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With the rapid development of urbanization, the living standard has been improved on a continued basis for urban residents and their activities have become increasingly frequent. Therefore, it is of massive significance to study the hot spots of urban residents' activities and enforce effective planning and decision-making for urban and traffic departments. In this paper, the data is preprocessed in the first place. Then, the passengers' pick-up points and travel track points are extracted, and the statistical analysis method is employed to analyze the travel length and travel time of urban residents. Finally, an improved FCM algorithm is proposed. The conventional Fuzzy c-means (FCM) clustering algorithm is classed as a local optimal algorithm, and the number of clustering is made uncertain. In view of the shortcomings as mentioned above, an improved (FCM) clustering algorithm is suggested in this paper, which adopts adaptive distance norm and adds its own norm induction matrix to each cluster in order to ensure global optimization. The partition coefficient (PC), classification entropy (CE) and (XB) index are introduced to assist in determining the optimal number of clustering. According to the statistical analysis of GPS track data, the morning peak and evening peak in a day is 7:00-10:00 and 17:00-20:00, respectively. Cluster analysis is carried out for each time period and the whole day using the model proposed in this paper. The results show that the number of hot spots in each time period and the whole day is 12, 10, 8, 13, 13, respectively. The hot spots are distributed in the business center, office and residential areas, which are consistent with the actual situation. It shows that the model proposed in this paper can effectively and accurately mine the hot spots of urban residents' activities. It plays an important role in urban planning and commercial layout.

Research on Urban Resident Activity Patterns and Hotspot Area Based on GPS Floating Car Data