n",
- " write_to_file=True)\n",
- "\n",
- "w_asym_mat.process()\n",
- "\n",
- "#The output is walk_asym_health_tracts.csv (used in the calculation of the metrics)"
+ "# Saved as walk_asym_health_tracts.tmx"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 7,
"metadata": {
"code_folding": []
},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:p2p:Processing network (drive) in format: csv with epsilon: 0.05\n"
- ]
- },
{
"name": "stdout",
"output_type": "stream",
@@ -387,16 +313,17 @@
"> lat\n",
"> Pop2014\n",
"> Pov14\n",
- "> community\n"
+ "> community\n",
+ "> ID\n"
]
},
{
- "name": "stderr",
+ "name": "stdin",
"output_type": "stream",
"text": [
- "INFO:p2p:Total number of rows in the dataset: 801\n",
- "INFO:p2p:Complete number of rows for computing the matrix: 801\n",
- "INFO:p2p:Total number of rows dropped due to missing latitude or longitude values: 0\n"
+ "Enter the longitude coordinate: lon\n",
+ "Enter the latitude coordinate: lat\n",
+ "Enter the index name: ID\n"
]
},
{
@@ -415,145 +342,80 @@
]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:p2p:Total number of rows in the dataset: 199\n",
- "INFO:p2p:Complete number of rows for computing the matrix: 199\n",
- "INFO:p2p:Total number of rows dropped due to missing latitude or longitude values: 0\n",
- "INFO:osmnet:Requesting network data within bounding box from Overpass API in 1 request(s)\n",
- "INFO:osmnet:Posting to http://www.overpass-api.de/api/interpreter with timeout=180, \"{'data': '[out:json][timeout:180];(way[\"highway\"][\"highway\"!~\"cycleway|footway|path|pedestrian|steps|track|proposed|construction|bridleway|abandoned|platform|raceway|service\"][\"motor_vehicle\"!~\"no\"][\"motorcar\"!~\"no\"][\"service\"!~\"parking|parking_aisle|driveway|emergency_access\"](41.55758000,-87.95448850,42.11430300,-87.48049640);>;);out;'}\"\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Requesting network data within bounding box from Overpass API in 1 request(s)\n",
- "Posting to http://www.overpass-api.de/api/interpreter with timeout=180, \"{'data': '[out:json][timeout:180];(way[\"highway\"][\"highway\"!~\"cycleway|footway|path|pedestrian|steps|track|proposed|construction|bridleway|abandoned|platform|raceway|service\"][\"motor_vehicle\"!~\"no\"][\"motorcar\"!~\"no\"][\"service\"!~\"parking|parking_aisle|driveway|emergency_access\"](41.55758000,-87.95448850,42.11430300,-87.48049640);>;);out;'}\"\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:osmnet:Downloaded 53,487.4KB from www.overpass-api.de in 8.82 seconds\n",
- "INFO:osmnet:Downloaded OSM network data within bounding box from Overpass API in 1 request(s) and 9.59 seconds\n"
- ]
- },
- {
- "name": "stdout",
+ "name": "stdin",
"output_type": "stream",
"text": [
- "Downloaded 53,487.4KB from www.overpass-api.de in 8.82 seconds\n",
- "Downloaded OSM network data within bounding box from Overpass API in 1 request(s) and 9.59 seconds\n"
+ "Enter the longitude coordinate: lon\n",
+ "Enter the latitude coordinate: lat\n",
+ "Enter the index name: ID\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:osmnet:Returning OSM data with 282,768 nodes and 60,979 ways...\n"
+ "INFO:p2p:Approx area of bounding box: 2,445.05 sq. km\n",
+ "INFO:p2p:All operations completed in 7.94 seconds\n"
]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Returning OSM data with 282,768 nodes and 60,979 ways...\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:osmnet:Edge node pairs completed. Took 67.06 seconds\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Edge node pairs completed. Took 67.06 seconds\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:osmnet:Returning processed graph with 87,248 nodes and 131,181 edges...\n",
- "INFO:osmnet:Completed OSM data download and Pandana node and edge table creation in 84.89 seconds\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Returning processed graph with 87,248 nodes and 131,181 edges...\n",
- "Completed OSM data download and Pandana node and edge table creation in 84.89 seconds\n",
- "Number of islands initially found: 254\n",
- "Number of disconnected nodes removed: 333\n"
- ]
- },
+ }
+ ],
+ "source": [
+ "# Calculate asymmetric distance matrix for driving (takes ~1.5 minutes to run) \n",
+ "\n",
+ "d_asym_mat = TransitMatrix('drive', \n",
+ " primary_input='/Users/whlu/spatial_access/data/input_data/tracts2010.csv', \n",
+ " secondary_input='/Users/whlu/spatial_access/data/input_data/health_chicago.csv')\n",
+ "\n",
+ "d_asym_mat.process()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:p2p:Prepared raw network in 4.47 seconds and wrote to: data/matrices/raw_network_1.csv\n",
- "/Users/irene/Documents/GitHub/access/travel_times/scripts/p2p.py:892: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
- " node_array = pd.DataFrame.as_matrix(nodes)\n",
- "INFO:p2p:Nearest Neighbor matching completed in 0.23 seconds\n",
- "INFO:p2p:Nearest Neighbor matching completed in 0.07 seconds\n",
- "INFO:p2p:Writing to file: data/matrices/drive_full_results_0.csv\n",
- "INFO:p2p:Shortest path matrix computed in 2.27 seconds\n",
- "INFO:p2p:All operations completed in 98.75 seconds\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Cleaned up calculation artifacts\n"
+ "INFO:p2p:Wrote to /Users/whlu/spatial_access/spatial_access/data/matrices/drive_asym_health_tracts.csv in 0.04 seconds\n"
]
}
],
"source": [
- "# Calculate asymmetric distance matrix for driving (takes ~1.5 minutes to run) \n",
+ "d_asym_mat.write_csv(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/drive_asym_health_tracts.csv\")\n",
"\n",
- "d_asym_mat = TransitMatrix(network_type='drive',\n",
- " primary_input='data/ORIG/tracts2010.csv',\n",
- " secondary_input='data/DEST/health_chicago.csv', \n",
- " write_to_file=True)\n",
- "\n",
- "d_asym_mat.process()\n",
+ "#Saved as drive_asym_health_tracts.csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "d_asym_mat.write_tmx(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/drive_asym_health_tracts.tmx\")\n",
"\n",
- "#The output is drive_asym_health_tracts.csv (used in the calculation of the metrics)"
+ "# Saved as drive_asym_health_tracts.tmx"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
+ "## Model 2: Symmetric Matrix\n",
"\n",
- "### Model 2: Symmetric Matrix \n",
- "---\n",
- "The second model creates a symmetric distance travel matrix from block to block (801 x 801 matrix). Then, we snap the destination points to the area of analysis (blocks), getting a matrix that calculates the distance between the destinations and every block in the dataset. \n"
+ "--\n",
+ "\n",
+ "The second model creates a symmetric distance travel matrix from block to block (801 x 801 matrix). Then, we snap the destination points to the area of analysis (blocks), getting a matrix that calculates the distance between the destinations and every block in the dataset.\n"
]
},
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "code_folding": []
- },
+ "execution_count": 13,
+ "metadata": {},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:p2p:Processing network (walk) in format: csv with epsilon: 0.05\n"
- ]
- },
{
"name": "stdout",
"output_type": "stream",
@@ -564,86 +426,72 @@
"> lat\n",
"> Pop2014\n",
"> Pov14\n",
- "> community\n"
+ "> community\n",
+ "> ID\n"
]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:p2p:Total number of rows in the dataset: 801\n",
- "INFO:p2p:Complete number of rows for computing the matrix: 801\n",
- "INFO:p2p:Total number of rows dropped due to missing latitude or longitude values: 0\n",
- "INFO:osmnet:Requesting network data within bounding box from Overpass API in 1 request(s)\n",
- "INFO:osmnet:Posting to http://www.overpass-api.de/api/interpreter with timeout=180, \"{'data': '[out:json][timeout:180];(way[\"highway\"][\"highway\"!~\"motor|proposed|construction|abandoned|platform|raceway\"][\"foot\"!~\"no\"][\"pedestrians\"!~\"no\"](41.60021990,-87.85448850,42.07126140,-87.58049640);>;);out;'}\"\n"
- ]
- },
- {
- "name": "stdout",
+ "name": "stdin",
"output_type": "stream",
"text": [
- "Requesting network data within bounding box from Overpass API in 1 request(s)\n",
- "Posting to http://www.overpass-api.de/api/interpreter with timeout=180, \"{'data': '[out:json][timeout:180];(way[\"highway\"][\"highway\"!~\"motor|proposed|construction|abandoned|platform|raceway\"][\"foot\"!~\"no\"][\"pedestrians\"!~\"no\"](41.60021990,-87.85448850,42.07126140,-87.58049640);>;);out;'}\"\n"
+ "Enter the longitude coordinate: lon\n",
+ "Enter the latitude coordinate: lat\n",
+ "Enter the index name: ID\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "ERROR:p2p:Error trying to download OSM network. \n",
- " Did you reverse lat/long? \n",
- " Is your network connection functional?\n",
- " \n"
- ]
- },
- {
- "ename": "SystemExit",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "An exception has occurred, use %tb to see the full traceback.\n",
- "\u001b[0;31mSystemExit\u001b[0m\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/usr/local/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2971: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
- " warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
+ "INFO:p2p:Approx area of bounding box: 2,067.39 sq. km\n",
+ "INFO:p2p:All operations completed in 9.09 seconds\n"
]
}
],
"source": [
"# Specify walking distance matrix (takes ~3 min to run) \n",
- "w_sym_mat = TransitMatrix(network_type='walk',\n",
- " primary_input='data/ORIG/tracts2010.csv',\n",
- " write_to_file=True,\n",
- " load_to_mem=True)\n",
+ "w_sym_mat = TransitMatrix('walk', \n",
+ " primary_input='/Users/whlu/spatial_access/data/input_data/tracts2010.csv')\n",
"\n",
"# Run process\n",
- "w_sym_mat.process()\n",
- "\n",
- "# Saved as walk_sym_health_tracts.csv"
+ "w_sym_mat.process()"
]
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "code_folding": [
- 0
- ],
- "collapsed": true
- },
+ "execution_count": 14,
+ "metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:p2p:Processing network (drive) in format: csv with epsilon: 0.05\n"
+ "INFO:p2p:Wrote to /Users/whlu/spatial_access/spatial_access/data/matrices/walk_sym_health_tracts.csv in 0.14 seconds\n"
]
- },
+ }
+ ],
+ "source": [
+ "w_sym_mat.write_csv(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/walk_sym_health_tracts.csv\")\n",
+ "\n",
+ "# Saved as walk_sym_health_tracts.csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "w_sym_mat.write_tmx(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/walk_sym_health_tracts.tmx\")\n",
+ "\n",
+ "# Saved as walk_sym_health_tracts.tmx"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
{
"name": "stdout",
"output_type": "stream",
@@ -654,34 +502,25 @@
"> lat\n",
"> Pop2014\n",
"> Pov14\n",
- "> community\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:p2p:Total number of rows in the dataset: 801\n",
- "INFO:p2p:Complete number of rows for computing the matrix: 801\n",
- "INFO:p2p:Total number of rows dropped due to missing latitude or longitude values: 0\n"
+ "> community\n",
+ "> ID\n"
]
},
{
- "name": "stdout",
+ "name": "stdin",
"output_type": "stream",
"text": [
- "The variable names in your speed limit data set are:\n",
- "> Unnamed: 0\n",
- "> FULLSTNA\n",
- "> SPDLIMIT\n"
+ "Enter the longitude coordinate: lopn\n",
+ "Enter the longitude coordinate: lon\n",
+ "Enter the latitude coordinate: lat\n",
+ "Enter the index name: ID\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:osmnet:Requesting network data within bounding box from Overpass API in 1 request(s)\n",
- "INFO:osmnet:Posting to http://www.overpass-api.de/api/interpreter with timeout=180, \"{'data': '[out:json][timeout:180];(way[\"highway\"][\"highway\"!~\"cycleway|footway|path|pedestrian|steps|track|proposed|construction|bridleway|abandoned|platform|raceway|service\"][\"motor_vehicle\"!~\"no\"][\"motorcar\"!~\"no\"][\"service\"!~\"parking|parking_aisle|driveway|emergency_access\"](41.60021990,-87.85448850,42.07126140,-87.58049640);>;);out;'}\"\n"
+ "INFO:p2p:Approx area of bounding box: 2,067.39 sq. km\n"
]
},
{
@@ -689,94 +528,68 @@
"output_type": "stream",
"text": [
"Requesting network data within bounding box from Overpass API in 1 request(s)\n",
- "Posting to http://www.overpass-api.de/api/interpreter with timeout=180, \"{'data': '[out:json][timeout:180];(way[\"highway\"][\"highway\"!~\"cycleway|footway|path|pedestrian|steps|track|proposed|construction|bridleway|abandoned|platform|raceway|service\"][\"motor_vehicle\"!~\"no\"][\"motorcar\"!~\"no\"][\"service\"!~\"parking|parking_aisle|driveway|emergency_access\"](41.60021990,-87.85448850,42.07126140,-87.58049640);>;);out;'}\"\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:osmnet:Downloaded 33,501.4KB from www.overpass-api.de in 7.87 seconds\n",
- "INFO:osmnet:Downloaded OSM network data within bounding box from Overpass API in 1 request(s) and 8.25 seconds\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Downloaded 33,501.4KB from www.overpass-api.de in 7.87 seconds\n",
- "Downloaded OSM network data within bounding box from Overpass API in 1 request(s) and 8.25 seconds\n"
+ "Posting to http://www.overpass-api.de/api/interpreter with timeout=180, \"{'data': '[out:json][timeout:180];(way[\"highway\"][\"highway\"!~\"cycleway|footway|path|pedestrian|steps|track|proposed|construction|bridleway|abandoned|platform|raceway|service\"][\"motor_vehicle\"!~\"no\"][\"motorcar\"!~\"no\"][\"service\"!~\"parking|parking_aisle|driveway|emergency_access\"](41.60021990,-87.95448850,42.07126140,-87.48049640);>;);out;'}\"\n",
+ "Downloaded 48,348.1KB from www.overpass-api.de in 4.67 seconds\n",
+ "Downloaded OSM network data within bounding box from Overpass API in 1 request(s) and 5.56 seconds\n",
+ "Returning OSM data with 249,806 nodes and 55,393 ways...\n",
+ "Edge node pairs completed. Took 79.76 seconds\n",
+ "Returning processed graph with 77,575 nodes and 117,269 edges...\n",
+ "Completed OSM data download and Pandana node and edge table creation in 89.67 seconds\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:osmnet:Returning OSM data with 170,563 nodes and 38,972 ways...\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Returning OSM data with 170,563 nodes and 38,972 ways...\n"
+ "/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py:2531: PerformanceWarning: \n",
+ "your performance may suffer as PyTables will pickle object types that it cannot\n",
