https://github.com/fbxyz/sds_bootcamp
Leveraging Python for Spatial Data Science
https://github.com/fbxyz/sds_bootcamp
bootcamp matplotlib python spatial-data-science
Last synced: about 1 year ago
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Leveraging Python for Spatial Data Science
- Host: GitHub
- URL: https://github.com/fbxyz/sds_bootcamp
- Owner: fbxyz
- Created: 2023-10-12T13:56:26.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-27T07:52:35.000Z (over 2 years ago)
- Last Synced: 2025-03-30T13:03:20.306Z (about 1 year ago)
- Topics: bootcamp, matplotlib, python, spatial-data-science
- Language: HTML
- Homepage:
- Size: 50.8 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Leveraging Python for Spatial Data Science
## [Spatial Data Science Bootcamp Paris](https://spatial-data-science-conference.com/bootcamp/paris) October 26th, 2023
[Florian Bayer](https://www.linkedin.com/in/florian-bayer-a4117b30/), 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](BC_1_GPs.ipynb)
- Or the [html export](https://rawcdn.githack.com/fbxyz/SDS_Bootcamp/ff02e71ea015dfef115c6afadd3f2ea7f2fb8c12/BC_1_GPs.html)
- Or using [Rise](https://rise.readthedocs.io/en/latest/) 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.](https://annuaire.sante.fr/web/site-pro/extractions-publiques)
## 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](https://github.com/geopandas/geopandas) for efficient geographic data handling
- [h3pandas](https://github.com/DahnJ/H3-Pandas) for spatial aggregation
- [matplotlib](https://github.com/matplotlib/matplotlib) for creating maps and visualizations
- [scipy](https://github.com/scipy/scipy) and [splot](https://github.com/pysal/splot) and [esda](https://github.com/pysal/esda) for performing statistical calculations
- [libpysal](https://github.com/pysal/libpysal) for constructing spatial weight matrices
- [geopy](https://github.com/geopy/geopy) for distance calculations based on geographic coordinates
- Additionally, [Jupyter lab](https://github.com/jupyterlab/jupyterlab) is recommended for an interactive environment
Feel free to clone the repository :
[https://github.com/fbxyz/SDS_Bootcamp](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
```