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Mining hourly population dynamics by activity type based on decomposition of sequential snapshot data

International Journal of Digital Earth 2022年第1期15卷 1395-1416页

作者： Qingli Shi Li Zhuo Haiyan Tao Qiuping Li Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and PlanningSun Yat-sen UniversityGuangzhouPeople’s Republic of China Guangdong Provincial Engineering Research Centre for Public Security and Disaster GuangzhouPeople’s Republic of China Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) ZhuhaiPeople’s Republic of China

The dynamic population distributions by activity type(***,shopping or in-home)are vital for resource allocation,urban planning and epidemic *** studies have incorporated individual-level human mobility data to map population distribution by activity type,access to such data is hindered due to privacy issues and they rely on auxiliary data to provide priori activity *** paper presents a method for generating the population dynamics by activity *** first introduce more readily available sequential snapshot data to construct the population mixture model,then decompose the population mixture,and finally estimate the dynamic population size for each *** test the method in the central districts of Guangzhou city,China,based on real-time Tencent user density *** analysis and accuracy assessment prove that our method can accurately estimate hourly distributions for populations engaging in working,stay-at-home,and socializing *** temporal distribution of the working population reproduces the regular work scenarios and socializing population displays complex spatial *** also find that there is an underlying relationship between a region’s function and its dynamic population *** presented method has great potential for application and could provide new insight for studying urban dynamic functions.

关键词： hourly population dynamics activity type population mixture model real-time Tencent user density data

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