02.528 Spatial Analysis of Urban Data

This course focuses on the use of GIS (Geographic Information Systems) in the urban context, applying GIS tools and techniques for analysing, modelling, and visualizing geospatial data in urban environments. The most fundamental concepts and techniques in GIS and spatial data analysis are covered in this course, including data collection, data management, and data visualization. Additionally, specific applications of GIS in the urban studies will be taught, namely 3D mapping, basic network analysis, and advanced spatial analysis.

This course also introduces GeoAI, showing how AI can be used alongside GIS to support mapping, spatial analysis, and the interpretation of urban patterns. Students will learn how these tools can help generate ideas and insights for urban planning and policy, while also thinking critically about their limits, assumptions, and possible errors.

Through a hands-on group final project, students will develop and apply a reusable GeoAI-supported analytical workflow to investigate a real-world urban issue in Singapore. The project will require each group to define a focused research problem and work exclusively with geospatial datasets obtained from publicly available governmental sources. Emphasis will be placed on methodological transparency, reproducibility, and critical assessment of the assumptions and limitations of AI-assisted geospatial analysis. Each group will publish its workflow as an open-source project.

The course is delivered as a combination of lectures and hands-on tutorials. Students will get to apply methods covered in the lectures during the tutorials and solve a geospatial problem through a final project.

Instructors: Mr Bayi Li and Ms Xiaohan Liu
Time: Wednesday (10AM-1PM)
Venue: TBC

Week 1 Introduction to Urban Data Analytics
Week 2 Urban Data Sources and Open Data in Singapore
Week 3 Introduction to Python in Urban Data Analytics
Week 4 Spatial Data Processing and Cleaning
Week 5 Cartographic Design
Week 6 Spatial statistics and analysis of vector data
Week 7 Recess Week
Week 8 Spatial analysis of raster data
Week 9 Spatial Data Integration and Feature Engineering
Week 10 Network Analysis for Urban Systems
Week 11 Computer Vision and Urban Case Study
Week 12 Web-Based Spatial Visualization
Week 13 Final Project Tutorial
Week 14 Final Presentation