Discovering Urban Functional Zones Using Latent Activity Trajectories
The step of urbanization and modern civilization fosters different functional zones in a city, such as residential areas, business districts, and educational areas. In a metropolis, people commute between these functional zones every day to engage in different socioeconomic activities, e.g., working...
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| Published in | IEEE transactions on knowledge and data engineering Vol. 27; no. 3; pp. 712 - 725 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.03.2015
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| Subjects | |
| Online Access | Get full text |
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| Abstract | The step of urbanization and modern civilization fosters different functional zones in a city, such as residential areas, business districts, and educational areas. In a metropolis, people commute between these functional zones every day to engage in different socioeconomic activities, e.g., working, shopping, and entertaining. In this paper, we propose a data-driven framework to discover functional zones in a city. Specifically, we introduce the concept of latent activity trajectory (LAT), which captures socioeconomic activities conducted by citizens at different locations in a chronological order. Later, we segment an urban area into disjointed regions according to major roads, such as highways and urban expressways. We have developed a topic-modeling-based approach to cluster the segmented regions into functional zones leveraging mobility and location semantics mined from LAT. Furthermore, we identify the intensity of each functional zone using Kernel Density Estimation. Extensive experiments are conducted with several urban scale datasets to show that the proposed framework offers a powerful ability to capture city dynamics and provides valuable calibrations to urban planners in terms of functional zones. |
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| AbstractList | The step of urbanization and modern civilization fosters different functional zones in a city, such as residential areas, business districts, and educational areas. In a metropolis, people commute between these functional zones every day to engage in different socioeconomic activities, e.g., working, shopping, and entertaining. In this paper, we propose a data-driven framework to discover functional zones in a city. Specifically, we introduce the concept of latent activity trajectory (LAT), which captures socioeconomic activities conducted by citizens at different locations in a chronological order. Later, we segment an urban area into disjointed regions according to major roads, such as highways and urban expressways. We have developed a topic-modeling-based approach to cluster the segmented regions into functional zones leveraging mobility and location semantics mined from LAT. Furthermore, we identify the intensity of each functional zone using Kernel Density Estimation. Extensive experiments are conducted with several urban scale datasets to show that the proposed framework offers a powerful ability to capture city dynamics and provides valuable calibrations to urban planners in terms of functional zones. |
| Author | Xing Xie Yingzi Wang Hui Xiong Yuan, Nicholas Jing Yu Zheng Kai Zheng |
| Author_xml | – sequence: 1 givenname: Nicholas Jing surname: Yuan fullname: Yuan, Nicholas Jing email: nicholas.yuan@microsoft.com organization: Microsoft Res. Asia, Beijing, China – sequence: 2 surname: Yu Zheng fullname: Yu Zheng email: yuzheng@microsoft.com organization: Microsoft Res. Asia, Beijing, China – sequence: 3 surname: Xing Xie fullname: Xing Xie email: xingx@microsoft.com organization: Microsoft Res. Asia, Beijing, China – sequence: 4 surname: Yingzi Wang fullname: Yingzi Wang email: yingzi@mail.ustc.edu.cn organization: Sch. of Comput. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei, China – sequence: 5 surname: Kai Zheng fullname: Kai Zheng email: kevinz@itee.uq.edu.au organization: ITEE, Univ. of Queensland, Brisbane, QLD, Australia – sequence: 6 surname: Hui Xiong fullname: Hui Xiong email: hxiong@rutgers.edu organization: State Univ. of New Jersey, Piscataway, NJ, USA |
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| SubjectTerms | Collaboration Image segmentation Roads Semantics Trajectory Urban areas |
| Title | Discovering Urban Functional Zones Using Latent Activity Trajectories |
| URI | https://ieeexplore.ieee.org/document/6871403 |
| Volume | 27 |
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