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‎.github/workflows/deploy.yml‎

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name: Deploy book to GitHub Pages
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on:
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push:
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branches: [main]
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jobs:
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deploy:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: "3.11"
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- name: Install dependencies
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run: pip install -r requirements.txt
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- name: Deploy to GitHub Pages
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run: mkdocs gh-deploy --force
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

‎.gitignore‎

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site/
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.venv/
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__pycache__/
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*.pyc
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.ipynb_checkpoints/
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.DS_Store

‎README.md‎

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# Geospatial Modeling with Python
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**Sergio Souza Costa** — LambdaGEO, UFMA
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A practical guide to geographic data science and discrete spatial simulation
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using the Python ecosystem and the [DisSModel](https://github.com/lambdageo/dissmodel) framework.
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## Build the book
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```bash
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pip install -r requirements.txt
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mkdocs serve # preview at http://127.0.0.1:8000
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mkdocs build # build static site to site/
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mkdocs gh-deploy # deploy to GitHub Pages
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```
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## Structure
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```
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docs/
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index.md
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part1/ ch01 … ch05 # Python for Data Science
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part2/ ch06 … ch10 # Geographic Data Science
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part3/ ch11 … ch20 # Discrete Spatial Modeling
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```
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## Contributing
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Each chapter lives on its own branch: `chapter/chNN-slug`.
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Open a PR to `main` when a chapter is ready for review.
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---
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*LambdaGEO Research Group · Federal University of Maranhão (UFMA)*

‎docs/index.md‎

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# Geospatial Modeling with Python
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**Sergio Souza Costa** — LambdaGEO, UFMA
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---
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This book is a practical guide to geographic data science and discrete spatial
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simulation using the Python ecosystem.
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It is organized in three parts:
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- **Part I** — Python for Data Science
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Core tools: Pandas, EDA, software engineering.
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- **Part II** — Geographic Data Science
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Spatial data types, GeoPandas, rasterio, libpysal, visualization.
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- **Part III** — Discrete Spatial Modeling
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Cellular automata, salabim, NumPy vectorization, and the
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[DisSModel](https://github.com/lambdageo/dissmodel) framework —
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including a full coastal dynamics case study.
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---
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## How to Use This Book
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Each chapter is a Jupyter notebook. You can read it as a book or run it
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interactively. Code cells are self-contained within each chapter.
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## Installation
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```bash
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pip install dissmodel>=0.2.0 geopandas rasterio libpysal salabim
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```
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For the coastal dynamics case study (Chapter 18):
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```bash
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pip install coastal-dynamics
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```
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---
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## Source Code
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All notebooks and supporting code are available at:
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- Book repository: [github.com/lambdageo/geospatial-modeling-book](https://github.com/lambdageo/geospatial-modeling-book)
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- DisSModel framework: [github.com/lambdageo/dissmodel](https://github.com/lambdageo/dissmodel)
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- Coastal dynamics models: [github.com/lambdageo/coastal-dynamics](https://github.com/lambdageo/coastal-dynamics)
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---
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## Citation
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If you use this material in your research or teaching, please cite:
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```
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Costa, S. S. (2026). Geospatial Modeling with Python.
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LambdaGEO Research Group, Federal University of Maranhão (UFMA).
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https://lambdageo.github.io/geospatial-modeling-book
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```
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---
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*LambdaGEO Research Group · Federal University of Maranhão (UFMA)*
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[lambdageo.github.io](https://lambdageo.github.io)

‎docs/part1/ch01_ecosystem.ipynb‎

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{
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"nbformat": 4,
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"nbformat_minor": 5,
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.11.0"
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},
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"book_metadata": {
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"part": 1,
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"chapter": 1,
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"title": "The Python Scientific Ecosystem",
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"path": "part1/ch01_ecosystem.ipynb"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Chapter 1: The Python Scientific Ecosystem\n",
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"\n",
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"*Part I — Python for Data Science*\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Learning Objectives\n",
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"\n",
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"By the end of this chapter you will be able to:\n",
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"\n",
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"- Set up a reproducible development environment (conda/mamba, VS Code, Jupyter)\n",
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"- Understand the role of each core library: NumPy, Pandas, Matplotlib, GeoPandas\n",
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"- Manage packages and environments for reproducible science\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"```{note}\n",
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"Keep tool installation instructions in a collapsible admonition block.\n",
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"```\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Standard imports — add chapter-specific imports below\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Why Python for Data Science\n",
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"\n",
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"*TODO: write content.*\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Code for section: Why Python for Data Science\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setting Up Your Environment\n",
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"\n",
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"*TODO: write content.*\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Code for section: Setting Up Your Environment\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Package Management with conda and pip\n",
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"\n",
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"*TODO: write content.*\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Code for section: Package Management with conda and pip\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Core Libraries at a Glance\n",
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"\n",
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"*TODO: write content.*\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Code for section: Core Libraries at a Glance\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Jupyter Notebooks and VS Code\n",
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"\n",
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"*TODO: write content.*\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Code for section: Jupyter Notebooks and VS Code\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Summary and Further Reading\n",
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"\n",
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"*TODO: write content.*\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Code for section: Summary and Further Reading\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Summary\n",
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"\n",
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"*TODO: summarise key takeaways.*\n",
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"\n",
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"### Key concepts introduced\n",
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"\n",
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"- TODO\n- TODO\n- TODO\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Further Reading\n",
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"\n",
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"- *TODO: add references.*\n"
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]
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}
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]
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}

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