ArcGIS is a comprehensive analytics platform for data scientists. This repository contains examples covering the analytical capabilities of ArcGIS
At the heart of Spatial Data Science in ArcGIS lies the Geographic Approach — a powerful framework that integrates diverse factors across social, economic, and environmental domains. Geography is not just a backdrop; it provides the science and language to understand and solve complex problems by organizing, integrating, and reflecting the multifaceted nature of our world.
This approach enables us to:
- Illuminate patterns hidden in spatial data
- Discover relationships across disciplines
- Make informed decisions grounded in location intelligence
Whether we're analyzing:
- Social factors like health, welfare, demographics, and education
- Economic dimensions such as infrastructure, energy, and development
- Or environmental systems including climate, biodiversity, and ecosystem services
ArcGIS empowers us to bring these dimensions together — bridging data silos and revealing insights that are only possible when we think spatially.
To run the sample scripts, make sure the required environment variables are set. For development purposes, you can define these variables in a .env file located at the root of the repository.
ARCGIS_API_KEY=<secret-api-key>
PYTHONPATH=./src/data-engineering/data_engineering/src
TRAFFIC_DATA_FILE="/data/Frankfurt-Main-traffic.sqlite"
TRAFFIC_FEATURES="/temp/Spatial_Data_Science.gdb/Weekday_Traffic_2025"