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Big Spatial Flow Data Analytics

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Handbook of Big Geospatial Data

Abstract

Spatial flow data represent meaningful interaction activities between two regions, such as exchange of population, goods, capital, and information. In recent years, the widespread adoption of location-aware technologies such as the GPS-enabled smartphones amass flow data at individual level, along with much finer spatiotemporal granularity and abundant semantic information. The increasing availability of big spatial flow has brought us with unprecedented opportunities to study all kinds of spatial interaction phenomena from new perspectives, as well as intellectual challenges to develop visualization and analytical methods to handle its unique geographic and geometric characteristics. This chapter introduces a collection of the latest methods and techniques specifically designed for big spatial flow data. Three major families of methods are reviewed, namely geovisualization, spatial data mining, and spatial statistics, to give readers a comprehensive picture of the available approaches that serve different study purposes. One representative approach from each family is selected to elaborate, so the readers can gain a deeper understanding to readily use the methods and potentially develop their own in the future.

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Author information

Authors and Affiliations

  1. School of Geosciences, University of South Florida, Tampa, FL, USA

    Ran Tao

Authors
  1. Ran Tao
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Correspondence to Ran Tao .

Editor information

Editors and Affiliations

  1. Professorship Big Geospatial Data Management, Technical University of Munich (TUM), Munich, Germany

    Martin Werner

  2. Spatial Sciences Institute, University of Southern California, Los Angeles, CA, USA

    Yao-Yi Chiang

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Tao, R. (2021). Big Spatial Flow Data Analytics. In: Werner, M., Chiang, YY. (eds) Handbook of Big Geospatial Data. Springer, Cham. https://doi.org/10.1007/978-3-030-55462-0_7

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