ISSS626 Geospatial Analytics and Applications

Hands-on Exercises
In-class Exercises
Take-home Exercises
Exploration

In-Class Exercise

In-Class Exercise 10

In this exercise, we will recap on the use of geocoding and learn how to work with Open Government Data.

In-Class Exercise 9

In this exercise, we will import geospatial data, create buffers for accessibility analysis, use spatial joins to assess facility proximity, visualize spatial data on maps, and calculate Hansen’s Accessibility metrics.

In-Class Exercise 8

In this exercise, we will go through a sample exercise for Take-Home Exercise 3B and In-Class Exercise 08, which supplement what we have learnt in Hands-On Exercise 8.

In-Class Exercise 7

In this exercise, we will explore Calibrating Hedonic Pricing Models for Private Highrise Property using the Geographically Weighted Regression (GWR) Method, focusing on spatially varying relationships in property pricing data.

In-Class Exercise 6

In this exercise, we will explore Emerging Hot Spot Analysis (EHSA), a spatio-temporal analysis method for identifying and categorizing hot and cold spot trends over time in a spatial dataset.

In-Class Exercise 5

In this exercise, we will perform global and local measures of spatial autocorrelation using sfdep package.

In-Class Exercise 4

In-Class Exercise 3

This session reviews past Hands-on exercises and address questions from classmates on Piazza.

In-Class Exercise 2

In this exercise, we will learn to analyze spatial point patterns using spatstat methods, including installing necessary packages, creating spatial objects, performing kernel density estimation, and applying edge correction methods.

In-Class Exercise 1

In the exercise, we will learn to handle geospatial data in R, create various maps, and perform statistical analysis using sf, tmap, and ggstatsplot.

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