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In the urbanization process of big cities in China, the rise of satellite towns, the migration of manufacturing and the relocation of residents to the suburban areas have accelerated the separation between workplace and residence and brought enormous changes to the activity patterns of Chinese cities. In this paper, we propose a novel and data-driven method of extracting individuals’ daily activities and identifying “anchor points” (home and workplace) from mobile phone data and survey data, and apply it to the Jinhe new town. People in study area are classified into three groups, and use time-geographic concept to depict individual activity pattern of each group. Furthermore, we focus on the residents with obviously separated home and workplace caused by suburbanization. For representing the cluster of these people’s activities in space-time, kernel density estimation is used to detect the intensity in space-time. The activity density profile facilitates finding the spatio-temporal characteristic of the demand deriving from suburban residents. The study shows that mobile phone data allows analyzing human activity pattern in space-time at very detailed scale but also require other data resources for comprehensiveness and visualization of all people across the city.
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