+ "map directly to c-types [inferred_type->mixed,key->block2_values] [items->['name', 'ref', 'highway', 'service', 'bridge', 'oneway', 'toll', 'area', 'junction']]\n",
+ "\n",
+ " pytables.to_hdf(path_or_buf, key, self, **kwargs)\n",
+ "INFO:p2p:Finished querying osm\n",
+ "INFO:p2p:All operations completed in 95.81 seconds\n"
]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:osmnet:Edge node pairs completed. Took 46.40 seconds\n",
- "INFO:osmnet:Returning processed graph with 57,572 nodes and 88,601 edges...\n",
- "INFO:osmnet:Completed OSM data download and Pandana node and edge table creation in 59.66 seconds\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Edge node pairs completed. Took 46.40 seconds\n",
- "Returning processed graph with 57,572 nodes and 88,601 edges...\n",
- "Completed OSM data download and Pandana node and edge table creation in 59.66 seconds\n"
- ]
- },
+ }
+ ],
+ "source": [
+ "# Specify driving distance matrix (takes ~1.5 minute to run) \n",
+ "d_sym_mat = TransitMatrix('drive', \n",
+ " primary_input='/Users/whlu/spatial_access/data/input_data/tracts2010.csv')\n",
+ "\n",
+ "# Run process. For driving, p2p queries OSM to fetch the street network and then output the shortest path transit matrix\n",
+ "d_sym_mat.process()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:p2p:Matching street network completed in \n",
- " 10.65 seconds: 4121 perfect matches, 118 near perfect matches,\n",
- " 649 good matches and 99 non matches\n",
- "INFO:p2p:Prepared raw network in 0.37 seconds and wrote to: data/raw_network_0.csv\n",
- "/Users/irene/Downloads/access/travel_times/scripts/p2p.py:643: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
- " node_array = pd.DataFrame.as_matrix(nodes)\n",
- "INFO:p2p:Nearest Neighbor matching completed in 0.24 seconds\n",
- "INFO:p2p:Writing to file: data/drive_full_results_0.csv\n",
- "INFO:p2p:Shortest path matrix computed in 1.27 seconds\n",
- "INFO:p2p:All operations completed in 72.20 seconds\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Cleaned up calculation artifacts\n"
+ "INFO:p2p:Wrote to /Users/whlu/spatial_access/spatial_access/data/matrices/drive_sym_health_tracts.csv in 0.13 seconds\n"
]
}
],
"source": [
- "# Specify driving distance matrix (takes ~1.5 minute to run) \n",
- "d_sym_mat = TransitMatrix(network_type='drive',\n",
- " primary_input='data/ORIG/tracts2010.csv',\n",
- " write_to_file=True,\n",
- " load_to_mem=True)\n",
- "\n",
- "# Run process. For driving, p2p queries OSM to fetch the street network and then output the shortest path transit matrix\n",
- "d_sym_mat.process()\n",
+ "d_sym_mat.write_csv(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/drive_sym_health_tracts.csv\")\n",
"\n",
"# Saved as drive_sym_health_tracts.csv"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "d_sym_mat.write_tmx(outfile = \"/Users/whlu/spatial_access/spatial_access/data/matrices/drive_sym_health_tracts.tmx\")\n",
+ "\n",
+ "# Saved as drive_sym_health_tracts.tmx"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -803,7 +616,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 47,
"metadata": {
"code_folding": [
0
@@ -813,17 +626,18 @@
"outputs": [],
"source": [
"# Read destination files to join with boundaries \n",
- "health_gdf = gpd.read_file('data/DEST/health_chicago.shp')\n",
+ "health_gdf = gpd.read_file('/Users/whlu/spatial_access/data/input_data/health_chicago-point.shp')\n",
"health_gdf.head()\n",
"#Use symmetric matrix calculated above or read your previously saved results:\n",
- "sym_walk=pd.read_csv('data/matrices/walk_sym_health_tracts.csv')\n",
+ "sym_walk=pd.read_csv('/Users/whlu/spatial_access/spatial_access/data/matrices/walk_sym_health_tracts.csv')\n",
"\n",
"# Read boundaries files \n",
- "boundaries_gdf = gpd.read_file('data/ORIG/tracts2010.shp')\n",
+ "boundaries_gdf = gpd.read_file('/Users/whlu/spatial_access/data/chicago_boundaries/chi_blocks.shp')\n",
"\n",
"# Rename the ID name in order to match both data frames. \n",
"sym_walk= sym_walk.rename(index=str, columns={\"Unnamed: 0\": \"geoid10\"})\n",
"\n",
+ "\n",
"# Spatial join of amenities within each area of analysis \n",
"#It drops values outside of the tracts shapefile. From 199 to 182 datapoints.\n",
"s_join = gpd.sjoin(health_gdf, boundaries_gdf, how='inner', op='within')\n",
@@ -831,6 +645,7 @@
"# Convert geopanda dataframe to non-spatial dataframe to join \n",
"jb_df = pd.DataFrame(s_join)\n",
"\n",
+ "\n",
"# Make sure the id is of the same data type in both data frames.\n",
"# sym_walk.dtypes\n",
"# jb_df.dtypes\n",
@@ -838,14 +653,14 @@
"jb_df=pd.DataFrame(jb_df['geoid10'])\n",
"\n",
"# Join the symmetric matrix with the spatially joined data (with geoid10 id)\n",
- "j_asym=pd.merge(sym_walk, jb_df, left_on='geoid10', right_on='geoid10', how='right')\n",
+ "j_asym=pd.merge(sym_walk, jb_df, left_on='geoid10', right_on='geoid10', how='left')\n",
"\n",
- "j_asym.to_csv('data/matrices/walk_asym_health_tracts_join.csv')"
+ "j_asym.to_csv('/Users/whlu/spatial_access/spatial_access/data/matrices/walk_asym_health_tracts_join.csv')"
]
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 48,
"metadata": {},
"outputs": [
{
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- "5 rows × 802 columns
\n",
+ "5 rows × 803 columns
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""
],
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+ "[5 rows x 803 columns]"
]
},
- "execution_count": 26,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
@@ -1069,16 +863,16 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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+ "(801, 803)"
]
},
- "execution_count": 31,
+ "execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
@@ -1093,13 +887,6 @@
"source": [
"Now that the user has the origin destination matrices, we can proceed to estimate metrics. For this demo's purpose, we will use only drive_asym_health_tracts.csv and walk_asym_health_tracts.csv to run the metrics."
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
diff --git a/docs/notebooks/p2p-TransitMatrix simple demo.ipynb b/docs/notebooks/p2p-TransitMatrix simple demo.ipynb
deleted file mode 100644
index 4e88103..0000000
--- a/docs/notebooks/p2p-TransitMatrix simple demo.ipynb
+++ /dev/null
@@ -1,556 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from spatial_access.p2p import TransitMatrix"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "sources_df = pd.read_csv('data/input_data/sources.csv')\n",
- "dests_df = pd.read_csv('data/input_data/dests.csv')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
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+ " 17031410900 \n",
+ " -87.576659 \n",
+ " 41.797874 \n",
+ " 2923 \n",
+ " 607 \n",
+ " 41 \n",
+ " \n",
+ " \n",
+ " 10 \n",
+ " 17031411000 \n",
+ " -87.576873 \n",
+ " 41.790716 \n",
+ " 3313 \n",
+ " 465 \n",
+ " 41 \n",
+ " \n",
+ " \n",
+ " 11 \n",
+ " 17031411200 \n",
+ " -87.594017 \n",
+ " 41.790556 \n",
+ " 1691 \n",
+ " 289 \n",
+ " 41 \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " geoid10 lon lat Pop2014 Pov14 community\n",
- "0 17031842400 -87.630040 41.742475 5157 769 44\n",
- "1 17031840300 -87.681882 41.832094 5881 1021 59\n",
- "2 17031841100 -87.635098 41.851006 3363 2742 34\n",
- "3 17031841200 -87.683342 41.855562 3710 1819 31\n",
- "4 17031838200 -87.675079 41.870416 3296 361 28"
+ " geoid10 lon lat Pop2014 Pov14 community\n",
+ "0 17031836300 -87.601757 41.801532 6465 234 41\n",
+ "1 17031836200 -87.601284 41.790469 1329 47 41\n",
+ "2 17031410100 -87.579323 41.801497 1956 551 41\n",
+ "3 17031410200 -87.594269 41.801668 1248 362 41\n",
+ "4 17031410500 -87.603745 41.797827 2630 717 41\n",
+ "5 17031410600 -87.598946 41.797971 2365 703 41\n",
+ "6 17031411100 -87.589702 41.790449 2246 154 41\n",
+ "7 17031410700 -87.594198 41.798040 1959 453 41\n",
+ "8 17031410800 -87.589626 41.797960 3201 741 41\n",
+ "9 17031410900 -87.576659 41.797874 2923 607 41\n",
+ "10 17031411000 -87.576873 41.790716 3313 465 41\n",
+ "11 17031411200 -87.594017 41.790556 1691 289 41"
]
},
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "sources_df.head()"
+ "sources_df"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -192,162 +299,119 @@
" \n",
" \n",
" ID \n",
- " Facility \n",
- " lat \n",
+ " name \n",
" lon \n",
- " Type \n",
- " target \n",
+ " lat \n",
" category \n",
- " community \n",
+ " target \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
- " 1 \n",
- " American Indian Health Service of Chicago, Inc. \n",
- " 41.956676 \n",
- " -87.651879 \n",
- " 5 \n",
- " 127000 \n",
- " Other Health Providers \n",
- " 3 \n",
+ " Museum of Science and Industry \n",
+ " -87.583131 \n",
+ " 41.790883 \n",
+ " Museum \n",
+ " 400 \n",
" \n",
" \n",
" 1 \n",
- " 2 \n",
- " Hamdard Center for Health and Human Services \n",
- " 41.997852 \n",
- " -87.669535 \n",
- " 5 \n",
- " 190000 \n",
- " Other Health Providers \n",
- " 77 \n",
+ " Medici \n",
+ " -87.593738 \n",
+ " 41.791438 \n",
+ " Restaurant \n",
+ " 50 \n",
" \n",
" \n",
" 2 \n",
- " 3 \n",
- " Infant Welfare Society of Chicago \n",
- " 41.924904 \n",
- " -87.717270 \n",
- " 5 \n",
- " 137000 \n",
- " Other Health Providers \n",
- " 22 \n",
+ " Valois \n",
+ " -87.588328 \n",
+ " 41.799663 \n",
+ " Restaurant \n",
+ " 30 \n",
" \n",
" \n",
" 3 \n",
- " 4 \n",
- " Mercy Family - Henry Booth House Family Health... \n",
- " 41.841694 \n",
- " -87.624790 \n",
- " 5 \n",
- " 159000 \n",
- " Other Health Providers \n",
- " 35 \n",
+ " DuSable Museum \n",
+ " -87.607132 \n",
+ " 41.791985 \n",
+ " Museum \n",
+ " 100 \n",
" \n",
" \n",
" 4 \n",
- " 6 \n",
- " Cook County - Dr. Jorge Prieto Health Center \n",
- " 41.847143 \n",
- " -87.724975 \n",
- " 5 \n",
- " 166000 \n",
- " Other Health Providers \n",
- " 30 \n",
+ " Whole Foods \n",
+ " -87.587949 \n",
+ " 41.801978 \n",
+ " Supermarket \n",
+ " 50 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " Hyde Park Produce \n",
+ " -87.595524 \n",
+ " 41.799942 \n",
+ " Supermarket \n",
+ " 35 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " Jewel Osco \n",
+ " -87.607225 \n",
+ " 41.784580 \n",
+ " Supermarket \n",
+ " 70 \n",
" \n",
" \n",
"\n",
""
],
"text/plain": [
- " ID Facility lat \\\n",
- "0 1 American Indian Health Service of Chicago, Inc. 41.956676 \n",
- "1 2 Hamdard Center for Health and Human Services 41.997852 \n",
- "2 3 Infant Welfare Society of Chicago 41.924904 \n",
- "3 4 Mercy Family - Henry Booth House Family Health... 41.841694 \n",
- "4 6 Cook County - Dr. Jorge Prieto Health Center 41.847143 \n",
- "\n",
- " lon Type target category community \n",
- "0 -87.651879 5 127000 Other Health Providers 3 \n",
- "1 -87.669535 5 190000 Other Health Providers 77 \n",
- "2 -87.717270 5 137000 Other Health Providers 22 \n",
- "3 -87.624790 5 159000 Other Health Providers 35 \n",
- "4 -87.724975 5 166000 Other Health Providers 30 "
+ " name lon lat category target\n",
+ "0 Museum of Science and Industry -87.583131 41.790883 Museum 400\n",
+ "1 Medici -87.593738 41.791438 Restaurant 50\n",
+ "2 Valois -87.588328 41.799663 Restaurant 30\n",
+ "3 DuSable Museum -87.607132 41.791985 Museum 100\n",
+ "4 Whole Foods -87.587949 41.801978 Supermarket 50\n",
+ "5 Hyde Park Produce -87.595524 41.799942 Supermarket 35\n",
+ "6 Jewel Osco -87.607225 41.784580 Supermarket 70"
]
},
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "dests_df.head()"
+ "dests_df"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#assymetrix matrix, different source and dest files\n",
- "matrix = TransitMatrix('walk', primary_input='data/input_data/tracts2010.csv', secondary_input='data/input_data/health_chicago.csv')"
+ "matrix = TransitMatrix(network_type='walk',\n",
+ " primary_hints={'idx' : 'geoid10', 'population': 'skip', 'lat': 'lat', 'lon': 'lon'},\n",
+ " secondary_hints={'idx': 'name', 'capacity': 'skip', 'category': 'category', 'lat': 'lat', 'lon': 'lon'},\n",
+ " primary_input='data/input_data/sources/hyde_park_tracts.csv',\n",
+ " secondary_input='data/input_data/destinations/hyde_park_dests.csv')\n"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 14,
"metadata": {},
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The variables in your data set are:\n",
- "> geoid10\n",
- "> lon\n",
- "> lat\n",
- "> Pop2014\n",
- "> Pov14\n",
- "> community\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:spatial_access.p2p:Total number of rows in the dataset: 801\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The variables in your data set are:\n",
- "> ID\n",
- "> Facility\n",
- "> lat\n",
- "> lon\n",
- "> Type\n",
- "> target\n",
- "> category\n",
- "> community\n"
- ]
- },
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:spatial_access.p2p:Total number of rows in the dataset: 199\n",
- "INFO:spatial_access.p2p:Approx area of bounding box: 117.50 sq. km\n",
- "INFO:spatial_access.p2p:Read network from data/osm_query_cache/walk41.55758_-87.9544885_42.114303_-87.4804964.h5\n",
- "INFO:spatial_access.p2p:Prepared raw network in 1.58 seconds\n",
- "INFO:spatial_access.p2p:Processing matrix with 35 threads\n",
- "INFO:spatial_access.p2p:Shortest path matrix computed in 1.38 seconds using 35 threads\n",
- "INFO:spatial_access.p2p:All operations completed in 13.18 seconds\n"
+ "INFO:spatial_access.p2p:Approx area of bounding box: 141.64 sq. km\n",
+ "INFO:spatial_access.p2p:All operations completed in 0.34 seconds\n"
]
}
],
@@ -357,38 +421,31 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:spatial_access.p2p:Wrote to data/output_1_walk.csv in 0.03 seconds\n"
+ "INFO:spatial_access.p2p:Wrote to data/output_data/matrices/simple_demo_matrix.csv in 0.00 seconds\n"
]
}
],
"source": [
- "matrix.write_csv('data/output_1_walk.csv')"
+ "matrix.write_csv('data/output_data/matrices/simple_demo_matrix.csv')"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Access Metrics (Origin Attributes)"
+ "## Access Metrics (Origin Attributes)"
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -396,132 +453,80 @@
]
},
{
- "cell_type": "code",
- "execution_count": 12,
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Access Model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "# Access Score"
+ "The first line of code is defining Access Model using the previously generated matrix above with a linear decay function. If the user specifies **`transit_matrix_filename=None`**, the matrix will be estimated 'on the fly'. \n",
+ "The second line of code is defining the density and variety weights."
