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Visual Data Mining in Spatial Interaction Analysis with Self-Organizing Maps

Author

Listed:
  • Jun Yan

    (Department of Geography and Geology, Western Kentucky University, 1906 College Heights Boulevard, Bowling Green, KY 42101, USA)

  • Jean-Claude Thill

    (Department of Geography and Earth Sciences, University of North Carolina—Charlotte, 9201 University City Boulevard, McEniry Building, Room 432, Charlotte, NC 28223, USA)

Abstract

Given that many spatial interaction (SI) systems are often constituted in large databases with high thematic dimensionality, data complexity reduction tasks are essential. The opportunity exists for researchers to examine the formation of different types of SIs as well as their interdependencies by exploring the patterns embedded in the data. To circumvent the limitations of existing methods of flow data compression and visual exploration, we propose an integrated computational and visual approach, known as VISIDAMIN, for handling both SI data projection and SI data quantization at once. The computational method of self-organizing maps serves as the data mining engine in this process. Using a large domestic air travel dataset as a case study, we examine how the characteristics of the air transport system interact with the SI system to create relationships and structures within the US domestic airline market.

Suggested Citation

  • Jun Yan & Jean-Claude Thill, 2009. "Visual Data Mining in Spatial Interaction Analysis with Self-Organizing Maps," Environment and Planning B, , vol. 36(3), pages 466-486, June.
    • Handle: RePEc:sae:envirb:v:36:y:2009:i:3:p:466-486
    DOI: 10.1068/b34019
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    References listed on IDEAS

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    1. Manfred M. Fischer & Yee Leung, 2001. "GeoComputational Modelling — Techniques and Applications: Prologue," Advances in Spatial Science, in: Manfred M. Fischer & Yee Leung (ed.), GeoComputational Modelling, chapter 1, pages 1-12, Springer.
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    Cited by:

    1. Francisco Javier Abarca-Alvarez & Francisco Sergio Campos-Sánchez & Fernando Osuna-Pérez, 2019. "Urban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligence," Sustainability, MDPI, vol. 11(23), pages 1-23, November.
    2. Ji Han & Jiabin Liu, 2018. "Urban Spatial Interaction Analysis Using Inter-City Transport Big Data: A Case Study of the Yangtze River Delta Urban Agglomeration of China," Sustainability, MDPI, vol. 10(12), pages 1-16, November.
    3. Mona Kashiha & Jean-Claude Thill, 2013. "The functional spaces of major European forwarding ports: study of competition for trade bound to the United States," Chapters, in: Thomas Vanoutrive & Ann Verhetsel (ed.), Smart Transport Networks, chapter 5, pages 68-98, Edward Elgar Publishing.

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