Skip to content

Latest commit

 

History

History
141 lines (108 loc) · 4.06 KB

README.md

File metadata and controls

141 lines (108 loc) · 4.06 KB
Raw

pygeoda

PyPI version PyPI - Downloads Anaconda-Server Badge Anaconda-Server Badge

pygeoda is a python library for spatial data analysis based on libgeoda and GeoDa. It provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code of GeoDa, which is an open-source software tool that serves as an introduction to spatial data analysis.

Installation

To install from PyPi:

pip install pygeoda

To install with conda run:

conda install -c conda-forge pygeoda

To install from source: (See more details: https://geodacenter.github.io/pygeoda/install.html)

pip install git+https://github.com/geodacenter/pygeoda

Documentation

https://geodacenter.github.io/pygeoda

Citation

Anselin, L., Li, X. and Koschinsky, J. (2022), GeoDa, From the Desktop to an Ecosystem for Exploring Spatial Data. Geogr Anal, 54: 439-466. Download Citation

Quick Start

  • pygeoda + ESRI Shapefile
import pygeoda
gda = pygeoda.open('./data/Guerry.shp')
w = pygeoda.queen_weights(gda)
lisa = pygeoda.local_moran(w, gda['Crm_prs'])
#lisa object:
#    lisa_values(): [0.516120231288079, 0.8182751384950308, ...]
#    lisa_pvalues(): [0.197, 0.013, ...]
#    lisa_num_nbrs(): [4, 6, ...]
#    lisa_clusters(): [0, 1, ...]
#    lisa_labels(): ('Not significant', 'High-High', 'Low-Low', 'High-Low', 'Low-High', 'Undefined', 'Isolated')
#    lisa_colors(): ('#eeeeee', '#FF0000', '#0000FF', '#a7adf9', '#f4ada8', '#464646', '#999999')
  • pygeoda + GeoPandas
import geopandas
df = geopandas.read_file('./data/Guerry.shp')

import pygeoda
gda = pygeoda.open(df)
w = pygeoda.queen_weights(gda)
lisa = pygeoda.local_moran(w, gda['Crm_prs'])
#lisa object:
#    lisa_values(): [0.516120231288079, 0.8182751384950308, ...]
#    lisa_pvalues(): [0.197, 0.013, ...]
#    lisa_num_nbrs(): [4, 6, ...]
#    lisa_clusters(): [0, 1, ...]
#    lisa_labels(): ('Not significant', 'High-High', 'Low-Low', 'High-Low', 'Low-High', 'Undefined', 'Isolated')
#    lisa_colors(): ('#eeeeee', '#FF0000', '#0000FF', '#a7adf9', '#f4ada8', '#464646', '#999999')

Current version 0.0.8

  • Spatial Weights

    • Queen
    • Rook
    • Distance based
    • K-Nearest Neighbor
    • Kernel

  • Local Indicators of Spatial Association (LISA)

    • Local Moran
    • Local Geary
    • Local Getis-Ord
    • Local Join Count
    • Multivariate Local Geary
    • Local Join Count
    • Bivariate Local Join Count
    • (Multivariate) Colocation Local Join Count
    • Quantile LISA
    • Multivariate Quantile LISA

  • Spatial Clustering

    • SCHC Spatial Constrained Hierarchical Clustering
      • Single-linkage
      • Complete-linkage
      • Average-linkage
      • Ward-linkage
    • SKATER
    • REDCAP
      • First-order and Single-linkage
      • Full-order and Complete-linkage
      • Full-order and Average-linkage
      • Full-order and Single-linkage
      • Full-order and Ward-linkage
    • AZP
      • greedy
      • Tabu Search
      • Simulated Annealing
    • Max-p
      • greedy
      • Tabu Search
      • Simulated Annealing

  • Map Classification

    • NaturalBreaks
    • QuantileBreaks
    • Hinge15Breaks
    • Hinge30Breaks
    • PercentileBreaks
    • StddevBreaks

  • Data

    • Demean standardize
    • Standardize data (Z)
    • Median absolute deviation

Authors

Xun Li and Luc Anselin

Contributors

Guanpeng Dong; Yong Liu; Hang Zhang; Yeqing Han;