Analyzing and geo-visualizing individual human mobility patterns using mobile call records

Understanding the dynamics of the individuals' daily mobility patterns is very important in a wide range of fields. However, lacking of tools to monitor the time-resolved location of individuals makes this research consume both tremendous time and money in the past. Nowadays, the rapidly developing ability to collect space-time activity (STA) data through new information technologies such as cellular phones, WiFi, and GPS methods is improving the quantity and quality of these data and reducing their cost. Also, “People-Based GIS” gives us a new perspective for analyzing and visualizing these data. In this paper, we introduce several preprocessing and spatiotemporal analysis methods for such new ICT data in individual human mobility patterns mining and urban analysis under the time geography framework. By using millions of raw mobile call records of a large city in China, we compute statistical characteristic of cell phone usage both at different time of a day and on different days of a week to derive aggregated mobility patterns for millions of mobile phone users and individual mobility patterns for different groups which are divided by some selected social factors, such as gender and age. The research methods and results also illustrate how to make 3D or 2D graphic representations of individual activities patterns and their evolution through space and time.