Classification of Changing Regions Using a Temporal Signature of Local Spatial Association

Abstract

Spatially associated patterns are often found in geographical phenomena, since nearby entities are often more related than distant ones. Such spatial association also changes over time; hence, the temporal aspect of spatial association needs to be examined using both spatiality and temporality. This paper describes a method of modeling the temporal signatures of spatial association, and thus of grouping similar changes. We employed a Moran scatterplot to assess the local characteristics of a spatial association and then extended it to a time-series Moran scatterplot quadrant signature (MSQS) to capture spatiotemporal changes in regions categorically. We used sequence comparison and data grouping techniques to classify similar regions in terms of the time-series MSQS. We tested the feasibility of the proposed method using a case study of a twenty-four-month (June 2004–May 2006) housing price index for sixty-nine administrative units in the Seoul Metropolitan Area, South Korea.