Geospatial Data Science
Definition
Geospatial data science merges spatial thinking with statistics, machine learning, and computing. It wrangles messy location data, engineers features from geography (proximity, network centrality, morphology), and builds predictive and explanatory models. The craft spans notebooks to production pipelines and insists on rigorous validation that respects spatial dependence.
Application
Applications include demand forecasting, deforestation detection, disaster damage mapping, traffic prediction, and urban equity metrics. Collaboration with domain experts ensures models translate into action.
FAQ
How is cross‑validation adapted for spatial data?
Use spatial or block cross‑validation to avoid training and test points that are neighbors, which would inflate performance estimates.
What spatial features help models?
Distance to POIs, land‑use mix, network reach, elevation, and texture from imagery. Multi‑scale features capture context at neighborhood and city scales.
How do you explain model outputs to planners?
Partial dependence, SHAP maps, and counterfactual scenarios show how changing features—like new transit—might affect outcomes.
What are ethical pitfalls?
Biased labels, sensitive locations, and feedback loops. Perform fairness checks, minimize precision for sensitive sites, and document model cards.
SUPPORT
© 2025 GISCARTA