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Measuring spatio-temporal autocorrelation in time series data of collective human mobility

Geo-Spatial Information Science 2019年第3期22卷 166-173页

作者： Yong Gao Jing Cheng Haohan Meng Yu Liu Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijingChina

Massive spatio-temporal big data about human mobility have become increasingly available.Revealing underlying dynamic patterns from these data is essential for understanding people’s behavior and urban deployment.spatio-temporal autocorrelation analysis is an exploratory approach to recognizing data distribution in space and time.The most widely used spatial autocorrelation measurements,such as Moran’s I and local indicators of spatial association(LISA),only apply to static data,so are powerless to spatio-temporal big data about human mobility.Thus,we proposed a new method by extending Moran’s I to measure the spatial autocorrelation of time series data.Then the method was applied to taxi ride data in Beijing,China to reveal the spatial pattern of collective human mobility.The result shows that there is strong positive spatio-temporal autocorrelation within the 5th Ring Road,weak negative spatio-temporal autocorrelation nearby the Sixth Ring Road,and almost no spatiotemporal autocorrelation between the roads.Local spatial patterns of taxi travel were also recognized.This method is useful for discovering underlying patterns from spatio-temporal big data to understand human mobility.

关键词： Taxi rides human mobility spatio-temporal autocorrelation Moran index

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spatio-temporal association analysis of county potential in the Pearl River Delta during 1990-2009

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Journal of Geographical Sciences 2015年第3期25卷 319-336页

作者： MEI Zhixiong XU Songjun OUYANG Jun School of Geography South China Normal University Guangzhou 510631 China

According to the highway data and some socioeconomic data of 1990, 1994, 2000, 2005 and 2009 of county units in the Pearl River Delta, this paper measured urban integrated power of different counties in different years by factor analysis, and estimated each county＇s potential in each year by means of expanded potential model. Based on that, the spa- tio-temporal association patterns and evolution of county potential were analyzed using spa- tio-temporal autocorrelation methods, and the validity of spatio-temporal association patterns was verified by comparing with spatial association patterns and cross-correlation function. The main results are shown as follows：（1） The global spatio-temporal association of county po- tential showed a positive effect during the study period. But this positive effect was not strong, and it had been slowly strengthened during 1994-2005 and decayed during 2005-2009. The local spatio-temporal association characteristics of most counties＇ potential kept relatively stable and focused on a positive autocorrelation, however, there were obvious transformations in some counties among four types of local spatio-temporal association （i.e., HH, LL, HL and LH）. （2） The distribution difference and its change of local spatio-temporal association types of county potential were obvious. spatio-temporal HH type units were located in the central zone and Shenzhen-Dongguan region of the eastern zone, but the central spatio-temporal HH area shrunk to the Guangzhou-Foshan core metropolitan region only after 2000; the spatio-temporal LL area in the western zone kept relatively stable with a surface-shaped continuous distribu- tion pattern, new LL type units emerged in the south-central zone since 2005, the eastern LL area expanded during 1994-2000, but then gradually shrunk and scattered at the eastern edge in 2009; the spatio-temporal HL and LH areas varied significantly. （3） The local spa- tio-temporal association patterns of county potential among the three zones presented significant disparity, and

关键词： county potential association spatio-temporal autocorrelation spatio-temporal Moran＇s I Pearl RiverDelta

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A general method of spatio-temporal clustering analysis

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Science China(Information Sciences) 2013年第10期56卷 158-171页

作者： DENG Min LIU QiLiang WANG JiaQiu SHI Yan Department of Surveying and Geo-informatics Central South University

spatio-temporal clustering has been a hot topic in the feld of spatio-temporal data mining and knowledge discovery.It can be employed to uncover and interpret developmental trends of geographic phenomenon in the real world.However,existing spatio-temporal clustering methods seldom consider both spatiotemporal autocorrelations and heterogeneities among spatio-temporal entities,and the coupling in space and time has not been well highlighted.In this paper,a unifed framework for the clustering analysis of spatio-temporal data is proposed,and a novel spatio-temporal clustering algorithm is developed by means of a spatio-temporal statistics methodology and intelligence computation technology.Our method is applied successfully to fnding spatio-temporal cluster in China’s annual temperature database for the period 1951 1992.

关键词： spatio-temporal clustering spatio-temporal autocorrelation spatio-temporal heterogeneity spatio-temporal data mining

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