]
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "DEBUG:spatial_access.p2p:Running in debug mode\n",
- "DEBUG:spatial_access.p2p:Processing network (walk) with epsilon: 0.050000\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The variables in your data set are:\n",
- "> geoid10\n",
- "> lon\n",
- "> lat\n",
- "> Pop2014\n",
- "> Pov14\n",
- "> community\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:spatial_access.p2p:Total number of rows in the dataset: 801\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The variables in your data set are:\n",
- "> ID\n",
- "> Facility\n",
- "> lat\n",
- "> lon\n",
- "> Type\n",
- "> target\n",
- "> category\n",
- "> community\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:spatial_access.p2p:Total number of rows in the dataset: 199\n",
- "INFO:spatial_access.p2p:Approx area of bounding box: 117.50 sq. km\n",
- "DEBUG:spatial_access.p2p:set bbox: [41.55758, -87.9544885, 42.114303, -87.4804964]\n",
- "INFO:spatial_access.p2p:Read network from data/osm_query_cache/walk41.55758_-87.9544885_42.114303_-87.4804964.h5\n",
- "DEBUG:spatial_access.p2p:Nearest Neighbor matching completed in 0.42 seconds\n",
- "DEBUG:spatial_access.p2p:Nearest Neighbor matching completed in 0.15 seconds\n",
- "INFO:spatial_access.p2p:Prepared raw network in 1.62 seconds\n",
- "INFO:spatial_access.p2p:Processing matrix with 35 threads\n",
- "INFO:spatial_access.p2p:Shortest path matrix computed in 1.37 seconds using 35 threads\n",
- "INFO:spatial_access.p2p:All operations completed in 11.63 seconds\n"
+ "WARNING:spatial_access.p2p:You should use tmx instead of csv for significantly better performance\n"
]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The variables in your data set are:\n",
- "> geoid10\n",
- "> lon\n",
- "> lat\n",
- "> Pop2014\n",
- "> Pov14\n",
- "> community\n",
- "If you have no population variable, write \"skip\" (no quotations)\n",
- "The variables in your data set are:\n",
- "> ID\n",
- "> Facility\n",
- "> lat\n",
- "> lon\n",
- "> Type\n",
- "> target\n",
- "> category\n",
- "> community\n",
- "If you have no capacity variable, write \"skip\" (no quotations)\n",
- "If you have no category variable, write \"skip\" (no quotations)\n"
+ "ename": "NameError",
+ "evalue": "name 'categories' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0msource_column_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'idx'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;34m'geoid10'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'population'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'skip'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lat'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'lat'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lon'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'lon'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mdest_column_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'idx'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'name'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'capacity'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'skip'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'category'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'category'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lat'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'lat'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lon'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'lon'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m decay_function = 'linear')\n\u001b[0m",
+ "\u001b[0;32m~/Downloads/push/spatial_access/spatial_access/Models.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, network_type, sources_filename, source_column_names, destinations_filename, dest_column_names, transit_matrix_filename, decay_function, configs, debug)\u001b[0m\n\u001b[1;32m 483\u001b[0m debug=debug)\n\u001b[1;32m 484\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_transit_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransit_matrix_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 485\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_focus_categories\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcategories\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcategories\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 486\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result_column_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'score'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'categories' is not defined"
]
}
],
"source": [
"access = AccessModel(network_type='walk',\n",
- " sp_matrix_filename=None,\n",
- " sources_filename='data/input_data/tracts2010.csv',\n",
- " destinations_filename='data/input_data/health_chicago.csv',\n",
- " decay_function='logit')"
+ " transit_matrix_filename='data/output_data/matrices/simple_demo_matrix.csv',\n",
+ " sources_filename='data/input_data/sources/hyde_park_tracts.csv',\n",
+ " destinations_filename='data/input_data/destinations/hyde_park_dests.csv',\n",
+ " source_column_names={'idx' : 'geoid10', 'population': 'skip', 'lat': 'lat', 'lon': 'lon'},\n",
+ " dest_column_names={'idx': 'name', 'capacity': 'skip', 'category': 'category', 'lat': 'lat', 'lon': 'lon'},\n",
+ " decay_function = 'linear')"
]
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"category_dict = {\n",
- "\"Hospitals\": [10,10,10,10,10],\n",
- "\"Federally Qualified Health Centers\": [8, 7, 6, 5, 4],\n",
- "\"School-Based Health Centers\": [7, 7, 6, 6, 5],\n",
- "\"All Free Health Clinics\": [5, 5, 5, 4, 4],\n",
- "\"Other Health Providers\": [4,3,2,1,1]\n",
+ " \"Museum\": [5, 5, 3],\n",
+ " \"Restaurant\": [10, 10],\n",
+ " \"Supermarket\": [10, 7, 5]\n",
"}"
]
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 101,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:spatial_access.BaseModel:Using weights: {'Museum': [5, 5, 3], 'Supermarket': [10, 7, 5], 'Restaurant': [10, 10]}\n"
+ ]
+ },
{
"data": {
"text/html": [
@@ -544,1524 +549,288 @@
" \n",
" all_categories_score \n",
- " Hospitals_score \n",
- " Federally Qualified Health Centers_score \n",
- " School-Based Health Centers_score \n",
- " All Free Health Clinics_score \n",
- " Other Health Providers_score \n",
- " all_categories_good_access \n",
- " Hospitals_good_access \n",
- " Federally Qualified Health Centers_good_access \n",
- " School-Based Health Centers_good_access \n",
- " All Free Health Clinics_good_access \n",
- " Other Health Providers_good_access \n",
+ " Museum_score \n",
+ " Supermarket_score \n",
+ " Restaurant_score \n",
" \n",
" \n",
" \n",
" \n",
- " 17031842400 \n",
- " 0.240321 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 0.896108 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " False \n",
- " False \n",
- " False \n",
- " False \n",
- " False \n",
- " False \n",
- " \n",
- " \n",
- " 17031840300 \n",
- " 11.805055 \n",
- " 0.000000 \n",
- " 28.691670 \n",
- " 0.778580 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " False \n",
- " False \n",
- " False \n",
- " False \n",
- " False \n",
- " False \n",
- " \n",
- " \n",
- " 17031841100 \n",
- " 31.087457 \n",
- " 10.433250 \n",
- " 11.210502 \n",
- " 39.557094 \n",
- " 27.087906 \n",
- " 70.608099 \n",
- " False \n",
- " False \n",
- " False \n",
- " False \n",
- " False \n",
- " True \n",
- " \n",
- " \n",
- " 17031841200 \n",
- " 74.932768 \n",
- " 50.785100 \n",
- " 85.340477 \n",
- " 47.998395 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " True \n",
- " True \n",
- " True \n",
- " True \n",
- " False \n",
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+ " all_categories_score Museum_score Supermarket_score \\\n",
+ "17031836300 19.318333 2.108333 9.826667 \n",
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+ "17031411000 10.296667 3.219444 1.605000 \n",
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+ "\n",
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+ "execution_count": 101,
+ "metadata": {},
+ "output_type": "execute_result"
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+ "source": [
+ "access.calculate(upper_threshold=1800,\n",
+ " normalize=False,\n",
+ " category_weight_dict=category_dict)"
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+ "execution_count": 17,
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801 rows × 12 columns
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""
],
"text/plain": [
- " all_categories_score Hospitals_score \\\n",
- "17031842400 0.240321 0.00000 \n",
- "17031840300 11.805055 0.00000 \n",
- "17031841100 31.087457 10.43325 \n",
- "17031841200 74.932768 50.78510 \n",
- "17031838200 96.805988 100.00000 \n",
- "\n",
- " Federally Qualified Health Centers_score \\\n",
- "17031842400 0.000000 \n",
- "17031840300 28.691670 \n",
- "17031841100 11.210502 \n",
- "17031841200 85.340477 \n",
- "17031838200 35.561236 \n",
- "\n",
- " School-Based Health Centers_score All Free Health Clinics_score \\\n",
- "17031842400 0.896108 0.000000 \n",
- "17031840300 0.778580 0.000000 \n",
- "17031841100 39.557094 27.087906 \n",
- "17031841200 47.998395 0.000000 \n",
- "17031838200 73.486427 22.684526 \n",
- "\n",
- " Other Health Providers_score all_categories_good_access \\\n",
- "17031842400 0.000000 False \n",
- "17031840300 0.000000 False \n",
- "17031841100 70.608099 False \n",
- "17031841200 0.000000 True \n",
- "17031838200 57.301925 True \n",
- "\n",
- " Hospitals_good_access \\\n",
- "17031842400 False \n",
- "17031840300 False \n",
- "17031841100 False \n",
- "17031841200 True \n",
- "17031838200 True \n",
- "\n",
- " Federally Qualified Health Centers_good_access \\\n",
- "17031842400 False \n",
- "17031840300 False \n",
- "17031841100 False \n",
- "17031841200 True \n",
- "17031838200 False \n",
+ " time_to_nearest_Museum time_to_nearest_Supermarket \\\n",
+ "17031836300 1041 545 \n",
+ "17031836200 547 906 \n",
+ "17031410100 1076 765 \n",
+ "17031410200 1371 474 \n",
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"\n",
- " School-Based Health Centers_good_access \\\n",
- "17031842400 False \n",
- "17031840300 False \n",
- "17031841100 False \n",
- "17031841200 True \n",
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- "\n",
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- "17031840300 False \n",
- "17031841100 False \n",
- "17031841200 False \n",
- "17031838200 False \n",
- "\n",
- " Other Health Providers_good_access \n",
- "17031842400 False \n",
- "17031840300 False \n",
- "17031841100 True \n",
- "17031841200 False \n",
- "17031838200 True "
+ " time_to_nearest_Restaurant time_to_nearest_all_categories \n",
+ "17031836300 994 545 \n",
+ "17031836200 517 517 \n",
+ "17031410100 629 629 \n",
+ "17031410200 506 474 \n",
+ "17031410500 1056 643 \n",
+ "17031410600 756 343 \n",
+ "17031411100 449 449 \n",
+ "17031410700 480 268 \n",
+ "17031410800 238 238 \n",
+ "17031410900 1041 897 \n",
+ "17031411000 1235 641 \n",
+ "17031411200 89 89 "
]
},
- "execution_count": 9,
+ "execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "access.model_results.head()"
+ "accessT.calculate()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "accessT.model_results.head()"
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
- "access.model_results.to_csv('access2010.csv')"
+ "accessT.model_results.to_csv('data/output_data/simple_demo_accessT.csv')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Access Count: Number of Destinations in Range"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Access time to closest destination"
+ "Access Count measures the number of destinations within a catchment area. In this case, the catchment area is 1800 seconds (30 minutes) of walking."
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 99,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "DEBUG:spatial_access.p2p:Running in debug mode\n",
- "DEBUG:spatial_access.p2p:Processing network (walk) with epsilon: 0.050000\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The variables in your data set are:\n",
- "> geoid10\n",
- "> lon\n",
- "> lat\n",
- "> Pop2014\n",
- "> Pov14\n",
- "> community\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:spatial_access.p2p:Total number of rows in the dataset: 801\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The variables in your data set are:\n",
- "> ID\n",
- "> Facility\n",
- "> lat\n",
- "> lon\n",
- "> Type\n",
- "> target\n",
- "> category\n",
- "> community\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:spatial_access.p2p:Total number of rows in the dataset: 199\n",
- "INFO:spatial_access.p2p:Approx area of bounding box: 117.50 sq. km\n",
- "DEBUG:spatial_access.p2p:set bbox: [41.55758, -87.9544885, 42.114303, -87.4804964]\n",
- "INFO:spatial_access.p2p:Read network from data/osm_query_cache/walk41.55758_-87.9544885_42.114303_-87.4804964.h5\n",
- "DEBUG:spatial_access.p2p:Nearest Neighbor matching completed in 0.42 seconds\n",
- "DEBUG:spatial_access.p2p:Nearest Neighbor matching completed in 0.15 seconds\n",
- "INFO:spatial_access.p2p:Prepared raw network in 1.60 seconds\n",
- "INFO:spatial_access.p2p:Processing matrix with 35 threads\n",
- "INFO:spatial_access.p2p:Shortest path matrix computed in 1.38 seconds using 35 threads\n",
- "INFO:spatial_access.p2p:All operations completed in 11.46 seconds\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The variables in your data set are:\n",
- "> geoid10\n",
- "> lon\n",
- "> lat\n",
- "> Pop2014\n",
- "> Pov14\n",
- "> community\n",
- "If you have no population variable, write \"skip\" (no quotations)\n",
- "The variables in your data set are:\n",
- "> ID\n",
- "> Facility\n",
- "> lat\n",
- "> lon\n",
- "> Type\n",
- "> target\n",
- "> category\n",
- "> community\n",
- "If you have no capacity variable, write \"skip\" (no quotations)\n",
- "If you have no category variable, write \"skip\" (no quotations)\n"
+ "INFO:spatial_access.p2p:Approx area of bounding box: 141.64 sq. km\n",
+ "INFO:spatial_access.p2p:All operations completed in 0.42 seconds\n"
]
}
],
"source": [
- "accessT = AccessTime(network_type='walk',\n",
- " sp_matrix_filename=None,\n",
- " sources_filename='data/input_data/tracts2010.csv',\n",
- " destinations_filename='data/input_data/health_chicago.csv')"
+ "accessC = AccessCount(network_type='walk',\n",
+ " transit_matrix_filename='data/output_data/matrices/simple_demo_matrix.csv',\n",
+ " sources_filename='data/input_data/sources/hyde_park_tracts.csv',\n",
+ " destinations_filename='data/input_data/destinations/hyde_park_dests.csv',\n",
+ " source_column_names={'idx' : 'geoid10', 'population': 'skip', 'lat': 'lat', 'lon': 'lon'},\n",
+ " dest_column_names={'idx': 'name', 'capacity': 'skip', 'category': 'category', 'lat': 'lat', 'lon': 'lon'}\n",
+ " )"
]
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
@@ -2392,333 +1070,235 @@
" \n",
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+ " count_in_range_Museum \n",
+ " count_in_range_Supermarket \n",
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+ " Restaurant \n",
+ " all_categories \n",
+ " Museum_per_capita \n",
+ " Supermarket_per_capita \n",
+ " Restaurant_per_capita \n",
+ " all_categories_per_capita \n",
" \n",
" \n",
- " 17031071200 \n",
- " 1034000 \n",
+ " spatial_index \n",
+ " \n",
+ " \n",
+ " \n",
" \n",
- " 17031130300 \n",
- " 1174000 \n",
+ " HYDE PARK \n",
+ " 400.0 \n",
+ " 85.0 \n",
+ " 80.0 \n",
+ " 565.0 \n",
+ " 0.017291 \n",
+ " 0.003674 \n",
+ " 0.003458 \n",
+ " 0.024423 \n",
" \n",
" \n",
- " 17031292200 \n",
- " 1882000 \n",
+ " WASHINGTON PARK \n",
+ " 100.0 \n",
