Clustering temporal population patterns in Switzerland (1850-2000)

Abstract

Spatial planning and quantitative geography face a great challenge to handle the growing amount of geospatial data and new statistics. Techniques of data mining and knowledge discovery are therefore presented to examine by time intervals (=15 decades) the population development of 2,896 Swiss communities. The key questions are how many temporal patterns will occur and what are their characteristics? Relative difference (RelDiff) is proposed as an alternative to relative change calculation. The detection of temporal patterns is based on mixture models and the Bayes theorem. A procedure of information optimization aims at selecting relevant temporal patterns for clustering. The use of a k-Nearest Neighbor classifier is based on the assumption that similar relevant temporal patterns are a good point of reference for the whole population development. The classification result is explained by significance with already existing classifications (e.g. central-periphery). Spatial visualization leads to the verification in mind of the spatial analyst and provides the process of knowledge conversion. © 2012 Springer-Verlag Berlin Heidelberg.