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Harvesting Big Geospatial Data from Natural Language Texts

In: Handbook of Big Geospatial Data

Author

Listed:
  • Yingjie Hu

    (University at Buffalo, Department of Geography)

  • Benjamin Adams

    (University of Canterbury, Department of Computer Science and Software Engineering)

Abstract

A vast amount of geospatial data exists in natural language texts, such as newspapers, Wikipedia articles, social media posts, travel blogs, online reviews, and historical archives. Compared with more traditional and structured geospatial data, such as those collected by the US Geological Survey and the national statistics offices, geospatial data harvested from these unstructured texts have unique merits. They capture valuable human experiences toward places, reflect near real-time situations in different geographic areas, or record important historical information that is otherwise not available. In addition, geospatial data from these unstructured texts are often big, in terms of their volume, velocity, and variety. This chapter presents the motivations of harvesting big geospatial data from natural language texts, describes typical methods and tools for doing so, summarizes a number of existing applications, and discusses challenges and future directions.

Suggested Citation

  • Yingjie Hu & Benjamin Adams, 2021. "Harvesting Big Geospatial Data from Natural Language Texts," Springer Books, in: Martin Werner & Yao-Yi Chiang (ed.), Handbook of Big Geospatial Data, chapter 0, pages 487-507, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-55462-0_19
    DOI: 10.1007/978-3-030-55462-0_19
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