Accessibility Analysis with Isocalors

Introduction

This repository contains the Jupyter notebooks for conducting a accessibility analysis under heat stress conditions using isocalors. It is part of a larger project investigating the effects of heat stress on the accessibility of essential services in Heidelberg. As urban heat waves become more frequent due to climate change, it is increasingly important to understand how heat impacts access to critical services, particularly for vulnerable populations. To address this challenge, the traditional concept of isochrones—commonly used to map accessibility based on travel time—is extended by incorporating heat stress factors. This innovative approach, known as isocalor analysis, is implemented using a heat-sensitive routing tool. By applying this method to the city of Heidelberg, the project provides valuable insights into how varying levels of heat stress influence access to essential health and livelihood services, offering guidance for strategies aimed at enhancing urban resilience in a warming climate.

The project consists of three main steps:

1. Data Collection: The first step involves collecting data on essential services, such as kindergartens, social facilities for the elderly, as well as population data and Local Climate Zones (LCZs) for Heidelberg.

2. Isocalor Analysis: The second step calculates isocalors for the essential services using the HEAL-API, which provides heat-sensitive routing based on OpenRouteService. The analysis considers different heat sensitivity factors, heat stress walking distance equivalents, and times of day to assess the impact of heat stress on accessibility.

3. LCZ Analysis: The third step uses Local Climate Zones (LCZs) to analyze the isocalors and assess the affected population by heat sensitivity factor, heat stress walking distance equivalent, and time of day. The analysis provides insights into the distribution of heat stress across different urban zones and highlights areas with high vulnerability to heat waves.

Usage

To use this project, follow these instructions:

• Download the whole repository.
• Download population data from Zensus 2022.
• Download the LCZ map for Heidelberg here.
• Download the city boundaries and the city district boundaries for Heidelberg here.
• Population data, LVZ map and the city boundary files are prepared as well in the data folder of this repository.
• Run the Get_POIs.ipynb notebook to download the POI data from openstreetmap via the Overpass API. Beware of storing the file for manual addition to the senior living facilities in the data folder before running the script.
• Run the Isocalors_POI.ipynb. Note: Computing isocalors may be resource-intensive and time-consuming. If functionality is the primary concern, creating a subset of essential services can significantly speed up the process. However, be aware that this will lead to substantial differences in the results. To create a subset, uncomment Box 6 (# Define a subset of the coordinates) in the Jupyter Notebook before running the script.
• Run lcz_analysis.ipynb.
• Create the layers vul_pop_grid_hd_fct5_noon.tif and vul_pop_grid_hd_fct3_noon.tif in QGIS.
  • Load the population raster (zensus_2022_hd_LAEA.tif) and the mask layer (vul_area_transport_all.json).
  • Select relevant areas by filtering the polygons in vul_area_transport_all.json based on shadow_factor, timeofday, and value.
  • Clip the population raster with Clip Raster by Mask Layer tool to extract the vulnerable population, ensuringthat only the selected features are used as the mask.
  • Export the clipped rasters as vul_pop_grid_hd_fct5_noon.tif and vul_pop_grid_hd_fct3_noon.tif.
• Open the QGIS project found in the img folder.
• The figures from the paper can be recreated without running the script by downloading the data from the data folder. The already calculated isocalors can be downloaded from this large file storage.

Requirements

To run the notebooks, the following Python packages are required:

• Jupyter Notebook
• OpenRouteService
• GeoPandas
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Pyogrio
• Rasterio
• Rasterstats
• OverPy
• Shapely

Content

1. Get_POIs:
   The notebook Get_POIs.ipynb retrieves POIs for kindergartens, senior living facilities, transport stops, etc. using the Overpass-API from OpenStreetMap and saves them to respective geojson files. The manual additions to the senior living facilities from seniorenheime_hd are integrated into the senior living facilities retrieved from osm. In the end all POIs except the transport stations are stored in one GeoJSON. The transport POIs are stored within a seperate GeoJSON.

2. Isocalors_POI:
   Isocalors_POI.ipynb calculates isocalors for input shapefiles downloaded from Geofabrik or with Get_POIs.ipynb via the HEAL-API. Ensure the HEAL-API URL is inserted into the corresponding code block. To obtain the HEAL URL, contact the HeiGIT team or the author of this repository. The HEAL-API is based on OpenRouteService. The notebook will call the Isocalors from the HEAL-API and perform subsequent calculations. The output will be stored as GeoJSON files.

3. lcz_analysis:
   The lcz_analysis.ipynb performs calculations using Local Climate Zones (LCZs) to analyze the isocalors created in Isocalors_POI. It calculates population, area, population density, transport stop number, transport stop density, and affected population by heat sensitivity factor, heat stress walking distance equivalent, and time of day, and outputs two GeoJSON files containing the results. A plot is generated showing the absolute number of affected population for heat sensitivity factors 3 and 5, averaged by LCZ.

Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Commit your changes (git commit -m 'Add your feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.

License

This project is licensed under the GNU GENERAL PUBLIC LICENSE - see the LICENSE file for details.