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Taking Shanghai Central City as its case study, this paper presents an approach to exploring the urban spatial structure through mobile phone positioning data. Firstly, based on base station location data and mobile phone signaling data, the paper analyses the number of users connecting to each base station, and further generates the maps of mobile phone user density through kernel density analysis. We move on to calculate the multi-day average user density based on a time frame of 10:00 and 23:00 at workdays and 15:00 and 23:00 at weekends for Shanghai Central City. Then, through spatial aggregation and density classifi cation on the density maps of 10:00 at workdays and 15:00 at weekends, we identify the ranks and functions of public centers within Shanghai Central City. Lastly, we identify residential areas, business off ice areas, and leisure areas in Shanghai Central City and measure the degree of functional mix by comparing the ratio of day and night user density as well as the user density at nighttime of workdays and weekends.
Taking Shanghai Central City as its case study, this paper presents an approach to exploring the urban spatial structure through mobile phone signaling data, the paper analyzes the number of users connecting to each base station, and further generates the maps of mobile phone user density through kernel density analysis. We move on to calculate the multi-day average user density based on a time frame of 10:00 and 23:00 at workdays and 15:00 and 23:00 at weekends for Shanghai Central City. Then, through spatial aggregation and density classifi cation on the density maps of 10:00 at workdays and 15:00 at weekends, we identify the ranks and functions of public centers within Shanghai Central City. Lastly, we identify residential areas, business off ice areas, and leisure areas in Shanghai Central City and measure the degree of functional mix by comparing the ratio of day and night user density as well as the u ser density at nighttime of workdays and weekends.