A workshop on applied spatial analysis and urban informatics.
This repository contains the materials and instructions for the PySAL workshop at SciPy 2022.
Geographic Data Science with Python
Fundamentals of Spatial Analysis
Applied Spatial Analysis: Neighborhood Analytics
Applied Spatial Analysis: Neighborhood 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
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
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