Six geographic application paradigms of big data

doi:10.11947/j.AGCS.2024.20230199

Abstract

Abstract:

With the advent of the big data era, multi-source big data is on the rise, leading to the integration of data-driven research paradigms with geography. Geospatial big data based on individual behavior offers observations of massive individual behavior 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 decision-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-temporal distribution characteristics. The last two directions provide support at the decision-making level. Furthermore, this article highlights issues in data acquisition, analysis methods, and application goals in big data applications.

Key words: geospatial big data, spatio-temporal distributions, abnormal objects, universal laws, correlations, future trends, spatial decision-making

CLC Number:

P208

Lun WU, Yuanqiao HOU, Yu LIU. Six geographic application paradigms of big data[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(8): 1465-1479.

Figures/Tables 8

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