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 develo...

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Bibliographic Details
Published in2010 18th International Conference on Geoinformatics pp. 1 - 7
Main Authors Chaogui Kang, Song Gao, Xing Lin, Yu Xiao, Yihong Yuan, Yu Liu, Xiujun Ma
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Cities and towns
geo-visualization
Humans
individual mobility patterns
mobile call records
Mobile communication
Mobile handsets
Poles and towers
spatio-temporal data mining
Three dimensional displays
time geography
Trajectory
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ISBN1424473012
9781424473014
ISSN2161-024X
DOI10.1109/GEOINFORMATICS.2010.5567857

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Summary: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.
ISBN:1424473012
9781424473014
ISSN:2161-024X
DOI:10.1109/GEOINFORMATICS.2010.5567857