Identification of Spatiotemporal Characteristics of Crowd Activity in County with Non-negative Matrix Factorization
The identification of spatiotemporal characteristics of crowd activity is conducive to the understanding of regional spatial structure characteristics.However,the existing studies have not paid enough attention to the county level and below.Moreover,the mobile signaling data used to characterize the crowd activities at the county level are sparse to a certain ex-tent and systematic data processing methods are lacking.We take Xinxing County in Guangdong Province as an example,select the mobile signaling data of two characteristic days of working day and weekend in 2020 to construct a spatiotemporal matrix,and use the non-negative matrix factorization method to extract the implied activity pattern features.Based on these features,the spatial distribution of various functional areas in this county is obtained by k-means clustering algorithm.The results show that Xinxing County has prosperous economy at night and abundant crowd activity mode on weekends.The in-tensity of crowd activity in the central city is high,but the con-nection with the surrounding villages and towns is weak.The county is dominated by residential function,and there is no significant functional partition.The results demonstrate that the non-negative matrix factorization method can effectively extract the pattern features in the sparse spatiotemporal ma-trix,which can provide scientific support and help for the county land spatial planning.
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