Classification of Changing Regions Based on Temporal Context in Local Spatial Association
We propose a method of modeling regional changes in local spatial association and classifying the changing regions based on the similarity of time-series signature of local spatial association. For intuitive recognition of time-series local spatial association, we employ Moran scatterplot and extend it to QS-TiMoS (Quadrant Sequence on Time-series Moran Scatterplot) that allows for examining temporal context in local spatial association using a series of categorical variables. Based on the QS-TiMoS signature of nodes and edges, we develop the similarity measures for “state sequence” and “clustering transition” of time-series local spatial association. The similarity matrices generated from the similarity measures are then used for producing the classification maps of time-series local spatial association that present the history of changing regions in clusters. The feasibility of the proposed method is tested by a case study on the rate of land price fluctuation of 232 administrative units in Korea, 1995-2004.
KeywordsState Sequence Administrative Unit Spatial Association Similarity Matrice Temporal Context
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