Inferring land use from mobile phone activity

Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. At the same time ubiquitous mobile sensors from personal GPS devices to mobile phones are collecting massive amounts of data on urban systems. The locations, communications, and activities of millions of people are recorded and stored by new information technologies. This work utilizes novel dynamic data, generated by mobile phone users, to measure spatiotemporal changes in population. In the process, we identify the relationship between land use and dynamic population over the course of a typical week. A machine learning classification algorithm is used to identify clusters of locations with similar zoned uses and mobile phone activity patterns. It is shown that the mobile phone data is capable of delivering useful information on actual land use that supplements zoning regulations.

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

[2]  Víctor Soto,et al.  Automated land use identification using cell-phone records , 2011, HotPlanet '11.

[3]  Carlo Ratti,et al.  Eigenplaces: Segmenting Space through Digital Signatures , 2010, IEEE Pervasive Computing.

[4]  Dominic Stead,et al.  Land Use and Travel Behaviour: Expected Effects from the Perspective of Utility Theory and Activity-Based Theories , 2005 .

[5]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[6]  Liang Liu,et al.  Estimating Origin-Destination Flows Using Mobile Phone Location Data , 2011, IEEE Pervasive Computing.

[7]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[8]  R. Cervero,et al.  TRAVEL DEMAND AND THE 3DS: DENSITY, DIVERSITY, AND DESIGN , 1997 .

[9]  R Krzyczkowski,et al.  Reducing the need for travel , 1974 .

[10]  Bert van Wee,et al.  Accessibility evaluation of land-use and transport strategies: review and research directions , 2004 .

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

[12]  Hunter N. B. Moseley,et al.  Limits of Predictability in Human Mobility , 2010 .

[13]  J. Jacobs.  The Death and Life of Great American Cities , 1962 .

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