+ " 0.0 \n",
+ " 0.0 \n",
+ " 100.0 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
" \n",
" \n",
- " 17031630900 \n",
- " 677000 \n",
+ " WOODLAWN \n",
+ " 0.0 \n",
+ " 70.0 \n",
+ " 0.0 \n",
+ " 70.0 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
" \n",
" \n",
"
\n",
- "
801 rows × 1 columns
\n",
"
\n",
" \n",
" percap_spend_all_categories \n",
+ " service_pop \n",
+ " percap_spending \n",
+ " category \n",
" \n",
" \n",
" \n",
" \n",
- " 17031842400 \n",
- " 2.105633 \n",
- " \n",
- " \n",
- " 17031840300 \n",
- " 8.006214 \n",
- " \n",
- " \n",
- " 17031841100 \n",
- " 14.469868 \n",
- " \n",
- " \n",
- " 17031841200 \n",
- " 26.235742 \n",
- " \n",
- " \n",
- " 17031838200 \n",
- " 34.861894 \n",
- " \n",
- " \n",
- " 17031650301 \n",
- " 1.852332 \n",
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- " 46.731572 \n",
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- " 46.731572 \n",
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- " \n",
- " 17031440201 \n",
- " 0.000000 \n",
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- " 17031839000 \n",
- " 6.608003 \n",
- " \n",
- " \n",
- " 17031030601 \n",
- " 13.836369 \n",
- " \n",
- " \n",
- " 17031030604 \n",
- " 12.081200 \n",
- " \n",
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- " 17031020801 \n",
- " 11.754579 \n",
- " \n",
- " \n",
- " 17031843300 \n",
- " 32.155156 \n",
- " \n",
- " \n",
- " 17031080202 \n",
- " 7.445448 \n",
- " \n",
- " \n",
- " 17031070102 \n",
- " 5.950369 \n",
- " \n",
- " \n",
- " 17031031501 \n",
- " 16.031848 \n",
- " \n",
- " \n",
- " 17031031502 \n",
- " 16.887460 \n",
- " \n",
- " \n",
- " 17031834900 \n",
- " 7.318661 \n",
- " \n",
- " \n",
- " 17031834800 \n",
- " 10.815228 \n",
- " \n",
- " \n",
- " 17031160502 \n",
- " 6.260889 \n",
- " \n",
- " \n",
- " 17031140702 \n",
- " 7.255581 \n",
- " \n",
- " \n",
- " 17031842000 \n",
- " 19.490115 \n",
- " \n",
- " \n",
- " 17031150402 \n",
- " 3.888481 \n",
- " \n",
- " \n",
- " 17031834400 \n",
- " 11.752635 \n",
- " \n",
- " \n",
- " 17031040201 \n",
- " 17.426541 \n",
- " \n",
- " \n",
- " 17031040202 \n",
- " 16.561329 \n",
- " \n",
- " \n",
- " 17031020702 \n",
- " 4.615418 \n",
+ " Museum of Science and Industry \n",
+ " 24861 \n",
+ " 0.016089 \n",
+ " Museum \n",
" \n",
" \n",
- " ... \n",
- " ... \n",
+ " DuSable Museum \n",
+ " 23134 \n",
+ " 0.004323 \n",
+ " Museum \n",
" \n",
" \n",
- " 17031291200 \n",
- " 17.834378 \n",
+ " Whole Foods \n",
+ " 31326 \n",
+ " 0.001596 \n",
+ " Supermarket \n",
" \n",
" \n",
- " 17031620300 \n",
- " 3.630258 \n",
+ " Hyde Park Produce \n",
+ " 31326 \n",
+ " 0.001117 \n",
+ " Supermarket \n",
" \n",
" \n",
- " 17031620400 \n",
- " 4.910887 \n",
+ " Jewel Osco \n",
+ " 16726 \n",
+ " 0.004185 \n",
+ " Supermarket \n",
" \n",
" \n",
- " 17031630100 \n",
- " 8.214787 \n",
+ " Medici \n",
+ " 31326 \n",
+ " 0.001596 \n",
+ " Restaurant \n",
" \n",
" \n",
- " 17031630300 \n",
- " 5.172186 \n",
- " \n",
- " \n",
- " 17031630400 \n",
- " 6.282396 \n",
- " \n",
- " \n",
- " 17031630500 \n",
- " 7.054518 \n",
- " \n",
- " \n",
- " 17031110300 \n",
- " 0.000000 \n",
- " \n",
- " \n",
- " 17031110400 \n",
- " 0.000000 \n",
- " \n",
- " \n",
- " 17031120100 \n",
- " 0.000000 \n",
- " \n",
- " \n",
- " 17031120200 \n",
- " 0.000000 \n",
- " \n",
- " \n",
- " 17031271400 \n",
- " 10.967600 \n",
- " \n",
- " \n",
- " 17031280100 \n",
- " 5.727133 \n",
- " \n",
- " \n",
- " 17031280800 \n",
- " 26.394242 \n",
- " \n",
- " \n",
- " 17031281900 \n",
- " 5.232678 \n",
- " \n",
- " \n",
- " 17031550100 \n",
- " 0.000000 \n",
- " \n",
- " \n",
- " 17031611500 \n",
- " 12.918862 \n",
- " \n",
- " \n",
- " 17031611800 \n",
- " 12.918862 \n",
- " \n",
- " \n",
- " 17031611900 \n",
- " 14.791521 \n",
- " \n",
- " \n",
- " 17031612000 \n",
- " 16.723639 \n",
- " \n",
- " \n",
- " 17031620100 \n",
- " 5.172186 \n",
- " \n",
- " \n",
- " 17031620200 \n",
- " 3.630258 \n",
- " \n",
- " \n",
- " 17031070200 \n",
- " 8.400801 \n",
- " \n",
- " \n",
- " 17031070400 \n",
- " 6.867317 \n",
- " \n",
- " \n",
- " 17031070500 \n",
- " 5.682349 \n",
- " \n",
- " \n",
- " 17031071000 \n",
- " 5.821422 \n",
- " \n",
- " \n",
- " 17031071200 \n",
- " 8.400801 \n",
- " \n",
- " \n",
- " 17031130300 \n",
- " 12.530199 \n",
- " \n",
- " \n",
- " 17031292200 \n",
- " 18.312486 \n",
- " \n",
- " \n",
- " 17031630900 \n",
- " 8.134728 \n",
+ " Valois \n",
+ " 31326 \n",
+ " 0.000958 \n",
+ " Restaurant \n",
" \n",
" \n",
"\n",
- "801 rows × 1 columns
\n",
""
],
"text/plain": [
- " percap_spend_all_categories\n",
- "17031842400 2.105633\n",
- "17031840300 8.006214\n",
- "17031841100 14.469868\n",
- "17031841200 26.235742\n",
- "17031838200 34.861894\n",
- "17031650301 1.852332\n",
- "17031530503 0.000000\n",
- "17031760803 0.000000\n",
- "17031540102 46.731572\n",
- "17031540101 46.731572\n",
- "17031440201 0.000000\n",
- "17031839000 6.608003\n",
- "17031030601 13.836369\n",
- "17031030604 12.081200\n",
- "17031020801 11.754579\n",
- "17031843300 32.155156\n",
- "17031080202 7.445448\n",
- "17031070102 5.950369\n",
- "17031031501 16.031848\n",
- "17031031502 16.887460\n",
- "17031834900 7.318661\n",
- "17031834800 10.815228\n",
- "17031160502 6.260889\n",
- "17031140702 7.255581\n",
- "17031842000 19.490115\n",
- "17031150402 3.888481\n",
- "17031834400 11.752635\n",
- "17031040201 17.426541\n",
- "17031040202 16.561329\n",
- "17031020702 4.615418\n",
- "... ...\n",
- "17031291200 17.834378\n",
- "17031620300 3.630258\n",
- "17031620400 4.910887\n",
- "17031630100 8.214787\n",
- "17031630300 5.172186\n",
- "17031630400 6.282396\n",
- "17031630500 7.054518\n",
- "17031110300 0.000000\n",
- "17031110400 0.000000\n",
- "17031120100 0.000000\n",
- "17031120200 0.000000\n",
- "17031271400 10.967600\n",
- "17031280100 5.727133\n",
- "17031280800 26.394242\n",
- "17031281900 5.232678\n",
- "17031550100 0.000000\n",
- "17031611500 12.918862\n",
- "17031611800 12.918862\n",
- "17031611900 14.791521\n",
- "17031612000 16.723639\n",
- "17031620100 5.172186\n",
- "17031620200 3.630258\n",
- "17031070200 8.400801\n",
- "17031070400 6.867317\n",
- "17031070500 5.682349\n",
- "17031071000 5.821422\n",
- "17031071200 8.400801\n",
- "17031130300 12.530199\n",
- "17031292200 18.312486\n",
- "17031630900 8.134728\n",
- "\n",
- "[801 rows x 1 columns]"
+ " service_pop percap_spending category\n",
+ "Museum of Science and Industry 24861 0.016089 Museum\n",
+ "DuSable Museum 23134 0.004323 Museum\n",
+ "Whole Foods 31326 0.001596 Supermarket\n",
+ "Hyde Park Produce 31326 0.001117 Supermarket\n",
+ "Jewel Osco 16726 0.004185 Supermarket\n",
+ "Medici 31326 0.001596 Restaurant\n",
+ "Valois 31326 0.000958 Restaurant"
]
},
- "execution_count": 46,
+ "execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "tsfca.calculate(upper_threshold=1800)"
+ "cov.calculate(upper_threshold=1800)"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
- "tsfca.model_results.to_csv('tsfca2010.csv')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Coverage Metrics (Destinations)"
+ "cov.model_results.to_csv('data/output_data/simple_demo_cov.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Coverage Score"
+ "### Two Stage Floating Catchment Area"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 48,
"metadata": {},
"outputs": [
{
@@ -4145,10 +2134,12 @@
]
},
{
- "name": "stderr",
+ "name": "stdin",
"output_type": "stream",
"text": [
- "INFO:spatial_access.p2p:Total number of rows in the dataset: 801\n"
+ "Enter the longitude coordinate: lon\n",
+ "Enter the latitude coordinate: lat\n",
+ "Enter the index name: geoid10\n"
]
},
{
@@ -4156,27 +2147,28 @@
"output_type": "stream",
"text": [
"The variables in your data set are:\n",
- "> ID\n",
- "> Facility\n",
- "> lat\n",
+ "> name\n",
"> lon\n",
- "> Type\n",
- "> target\n",
+ "> lat\n",
"> category\n",
- "> community\n"
+ "> target\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the longitude coordinate: lon\n",
+ "Enter the latitude coordinate: lat\n",
+ "Enter the index name: name\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:spatial_access.p2p:Total number of rows in the dataset: 199\n",
- "INFO:spatial_access.p2p:Approx area of bounding box: 117.50 sq. km\n",
- "INFO:spatial_access.p2p:Read network from data/osm_query_cache/walk41.55758_-87.9544885_42.114303_-87.4804964.h5\n",
- "INFO:spatial_access.p2p:Prepared raw network in 1.58 seconds\n",
- "INFO:spatial_access.p2p:Processing matrix with 35 threads\n",
- "INFO:spatial_access.p2p:Shortest path matrix computed in 1.38 seconds using 35 threads\n",
- "INFO:spatial_access.p2p:All operations completed in 13.92 seconds\n"
+ "INFO:spatial_access.p2p:Approx area of bounding box: 141.64 sq. km\n",
+ "INFO:spatial_access.p2p:All operations completed in 8.62 seconds\n"
]
},
{
@@ -4190,31 +2182,64 @@
"> Pop2014\n",
"> Pov14\n",
"> community\n",
- "If you have no population variable, write \"skip\" (no quotations)\n",
+ "If you have no population variable, write \"skip\" (no quotations)\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the population variable: Pop2014\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"The variables in your data set are:\n",
- "> ID\n",
- "> Facility\n",
- "> lat\n",
+ "> name\n",
"> lon\n",
- "> Type\n",
- "> target\n",
+ "> lat\n",
"> category\n",
- "> community\n",
- "If you have no capacity variable, write \"skip\" (no quotations)\n",
+ "> target\n",
+ "If you have no capacity variable, write \"skip\" (no quotations)\n"
+ ]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the capacity variable: target\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"If you have no category variable, write \"skip\" (no quotations)\n"
]
+ },
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter the category variable: category\n"
+ ]
}
],
"source": [
- "cov = Coverage(network_type='walk',\n",
- " sp_matrix_filename=None,\n",
- " sources_filename='data/input_data/tracts2010.csv',\n",
- " destinations_filename='data/input_data/health_chicago.csv')"
+ "tsfca = TSFCA(network_type='walk',\n",
+ " transit_matrix_filename='data/output_data/matrices/simple_demo_matrix.csv',\n",
+ " sources_filename='data/input_data/sources/hyde_park_tracts.csv',\n",
+ " destinations_filename='data/input_data/destinations/hyde_park_dests.csv',\n",
+ " source_column_names={'idx' : 'geoid10', 'population': 'Pop2014', 'lat': 'lat', 'lon': 'lon'},\n",
+ " dest_column_names={'idx': 'name', 'capacity': 'target', 'category': 'category', 'lat': 'lat', 'lon': 'lon'}\n",
+ " )"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 49,
"metadata": {},
"outputs": [
{
@@ -4238,2008 +2263,148 @@
" \n",
" \n",
" service_pop \n",
- " percap_spending \n",
- " category \n",
+ " percap_spend_Museum \n",
+ " percap_spend_Supermarket \n",
+ " percap_spend_Restaurant \n",
+ " percap_spend_all_categories \n",
" \n",
" \n",
" \n",
" \n",
- " 1 \n",
- " 113180 \n",
- " 1.122106 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 2 \n",
- " 120026 \n",
- " 1.582990 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 3 \n",
- " 102031 \n",
- " 1.342729 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 4 \n",
- " 77870 \n",
- " 2.041865 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 6 \n",
- " 99714 \n",
- " 1.664761 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 8 \n",
- " 80505 \n",
- " 2.384945 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 9 \n",
- " 90154 \n",
- " 1.242319 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 10 \n",
- " 108511 \n",
- " 1.354701 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 11 \n",
- " 85348 \n",
- " 2.179313 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 12 \n",
- " 47348 \n",
- " 3.484836 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 14 \n",
- " 110148 \n",
- " 0.916948 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 15 \n",
- " 49853 \n",
- " 2.226546 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 16 \n",
- " 51802 \n",
- " 2.721903 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 17 \n",
- " 82132 \n",
- " 1.911557 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 18 \n",
- " 41958 \n",
- " 4.004004 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 19 \n",
- " 65553 \n",
- " 3.050966 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 20 \n",
- " 113896 \n",
- " 0.904334 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 21 \n",
- " 99594 \n",
- " 1.666767 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 22 \n",
- " 40540 \n",
- " 2.984706 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 23 \n",
- " 60988 \n",
- " 1.983997 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 24 \n",
- " 62108 \n",
- " 1.932118 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 25 \n",
- " 55997 \n",
- " 1.803668 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 26 \n",
- " 102857 \n",
- " 1.438891 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 27 \n",
- " 82449 \n",
- " 2.413613 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 28 \n",
- " 83013 \n",
- " 1.541927 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 29 \n",
- " 79690 \n",
- " 1.279960 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 30 \n",
- " 66895 \n",
- " 1.793856 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 31 \n",
- " 72398 \n",
- " 2.044255 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 32 \n",
- " 105758 \n",
- " 1.352144 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 33 \n",
- " 106516 \n",
- " 1.304968 \n",
- " all_categories \n",
- " \n",
- " \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " ... \n",
- " \n",
- " \n",
- " 176 \n",
- " 57777 \n",
- " 3.167350 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 177 \n",
- " 107860 \n",
- " 1.603931 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 178 \n",
- " 60298 \n",
- " 2.288633 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 179 \n",
- " 39590 \n",
- " 3.561505 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 180 \n",
- " 79849 \n",
- " 2.304349 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 181 \n",
- " 67750 \n",
- " 2.804428 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 182 \n",
- " 128563 \n",
- " 0.855612 \n",
- " all_categories \n",
+ " 17031836300 \n",
+ " 0.004323 \n",
+ " 0.006899 \n",
+ " 0.002554 \n",
+ " 0.013775 \n",
" \n",
" \n",
- " 183 \n",
- " 111827 \n",
- " 1.251934 \n",
- " all_categories \n",
+ " 17031836200 \n",
+ " 0.020412 \n",
+ " 0.006899 \n",
+ " 0.002554 \n",
+ " 0.029864 \n",
" \n",
" \n",
- " 184 \n",
- " 79549 \n",
- " 1.558788 \n",
- " all_categories \n",
+ " 17031410100 \n",
+ " 0.016089 \n",
+ " 0.002713 \n",
+ " 0.002554 \n",
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" \n",
" \n",
- " 185 \n",
- " 103798 \n",
- " 1.647431 \n",
- " all_categories \n",
+ " 17031410200 \n",
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+ " 0.002713 \n",
+ " 0.002554 \n",
+ " 0.025679 \n",
" \n",
" \n",
- " 186 \n",
- " 77493 \n",
- " 1.535623 \n",
- " all_categories \n",
+ " 17031410500 \n",
+ " 0.020412 \n",
+ " 0.006899 \n",
+ " 0.002554 \n",
+ " 0.029864 \n",
" \n",
" \n",
- " 187 \n",
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- " 2.641930 \n",
- " all_categories \n",
+ " 17031410600 \n",
+ " 0.020412 \n",
+ " 0.006899 \n",
+ " 0.002554 \n",
+ " 0.029864 \n",
" \n",
" \n",
- " 188 \n",
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- " all_categories \n",
+ " 17031411100 \n",
+ " 0.020412 \n",
+ " 0.006899 \n",
+ " 0.002554 \n",
+ " 0.029864 \n",
" \n",
" \n",
- " 189 \n",
- " 106126 \n",
- " 1.875130 \n",
- " all_categories \n",
