Classification of Changing Regions Based on Temporal Context in Local Spatial Association

  • Jae-Seong Ahn
  • Yang-Won Lee
  • Key-Ho Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4265)


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.


State Sequence Administrative Unit Spatial Association Similarity Matrice Temporal Context 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jae-Seong Ahn
    • 1
  • Yang-Won Lee
    • 2
  • Key-Ho Park
    • 1
  1. 1.Department of Geography, College of Social SciencesSeoul National University 
  2. 2.Center for Spatial Information ScienceUniversity of TokyoTokyoJapan

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