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Study / Spatial Analysis
Spatial Analysis
The analysis methods behind a GIS project. Each topic explains the idea, gives a worked example and links to the matching practical.
9 theory notes
Querying and overlay
3 guides- 01Attribute Queries and Selection: Finding the Features You NeedHow attribute tables are organised, how selecting differs from filtering, and how to write queries that combine conditions on text, numbers and missing values.
- 02Joining and Relating Tables: Keys, Matches and CardinalityHow a table of values is attached to a layer through a shared key, what goes wrong when keys do not match, and when to use a join, a relate or a spatial join.
- 03Vector Overlay and Proximity Analysis: Clip, Intersect, Buffer and JoinHow overlay tools combine two vector layers, how buffers and nearest-feature joins measure closeness, and what to check so the results are right.
Rasters and surfaces
3 guides- 04Raster Analysis and Map Algebra: Reclassify, Combine and SummariseHow local, focal, zonal and global operations work on a grid of cells, how to reclassify and combine rasters, and what to check before you trust the output.
- 05Terrain Analysis and Hydrology: Slope, Aspect, Viewshed and FlowHow slope, aspect, hillshade and contours are derived from an elevation model, how a viewshed is calculated, and how flow direction and accumulation outline streams and watersheds.
- 06Interpolation and Density Analysis: From Points to SurfacesHow IDW, TIN and kriging estimate values between measurements, why density analysis answers a different question, and how to check an interpolation against held-back points.
Decisions and networks
2 guides- 07Site Suitability and Multi-Criteria Analysis: Distance, Cost and Weighted OverlayHow to turn several criteria into one ranked map: distance surfaces, cost distance, standardised scores, weights and constraints, and how to test how sensitive the answer is.
- 08Network Analysis and Routing: Shortest Paths, Service Areas and OD MatricesHow a road network is modelled as a graph, how shortest paths and service areas are found, and why the quality of the network data decides the answer.
Patterns and statistics
1 guide- 09Spatial Statistics and Autocorrelation: Nearest Neighbour, Moran's I and VariogramsWhy nearby values tend to be alike, how the nearest neighbour index and Moran's I measure that, how a variogram describes it, and what a simple simulation can and cannot show.