Welcome to ISSS626 Geospatial Analytics and Applications

1 Welcome to my Geospatial Analytics

Hello there! I am Shubham Sinha, and this is my homepage for ISSS626: Geospatial Analysis and Application, a course taught by Prof KAM Tin Seong at Singapore Management University as part of my Master of IT in Business program (Data Science & Analytics). This site showcases my coursework, where I apply a range of geospatial techniques to transform, visualize, and analyze spatial data in meaningful ways.

2 Course Overview

Hands-on Exercise: Guided practice using R and public datasets to perform geospatial analysis.
In-class Exercise: Builds on hands-on methods, focusing on data visualization and communication.
Take-home Exercise: Integrates course concepts to tackle real-world spatial analysis challenges.

Hands-on Exercise

Hands-on Exercise 9A: Modelling Geographical Accessibility

Hands-on Exercise 8A: Geographically Weighted Predictive Models

Hands-on Exercise 5B: Local Measures of Spatial Autocorrelation

Hands-on Exercise 5A: Global Measures of Spatial Autocorrelation

Hands-on Exercise 7A: Calibrating Hedonic Pricing Model for Private Highrise Property with GWR Method

Hands-on Exercise 6A: Geographical Segmentation with Spatially Constrained Clustering Techniques

Hands-on Exercise 4: Spatial Weights and Applications

Hands-on Exercise 3 - Spatio-Temporal Point Patterns Analysis

Hands-on Exercise 2B - Second-order Spatial Point Patterns Analysis Methods

Hands-on Exercise 2A - First-order Spatial Point Patterns Analysis Methods

Hands-on Exercise 1B - Choropleth Mapping with R

Hands-on Exercise 1A - Geospatial Data Wrangling with R

In-class Exercise

In-Class Exercise 10 - Market Area Analysis for Retail and Service Locations using Huff model

Hands-on Exercise 9A: Modelling Geographical Accessibility

Hands-on Exercise 8A: Geographically Weighted Predictive Models

In-Class Exercise 6 - Kick Start for Take home 2

In-Class Exercise 5 - Global and Local measures of spatial autocorrelation using sfdep package AND Emerging Hot Spot Analysis (EHSA)

In-class Exercise 4: Geographically Weighted Summary Statistics - GWmodel methods

In-class Exercise 3b : Working with Open Government Data

In-class Exercise 3a: Interactive K-function

In-class Exercise 2: Spatial Point Patterns Analysis

In-class Exercise 1: Geospatial Analytics

In-class Exercise 7 - Calibrating Hedonic Pricing Model

Take-home Exercise

Take-home Exercise 3 - Modelling Spatial Heterogeneity in HDB Resale Prices via GWR

Take-home Exercise 2 : Uncovering Spatial and Spatio-Temporal Patterns of Bus Passenger Demand in Singapore Using Advanced Geospatial Analytics

Take-home Exercise 1: Geospatial Analytics for Public Good