+ " 17031410700 \n",
+ " 0.020412 \n",
+ " 0.002713 \n",
+ " 0.002554 \n",
+ " 0.025679 \n",
" \n",
" \n",
- " 190 \n",
- " 89400 \n",
- " 1.454139 \n",
- " all_categories \n",
+ " 17031410800 \n",
+ " 0.020412 \n",
+ " 0.002713 \n",
+ " 0.002554 \n",
+ " 0.025679 \n",
" \n",
" \n",
- " 191 \n",
- " 98164 \n",
- " 2.037407 \n",
- " all_categories \n",
+ " 17031410900 \n",
+ " 0.016089 \n",
+ " 0.002713 \n",
+ " 0.002554 \n",
+ " 0.021357 \n",
" \n",
" \n",
- " 192 \n",
- " 103726 \n",
- " 1.465399 \n",
- " all_categories \n",
+ " 17031411000 \n",
+ " 0.016089 \n",
+ " 0.002713 \n",
+ " 0.002554 \n",
+ " 0.021357 \n",
" \n",
" \n",
- " 194 \n",
- " 59553 \n",
- " 2.098971 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 195 \n",
- " 80321 \n",
- " 1.431755 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 196 \n",
- " 77038 \n",
- " 2.011994 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 197 \n",
- " 100239 \n",
- " 1.087401 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 198 \n",
- " 75620 \n",
- " 2.618355 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 199 \n",
- " 74965 \n",
- " 1.587407 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 200 \n",
- " 80219 \n",
- " 2.393448 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 201 \n",
- " 7190 \n",
- " 26.703755 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 202 \n",
- " 51061 \n",
- " 3.192260 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 203 \n",
- " 79301 \n",
- " 2.433765 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 204 \n",
- " 82505 \n",
- " 1.757469 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 205 \n",
- " 76715 \n",
- " 1.603337 \n",
- " all_categories \n",
- " \n",
- " \n",
- " 206 \n",
- " 56990 \n",
- " 2.105633 \n",
- " all_categories \n",
+ " 17031411200 \n",
+ " 0.020412 \n",
+ " 0.006899 \n",
+ " 0.002554 \n",
+ " 0.029864 \n",
" \n",
" \n",
"\n",
- "199 rows × 3 columns
\n",
""
],
"text/plain": [
- " service_pop percap_spending category\n",
- "1 113180 1.122106 all_categories\n",
- "2 120026 1.582990 all_categories\n",
- "3 102031 1.342729 all_categories\n",
- "4 77870 2.041865 all_categories\n",
- "6 99714 1.664761 all_categories\n",
- "8 80505 2.384945 all_categories\n",
- "9 90154 1.242319 all_categories\n",
- "10 108511 1.354701 all_categories\n",
- "11 85348 2.179313 all_categories\n",
- "12 47348 3.484836 all_categories\n",
- "14 110148 0.916948 all_categories\n",
- "15 49853 2.226546 all_categories\n",
- "16 51802 2.721903 all_categories\n",
- "17 82132 1.911557 all_categories\n",
- "18 41958 4.004004 all_categories\n",
- "19 65553 3.050966 all_categories\n",
- "20 113896 0.904334 all_categories\n",
- "21 99594 1.666767 all_categories\n",
- "22 40540 2.984706 all_categories\n",
- "23 60988 1.983997 all_categories\n",
- "24 62108 1.932118 all_categories\n",
- "25 55997 1.803668 all_categories\n",
- "26 102857 1.438891 all_categories\n",
- "27 82449 2.413613 all_categories\n",
- "28 83013 1.541927 all_categories\n",
- "29 79690 1.279960 all_categories\n",
- "30 66895 1.793856 all_categories\n",
- "31 72398 2.044255 all_categories\n",
- "32 105758 1.352144 all_categories\n",
- "33 106516 1.304968 all_categories\n",
- ".. ... ... ...\n",
- "176 57777 3.167350 all_categories\n",
- "177 107860 1.603931 all_categories\n",
- "178 60298 2.288633 all_categories\n",
- "179 39590 3.561505 all_categories\n",
- "180 79849 2.304349 all_categories\n",
- "181 67750 2.804428 all_categories\n",
- "182 128563 0.855612 all_categories\n",
- "183 111827 1.251934 all_categories\n",
- "184 79549 1.558788 all_categories\n",
- "185 103798 1.647431 all_categories\n",
- "186 77493 1.535623 all_categories\n",
- "187 68132 2.641930 all_categories\n",
- "188 73569 1.372861 all_categories\n",
- "189 106126 1.875130 all_categories\n",
- "190 89400 1.454139 all_categories\n",
- "191 98164 2.037407 all_categories\n",
- "192 103726 1.465399 all_categories\n",
- "194 59553 2.098971 all_categories\n",
- "195 80321 1.431755 all_categories\n",
- "196 77038 2.011994 all_categories\n",
- "197 100239 1.087401 all_categories\n",
- "198 75620 2.618355 all_categories\n",
- "199 74965 1.587407 all_categories\n",
- "200 80219 2.393448 all_categories\n",
- "201 7190 26.703755 all_categories\n",
- "202 51061 3.192260 all_categories\n",
- "203 79301 2.433765 all_categories\n",
- "204 82505 1.757469 all_categories\n",
- "205 76715 1.603337 all_categories\n",
- "206 56990 2.105633 all_categories\n",
+ " percap_spend_Museum percap_spend_Supermarket \\\n",
+ "17031836300 0.004323 0.006899 \n",
+ "17031836200 0.020412 0.006899 \n",
+ "17031410100 0.016089 0.002713 \n",
+ "17031410200 0.020412 0.002713 \n",
+ "17031410500 0.020412 0.006899 \n",
+ "17031410600 0.020412 0.006899 \n",
+ "17031411100 0.020412 0.006899 \n",
+ "17031410700 0.020412 0.002713 \n",
+ "17031410800 0.020412 0.002713 \n",
+ "17031410900 0.016089 0.002713 \n",
+ "17031411000 0.016089 0.002713 \n",
+ "17031411200 0.020412 0.006899 \n",
"\n",
- "[199 rows x 3 columns]"
+ " percap_spend_Restaurant percap_spend_all_categories \n",
+ "17031836300 0.002554 0.013775 \n",
+ "17031836200 0.002554 0.029864 \n",
+ "17031410100 0.002554 0.021357 \n",
+ "17031410200 0.002554 0.025679 \n",
+ "17031410500 0.002554 0.029864 \n",
+ "17031410600 0.002554 0.029864 \n",
+ "17031411100 0.002554 0.029864 \n",
+ "17031410700 0.002554 0.025679 \n",
+ "17031410800 0.002554 0.025679 \n",
+ "17031410900 0.002554 0.021357 \n",
+ "17031411000 0.002554 0.021357 \n",
+ "17031411200 0.002554 0.029864 "
]
},
- "execution_count": 19,
+ "execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "cov.calculate(upper_threshold=1800)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Destination Sum: capacity and capacity per capita of providers in area"
+ "tsfca.calculate(upper_threshold=1800)"
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 11,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The variables in your data set are:\n",
- "> geoid10\n",
- "> lon\n",
- "> lat\n",
- "> Pop2014\n",
- "> Pov14\n",
- "> community\n",
- "If you have no population variable, write \"skip\" (no quotations)\n",
- "The variables in your data set are:\n",
- "> ID\n",
- "> Facility\n",
- "> lat\n",
- "> lon\n",
- "> Type\n",
- "> target\n",
- "> category\n",
- "> community\n",
- "If you have no capacity variable, write \"skip\" (no quotations)\n",
- "If you have no category variable, write \"skip\" (no quotations)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "d_sum = DestSum(network_type='walk',\n",
- " sources_filename='data/input_data/tracts2010.csv',\n",
- " destinations_filename='data/input_data/health_chicago.csv')"
+ "tsfca.model_results.to_csv('data/output_data/simple_demo_tsfca.csv')"
]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " All Free Health Clinics \n",
- " Hospitals \n",
- " School-Based Health Centers \n",
- " Federally Qualified Health Centers \n",
- " Other Health Providers \n",
- " all_categories \n",
- " All Free Health Clinics_per_capita \n",
- " Hospitals_per_capita \n",
- " School-Based Health Centers_per_capita \n",
- " Federally Qualified Health Centers_per_capita \n",
- " Other Health Providers_per_capita \n",
- " all_categories_per_capita \n",
- " \n",
- " \n",
- " spatial_index \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " ALBANY PARK \n",
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- " 0.0 \n",
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- " 0.0 \n",
- " 500000.0 \n",
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- " ARMOUR SQUARE \n",
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- " 0.000000 \n",
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- " \n",
- " AUBURN GRESHAM \n",
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- " \n",
- " AUSTIN \n",
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- " 1.216545 \n",
- " 1.849148 \n",
- " 3.678832 \n",
- " 0.000000 \n",
- " 6.744526 \n",
- " \n",
- " \n",
- " BELMONT CRAGIN \n",
- " 0.0 \n",
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- " 0.0 \n",
- " 178000.0 \n",
- " 0.0 \n",
- " 178000.0 \n",
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- " \n",
- " \n",
- " BRIGHTON PARK \n",
- " 0.0 \n",
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- " 163000.0 \n",
- " 128000.0 \n",
- " 0.0 \n",
- " 291000.0 \n",
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- " 0.000000 \n",
- " 3.622303 \n",
- " 2.844508 \n",
- " 0.000000 \n",
- " 6.466810 \n",
- " \n",
- " \n",
- " CALUMET HEIGHTS \n",
- " 0.0 \n",
- " 112000.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 112000.0 \n",
- " 0.000000 \n",
- " 8.358209 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 8.358209 \n",
- " \n",
- " \n",
- " CHICAGO LAWN \n",
- " 198000.0 \n",
- " 198000.0 \n",
- " 180000.0 \n",
- " 206000.0 \n",
- " 0.0 \n",
- " 782000.0 \n",
- " 3.622128 \n",
- " 3.622128 \n",
- " 3.292844 \n",
- " 3.768477 \n",
- " 0.000000 \n",
- " 14.305576 \n",
- " \n",
- " \n",
- " DOUGLAS \n",
- " 0.0 \n",
- " 0.0 \n",
- " 363000.0 \n",
- " 199000.0 \n",
- " 345000.0 \n",
- " 907000.0 \n",
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- " 0.000000 \n",
- " 9.490196 \n",
- " 5.202614 \n",
- " 9.019608 \n",
- " 23.712418 \n",
- " \n",
- " \n",
- " DUNNING \n",
- " 0.0 \n",
- " 183000.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 183000.0 \n",
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- " 4.250575 \n",
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- " 0.000000 \n",
- " 0.000000 \n",
- " 4.250575 \n",
- " \n",
- " \n",
- " EAST GARFIELD PARK \n",
- " 128000.0 \n",
- " 102000.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 230000.0 \n",
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- " \n",
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- " 3.310659 \n",
- " 3.618207 \n",
- " 0.000000 \n",
- " 3.437296 \n",
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- " \n",
- " \n",
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- " 0.0 \n",
- " 608000.0 \n",
- " 3.828171 \n",
- " 5.698422 \n",
- " 0.000000 \n",
- " 8.240795 \n",
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- " 17.767387 \n",
- " \n",
- " \n",
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- " 0.0 \n",
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- " 0.0 \n",
- " 120000.0 \n",
- " 0.0 \n",
- " 120000.0 \n",
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- " \n",
- " \n",
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- " 0.0 \n",
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- " 2.575000 \n",
- " \n",
- " \n",
- " GARFIELD RIDGE \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 260000.0 \n",
- " 0.0 \n",
- " 260000.0 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 5.690772 \n",
- " 0.000000 \n",
- " 5.690772 \n",
- " \n",
- " \n",
- " GRAND BOULEVARD \n",
- " 0.0 \n",
- " 134000.0 \n",
- " 101000.0 \n",
- " 157000.0 \n",
- " 112000.0 \n",
- " 504000.0 \n",
- " 0.000000 \n",
- " 3.533010 \n",
- " 2.662940 \n",
- " 4.139422 \n",
- " 2.952964 \n",
- " 13.288336 \n",
- " \n",
- " \n",
- " GREATER GRAND CROSSING \n",
- " 0.0 \n",
- " 0.0 \n",
- " 183000.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 183000.0 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 5.289017 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 5.289017 \n",
- " \n",
- " \n",
- " HEGEWISCH \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 179000.0 \n",
- " 0.0 \n",
- " 179000.0 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 19.168987 \n",
- " 0.000000 \n",
- " 19.168987 \n",
- " \n",
- " \n",
- " HUMBOLDT PARK \n",
- " 0.0 \n",
- " 146000.0 \n",
- " 238000.0 \n",
- " 708000.0 \n",
- " 0.0 \n",
- " 1092000.0 \n",
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- " 2.347682 \n",
- " 3.827043 \n",
- " 11.384650 \n",
- " 0.000000 \n",
- " 17.559375 \n",
- " \n",
- " \n",
- " HYDE PARK \n",
- " 0.0 \n",
- " 129000.0 \n",
- " 0.0 \n",
- " 151000.0 \n",
- " 0.0 \n",
- " 280000.0 \n",
- " 0.000000 \n",
- " 5.576208 \n",
- " 0.000000 \n",
- " 6.527189 \n",
- " 0.000000 \n",
- " 12.103398 \n",
- " \n",
- " \n",
- " IRVING PARK \n",
- " 0.0 \n",
- " 187000.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 187000.0 \n",
- " 0.000000 \n",
- " 3.310320 \n",
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- " 3.310320 \n",
- " \n",
- " \n",
- " KENWOOD \n",
- " 109000.0 \n",
- " 0.0 \n",
- " 155000.0 \n",
- " 191000.0 \n",
- " 0.0 \n",
- " 455000.0 \n",
- " 9.026915 \n",
- " 0.000000 \n",
- " 12.836439 \n",
- " 15.817805 \n",
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- " 37.681159 \n",
- " \n",
- " \n",
- " LAKE VIEW \n",
- " 0.0 \n",
- " 262000.0 \n",
- " 110000.0 \n",
- " 480000.0 \n",
- " 0.0 \n",
- " 852000.0 \n",
- " 0.000000 \n",
- " 2.521025 \n",
- " 1.058445 \n",
- " 4.618671 \n",
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- " 8.198141 \n",
- " \n",
- " \n",
- " LINCOLN PARK \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 101000.0 \n",
- " 0.0 \n",
- " 101000.0 \n",
- " 0.000000 \n",
- " 0.000000 \n",
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- " 1.460551 \n",
- " \n",
- " \n",
- " LINCOLN SQUARE \n",
- " 0.0 \n",
- " 293000.0 \n",
- " 124000.0 \n",
- " 383000.0 \n",
- " 0.0 \n",
- " 800000.0 \n",
- " 0.000000 \n",
- " 6.828723 \n",
- " 2.889971 \n",
- " 8.926282 \n",
- " 0.000000 \n",
- " 18.644976 \n",
- " \n",
- " \n",
- " LOGAN SQUARE \n",
- " 0.0 \n",
- " 0.0 \n",
- " 109000.0 \n",
- " 198000.0 \n",
- " 284000.0 \n",
- " 591000.0 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 1.362483 \n",
- " 2.474969 \n",
- " 3.549956 \n",
- " 7.387408 \n",
- " \n",
- " \n",
- " LOWER WEST SIDE \n",
- " 0.0 \n",
- " 0.0 \n",
- " 322000.0 \n",
- " 369000.0 \n",
- " 0.0 \n",
- " 691000.0 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 8.764528 \n",
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- " \n",
- " \n",
- " MCKINLEY PARK \n",
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- " 0.0 \n",
- " 0.0 \n",
- " 200000.0 \n",
- " 0.0 \n",
- " 200000.0 \n",
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- " 10.800886 \n",
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- " \n",
- " \n",
- " MONTCLARE \n",
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- " 111000.0 \n",
- " 0.0 \n",
- " 0.0 \n",
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- " 111000.0 \n",
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- " 13.034288 \n",
- " \n",
- " \n",
- " NEAR NORTH SIDE \n",
- " 0.0 \n",
- " 486000.0 \n",
- " 0.0 \n",
- " 305000.0 \n",
- " 0.0 \n",
- " 791000.0 \n",
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- " 5.312637 \n",
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- " 3.334062 \n",
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- " 8.646699 \n",
- " \n",
- " \n",
- " NEAR SOUTH SIDE \n",
- " 0.0 \n",
- " 131000.0 \n",
- " 145000.0 \n",
- " 193000.0 \n",
- " 0.0 \n",
- " 469000.0 \n",
- " 0.000000 \n",
- " 4.973802 \n",
- " 5.505353 \n",
- " 7.327815 \n",
- " 0.000000 \n",
- " 17.806971 \n",
- " \n",
- " \n",
- " NEAR WEST SIDE \n",
- " 339000.0 \n",
- " 614000.0 \n",
- " 583000.0 \n",
- " 292000.0 \n",
- " 192000.0 \n",
- " 2020000.0 \n",
- " 4.721251 \n",
- " 8.551175 \n",
- " 8.119438 \n",
- " 4.066682 \n",
- " 2.673983 \n",
- " 28.132529 \n",
- " \n",
- " \n",
- " NEW CITY \n",
- " 257000.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 538000.0 \n",
- " 0.0 \n",
- " 795000.0 \n",
- " 5.056567 \n",
- " 0.000000 \n",
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- " 10.585342 \n",
- " 0.000000 \n",
- " 15.641909 \n",
- " \n",
- " \n",
