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

In-Class Exercise

Take Home Exercise 3
70 min
Oct 29, 2024

Take Home Exercise 2
In this exercise, we will explore how COVID-19 affected Thailand’s tourism economy using spatial and spatio-temporal analysis, focusing on how the impacts varied across different provinces and examining recovery patterns.
62 min
Sep 30, 2024

Take Home Exercise 1
In this exercise, we will apply spatial and spatio-temporal point pattern analysis methods to identify factors affecting road traffic accidents in the Bangkok Metropolitan Region (BMR), including visualizing spatio-temporal dynamics, conducting spatial analysis using Network Spatial Point Patterns, and analyzing spatio-temporal patterns using Temporal Network Spatial Point Patterns.
64 min
Sep 5, 2024
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