Spatial analysis

Spatial analysis examines locations, patterns, relationships, and processes represented by spatial data. It uses statistical, geometric, and computational methods to answer questions about where things are and how places relate.

Geographic information system: A computer system for capturing, managing, analyzing, and displaying geographically referenced data. GIS provides the data-management and mapping environment where many spatial analyses are performed.
Site selection: The process of choosing a location that best meets specified criteria. Spatial overlays and suitability models compare candidate locations against multiple constraints.
Geographic information system data models: Representations used to encode geographic features and their attributes in a GIS. Raster and vector structures shape which spatial operations are available and appropriate.
Exploratory spatial data analysis: Methods for discovering spatial structure and unusual patterns through maps and statistical summaries. It emphasizes open-ended pattern discovery before formal hypothesis testing.
Modifiable areal unit problem: The sensitivity of statistical results to the scale and zoning of geographic units. Changing area boundaries can alter measured correlations and apparent clusters.
Spatial autocorrelation: The degree to which nearby locations have similar or dissimilar values. It measures whether a mapped pattern is geographically clustered, dispersed, or random.
Location-allocation problem: An optimization problem that places facilities to serve demand under specified objectives and constraints. It helps determine where facilities should go and which populations they should serve.
Coordinate reference system: A framework that assigns coordinates to locations on Earth or in a local space. Compatible reference systems are necessary for valid distance, area, and overlay calculations.
Geostatistics: Statistical methods for spatially continuous variables, especially interpolation and spatial prediction. It is a specialized toolkit for measured surfaces, unlike the broader range of spatial analysis.
Ecological fallacy: The error of inferring individual-level relationships from aggregate data. Area-level patterns do not necessarily describe the people or objects within each area.

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