Leveraging Python for Spatial Data Science

Spatial Data Science Bootcamp Paris October 26th, 2023

Florian Bayer, PhD in Public Health, MSc in Geography

Health geographer at Agence de la biomédecine, University lecturer at Paris Panthéon Sorbonne and ENSG

To access the presentation

• Via BC_1_GPs.ipynb
• Or the html export
• Or using Rise in Jupyter

Objective

The aim of this workshop is to deliver an overview of spatial analysis tools that enable leveraging spatial dimension in your analyses.

Our primary focus will revolve around examining the spatial distribution of self-employed general practitioners (GPs), distinct from those employed by healthcare institutions, within the Paris metropolitan area and its surrounding regions.

The primary objective is to ascertain the presence of spatial disparities in the distribution of general practitioners throughout the Parisian region.

The data comes from the french health ministry.

Workshop Outline:

• Spatial data management and geocoding
• Introduction to the Modifiable Areal Unit Problem (MAUP)
• Spatial autocorrelation and Hot Spot Detection
• Appendix : travel time computing

Instructions

This workshop requires Python 3.x (specifically tested with version 3.9). The primary packages utilized in this workshop include:

• geopandas for efficient geographic data handling
• h3pandas for spatial aggregation
• matplotlib for creating maps and visualizations
• scipy and splot and esda for performing statistical calculations
• libpysal for constructing spatial weight matrices
• geopy for distance calculations based on geographic coordinates
• Additionally, Jupyter lab is recommended for an interactive environment

Feel free to clone the repository : https://github.com/fbxyz/SDS_Bootcamp

You can install them in your preferred Python virtual environment or use conda :

1. First clone this git repo

git clone https://github.com/fbxyz/SDS_Bootcamp
cd SDS_Bootcamp

2. Create and activate the conda environment

conda create -n bootcamp_level python=3.9 mamba -c conda-forge
conda activate bootcamp_level

3.Install packages with mamba

mamba install -c conda-forge geopandas h3pandas matplotlib scipy libpysal esda splot geopy notebook jupyterlab

4. Optionally, add the environment as a Jupyter kernel

python -m ipykernel install --sys-prefix --name bootcamp_level

5. Launch Jupyter Notebook or Jupyter Lab

jupyter lab