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Inferring land use from mobile phone activity

Inferring land use from mobile phone activity Inferring land use from mobile phone activity Jameson L. Toole Michael Ulm Austrian Institute of Technology Vienna, Austria Marta C. González Massachusetts Institute of Technology 77 Mass. Ave Cambridge, MA, USA Massachusetts Institute of Technology 77 Mass. Ave Cambridge, MA, USA michael.ulm@ait.ac.at Dietmar Bauer Austrian Institute of Technology Vienna, Austria jltoole@mit.edu martag@mit.edu dietmar.bauer@ait.ac.at ABSTRACT 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Inferring land use from mobile phone activity

Association for Computing Machinery — Aug 12, 2012

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References (14)

Datasource
Association for Computing Machinery
Copyright
Copyright © 2012 by ACM Inc.
ISBN
978-1-4503-1542-5
doi
10.1145/2346496.2346498
Publisher site
See Article on Publisher Site

Abstract

Inferring land use from mobile phone activity Jameson L. Toole Michael Ulm Austrian Institute of Technology Vienna, Austria Marta C. González Massachusetts Institute of Technology 77 Mass. Ave Cambridge, MA, USA Massachusetts Institute of Technology 77 Mass. Ave Cambridge, MA, USA michael.ulm@ait.ac.at Dietmar Bauer Austrian Institute of Technology Vienna, Austria jltoole@mit.edu martag@mit.edu dietmar.bauer@ait.ac.at ABSTRACT 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

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