Geospatial Analysis
Definition
Geospatial analysis is the examination of phenomena through their locations, relationships, and spatial structure. It spans vector and raster operations, spatial statistics, network modeling, and time‑enabled analytics, turning raw coordinates into insights about proximity, clustering, connectivity, and change. The map is not the end product; it is a lens for inference.
Application
Applications include public health hotspot detection, transportation accessibility, habitat suitability, urban equity audits, and supply‑chain resilience. Analysts integrate satellite imagery, sensors, surveys, and administrative data to build evidence for policy and business decisions.
FAQ
What makes spatial data special compared to typical tables?
Spatial dependence—nearby things are related—and varying support/scale. Analyses must respect topology, resolution, and modifiable areal unit effects to avoid spurious conclusions.
How do you choose between vector and raster methods?
Use vector when discrete features and topology matter; raster for continuous surfaces or intensive cell math. Many workflows mix both via rasterization or zonal stats.
Why is scale critical in spatial clustering?
Clusters can appear or vanish as you change bandwidth or aggregation units. Sensitivity tests and multi‑scale methods help avoid overfitting a single view.
How can time be integrated cleanly?
Use time‑indexed geometries, trajectories, and event series. Visualize with time sliders, but also model with temporal joins, lagged predictors, and space‑time cubes.