GEOSPATIAL FINANCE
Foundations and Applications
Spatial context is fundamental to financial risk assessment. Geospatial data — vector, raster, sensor networks, time series — exposes supply-chain vulnerability, asset-level climate exposure, biodiversity loss, and development impact at locations regulators and investors can act on.
- [01]
Spatial data types: vector (points, lines, polygons), raster (gridded — satellite imagery, elevation, temperature), sensor networks, temporal layers.
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MAUP — the Modifiable Areal Unit Problem: same data at different aggregation scales yield different statistical conclusions; critical for spatial finance.
- [03]
Spatial autocorrelation (Moran's I, LISA, Geary's C) reveals clustering of risk and opportunity (e.g., poverty + pollution co-location).
- [04]
GWR (Geographically Weighted Regression) lets coefficients vary locally — proximity to forest is worth different amounts in different regions.
- [05]
China's Belt and Road Initiative — geospatial analysis of deforestation, water pollution, and land-system change along corridors.
"Spatial context is fundamental to financial risk assessment. Location reveals supply-chain vulnerability, climate exposure, biodiversity risk, and development impact."
Manfred Fischer · Ph.D.
Emeritus Professor of Economic Geography · Vienna University of Economics and Business
"Geospatial data is solving today's societal and environmental challenges. Location is the unifying coordinate of every climate question worth asking."