 Big Spatial Flow Data AnalyticsRan Tao ()
Chapter Chapter 7 in Handbook of Big Geospatial Data, 2021, pp 163-183 from Springer Abstract: 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. Date: 2021 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-55462-0_7 Ordering information: This item can be ordered from DOI: 10.1007/978-3-030-55462-0_7 Access Statistics for this chapter More chapters in Springer Books from Springer |
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