Abstract
The fine detection of human activity sub-areas of the city is an effective basis for realizing the appropriate city resource allocation and scientific management. In this paper, we proposed a method of finely dividing the urban area using social media data. First, we build a spatial interactive network based on a large amount of user movement data obtained from geo-tagged social media data. Second, community discovery algorithm in complex networks is used to detect the community structure. Then mapping the network communities to geospatial and revealing the urban sub-regions structure. We detected urban sub-regions in metropolitan area of Wuhan. The experimental results show that compared with single travel data, social media data covers a wider range of user activity types, which can divide urban space into fine-grained sub-regions reflecting people’s real activity space with closer interaction. Compared with other top-down urban structures, such as administrative divisions, the fine-grained urban sub-region structure detected in this paper can provide a more detailed basis for urban management.
