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Geospatial Modeling with Python

Sergio Souza Costa — LambdaGEO, UFMA

This book is a practical guide to geographic data science and discrete spatial simulation using the Python ecosystem.

It is organized in four parts:

  • Part I — Scientific Python for Researchers
    Core tools and practices: Python fundamentals, Pandas, data cleaning, EDA, and software engineering for reproducible science.

  • Part II — Geographic Data Science
    A dual-substrate approach covering both vector and raster data models, multidimensional arrays, spatial relationships, and raster-vector integration.

  • Part III — Discrete Spatial Modeling
    Cellular automata, discrete-event simulation with salabim, NumPy vectorization, and the DisSModel framework — including a full coastal dynamics case study.

  • Part IV — DisSModel in Practice
    The Brazilian Earth Observation ecosystem, the DisSModel Platform, and ensemble scenario analysis.

Part I — Scientific Python for Researchers

Chapters 1–5 are fully independent of DisSModel and can be read standalone.

Ch Title Notebook
1 The Scientific Python Ecosystem part1/ch01_ecosystem.ipynb
2 The Geospatial Python Toolbox part1/ch02_toolbox.ipynb
3 Tabular Data with Pandas part1/ch03_pandas.ipynb
4 Data Cleaning and Exploratory Analysis part1/ch04_cleaning_eda.ipynb
5 Software Engineering for Scientific Python part1/ch05_software_eng.ipynb

Part II — Geographic Data Science

A dual-substrate treatment of spatial data: vector and raster as complementary models. Chapters 6–13 are fully independent of DisSModel.

Ch Title Substrate Notebook
6 Introduction to Spatial Data Both part2/ch06_spatial_intro.ipynb
7 Vector Data with GeoPandas Vector part2/ch07_vector.ipynb
8 Raster Data with NumPy and rasterio Raster part2/ch08_raster.ipynb
9 Multidimensional Arrays with Xarray Raster part2/ch09_xarray.ipynb ⭐
10 Spatial Relationships and Weights Both part2/ch10_weights.ipynb
11 Exploratory Spatial Data Analysis Both part2/ch11_esda.ipynb
12 Visualizing Spatial Data Both part2/ch12_visualization.ipynb
13 Raster-Vector Integration Patterns Both part2/ch13_integration.ipynb ⭐

⭐ New chapters added in this edition.

Part III — Discrete Spatial Modeling

Chapters 14–18 introduce simulation paradigms and build toward the DisSModel framework. Chapters 14–16 can be read without prior DisSModel knowledge.

Ch Title DisSModel dependency Notebook
14 Paradigms of Spatial Simulation None part3/ch14_paradigms.ipynb
15 Cellular Automata from Scratch None part3/ch15_ca.ipynb
16 Discrete-Event Simulation with salabim None part3/ch16_des.ipynb
17 The Performance Problem — and the Solution None part3/ch17_performance.ipynb
18 Introducing DisSModel API part3/ch18_dissmodel.ipynb
19 Building Models with DisSModel API part3/ch19_building.ipynb
20 Land Use and Cover Change Modeling API part3/ch20_lucc.ipynb
21 Case Study — Coastal Dynamics API part3/ch21_coastal.ipynb
22 Reproducibility and Experiment Provenance API part3/ch22_provenance.ipynb

Part IV — DisSModel in Practice

Ch Title Notebook
23 DisSModel and the Brazilian Earth Observation Ecosystem part4/ch23_ecosystem.ipynb
24 Running Models with the DisSModel Platform part4/ch24_platform.ipynb
25 Ensemble Scenarios and Sensitivity Analysis part4/ch25_ensemble.ipynb

How to Use This Book

Each chapter is a Jupyter notebook. You can read it as a book or run it interactively. Code cells are self-contained within each chapter.

Parts I and II require no knowledge of DisSModel and are suitable for readers interested in geographic data science alone. Part III introduces simulation concepts independently before coupling to the framework. Part IV assumes familiarity with the full DisSModel API.

Installation

pip install geopandas rasterio xarray zarr libpysal salabim

For chapters in Part III and IV that use DisSModel:

pip install dissmodel>=0.4.0

For the coastal dynamics case study (Chapter 21):

pip install coastal-dynamics

Source Code

All notebooks and supporting code are available at:

Citation

If you use this material in your research or teaching, please cite:

Costa, S. S. (2028). Geospatial Modeling with Python.
LambdaGEO Research Group, Federal University of Maranhão (UFMA).
https://lambdageo.github.io/geospatial-modeling-python

LambdaGEO Research Group · Federal University of Maranhão (UFMA)
lambdageo.github.io