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Harnessing Heterogeneous Big Geospatial Data

In: Handbook of Big Geospatial Data

Author

Listed:
  • Bo Yan

    (University of California)

  • Gengchen Mai

    (University of California)

  • Yingjie Hu

    (University at Buffalo)

  • Krzysztof Janowicz

    (University of California)

Abstract

The heterogeneity of geospatial datasets is a mixed blessing in that it theoretically enables researchers to gain a more holistic picture by providing different (cultural) perspectives, media formats, resolutions, thematic coverage, and so on, but at the same time practice shows that this heterogeneity may hinder the successful combination of data, e.g., due to differences in data representation and underlying conceptual models. Three different aspects are usually distinguished in processing big geospatial data from heterogeneous sources, namely geospatial data conflation, integration, and enrichment. Each step is a progression on the previous one by taking the result of the last step, extracting useful information, and incorporating additional information to solve specific questions. This chapter introduces and clarifies the scope and goal of each of these aspects, presents existing methods, and outlines current research trends.

Suggested Citation

  • Bo Yan & Gengchen Mai & Yingjie Hu & Krzysztof Janowicz, 2021. "Harnessing Heterogeneous Big Geospatial Data," Springer Books, in: Martin Werner & Yao-Yi Chiang (ed.), Handbook of Big Geospatial Data, chapter 0, pages 459-473, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-55462-0_17
    DOI: 10.1007/978-3-030-55462-0_17
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