With the advent of the big data era,multi-source big data is on the rise,leading to the integration of data-driven re-search paradigms with geography.Geospatial big data based on individual behavior offers observations of massive individual be-havior patterns,thereby achieving"from people to places"social perception and supporting various applications such as urban management,transportation,and public health.This article delineates six application paradigms focusing on geospatial big data from an application perspective,ranging from describing spatio-temporal distributions at a low level to optimizing spatial deci-sion-making at a high level.The first direction involves a simple characterization of the spatio-temporal features of geographic phenomena and elements,while the second to fourth directions focus on exploring the rules and mechanisms behind spatio-tem-poral distribution characteristics.The last two directions provide support at the decision-making level.Furthermore,this arti-cle highlights issues in data acquisition,analysis methods,and application goals in big data applications.
geospatial big dataspatio-temporal distributionsabnormal objectsuniversal lawscorrelationsfuture trendsspatial decision-making
万方数据
维普
