Geospatial Data Visualization

Geographic data visualization is essential for spatial analysis in research^[1]. This page demonstrates mapping capabilities using Observable Framework.

Choropleth Maps

Point Clustering

Density Heatmaps

Geographic Patterns

Spatial analysis reveals patterns that might be hidden in non-geographic representations^[2]. Key applications include:

Environmental Science: Climate zones, pollution dispersion
Public Health: Disease spread, healthcare accessibility
Economics: Regional development, trade flows
Urban Planning: Population density, infrastructure planning

Geospatial visualization combines cartography with data visualization. See Tufte, E.R. (1983). The Visual Display of Quantitative Information for foundational principles. ↩︎
Spatial autocorrelation and clustering analysis are fundamental to geographic data science. Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93-115. ↩︎