Unsupervised representation learning of spatial data via multimodal embedding

Porter Jenkins, Ahmad Farag, Suhang Wang, Zhenhui Li

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

Abstract

Increasing urbanization across the globe has coincided with greater access to urban data; this enables researchers and city administrators with better tools to understand urban dynamics, such as crime, traffic, and living standards. In this paper, we study the Learning an Embedding Space for Regions (LESR) problem, wherein we aim to produce vector representations of discrete regions. Recent studies have shown that embedding geospatial regions in a latent vector space can be useful in a variety of urban computing tasks. However, previous studies do not consider regions across multiple modalities in an end-to-end framework. We argue that doing so facilitates the learning of greater semantic relationships among regions. We propose a novel method, RegionEncoder, that jointly learns region representations from satellite image, point-of-interest, human mobility, and spatial graph data. We demonstrate that these region embeddings are useful as features in two regression tasks and across two distinct urban environments. Additionally, we perform an ablation study that evaluates each major architectural component. Finally, we qualitatively explore the learned embedding space, and show that semantic relationships are discovered across modalities.

Original languageEnglish (US)
Title of host publicationCIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages1993-2002
Number of pages10
ISBN (Electronic)9781450369763
DOIs
StatePublished - Nov 3 2019
Event28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
Duration: Nov 3 2019Nov 7 2019

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019
CountryChina
CityBeijing
Period11/3/1911/7/19

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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  • Cite this

    Jenkins, P., Farag, A., Wang, S., & Li, Z. (2019). Unsupervised representation learning of spatial data via multimodal embedding. In CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 1993-2002). (International Conference on Information and Knowledge Management, Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3357384.3358001