ISSS626 Geospatial Analytics and Applications
  • Hands-on Exercises
    • 1A: Geospatial Data Wrangling with R
    • 1B: Choropleth Mapping with R
    • 2A: 1st Order Spatial Point Patterns Analysis
    • 2B: 2nd Order Spatial Point Patterns Analysis
    • 3A: Network Constrained Spatial Point Patterns Analysis
    • 4A: Spatial Weights and Applications
    • 5A: Global Measures of Spatial Autocorrelation
    • 5B: Local Measures of Spatial Autocorrelation
    • 6A: Geographical Segmentation with Spatially Constrained Clustering Techniques
    • 7A: Calibrating Hedonic Pricing Model for Private Highrise Property with GWR Method
    • 8A: Geographically Weighted Predictive Models
    • 9A: Modelling Geographical Accessibility
    • 10A: Processing and Visualising Flow Data
    • 10B: Calibrating Spatial Interaction Models with R
  • In-class Exercises
    • In-class Exercise 01
    • In-class Exercise 02
    • In-class Exercise 03
    • In-class Exercise 04
    • In-class Exercise 05
    • In-class Exercise 06
    • In-class Exercise 07
    • In-class Exercise 08
    • In-class Exercise 09
    • In-class Exercise 10
  • Take-home Exercises
    • Take-home Exercise 01
    • Take-home Exercise 02
    • Take-home Exercise 03
  • Exploration

On this page

  • Introduction to Spatial Data Science

Exploration

This page shows additional exploration using online resources as follows:

  • Tutorials - Learn Spatial Analysis | Center for Spatial Data Science (CSDS)

Introduction to Spatial Data Science

01 Spatial Data Handling
In this exercise, we will handle spatial data to create a choropleth map of abandoned vehicles per capita in Chicago by downloading, filtering, transforming data, and using spatial join and aggregation techniques.
33 min
Sep 5, 2024
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