- " NORTH CENTER \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 132000.0 \n",
- " 0.0 \n",
- " 132000.0 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 3.461841 \n",
- " 0.000000 \n",
- " 3.461841 \n",
- " \n",
- " \n",
- " NORTH LAWNDALE \n",
- " 0.0 \n",
- " 346000.0 \n",
- " 140000.0 \n",
- " 556000.0 \n",
- " 0.0 \n",
- " 1042000.0 \n",
- " 0.000000 \n",
- " 6.058059 \n",
- " 2.451238 \n",
- " 9.734916 \n",
- " 0.000000 \n",
- " 18.244213 \n",
- " \n",
- " \n",
- " NORWOOD PARK \n",
- " 0.0 \n",
- " 152000.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 152000.0 \n",
- " 0.000000 \n",
- " 4.165981 \n",
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- " 4.165981 \n",
- " \n",
- " \n",
- " OAKLAND \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 297000.0 \n",
- " 0.0 \n",
- " 297000.0 \n",
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- " 0.000000 \n",
- " 0.000000 \n",
- " 91.950464 \n",
- " 0.000000 \n",
- " 91.950464 \n",
- " \n",
- " \n",
- " PORTAGE PARK \n",
- " 0.0 \n",
- " 184000.0 \n",
- " 0.0 \n",
- " 134000.0 \n",
- " 0.0 \n",
- " 318000.0 \n",
- " 0.000000 \n",
- " 2.739563 \n",
- " 0.000000 \n",
- " 1.995116 \n",
- " 0.000000 \n",
- " 4.734679 \n",
- " \n",
- " \n",
- " PULLMAN \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 111000.0 \n",
- " 0.0 \n",
- " 111000.0 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 15.925395 \n",
- " 0.000000 \n",
- " 15.925395 \n",
- " \n",
- " \n",
- " RIVERDALE \n",
- " 0.0 \n",
- " 0.0 \n",
- " 192000.0 \n",
- " 144000.0 \n",
- " 0.0 \n",
- " 336000.0 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 26.703755 \n",
- " 20.027816 \n",
- " 0.000000 \n",
- " 46.731572 \n",
- " \n",
- " \n",
- " ROGERS PARK \n",
- " 0.0 \n",
- " 0.0 \n",
- " 173000.0 \n",
- " 551000.0 \n",
- " 0.0 \n",
- " 724000.0 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 3.446078 \n",
- " 10.975658 \n",
- " 0.000000 \n",
- " 14.421736 \n",
- " \n",
- " \n",
- " ROSELAND \n",
- " 0.0 \n",
- " 181000.0 \n",
- " 0.0 \n",
- " 281000.0 \n",
- " 0.0 \n",
- " 462000.0 \n",
- " 0.000000 \n",
- " 4.091228 \n",
- " 0.000000 \n",
- " 6.351574 \n",
- " 0.000000 \n",
- " 10.442802 \n",
- " \n",
- " \n",
- " SOUTH CHICAGO \n",
- " 0.0 \n",
- " 138000.0 \n",
- " 0.0 \n",
- " 301000.0 \n",
- " 0.0 \n",
- " 439000.0 \n",
- " 0.000000 \n",
- " 4.221862 \n",
- " 0.000000 \n",
- " 9.208554 \n",
- " 0.000000 \n",
- " 13.430416 \n",
- " \n",
- " \n",
- " SOUTH LAWNDALE \n",
- " 0.0 \n",
- " 100000.0 \n",
- " 141000.0 \n",
- " 859000.0 \n",
- " 166000.0 \n",
- " 1266000.0 \n",
- " 0.000000 \n",
- " 1.220614 \n",
- " 1.721065 \n",
- " 10.485072 \n",
- " 2.026219 \n",
- " 15.452970 \n",
- " \n",
- " \n",
- " SOUTH SHORE \n",
- " 0.0 \n",
- " 195000.0 \n",
- " 0.0 \n",
- " 104000.0 \n",
- " 0.0 \n",
- " 299000.0 \n",
- " 0.000000 \n",
- " 5.176945 \n",
- " 0.000000 \n",
- " 2.761038 \n",
- " 0.000000 \n",
- " 7.937983 \n",
- " \n",
- " \n",
- " UPTOWN \n",
- " 0.0 \n",
- " 642000.0 \n",
- " 152000.0 \n",
- " 753000.0 \n",
- " 127000.0 \n",
- " 1674000.0 \n",
- " 0.000000 \n",
- " 12.092445 \n",
- " 2.863009 \n",
- " 14.183195 \n",
- " 2.392119 \n",
- " 31.530768 \n",
- " \n",
- " \n",
- " WASHINGTON HEIGHTS \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 174000.0 \n",
- " 0.0 \n",
- " 174000.0 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 6.582183 \n",
- " 0.000000 \n",
- " 6.582183 \n",
- " \n",
- " \n",
- " WASHINGTON PARK \n",
- " 160000.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 160000.0 \n",
- " 8.515621 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 8.515621 \n",
- " \n",
- " \n",
- " WEST ELSDON \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 143000.0 \n",
- " 0.0 \n",
- " 143000.0 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 7.461518 \n",
- " 0.000000 \n",
- " 7.461518 \n",
- " \n",
- " \n",
- " WEST GARFIELD PARK \n",
- " 0.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 102000.0 \n",
- " 0.0 \n",
- " 102000.0 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 5.401112 \n",
- " 0.000000 \n",
- " 5.401112 \n",
- " \n",
- " \n",
- " WEST RIDGE \n",
- " 295000.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 246000.0 \n",
- " 0.0 \n",
- " 541000.0 \n",
- " 4.040044 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 3.368986 \n",
- " 0.000000 \n",
- " 7.409031 \n",
- " \n",
- " \n",
- " WEST TOWN \n",
- " 155000.0 \n",
- " 397000.0 \n",
- " 171000.0 \n",
- " 911000.0 \n",
- " 0.0 \n",
- " 1634000.0 \n",
- " 1.917842 \n",
- " 4.912150 \n",
- " 2.115813 \n",
- " 11.271962 \n",
- " 0.000000 \n",
- " 20.217768 \n",
- " \n",
- " \n",
- " WOODLAWN \n",
- " 0.0 \n",
- " 126000.0 \n",
- " 0.0 \n",
- " 0.0 \n",
- " 165000.0 \n",
- " 291000.0 \n",
- " 0.000000 \n",
- " 5.119662 \n",
- " 0.000000 \n",
- " 0.000000 \n",
- " 6.704319 \n",
- " 11.823981 \n",
- " \n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " geoid10 \n",
- " lon \n",
- " lat \n",
- " Pop2014 \n",
- " Pov14 \n",
- " community \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 0 \n",
- " 17031842400 \n",
- " -87.630040 \n",
- " 41.742475 \n",
- " 5157 \n",
- " 769 \n",
- " 44 \n",
- " \n",
- " \n",
- " 1 \n",
- " 17031840300 \n",
- " -87.681882 \n",
- " 41.832094 \n",
- " 5881 \n",
- " 1021 \n",
- " 59 \n",
- " \n",
- " \n",
- " 2 \n",
- " 17031841100 \n",
- " -87.635098 \n",
- " 41.851006 \n",
- " 3363 \n",
- " 2742 \n",
- " 34 \n",
- " \n",
- " \n",
- " 3 \n",
- " 17031841200 \n",
- " -87.683342 \n",
- " 41.855562 \n",
- " 3710 \n",
- " 1819 \n",
- " 31 \n",
- " \n",
- " \n",
- " 4 \n",
- " 17031838200 \n",
- " -87.675079 \n",
- " 41.870416 \n",
- " 3296 \n",
- " 361 \n",
- " 28 \n",
- " \n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " ID \n",
- " Facility \n",
- " lat \n",
- " lon \n",
- " Type \n",
- " target \n",
- " category \n",
- " community \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 0 \n",
- " 1 \n",
- " American Indian Health Service of Chicago, Inc. \n",
- " 41.956676 \n",
- " -87.651879 \n",
- " 5 \n",
- " 127000 \n",
- " Other Health Providers \n",
- " 3 \n",
- " \n",
- " \n",
- " 1 \n",
- " 2 \n",
- " Hamdard Center for Health and Human Services \n",
- " 41.997852 \n",
- " -87.669535 \n",
- " 5 \n",
- " 190000 \n",
- " Other Health Providers \n",
- " 77 \n",
- " \n",
- " \n",
- " 2 \n",
- " 3 \n",
- " Infant Welfare Society of Chicago \n",
- " 41.924904 \n",
- " -87.717270 \n",
- " 5 \n",
- " 137000 \n",
- " Other Health Providers \n",
- " 22 \n",
- " \n",
- " \n",
- " 3 \n",
- " 4 \n",
- " Mercy Family - Henry Booth House Family Health... \n",
- " 41.841694 \n",
- " -87.624790 \n",
- " 5 \n",
- " 159000 \n",
- " Other Health Providers \n",
- " 35 \n",
- " \n",
- " \n",
- " 4 \n",
- " 6 \n",
- " Cook County - Dr. Jorge Prieto Health Center \n",
- " 41.847143 \n",
- " -87.724975 \n",
- " 5 \n",
- " 166000 \n",
- " Other Health Providers \n",
- " 30 \n",
- " \n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " Hospitals \n",
- " All Free Health Clinics \n",
- " Other Health Providers \n",
- " Federally Qualified Health Centers \n",
- " School-Based Health Centers \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 17031010100 \n",
- " 1 \n",
- " 0 \n",
- " 0 \n",
- " 4 \n",
- " 1 \n",
- " \n",
- " \n",
- " 17031010201 \n",
- " 1 \n",
- " 0 \n",
- " 0 \n",
- " 4 \n",
- " 1 \n",
- " \n",
- " \n",
- " 17031010202 \n",
- " 1 \n",
- " 0 \n",
- " 1 \n",
- " 4 \n",
- " 1 \n",
- " \n",
- " \n",
- " 17031010300 \n",
- " 0 \n",
- " 0 \n",
- " 1 \n",
- " 5 \n",
- " 1 \n",
- " \n",
- " \n",
- " 17031010400 \n",
- " 1 \n",
- " 0 \n",
- " 1 \n",
- " 3 \n",
- " 1 \n",
- " \n",
- " \n",
- "
\n",
- "
\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " Hospitals \n",
- " Federally Qualified Health Centers \n",
- " School-Based Health Centers \n",
- " All Free Health Clinics \n",
- " Other Health Providers \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 17031010100 \n",
- " 1195 \n",
- " 200 \n",
- " 1722 \n",
- " 3140 \n",
- " 2008 \n",
- " \n",
- " \n",
- " 17031010201 \n",
- " 1010 \n",
- " 469 \n",
- " 1791 \n",
- " 2377 \n",
- " 1907 \n",
- " \n",
- " \n",
- " 17031010202 \n",
- " 1401 \n",
- " 396 \n",
- " 1453 \n",
- " 2705 \n",
- " 1672 \n",
- " \n",
- " \n",
- " 17031010300 \n",
- " 1802 \n",
- " 531 \n",
- " 1377 \n",
- " 3140 \n",
- " 1746 \n",
- " \n",
- " \n",
- " 17031010400 \n",
- " 1407 \n",
- " 921 \n",
- " 979 \n",
- " 2882 \n",
- " 1317 \n",
- " \n",
- " \n",
- "
\n",
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:ScoreModel:Plot was saved to: data/figures/fig_0.png\n"
- ]
- }
- ],
- "source": [
- "tt.plot_nearest_providers(limit_categories=None,\n",
- " n_bins=300,\n",
- " resolution='block',\n",
- " title=\"Time to Nearest Neighbor Health Provider - CDF by Area\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:ScoreModel:Plot was saved to: data/figures/fig_1.png\n"
- ]
- }
- ],
- "source": [
- "tt.plot_nearest_providers(limit_categories=None,\n",
- " n_bins=300,\n",
- " resolution='population',\n",
- " title=\"Time to Nearest Neighbor Health Provider- CDF by Population\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Other metrics results are saved under Access_health_tracts.shp. \n",
- "The prefix nd stands for the count of nearby destinations metric and the prefix nn stands for the time to the closest destination."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
![](\"figures/infrastructure2.png\" "\\"Optional")
"
+ "![](\"./figures/diagram_code.png\" "\\"Optional")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The workflow first estimates a point to point shortest path [**(p2p)**](./scripts/p2p.py) algorithm for creating the travel-time distance matrix by travel mode (walking and driving). Then, it creates a score infrastructure [**(ScoreModel)**](./scripts/ScoreModel.py) for creating the metrics, using the ModelData class (parent of Access and Coverage Models created below). Specifically, this class allows the user to generate any type of metric, suiting each user's needs. Finally, it creates the models [**(CommunityAnalytics)**](./scripts/CommunityAnalytics.py) for creating aggregate measures of the access score and coverage score (creating the Access and Coverage Models classes). \n",
+ "The workflow first estimates a point to point shortest path [**(p2p)**](./spatial_access/p2p.py) algorithm for creating the travel-time distance matrix by travel mode (walking and driving). Then, it creates a base model infrastructure [**(BaseModel)**](./spatial_access/BaseModel.py) for creating the metrics, using the ModelData class (parent of Access and Coverage Models created below). Specifically, this class allows the user to generate any type of metric, suiting each user's needs. Finally, it creates the models [**(Models)**](./spatial_access/Models.py) for creating aggregate measures of the access score, AccessTime, AccessCount, AccessSum, DestinationSum, coverage score, and TSFCA output score (creating the Models classes). \n",
"\n",
"This framework provides the user with the flexibility to start at different stages along the process: \n",
"\n",
@@ -105,19 +99,19 @@
"source": [
"**The folder is organized as follows:**\n",
"\n",
- "Under the **demo** folder, the notebooks run through Chicago's health example: \n",
- "* [reqs.ipynb](./reqs.ipynb) : This notebook shows the installation and files requirements in order to run the demos. \n",
- "* [matrix.ipynb](./matrix.ipynb) : This notebook shows how to run the travel time distance matrices. It uses the [p2p](./scripts/p2p.py) script.\n",
- "* [access_score.ipynb](./access_score.ipynb) : This notebook shows how to run the access score and the specific parameters that might be tweaked depending on the user's interest. It uses the [ScoreModel](./scripts/ScoreModel.py) and [CommunityAnalytics](./scripts/CommunityAnalytics.py) scripts.\n",
- "* [coverage_score.ipynb](./coverage_score.ipynb) :This notebook shows how to run the coverage score and the specific parameters that might be tweaked depending on the user's interest. It uses the [ScoreModel](./scripts/ScoreModel.py) and [CommunityAnalytics](./scripts/CommunityAnalytics.py) scripts.\n",
- "* [travel_time_metrics.ipynb](./travel_time_metrics.ipynb) :This notebook shows how to run the travel time distance matrices. It uses the [ScoreModel](./scripts/ScoreModel.py) and [CommunityAnalytics](./scripts/CommunityAnalytics.py) scripts. \n",
- "* [calibration.ipynb](./calibration.ipynb) : **? Have it as a notebook/within the demos/in this notebook or not at all?** Comparison (validation and calibration) with GoogleMaps.\n",
+ "In the **docs** folder, under **notebooks**, the notebooks run through Chicago's health example: \n",
+ "* [reqs.ipynb](./docs/notebooks/reqs.ipynb) : This notebook shows the installation and files requirements in order to run the demos. \n",
+ "* [matrix.ipynb](./docs/notebooks/matrix.ipynb) : This notebook shows how to run the travel time distance matrices. It uses the [p2p](./spatial_access/p2p.py) script.\n",
+ "* [access_score.ipynb](./docs/notebooks/access_score.ipynb) : This notebook shows how to run the access score and the specific parameters that might be tweaked depending on the user's interest. It uses the [BaseModel](./spatial_access/Basemodel.py) and [Models](./spatial_access/Models.py) scripts.\n",
+ "* [coverage_score.ipynb](./coverage_score.ipynb) :This notebook shows how to run the coverage score and the specific parameters that might be tweaked depending on the user's interest. It uses the [BaseModel](./spatial_access/Basemodel.py) and [Models](./spatial_access/Models.py) scripts.\n",
+ "* [travel_time_metrics.ipynb](./docs/notebooks/travel_time_metrics.ipynb) :This notebook shows how to run the travel time distance matrices. It uses the [BaseModel](./spatial_access/Basemodel.py) and [Models](./spatial_access/Models.py) scripts. \n",
+ "* [calibration.ipynb](./docs/notebooks/calibration.ipynb) : **? Have it as a notebook/within the demos/in this notebook or not at all?** Comparison (validation and calibration) with GoogleMaps.\n",
"\n",
- "The **data** folder contains the files needed for the estimation of the metrics under **ORIG** (for origins) and **DEST** (for destinations). The **matrices** folder contains the estimated symmetric and asymmetric matrices. The **access_score**, **coverage_score**, and **travel_time_metrics** folders contain the results of the analyses. Finally, **figures** contain the results of maps and plots calculated during the process. \n",
+ "In the **data** folder, the **input_data** folder contains the files needed for the estimation of the metrics under **sources** (for origins) and **destinations** (for destinations). In the **data** folder within **spatial_access**, the **matrices** folder contains the estimated symmetric and asymmetric matrices. The **access_score**, **coverage score**, **tsfca**, and **travel_time_metrics** folders contain the results of the analyses. Finally, **figures** contain the results of maps and plots calculated during the process. \n",
"\n",
- "Further editing of the Python scripts is accessible under the folder **scripts**. \n",
+ "Further editing of the Python scripts is accessible under the folder **spatial_access**. \n",
"\n",
- "The rest of this notebook describes the reasoning of the Python modules p2p, ScoreModel, and CommunityAnalytics which compose the calculation of the algorithm of travel time **matrices** and the estimation of the **metrics**. Please note that this report stipulates the **_parameters_** specified within the estimations so the users can reproduce the calculations."
