- Describe the effect of non-stationarity on local indices of spatial association
- Decompose Moran’s I and Geary’s c into local measures of spatial association
- Compute the Gi and Gi* statistics
- Explain how geographically weighted regression provides a local measure of spatial association
- Explain how a weights matrix can be used to convert any classical statistic into a local measure of spatial association
- Compare and contrast global and local statistics and their uses
This knowledge area embodies a variety of data driven analytics, geocomputational methods, simulation and model driven approaches designed to study complex spatial-temporal problems, develop insights into characteristics of geospatial data sets, create and test geospatial process models, and construct knowledge of the behavior of geographically-explicit and dynamic processes and their patterns.
Topics in this Knowledge Area are listed thematically below. Existing topics are in regular font and linked directly to their original entries (published in 2006; these contain only Learning Objectives). Entries that have been updated and expanded are in bold. Forthcoming, future topics are italicized.