🗺️ Locating the Forgotten Data Type: Intro to Geospatial Analysis with Python

Welcome! This repository supports a talk given at the Python Athens Meetup, June 2025, titled:

"Locating the Forgotten Data Type: Intro to Geospatial Analysis with Python"
By Haris Ballis

This repo includes a practical notebook and code samples showcasing how to work with geospatial data in Python — from constructing simple geometries to performing real-world spatial queries and visualizations.

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🧠 What You’ll Learn

• What geospatial data types are and why they matter
• How to construct and store geometries using Shapely
• How spatial context reduces database complexity
• How to query and visualize real-world data (e.g., cafes along a road)
• Introduction to spatial indexing (H3), projections, and formats

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📦 Dependencies

Install the requirements using pip or uv:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

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📁 Key Notebook: cafes_along_road.ipynb

This notebook:

• Downloads an OpenStreetMap road network using osmnx
• Selects a target road (Δημητρίου Γούναρη) and creates a buffered area around it
• Queries Google Places API for cafes and bakeries along the road
• Uses GeoPandas for spatial filtering, intersection, and joining
• Calculates inter-cafe distances and density
• Aggregates points into H3 hexagonal cells for spatial summaries
• Visualizes the results with plotly

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🔐 API Access

This example uses the Google Places (New) API. Set your API key as an environment variable:

export GOOGLE_API_KEY=your_key_here

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🧭 License

MIT License. Feel free to adapt or build on this work — just give credit where it's due.

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🙋‍♂️ About the Author

Haris Ballis – geospatial analyst, Python enthusiast, and optimization geek.
🔗 github.com/harisbal