+ "The rest of this notebook describes the reasoning of the Python modules p2p, BaseModel, and Models which compose the calculation of the algorithm of travel time **matrices** and the estimation of the **metrics**. Please note that this report stipulates the **_parameters_** specified within the estimations so the users can reproduce the calculations."
]
},
{
@@ -138,7 +132,7 @@
"OSM's data structure is composed of four elements: nodes, ways, relations, and tags.\n",
"Nodes are latitude and longitude coordinates (projected in WGS 84) that represent the map’s features. Ways are a list of nodes that compose the geometry features (i.e. point, line, polygon) within a map, depicting streets, waterways, parks, etc. Relations express the relationship between nodes and ways. Lastly, tags are attached to nodes, mays or relations, storing metadata about the map objects. \n",
"\n",
- "Then, the OpenStreetMap network is downloaded using the area of the previously determined bounding box. The complexity of the network depends on the number of nodes on the generated bounding box (i.e. the area of interest defined by the latitude and longitude coordinates). Therefore, the number of observations should not affect the efficiency of the running times. In order to get the distances from OSM, OSM net calculates the distances of the relations, creating the edges that are queried for the travel time estimation. To estimate these distances, the user has to specify data into a WGS 84 projection. (Please find more specifics in the [requirements file](./reqs.ipynb).) "
+ "Then, the OpenStreetMap network is downloaded using the area of the previously determined bounding box. The complexity of the network depends on the number of nodes on the generated bounding box (i.e. the area of interest defined by the latitude and longitude coordinates). Therefore, the number of observations should not affect the efficiency of the running times. In order to get the distances from OSM, OSM net calculates the distances of the relations, creating the edges that are queried for the travel time estimation. To estimate these distances, the user has to specify data into a WGS 84 projection. (Please find more specifics in the [requirements file](./docs/notebooks/reqs.ipynb).) "
]
},
{
@@ -163,7 +157,7 @@
"metadata": {},
"source": [
"**Script** \n",
- "The p2p.py script runs the point to point (p2p) algorithm and creates two classes: **pyTMatrix** and **TransitMatrix**. The output of p2p is the travel time matrix which is computed in seconds. The **pyTMatrix** generates the environment in Python for estimating the time travel matrix by creating a wrapper for C++. The **TransitMatrix** unified class run manages all aspects of computing a transit time matrix where matrices can be symmetric or assymetric (as mentioned above). Therefore, load one input file if the user wants a symmetric distance graph, or two for an asymmetric matrix. Particularly, this class account for all the details that entail specifying the speed limits, creating the bounding box for the area of interest in order to run the OSM query and calculate the shortest path matrix. "
+ "The p2p.py script runs the point to point (p2p) algorithm and creates the class **TransitMatrix**. The output of p2p is the travel time matrix which is computed in seconds. The **TransitMatrix** unified class run manages all aspects of computing a transit time matrix where matrices can be symmetric or assymetric (as mentioned above). Therefore, load one input file if the user wants a symmetric distance graph, or two for an asymmetric matrix. Particularly, this class account for all the details that entail specifying the speed limits, creating the bounding box for the area of interest in order to run the OSM query and calculate the shortest path matrix. "
]
},
{
@@ -172,17 +166,17 @@
"source": [
"**Specifics of P2P parameters** \n",
"Several parameters should be taken into account when calculating the distance network matrix: \n",
- "* The **network type** can be determined for walking or driving. \n",
+ "* The **network type** can be determined for walking, biking, or driving. \n",
"\n",
"* Thresholds can be adjusted and are considered in the calculation of the distance matrix: the **average walking speed** is 5 km/h (3 mph) and the default **average driving speed** is of 40 km/h (25 mph). This parameter might be specified differently for different populations. * The City of Chicago estimates an average block dimension of 660 feet (200 m) by 330 feet (100 m).(https://www.cityofchicago.org/dam/city/depts/cdot/StreetandSitePlanDesignStandards407.pdf) STREET AND SITE PLAN DESIGN STANDARDS (2007:pg 2.) These dimensions might change across cities. >>> (Include table with conversions. Or reference to data on other cities) Therefore, the average walking speed of 3 mph estimates that a person, on average, walks a block in 72 to 144 seconds (1.2 - 2.4 min) to walk a block. The average speeds can be specified within the **p2p_parameters.json file**. For **driving** speed limits, open **speed_limit_dictionary.txt** file that contains speed limits for different OSM type of roads. \n",
" \n",
"* The **last mile** correction is the distance that is not accounted for when calculating the shortest path from the centroid of the block to the actual network; therefore, the wanting extra distance is added to the travel time distance(>>>). \n",
" \n",
- "* Also for walking and driving, a **node penalty** of X seconds can also be specified by the user for the number of intersections within the area of analysis. The logic is that having more intersections will increase the travel time due to crossings. However, by doing a time travel calibration between the p2p algorithm and GoogleMaps, there was no need for adding penalities for the city of Chicago. The calibration can be assessed for different cities by using a random set of latitude and longitude points and comparing both estimated travel times for p2p (using the default average speeds and speed limits) and the GoogleMaps API. **(PENDING)** (Show table in which the calibration was tested for different cities, suggesting values for each.) It can be specified within the **p2p_parameters.json file**. \n",
+ "* Also for walking and driving, a **node penalty** of X seconds can also be specified by the user for the number of intersections within the area of analysis. The logic is that having more intersections will increase the travel time due to crossings. However, by doing a time travel calibration between the p2p algorithm and GoogleMaps, there was no need for adding penalities for the city of Chicago. The calibration can be assessed for different cities by using a random set of latitude and longitude points and comparing both estimated travel times for p2p (using the default average speeds and speed limits) and the GoogleMaps API. **(PENDING)** (Show table in which the calibration was tested for different cities, suggesting values for each.) It can be specified within the **Configs.py** file.\n",
" \n",
"* The network is **directed**, meaning that one way streets are respected and A->B and B->A can have different edge traversal speeds. \n",
"\n",
- "* **Threshold constants**: the walk or drive average speed can be specified within the **p2p_parameters.json file**. \n",
+ "* **Threshold constants**: the walk or drive average speed can be specified within the **Configs.py** file.\n",
"\n",
"* **Epsilon**: Controls how large to make the network bounding box beyond your dataset. Larger epsilons result in longer computation times, but smaller epsilons result in slightly reduced accuracy at the very edges of the bounding box, especially for driving networks. The default is currently set at 0.05, which seems to balance the two reasonably well. (+/-) 0.02 will result in a large increase/decrease in computation time and accuracy. If too many values are defined as -1, it means that the epsilon is too small. Refer to the epsilon calibration to assess if this value must change and the matrix contains too many -1. The value of -1 is hardcoded in the tmat.h file and is considered as an NaN value of the origins when estimating the metrics. \n",
"\n",
@@ -197,9 +191,9 @@
"source": [
"**_Specifications_** \n",
"\n",
- "In the **demo prompt**, the user can specify: **network type, epsilon, primary input data (origin file), secondary input data (destination file if running an asymmetric matrix), and if the user wants to save the matrix to a csv file.** \n",
+ "In the **demo prompt**, the user can specify: **network type, primary input data (origin file), secondary input data (destination file if running an asymmetric matrix), and if the user wants to save the matrix to a csv file.** \n",
"\n",
- "In the **scripts**, the user can specify: **Average speeds, last mile corrections, node penalty, threshold constants, and output results** (meters instead of seconds - lines 580 - 599 of p2p.py). \n",
+ "In the **Configs**, the user can specify: **Average speeds, last mile corrections, node penalty, epsilon, threshold constants, and output results** (meters instead of seconds - lines 580 - 599 of p2p.py). \n",
"\n",
"In order to construct the matrices, the input table should contain **ID, latitude, longitude** for the origins and destinations.\n"
]
@@ -220,7 +214,7 @@
"(Disregard this section if you only care about the travel-time matrix.)\n",
"\n",
"**Scripts** \n",
- "CommunityAnalytics.py creates the classes: **AccessModel** and **CoverageModel**. As mentioned above, the access score, time to closest destination, and count of nearby destinations metrics are attributes of the origins while the coverage score is an attribute of the destinations in a catchment area. The access, coverage, and count of nearby destionation metrics are all estimated within a travel time network distance buffer. \n"
+ "Models.py creates the classes: **AccessModel** and **Coverage**. As mentioned above, the access score, time to closest destination, sum of nearby attributes, TSFCA (access to per capita spending), and count of nearby destinations metrics are attributes of the origins while the coverage score is an attribute of the destinations in a catchment area. The access to faciilities, acccess to per capita spending, coverage, and count of nearby destionation metrics are all estimated within a travel time network distance buffer. \n"
]
},
{
@@ -300,22 +294,20 @@
"In the **demo prompt**, the user can specify parameters in two different commands: \n",
"\n",
"**name.AccessModel( ):** \n",
- "- network_type ('drive' or 'walk')\n",
- "- source_filename (primary input data) \n",
- "- dest_filename (secondary input data) \n",
- "- sp_matrix_filename (origin-destination travel time matrix) \n",
- "- decay_function ('linear', 'root', 'logit') \n",
- "- limit_categories (only estimate model for specific categories)\n",
- "\n",
+ "- network_type ('walk', 'bike', 'drive', 'otp') \n",
+ "- sources_filename (primary input data)\n",
+ "- destinations_filename (secondary input data)\n",
+ "- source_column_names (dictionary that map column names to expected values)\n",
+ "- dest_column_names (dictionary that map column names to expected values)\n",
+ "- transit_matrix_filename (sources-destination travel time matrix)\n",
+ "- decay_function ('linear', 'root', 'logit', default is 'linear')\n",
"**name.calculate():**\n",
- "- custom_weight_dict (Specifies the weights of each facility/establishment defined as a previously generated dictionary. If custom_weight_dict=None, the dictionary will contain [1,1,1,1,1,1,1,1,1,1] weights.)\n",
- "- largest_weights_first=True sorts the weight array such that the largest values will be used first. If False, it will do the opposite.\n",
- "- limit_categories (None is defined as default, but user can delimit on particular categories of interest to calculate the access score.) \n",
- "- upper (is the time (in minutes) in which the origin and destinations are considered to be out of range of each other. As a default, if the travel time distance exceeds 30 minutes, the score will be zero.)\n",
- "- custom_threshold=40 (change binning threshold specifying it in calculate() function. Example: model_name.calculate(custom_threshold=40) for changing the percent of population above a certain threshold.)\n",
- "- normalize (Default is True and shows both normalized and non-normalized values. False shows only non-normalized results.) . \n",
+ "- upper_threshold (maximum time considered out of range in seconds, default is 30 minutes, when out of range, score will be zero)\n",
+ "- category_weight_dict (specifies the weights of each facility/establishment defined as dictionary, default dictionary will contain [1,1,1,1,1,1,1,1,1,1] weights.)\n",
+ "- normalize (accepts boolean, default is False and shows only non-normalized results, true shows normalized values.)\n",
+ "- normalize_type ('z_score' or 'minmax', default is 'minmax')\n",
"\n",
- "In the **scripts**, the user can specify: The default weights set before as [1,1,1,1,1,1,1,1,1,1] weights, different standarizations, and other modifications to the access score.\n",
+ "In the **Models**, the user can specify: The default weights set before as [1,1,1,1,1,1,1,1,1,1] weights, different standarizations, and other modifications to the access score.\n",
"\n",
"GO TO [ACCESS SCORE DEMO](./access_score.ipynb) "
]
@@ -357,21 +349,20 @@
"source": [
"---\n",
"**Destination-based** \n",
- "The **Coverage** model generates a coverage access which defines the reach of services by a specific targeted population. The score focuses on the coverage of the destination, scrutinizing how many people are within a catchment area. Specifically, the per capita spending score is the ratio of the coverage score by population. Specifically, it takes the magnitude of the facility/establishment and divides it by the total population it serves within a bufffer (in this case, 30 minutes). In the specifications, the magnitude of the destination is denominated as target. \n",
+ "The **Coverage** model generates a coverage access which shows the per capita spending available to a specific targeted population. The model focuses on the coverage of the destination, scrutinizing how many people are within a catchment area. Specifically, it takes the total spending of the facility/establishment and divides it by the total population it serves within a buffer (in this case, 30 minutes). In the specifications, the magnitude of the destination is denominated as target. \n",
"\n",
"**_Specifications_** \n",
"\n",
- "**name.CoverageModel( ):** \n",
- "- network_type ('drive' or 'walk')\n",
- "- source_filename (primary input data) \n",
- "- dest_filename (secondary input data) \n",
- "- upper (is the time (in minutes) in which the origin and destinations are considered to be out of range of each other. As a default, if the travel time distance exceeds 30 minutes, the score will be zero.)\n",
- "- The prompt will ask for the target which should be specified in the destination file, according to the user's needs. \n",
+ "**name.Coverage( ):** \n",
+ "- network_type ('walk', 'bike', 'drive', or 'otp')\n",
+ "- sources_filename (primary input data)\n",
+ "- destinations_filename (secondary input data)\n",
+ "- transit_matrix_filename (origin-destination transit matrix)\n",
" \n",
"**name.calculate():**\n",
- "- Just processes the model specified above. \n",
+ "- upper_threshold (the time (in seconds) in which the origin and destinations are considered to be out of range of each other)\n",
"\n",
- "In the **scripts**, the user can specify further changed to the coverage score.\n",
+ "In the **Models**, the user can specify further changed to the coverage score.\n",
"\n",
"GO TO [COVERAGE SCORE DEMO](./coverage_score.ipynb) "
]
diff --git a/docs/notebooks/0_reqs_install.ipynb b/docs/notebooks/0_reqs_install.ipynb
new file mode 100644
index 0000000..83a176d
--- /dev/null
+++ b/docs/notebooks/0_reqs_install.ipynb
@@ -0,0 +1,200 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Installation Setup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "--- \n",
+ "The package is written in Python 3.6, C++ 11 and Cython by [Logan Noel](https://www.linkedin.com/in/lmnoel/). (Minimum Python version 3.5) \n",
+ "Currently, the only supported operating systems are MacOS and Ubuntu (if you don't have either, a guide for installing Ubuntu 16.04 LTS is in README.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Note: More seasoned users can download installation requirements directly on the terminal.**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**For MacOS:**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install Python3\n",
+ "! brew install python3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install homebrew\n",
+ "! /usr/bin/ruby -e \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install pip3\n",
+ "! brew install pip3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install jupyter, jupyterlab, and jupyter hub\n",
+ "! brew install jupyter\n",
+ "! brew install jupyterlab"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Clone the repository:\n",
+ "! git clone https://github.com/jupyterhub/jupyterhub"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install spatial index package\n",
+ "! brew install spatialindex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install spatial access package\n",
+ "! pip3 install spatial_access"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install scipy package\n",
