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Discovering urban spatial-temporal structure from human activity patterns

Discovering urban spatial-temporal structure from human activity patterns Discovering Urban Spatial-Temporal Structure from Human Activity Patterns Shan Jiang Massachusetts Institute of Technology 77 Mass. Ave. E5519E Cambridge, MA 02142 USA +1 (857) 654-5066 Joseph Ferreira, Jr. Massachusetts Institute of Technology 77 Mass. Ave. 9-532 Cambridge, MA 02139 USA +1 (617) 253-7410 Marta C. Gonzalez Massachusetts Institute of Technology 77 Mass. Ave. 1153 Cambridge, MA 02139 USA +1 (617) 715-4140 shanjang@mit.edu ABSTRACT jf@mit.edu martag@mit.edu Urban geographers, planners, and economists have long been studying urban spatial structure to understand the development of cities. Statistical and data mining techniques, as proposed in this paper, go a long way in improving our knowledge about human activities extracted from travel surveys. As of today, most urban simulators have not yet incorporated the various types of individuals by their daily activities. In this work, we detect clusters of individuals by daily activity patterns, integrated with their usage of space and time, and show that daily routines can be highly predictable, with clear differences depending on the group, e.g. students vs. part time workers. This analysis presents the basis to capture collective activities at large scales and expand our perception of urban structure from the spatial dimension to spatial-temporal dimension. It will be http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Discovering urban spatial-temporal structure from human activity patterns

Association for Computing Machinery — Aug 12, 2012

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Datasource
Association for Computing Machinery
Copyright
Copyright © 2012 by ACM Inc.
ISBN
978-1-4503-1542-5
doi
10.1145/2346496.2346512
Publisher site
See Article on Publisher Site

Abstract

Discovering Urban Spatial-Temporal Structure from Human Activity Patterns Shan Jiang Massachusetts Institute of Technology 77 Mass. Ave. E5519E Cambridge, MA 02142 USA +1 (857) 654-5066 Joseph Ferreira, Jr. Massachusetts Institute of Technology 77 Mass. Ave. 9-532 Cambridge, MA 02139 USA +1 (617) 253-7410 Marta C. Gonzalez Massachusetts Institute of Technology 77 Mass. Ave. 1153 Cambridge, MA 02139 USA +1 (617) 715-4140 shanjang@mit.edu ABSTRACT jf@mit.edu martag@mit.edu Urban geographers, planners, and economists have long been studying urban spatial structure to understand the development of cities. Statistical and data mining techniques, as proposed in this paper, go a long way in improving our knowledge about human activities extracted from travel surveys. As of today, most urban simulators have not yet incorporated the various types of individuals by their daily activities. In this work, we detect clusters of individuals by daily activity patterns, integrated with their usage of space and time, and show that daily routines can be highly predictable, with clear differences depending on the group, e.g. students vs. part time workers. This analysis presents the basis to capture collective activities at large scales and expand our perception of urban structure from the spatial dimension to spatial-temporal dimension. It will be

There are no references for this article.