Towards Estimating Urban Population Distributions from Mobile Call Data

Today, large-volume mobile phone call datasets are widely applied to investigate the spatio-temporal characteristics of human urban activity. This paper discusses several fundamental issues in estimating population distributions based on mobile call data. By adopting an individual-based call activity dataset that consists of nearly two million mobile subscribers who made over one hundred million communications over seven consecutive days, we explore the relationships among the Erlang values, the number of calls, and the number of active mobile subscribers. Then, the LandScan population density dataset is introduced to evaluate the process of estimating the population. The empirical findings indicate that: (1) Temporal variation exists in the relation between the Erlang values and the number of calls; (2) The number of calls is linearly proportional to the number of active mobile subscribers; (3) The proportion between the mobile subscribers and the actual total population varies in different areas, thus failing to represent the underlying population. Hence, the call activity reflects “activity intensity” rather than population distribution. The Erlang is a defective indicator of population distribution, whereas the number of calls serves as a better measure. This research provides an explicit clarification with respect to using call activity data for estimating population distribution.

[1]  W. G. Hansen.  How Accessibility Shapes Land Use , 1959 .

[2]  D. McFadden.  MEASUREMENT OF URBAN TRAVEL DEMAND , 1974 .

[3]  Bruce W. Hamilton,et al.  Wasteful Commuting , 1982, Journal of Political Economy.

[4]  Ailsa Roell,et al.  Wasteful Commuting: Appendix , 1982 .

[5]  S. Hanson,et al.  Systematic variability in repetitious travel , 1988 .

[6]  H. M. Alexander,et al.  DISEASE SPREAD AND POPULATION DYNAMICS OF ANTHER-SMUT INFECTION OF SILENE ALBA CAUSED BY THE FUNGUS USTILAGO VIOLACEA , 1988 .

[7]  P. Gordon,et al.  The influence of metropolitan spatial structure on commuting time , 1989 .

[8]  M. Kwan.  Gender and Individual Access to Urban Opportunities: A Study Using Space–Time Measures , 1999 .

[9]  M. Kwan.  Gender, the Home-Work Link, and Space-Time Patterns of Nonemployment Activities , 1999 .

[10]  Guoqiang Shen.  Estimating nodal attractions with exogenous spatial interaction and impedance data using the gravity model , 1999 .

[11]  J. E. Dobson,et al.  LandScan: A Global Population Database for Estimating Populations at Risk , 2000 .

[12]  Yilin Zhao,et al.  Mobile phone location determination and its impact on intelligent transportation systems , 2000, IEEE Trans. Intell. Transp. Syst..

[13]  Mei-Po Kwan,et al.  Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set , 2000 .

[14]  Ryuichi Kitamura,et al.  Micro-simulation of daily activity-travel patterns for travel demand forecasting , 2000 .

[15]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[16]  K. Axhausen,et al.  Habitual travel behaviour: Evidence from a six-week travel diary , 2003 .

[17]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[18]  T. Geisel,et al.  Forecast and control of epidemics in a globalized world. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[19]  M. Wegener.  Overview of Land Use Transport Models , 2004 .

[20]  Ling Bian,et al.  A Conceptual Framework for an Individual-Based Spatially Explicit Epidemiological Model , 2004 .

[21]  Yasuo Asakura,et al.  TRACKING SURVEY FOR INDIVIDUAL TRAVEL BEHAVIOUR USING MOBILE COMMUNICATION INSTRUMENTS , 2004 .

[22]  M. Wegener,et al.  Land-Use Transport Interaction: State of the Art , 2004 .

[23]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[24]  R. Ahas,et al.  Location based services—new challenges for planning and public administration? , 2005 .

[25]  Xiaoling Chen,et al.  Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes , 2006 .

[26]  C. Ratti,et al.  Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis , 2006 .

[27]  Carlo Ratti,et al.  Cellular Census: Explorations in Urban Data Collection , 2007, IEEE Pervasive Computing.

[28]  J. Raymer,et al.  Combining census and registration data to estimate detailed elderly migration flows in England and Wales , 2007 .

[29]  Noam Shoval.  Sensing Human Society , 2007 .

[30]  M. Kwan.  Mobile Communications, Social Networks, and Urban Travel: Hypertext as a New Metaphor for Conceptualizing Spatial Interaction , 2007 .

[31]  Ling Bian,et al.  A Network Model for Dispersion of Communicable Diseases , 2007, Trans. GIS.

[32]  Noam Shoval,et al.  Tracking technologies and urban analysis , 2008 .

[33]  Rein Ahas,et al.  Evaluating passive mobile positioning data for tourism surveys: An Estonian case study , 2008 .

[34]  Carlo Ratti,et al.  Computing Urban Mobile Landscapes Through Monitoring Population Density Based On Cell-phone Chatting , 2008 .

[35]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[36]  Itzhak Benenson,et al.  PARKAGENT: An agent-based model of parking in the city , 2008, Comput. Environ. Urban Syst..

[37]  G. Madey,et al.  Uncovering individual and collective human dynamics from mobile phone records , 2007, 0710.2939.

[38]  Carlo Ratti,et al.  Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network , 2009 .

[39]  R. Ahas,et al.  The Seasonal Variability of Population in Estonian Municipalities , 2010 .

[40]  Carlo Ratti,et al.  Does Urban Mobility Have a Daily Routine? Learning from the Aggregate Data of Mobile Networks , 2010 .

[41]  R. Ahas,et al.  Daily rhythms of suburban commuters' movements in the Tallinn metropolitan area: Case study with mobile positioning data , 2010 .

[42]  Le Wang,et al.  Improving the housing-unit method for small-area population estimation using remote-sensing and GIS information , 2010 .

[43]  Gail K. Auslander,et al.  What can we learn about the mobility of the elderly in the GPS era , 2010 .

[44]  O. Järv,et al.  Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones , 2010 .

[45]  M. O'Kelly,et al.  New Estimates of Gravitational Attraction by Linear Programming , 2010 .

[46]  C. Dye,et al.  The Population Dynamics and Control of Tuberculosis , 2010, Science.

[47]  Jukka-Pekka Onnela,et al.  Geographic Constraints on Social Network Groups , 2010, PloS one.

[48]  Fahui Wang,et al.  Urban land uses and traffic 'source-sink areas': Evidence from GPS-enabled taxi data in Shanghai , 2012 .