- Sergio Rey - University of California, Riverside
- Elijah Knaap - University of California, Riverside
This repository contains the materials and instructions for the PySAL workshop at SciPy 2022.
Proposed Schedule:
-
Geographic Data Science with Python
- PySAL Overview
- Spatial data processing
- Choropleth mapping and geovisualization
- Break
-
Fundamentals of Spatial Analysis
- Spatial weights
- Global & Local spatial autocorrelation
- Break
-
Applied Spatial Analysis: Neighborhood Analytics
- Clustering/Geodemographic Analysis
- Segregation Analysis
- Break
-
Applied Spatial Analysis: Neighborhood Dynamics
- Modeling neighborhood change
- Measuring spatial and temporal segregation dynamics
Brief introduction to the PySAL ecosystem of packages for spatial data science
Reading and writing GIS file formats, spatial data wrangling, changing coordinate transformation systems.
Introduction to choropleth map classification using mapclassify. Basic visualization with GeoPandas, and matplotlib as well as interactive visualization via folium, leaflet and geoviews/hvplot,
Hands-on 1 Exploratory Geovisualization
Introduction to the spatial weights matrix for formally encoding geographic relationships.
Exploratory spatial data analysis and overview of measures of spatial autocorrelation statistics such as Moran's I and the join-count statistic.
Hands-on 2 Hot-spot detection
Exploring socio-spatial differentiation
Introduction to classic and spatially-constrained geodemographics (regionalization). This module provides an overview of integrating scikit-learn and pysal to develop socio-demographic cluster models that optionally include a spatial constraint.
Hands-on 3 Defining Neighborhoods
Applied segregation analysis including the calculation of classic, multigroup, and spatial indices. This module also includes analysis of spatial segregation dynamics, and comparative inference
Introduction to geosnap for creating geodemographic typologies over time and modeling neighborhood transitions
Examine changes in income segregation over space and time
Hands-on 4 Comparative segregation
If you are familiar with GitHub, you should clone or fork this GitHub repository to a specific directory. Cloning can be done by:
git clone https://github.com/sjsrey/pysal-scipy22.gitIf you are not using git, you can grab the workshop materials as a zip file. Extract the downloaded zip file to a working directory for the workshop.
We will be using a number of Python packages for geospatial analysis.
An easy way to install all of these packages is to use a Python distribution such as Anaconda. In this workshop we will use anaconda to build an environment for Python 3.9. It does not matter which version of anaconda is downloaded. We recommend installing Anaconda 3.9.
Once you have installed Anaconda, open a terminal and change into the directory where you downloaded the tutorial materials and create the workshop environment with:
conda env create -f environment.ymlThis will build a conda python 3.9 environment that sandboxes the installation of the required packages for this workshop so we don't break anything in your computer's system Python (if it has one).
This may take 10-15 minutes to complete depending on the speed of your network connection.
Once this completes, you can activate the workshop environment with:
conda activate pysal-workshopYou are ready for the workshop at this point.