Geography is crucial to conducting GIS analysis in the fields of landscape ecology, crime analysis, and health geography. Phenomena are often dependent upon the spatial nature of the data. Hence, spatial information must be included in the equation among other social, economic, and political factors to obtain all-encompassing and accurate results. The importance of geography, and thus spatial data, can be explained by the concept of spatial autocorrelation. It states that objects that are close together spatially have similar characteristics. Geostatistical methods such as kriging allow us to take geography into considerations when doing GIS analyses.

In particular, scale is an important factor in both landscape ecology and geography. In the context of GIS, scale refers to the spatial domain of the study. For example, an analysis on a watershed differs drastically from an analysis on a river in terms of the number of variables needed to be considered, the levels of interactions amongst those variables, the time duration required for the study, the variability in climate etc. Therefore, most explanatory models are scale-dependent.