Part 2. Geospatial Python Programming

Overview

This chapter introduces you to Python programming for geospatial analysis. Python has become the de facto standard for modern GIS work, offering powerful libraries for data manipulation, spatial analysis, and visualisation. You’ll learn the essential Python skills needed for working with geographic data, from basic programming concepts to specialised geospatial operations.

Learning Objectives

By the end of this chapter, you will be able to:

  • Write and execute Python code for geospatial analysis
  • Manipulate and analyse tabular data using pandas
  • Work with spatial data using geopandas
  • Perform common GIS operations programmatically
  • Create effective visualisations of spatial data
  • Structure code into reusable functions

Chapter Structure

This chapter is organised into five main sections:

  1. Basic Python: Core Python concepts including variables, data types, loops, and conditional statements
  2. DataFrames: Working with tabular data using pandas, including data cleaning, filtering, and aggregation
  3. Geospatial Data: Introduction to vector and raster data, coordinate systems, and spatial operations with geopandas
  4. Creating Functions: Writing reusable code to make your analyses more efficient and reproducible
  5. Urban Analytics: Applied examples using real geospatial datasets for urban and environmental analysis

Why Python for GIS?

Python has several advantages for geospatial analysis:

  • Open source: Free to use and modify
  • Extensive libraries: Rich ecosystem of spatial packages (geopandas, rasterio, shapely, etc.)
  • Reproducibility: Code-based workflows ensure your analysis can be repeated
  • Integration: Works seamlessly with other tools and platforms
  • Community: Large, active community providing support and new developments

Local Installation (Optional)

If you prefer working locally:

  • Install Python via Anaconda or Miniconda
  • Use Jupyter Lab, VS Code, or another IDE
  • Install required packages using conda or pip

Key Python Packages for GIS

This chapter focuses on these essential libraries:

  • pandas: Tabular data manipulation
  • geopandas: Spatial data operations
  • matplotlib: Basic plotting
  • seaborn: Statistical visualisation
  • shapely: Geometric operations
  • momepy: geomorphological analysis of urban form

Getting Help

As you work through this chapter, remember:

  • Documentation is your friend: https://www.python.org/doc
  • Stack Overflow is an excellent resource for troubleshooting
  • The Ed discussion forum for course-specific questions
  • Use AI assistants judiciously (ChatGPT, Gemini) but always understand the code they generate

Next Steps

Let’s begin with the fundamentals in Section 2.1 Basic Python, where you’ll learn the core Python programming concepts needed for geospatial analysis.