Hands-on Exercise

9A: Modelling Geographical Accessibility

In this exercise, we will learn to model geographical accessibility using Hansen’s potential model, Spatial Accessibility Measure (SAM), and other methods in R.

10B: Calibrating Spatial Interaction Models with R

In this exercise, we will learn to calibrate Spatial Interaction Models (SIMs) using various regression methods to determine factors affecting public bus passenger flows during the morning peak in Singapore.

10A: Processing and Visualising Flow Data

In this exercise, we will explore the concept of spatial interaction, and learn how to build an OD (origin/destination) matrix.

8A: Geographically Weighted Predictive Models

In this exercise, we will learn how to build predictive models using the geographical random forest method to predict outcomes based on geospatial factors and historical geospatial locations.

7A: Calibrating Hedonic Pricing Model for Private Highrise Property with GWR Method

In this exercise, we will learn to build hedonic pricing models for private high-rise property using Geographically Weighted Regression (GWR) methods to account for non-stationary variables.

6A: Geographical Segmentation with Spatially Constrained Clustering Techniques

In this exercise, we will learn to delineate homogeneous regions using hierarchical and spatially constrained clustering techniques on geographically referenced multivariate data.

5B: Local Measures of Spatial Autocorrelation

In this exercise, we will learn to compute Local Measures of Spatial Autocorrelation (LMSA) using the spdep package, including Local Moran’s I, Getis-Ord’s Gi-statistics, and their visualizations.

5A: Global Measures of Spatial Autocorrelation

In this exercise, we will learn to compute Global Measures of Spatial Autocorrelation using the spdep package, including Moran’s I and Geary’s C tests, spatial correlograms, and their statistical interpretation.

4A: Spatial Weights and Applications

In this exercise, we will learn to compute spatial weights, visualize spatial distributions, and create spatially lagged variables using various functions from R packages such as sf,spdep, and tmap.

3A: Network Constrained Spatial Point Patterns Analysis

In this exercise, we will learn to use R and the spNetwork package for analyzing network-constrained spatial point patterns, focusing on kernel density estimation and G- and K-function analysis.

2B: 2nd Order Spatial Point Patterns Analysis

In this exercise, we will learn to apply 2nd-order spatial point pattern analysis methods in R, including G, F, K, and L functions, to evaluate spatial point distributions and perform hypothesis testing using the spatstat package.

2A: 1st Order Spatial Point Patterns Analysis

In this exercise, we will learn to analyze spatial point patterns in R, including importing geospatial data, performing kernel density estimation and nearest neighbor analysis, and visualizing results using spatstat, sf, and tmap packages.

1B: Thematic Mapping and GeoVisualisation with R

In this exercise, we will learn to create thematic maps and perform geovisualization in R using the tmap package, including data preparation, classification, color schemes, and advanced mapping techniques.

1A: Geospatial Data Wrangling with R

In this exercise, we will learn to use R for geospatial data handling, including importing, transforming, wrangling, and visualizing data with sf, tidyverse, and ggplot2.