大数据的6种地理学应用范式  

Six geographic application paradigms of big data

作　　者：邬伦[1] 侯远樵 刘瑜[1] WU Lun;HOU Yuanqiao;LIU Yu(Institute of Remote Sensing and Geographical Information Systems,Peking University,Beijing 100871,China)

机构地区：[1]北京大学遥感与地理信息系统研究所,北京100871

出　　处：《测绘学报》2024年第8期1465-1479,共15页Acta Geodaetica et Cartographica Sinica

基　　金：国家自然科学基金(41830645,42271426)。

摘　　要：随着大数据时代的来临,多源大数据正在兴起,数据驱动研究范式与地理学日益融合。基于个体行为的地理空间大数据可提供对海量个体行为模式的观察,从而实现“由人及地”的社会感知,支持城市管理、交通、公共卫生等不同应用。本文从应用角度,以地理空间大数据为重点,梳理其支持的6种应用范式,按照层次从低到高依次为描述时空分布、识别异常对象、发现普适规律、揭示关联关系、预测未来趋势及优化空间决策。其中,第1个方向是对地理现象和地理要素时空特征的简单刻画;第2~4个方向则注重探寻时空分布特征背后的规律和机理;最后两项,则是在决策层面提供支持。继而,本文指出大数据应用中数据获取、分析方法和应用目标3方面的问题。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.

关 键 词：地理空间大数据 时空分布 异常对象 普适规律 关联关系 未来趋势 空间决策 

分 类 号：P208[天文地球—地图制图学与地理信息工程]