Chapter 2 presents the spatial data matrix as the most efficient structure for storage and analysis of data on different characteristics at different locations. Global Positioning Systems (GPS) help to locate a position on the Earth, and are commonly used for adding locational information to data. Different spatial datasets can be presented in the same frame of reference using a geographic coordinate system or projection (e.g. azimuthal, conical or cylindrical). To undertake spatial analysis, phenomena must first be conceptualised and represented as either raster (grid cells) or vector (point, line polygon) data. Important characteristics of spatial data are its measurement level, map scale and associated topological information. Nominal, ordinal, interval and ratio are the four levels of measurement for populating the spatial data matrix; they hold different amounts of information and determine what analysis can be performed. Map scale refers to the ratio between a distance represented on a map and the same distance on the ground. Spatial data topology governs how information is stored, spatial data are most commonly stored in a hierarchical GIS database.