A Novel Framework for Joint Learning of City Region Partition and Representation

Mingyu DENG, Wanyi ZHANG*, Jie ZHAO, Zhu WANG, Mingliang ZHOU, Jun LUO, Chao CHEN*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

The proliferation of multimodal big data in cities provides unprecedented opportunities for modeling and forecasting urban problems, such as crime prediction and house price prediction, through data-driven approaches. A fundamental and critical issue in modeling and forecasting urban problems lies in identifying suitable spatial analysis units, also known as city region partition. Existing works rely on subjective domain knowledge for static partitions, which is general and universal for all tasks. In fact, different tasks may need different city region partitions. To address this issue, we propose JLPR, a task-oriented framework for Joint Learning of region Partition and Representation. To make partitions fit tasks, JLPR integrates the region partition into the representation model training and learns region partitions using the supervision signal from the downstream task. We evaluate the framework on two prediction tasks (i.e., crime prediction and housing price prediction) in Chicago. Experiments show that JLPR consistently outperforms state-of-the-art partitioning methods in both tasks, which achieves above 25% and 70% performance improvements in terms of mean absolute error for crime prediction and house price prediction tasks, respectively. Additionally, we meticulously undertake three visualization case studies, which yield profound and illuminating findings from diverse perspectives, demonstrating the remarkable effectiveness and superiority of our approach.

Original languageEnglish
Article number210
JournalACM Transactions on Multimedia Computing, Communications and Applications
Volume20
Issue number7
DOIs
Publication statusPublished - 16 May 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Keywords

  • multimodal big data
  • partition learning
  • prediction task
  • Region partition
  • representation learning

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