Learning task-specific city region partition

Hongjian Wang, Porter Jenkins, Hua Wei, Fei Wu, Zhenhui Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The proliferation of publicly accessible urban data provide new insights on various urban tasks. A frequently used approach is to treat each region as a data sample and build a model over all the regions to observe the correlations between urban features (e.g., demographics) and the target variable (e.g., crime count). To define regions, most existing studies use fixed grids or pre-defined administrative boundaries (e.g., census tracts or community areas). In reality, however, definitions of regions should be different depending on tasks (e.g., regional crime count prediction vs. real estate prices estimation). In this paper, we propose a new problem of task-specific city region partitioning, aiming to find the best partition in a city w.r.t. a given task. We prove this is an NP-hard search problem with no trivial solution. To learn the partition, we first study two variants of Markov Chain Monte Carlo (MCMC). We further propose a reinforcement learning scheme for effective sampling the search space. We conduct experiments on two real datasets in Chicago (i.e., crime count and real estate price) to demonstrate the effectiveness of our proposed method.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
PublisherAssociation for Computing Machinery, Inc
Pages3300-3306
Number of pages7
ISBN (Electronic)9781450366748
DOIs
StatePublished - May 13 2019
Event2019 World Wide Web Conference, WWW 2019 - San Francisco, United States
Duration: May 13 2019May 17 2019

Publication series

NameThe Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019

Conference

Conference2019 World Wide Web Conference, WWW 2019
CountryUnited States
CitySan Francisco
Period5/13/195/17/19

Fingerprint

Crime
Reinforcement learning
Markov processes
Sampling
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

Cite this

Wang, H., Jenkins, P., Wei, H., Wu, F., & Li, Z. (2019). Learning task-specific city region partition. In The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019 (pp. 3300-3306). (The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3308558.3313704
Wang, Hongjian ; Jenkins, Porter ; Wei, Hua ; Wu, Fei ; Li, Zhenhui. / Learning task-specific city region partition. The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. Association for Computing Machinery, Inc, 2019. pp. 3300-3306 (The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019).
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Wang, H, Jenkins, P, Wei, H, Wu, F & Li, Z 2019, Learning task-specific city region partition. in The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, Association for Computing Machinery, Inc, pp. 3300-3306, 2019 World Wide Web Conference, WWW 2019, San Francisco, United States, 5/13/19. https://doi.org/10.1145/3308558.3313704

Learning task-specific city region partition. / Wang, Hongjian; Jenkins, Porter; Wei, Hua; Wu, Fei; Li, Zhenhui.

The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. Association for Computing Machinery, Inc, 2019. p. 3300-3306 (The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Wang H, Jenkins P, Wei H, Wu F, Li Z. Learning task-specific city region partition. In The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. Association for Computing Machinery, Inc. 2019. p. 3300-3306. (The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019). https://doi.org/10.1145/3308558.3313704