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Discovering regions of different functions in a city using human mobility and POIs

Discovering regions of different functions in a city using human mobility and POIs Discovering Regions of Different Functions in a City Using Human Mobility and POIs Jing Yuan v-jinyua@microsoft.com Microsoft Research Asia Yu Zheng yuzheng@microsoft.com Microsoft Research Asia Xing Xie xing.xie@microsoft.com Microsoft Research Asia ABSTRACT The development of a city gradually fosters different functional regions, such as educational areas and business districts. In this paper, we propose a framework (titled DRoF) that Discovers Regions of different Functions in a city using both human mobility among regions and points of interests (POIs) located in a region. Specifically, we segment a city into disjointed regions according to major roads, such as highways and urban express ways. We infer the functions of each region using a topic-based inference model, which regards a region as a document, a function as a topic, categories of POIs (e.g., restaurants and shopping malls) as metadata (like authors, affiliations, and key words), and human mobility patterns (when people reach/leave a region and where people come from and leave for) as words. As a result, a region is represented by a distribution of functions, and a function is featured by a distribution of mobility patterns. We further identify the intensity of each function in different locations. The results generated by http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Discovering regions of different functions in a city using human mobility and POIs

Association for Computing Machinery — Aug 12, 2012

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References (25)

Datasource
Association for Computing Machinery
Copyright
Copyright © 2012 by ACM Inc.
ISBN
978-1-4503-1462-6
doi
10.1145/2339530.2339561
Publisher site
See Article on Publisher Site

Abstract

Discovering Regions of Different Functions in a City Using Human Mobility and POIs Jing Yuan v-jinyua@microsoft.com Microsoft Research Asia Yu Zheng yuzheng@microsoft.com Microsoft Research Asia Xing Xie xing.xie@microsoft.com Microsoft Research Asia ABSTRACT The development of a city gradually fosters different functional regions, such as educational areas and business districts. In this paper, we propose a framework (titled DRoF) that Discovers Regions of different Functions in a city using both human mobility among regions and points of interests (POIs) located in a region. Specifically, we segment a city into disjointed regions according to major roads, such as highways and urban express ways. We infer the functions of each region using a topic-based inference model, which regards a region as a document, a function as a topic, categories of POIs (e.g., restaurants and shopping malls) as metadata (like authors, affiliations, and key words), and human mobility patterns (when people reach/leave a region and where people come from and leave for) as words. As a result, a region is represented by a distribution of functions, and a function is featured by a distribution of mobility patterns. We further identify the intensity of each function in different locations. The results generated by

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