+ "! brew install scipy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install geopy package\n",
+ "! pip install geopy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install geopandas package\n",
+ "! conda install geopandas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run setup.py to install all the packages\n",
+ "! sudo python setup.py install"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**For Ubuntu:**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "! sudo apt-get install libspatialindex-dev"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "! sudo apt-get install python-tk"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "! pip3 install spatial_access"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/1_matrix.ipynb b/docs/notebooks/1_matrix.ipynb
new file mode 100755
index 0000000..d91fd09
--- /dev/null
+++ b/docs/notebooks/1_matrix.ipynb
@@ -0,0 +1,913 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Transit Matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys, os\n",
+ "os.chdir('/Users/whlu/spatial_access/spatial_access')\n",
+ "from p2p import *\n",
+ "\n",
+ "import geopandas as gpd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "--- \n",
+ "![](\"figures/snap.png\" "\\"Optional")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Spatial join of health facilities and area of analysis**\n",
+ "\n",
+ "Finally, in order to get the matrix of origins to destinations, we need to join the health facilities by block with the distance matrix previously generated. This will generate an asymmetric matrix with all the distances from destinations to all the units of analysis in Chicago."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "code_folding": [
+ 0
+ ],
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "# Read destination files to join with boundaries \n",
+ "health_gdf = gpd.read_file('/Users/whlu/spatial_access/data/input_data/health_chicago-point.shp')\n",
+ "health_gdf.head()\n",
+ "#Use symmetric matrix calculated above or read your previously saved results:\n",
+ "sym_walk=pd.read_csv('/Users/whlu/spatial_access/spatial_access/data/matrices/walk_sym_health_tracts.csv')\n",
+ "\n",
+ "# Read boundaries files \n",
+ "boundaries_gdf = gpd.read_file('/Users/whlu/spatial_access/data/chicago_boundaries/chi_blocks.shp')\n",
+ "\n",
+ "# Rename the ID name in order to match both data frames. \n",
+ "sym_walk= sym_walk.rename(index=str, columns={\"Unnamed: 0\": \"geoid10\"})\n",
+ "\n",
+ "\n",
+ "# Spatial join of amenities within each area of analysis \n",
+ "#It drops values outside of the tracts shapefile. From 199 to 182 datapoints.\n",
+ "s_join = gpd.sjoin(health_gdf, boundaries_gdf, how='inner', op='within')\n",
+ "\n",
+ "# Convert geopanda dataframe to non-spatial dataframe to join \n",
+ "jb_df = pd.DataFrame(s_join)\n",
+ "\n",
+ "\n",
+ "# Make sure the id is of the same data type in both data frames.\n",
+ "# sym_walk.dtypes\n",
+ "# jb_df.dtypes\n",
+ "jb_df.geoid10=jb_df.geoid10.astype(int)\n",
+ "jb_df=pd.DataFrame(jb_df['geoid10'])\n",
+ "\n",
+ "# Join the symmetric matrix with the spatially joined data (with geoid10 id)\n",
+ "j_asym=pd.merge(sym_walk, jb_df, left_on='geoid10', right_on='geoid10', how='left')\n",
+ "\n",
+ "j_asym.to_csv('/Users/whlu/spatial_access/spatial_access/data/matrices/walk_asym_health_tracts_join.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "![](\"/Users/tienc/spatial_access/data/figures/Access_Model_all.png\" "\\"Optional")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 131,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'/Users/tienc/spatial_access'"
+ ]
+ },
+ "execution_count": 131,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pwd"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### **Aggregate to a Higher Level**\n",
+ "\n",
+ "The current data is at the tract level. To understand the data at a higher level, aggregate the ddata to a community level. Then write it as a a csv. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "![](\"/data/figures/access_mod_agg.png\" "\\"Optional")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### **Plot Aggregated Data**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following cumulative distribution function shows the percentage of tracts that fall within an access score."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:spatial_access.BaseModel:Plot was saved to: data/output_data/accessModel_CDFplot\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "![](\"figures/coverage.png\" "\\"Optional")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from spatial_access.Models import *"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#View sources and destinations for Chicago Health Facilities\n",
+ "sources=pd.read_csv('/Users/whlu/spatial_access/data/input_data/tracts2010.csv')\n",
+ "dest=pd.read_csv('/Users/whlu/spatial_access/data/input_data/health_chicago.csv')\n",
+ "dest= dest.rename(index=str, columns={\"target\": \"capacity\"})\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "![](\"figures/coverage_t.png\" "\\"Optional")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Aggregation by greater areal units"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "![](\"figures/cov_t_com.png\" "\\"Optional")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot\n",
+ "The following cumulative distribution function shows the number of tracts that fall below a certain level of per capita spending.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:spatial_access.BaseModel:Plot was saved to: /Users/whlu/spatial_access/data/coverage score/coverage_cdf_plot.png\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "![](\"/Users/whlu/spatial_access/docs/notebooks/figures/tsfca_figure_1.png\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Writes output to csv\n",
+ "tsfca.model_results.to_csv('/Users/whlu/spatial_access/data/tsfca/tsfca_results.csv')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Aggregation at a greater areal level"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "![](\"figures/access_sd_t.png\" "\\"Optional")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "By constructing the access model, we can do several things:\n",
- "- Write to csv\n",
- "- Aggregate\n",
- "- Get count of nearby destinations metric\n",
- "- Get time to closest destination metric"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Write output to csv\n",
- "access.write_csv()\n",
- "\n",
- "#Saved as Access_walk_health_tracts.csv"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Aggregation at a greater areal level"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "![](\"figures/access_comm.png\" "\\"Optional")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Plot"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The following cumulative distribution function shows the percentage of tracts that fall within an access score."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "![](\"figures/a_FQHC.png\" "\\"Optional")
"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/notebooks/calibration.ipynb b/docs/notebooks/calibration.ipynb
deleted file mode 100755
index 3cbc807..0000000
--- a/docs/notebooks/calibration.ipynb
+++ /dev/null
@@ -1,249 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## P2P Calibration \n",
- "put at the end of p2p in order to import transitmatrix\n",
- "\n",
- "We use Graphhopper in order to test the p2p calibration. A Graphhopper API is required to run this process and it can be obtained here:\n",
- "https://graphhopper.com/api/1/docs/FAQ/\n",
- "\n",
- "Note: You can also use a GoogleMaps API.\n",
- "\n",
- "\n",
- "Show the mean and stddev of the difference between p2p's route time\n",
- "and GraphHopper's route time, in seconds.\n",
- "\n",
- "IMPORTANT: To use this, must have a valid GraphHopper Matrix API key\n",
- "saved in a text file in this directory called GRAPHHOPPER_API_KEY.txt\n",
- "\n",
- "Positive differences indicate p2p's route was longer, negative times indicates\n",
- "that p2p's route was shorter."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# P2P Calibration \n",
- "#need this because p2p and graphhopper use synonyms for \n",
- "#vehicle/route types\n",
- "p2p_to_graphhopper_type_names = {\n",
- " 'drive' : 'car',\n",
- " 'walk' : 'foot',\n",
- " 'bike' : 'bike'\n",
- "}\n",
- "\n",
- "def sample_one_matrix(df, tm, network_type, api_key):\n",
- " '''\n",
- " Test one ditance matrix\n",
- " '''\n",
- "\n",
- " base_url = \"https://graphhopper.com/api/1/matrix\"\n",
- " first = True\n",
- " for data in df.itertuples():\n",
- " x_data = data[4]\n",
- " y_data = data[5]\n",
- " if first:\n",
- " point_string = \"?point={},{}\".format(x_data, y_data)\n",
- " first = False\n",
- " else:\n",
- " point_string = \"&point={},{}\".format(x_data, y_data)\n",
- "\n",
- " base_url += point_string\n",
- "\n",
- " param_string = \"&type=json&vehicle={}&debug=true&out_array=times&key={}\".format(network_type, api_key)\n",
- " base_url += param_string\n",
- "\n",
- "\n",
- " try:\n",
- " r = requests.get(base_url)\n",
- "\n",
- " results = r.json()['times']\n",
- " except:\n",
- " print('there was a problem fetching from GraphHopper. Exiting...')\n",
- " sys.exit()\n",
- "\n",
- "\n",
- " already_checked = set()\n",
- " diffs = []\n",
- " for i, row in enumerate(df.index):\n",
- " for j, col in enumerate(df.index):\n",
- " if (row, col) not in already_checked and row != col:\n",
- " calculated_time = tm.get(row, col)\n",
- " actual_time = results[i][j]\n",
- " diff = calculated_time - actual_time\n",
- " diffs.append(diff)\n",
- " already_checked.add((row, col))\n",
- " already_checked.add((col, row))\n",
- "\n",
- " stddev = np.std(diffs)\n",
- " mean = np.mean(diffs)\n",
- "\n",
- "\n",
- " print('diffs mean: {}, stddev: {}'.format(mean, stddev))\n",
- "\n",
- "\n",
- "def calibrate(network_type='walk', input_file='resources/LEHD_blocks.csv', \n",
- " sl_file='resources/condensed_street_data.csv', n=1):\n",
- " '''\n",
- " Show the mean and stddev of the difference between p2p's route time\n",
- " and GraphHopper's route time, in seconds.\n",
- "\n",
- " IMPORTANT: To use this, must have a valid GraphHopper Matrix API key\n",
- " saved in a text file in this directory called GRAPHHOPPER_API_KEY.txt\n",
- "\n",
- " Positive differences indicate p2p's route was longer, negative times indicates\n",
- " that p2p's route was shorter.\n",
- " '''\n",
- " if network_type == 'drive':\n",
- " assert sl_file is not None, 'must provide sl_file for use with driving network calibration'\n",
- " with open('GRAPHHOPPER_API_KEY.txt', 'r') as api_file:\n",
- " api_key = api_file.read()\n",
- " api_key = api_key.strip()\n",
- " gh_type_name = p2p_to_graphhopper_type_names[network_type]\n",
- "\n",
- " tm = TransitMatrix(network_type=network_type, primary_input=input_file)\n",
- " if network_type == 'drive':\n",
- " tm.process(speed_limit_filename=sl_file)\n",
- " else:\n",
- " tm.process()\n",
- "\n",
- " #extract the column names\n",
- " xcol = ''\n",
- " ycol = ''\n",
- " idx = ''\n",
- "\n",
- " df = pd.read_csv(input_file)\n",
- "\n",
- " print('The variables in your data set are:')\n",
- " df_cols = df.columns.values\n",
- " for var in df_cols:\n",
- " print('> ',var)\n",
- " while xcol not in df_cols:\n",
- " xcol = input('Enter the x coordinate (Latitude): ')\n",
- " while ycol not in df_cols:\n",
- " ycol = input('Enter the y coordinate (Longitude): ')\n",
- " while idx not in df_cols:\n",
- " idx = input('Enter the index name: ')\n",
- "\n",
- " df.rename(columns={xcol:'x',ycol:'y', idx:'idx'},inplace=True)\n",
- " df.set_index('idx', inplace=True)\n",
- "\n",
- " for i in range(n):\n",
- "\n",
- " sample_one_matrix(df.sample(24), tm, gh_type_name, api_key)\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "calibrate(network_type='walk',\n",
- " input_file='data/ORIG/tracts2010.csv',\n",
- " sl_file='data/DEST/health_chicago.csv',\n",
- " n=1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Epsilon Calibration"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(801, 200)"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Load the travel time distance matrix and assess dimensions: \n",
- "df = pd.read_csv('scripts/data/matrices/walk_asym_health_tracts.csv')\n",
- "df.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Identify percentage of values outside epsilon:\n",
- "p_eps=((df.groupby('1').count()).iloc[0][0])/len(df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Of the total matrix 0.25% of the values are outside the bounding box. If the value is below 1% it seems epsilon is appropriate for this particular dataset.\n"
- ]
- }
- ],
- "source": [
- "print (\"Of the total matrix \",\"{0:.2f}%\".format(p_eps* 100),\"of the values are outside the bounding box. If the value is below 1% it seems epsilon is appropriate for this particular dataset.\")"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/notebooks/coverage_score.ipynb b/docs/notebooks/coverage_score.ipynb
old mode 100755
new mode 100644
index e30156d..369a4be
--- a/docs/notebooks/coverage_score.ipynb
+++ b/docs/notebooks/coverage_score.ipynb
@@ -13,28 +13,43 @@
"source": [
"## Calculating the Coverage Score\n",
"---\n",
+ "#### **Column Inputs for Each Model**\n",
+ "Each model takes in inputs from both the sources and destination csv files. Each model takes in the standard inputs indicated below as well as additional inputs specified under each model\n",
+ "\n",
+ "* Source File\n",
+ " * Unique index identifier (geoid10) (integer or real) \n",
+ " * Latitude and longitude coordinates (real)\n",
+ " * To aggregate: larger areal ID\n",
+ " * Population within the areal unit\n",
+ "* Destination File\n",
+ " * Unique index identifier (ID) (integer or real)\n",
+ " * Latitude and longitude coordinates (real)\n",
+ " * Category for each type of facility\n",
+ " * To aggregate: larger areal ID\n",
+ " * Capacity for each facility\n",
+ "\n",
+ " \n",
+ "\n",
"**Specifications for Coverage Model:** \n",
"\n",
- "**name.CoverageModel( ):** \n",
- "- network_type ('drive' or 'walk')\n",
- "- source_filename (primary input data) \n",
- "- dest_filename (secondary input data) \n",
- "- upper (is the time (in minutes) in which the origin and destinations are considered to be out of range of each other. As a default, if the travel time distance exceeds 30 minutes, the score will be zero.)\n",
- "- The prompt will ask for the target which should be specified in the destination file, according to the user's needs. \n",
+ "**name.Coverage( ):** \n",
+ "- network_type ('walk', 'bike', 'drive', or 'otp')\n",
+ "- sources_filename (primary input data)\n",
+ "- destinations_filename (secondary input data)\n",
+ "- transit_matrix_filename (origin-destination transit matrix)\n",
" \n",
"**name.calculate():**\n",
- "- Just processes the model specified above. \n",
- "\n",
- "In the **scripts**, the user can specify further changed to the coverage score.\n",
+ "- upper_threshold (the time (in seconds) in which the origin and destinations are considered to be out of range of each other)\n",
"\n",
- "Functions within the PCSpend Model class (use as name_of_model.function()): \n",
- "- calculate ()\n",
- "- get_aggregate (aggregate_type) Aggregation can be by ('category') or ('lower_areal_unit') for the community level. If the user wants to modify the level of aggregation, change the lower_areal_unit field in destination csv to another unit of analysis.\n",
- "- agg_area_cat (aggregate by area and by category)\n",
- "- write_aggregate (aggregate_type, filename='name')\n",
- "- write_agg_area_cat (write the aggregation by lower areal unit and by category)\n",
- "- plot_cdf (title='Coverage Amount')\n",
- "- write_csv (filename='name') "
+ "Functions within the Coverage Model class (use as name.function()): \n",
+ "- calculate () \n",
+ "- model_results (results of the TSFCA calculations)\n",
+ "- write_csv (filename='name') \n",
+ "- aggregate ()\n",
+ "- write_aggregated_results()\n",
+ "- plot_cdf()\n",
+ "- plot_choropleth()\n",
+ "\n"
]
},
{
@@ -46,28 +61,24 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
- "import sys, os\n",
- "os.chdir('scripts')\n",
- "\n",
- "from CommunityAnalytics import *\n",
"import pandas as pd\n",
- "\n",
- "%matplotlib inline"
+ "from spatial_access.Models import *"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"#View sources and destinations for Chicago Health Facilities\n",
- "sources=pd.read_csv('data/ORIG/tracts2010.csv')\n",
- "dest=pd.read_csv('data/DEST/health_chicago.csv')"
+ "sources=pd.read_csv('/Users/whlu/spatial_access/data/input_data/tracts2010.csv')\n",
+ "dest=pd.read_csv('/Users/whlu/spatial_access/data/input_data/health_chicago.csv')\n",
+ "dest= dest.rename(index=str, columns={\"target\": \"capacity\"})"
]
},
{
@@ -102,6 +113,7 @@
" ![](\"figures/nd_2.png\" "\\"Optional")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Time to Closest Destination"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Use .near_nbr to create the time to the nearest neighbor within a buffer (time defined in upper)\n",
- "tt.near_nbr.to_csv('data/other_metrics/nn_health_tracts.csv')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "![](\"figures/nn_2.png\" "\\"Optional")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Empirical Cumulative Distribution Function Plots**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
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