short-paper

Representation learning of urban regions via mobility-signature-based zone embedding: a case study of Seoul, South Korea

Authors:

Yoonjin YoonAuthors Info & Claims

LocalRec '21: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising

Article No.: 6, Pages 1 - 4

https://doi.org/10.1145/3486183.3491001

Published: 19 November 2021 Publication History

Abstract

As urbanization continues to evolve and accelerate, understanding the interactions between urban geography and large-scale mobility data has generated a great interest in the urban studies in recent years. In this paper, we present a method to learn embeddings of urban zones by utilizing the spatiotemporal characteristics of urban mobility. We extract the mobility signature from taxi trajectory data in Seoul, South Korea. Then, we apply Skip-gram model on the mobility signature to obtain the embeddings of the urban zones. Finally, we apply the spherical k-means clustering on the learned embeddings of zones to identify the urban functional regions. Through proposed approach, region maps of Seoul that can readily identify regions of similar socio-economic activities such as mobility patterns are provided.

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Cited By

Li TXi YWang HLi YTarkoma SHui P(2023)Learning Representations of Satellite Imagery by Leveraging Point-of-InterestsACM Transactions on Intelligent Systems and Technology10.1145/358934414:4(1-32)Online publication date: 8-May-2023
https://dl.acm.org/doi/10.1145/3589344
Luo YLeong CJiao SChung FLi WLiu G(2023)Geo-Tile2Vec: A Multi-Modal and Multi-Stage Embedding Framework for Urban AnalyticsACM Transactions on Spatial Algorithms and Systems10.1145/35717419:2(1-25)Online publication date: 12-Apr-2023
https://dl.acm.org/doi/10.1145/3571741
Bouros PDasu TKanza YRenz MSacharidis D(2022)LocalRec 2021 Workshop Report: The Fifth ACM SIGSPATIAL Workshop on Location-Based Recommendations, Geosocial Networks and GeoadvertisingSIGSPATIAL Special10.1145/3578484.357848613:3(1-5)Online publication date: 23-Dec-2022
https://dl.acm.org/doi/10.1145/3578484.3578486

Index Terms

Representation learning of urban regions via mobility-signature-based zone embedding: a case study of Seoul, South Korea
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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Published In

cover image ACM Conferences

LocalRec '21: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising

November 2021

66 pages

ISBN:9781450391009

DOI:10.1145/3486183

Editors:
Panagiotis Bouros
Johannes Gutenberg University Mainz, Germany
,
Tamraparni Dasu
AT&T Labs - Research
,
Yaron Kanza
AT&T Labs - Research
,
Matthias Renz
Christian-Albrechts-Universität zu Kiel, Germany
,
Dimitris Sacharidis
Université Libre de Bruxelles, Belgium

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 November 2021

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National Research Foundation of Korea
National Research Foundation of Korea_

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SIGSPATIAL '21

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SIGSPATIAL

SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems

November 2, 2021

Beijing, China

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Overall Acceptance Rate 17 of 26 submissions, 65%

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Cited By

Li TXi YWang HLi YTarkoma SHui P(2023)Learning Representations of Satellite Imagery by Leveraging Point-of-InterestsACM Transactions on Intelligent Systems and Technology10.1145/358934414:4(1-32)Online publication date: 8-May-2023
https://dl.acm.org/doi/10.1145/3589344
Luo YLeong CJiao SChung FLi WLiu G(2023)Geo-Tile2Vec: A Multi-Modal and Multi-Stage Embedding Framework for Urban AnalyticsACM Transactions on Spatial Algorithms and Systems10.1145/35717419:2(1-25)Online publication date: 12-Apr-2023
https://dl.acm.org/doi/10.1145/3571741
Bouros PDasu TKanza YRenz MSacharidis D(2022)LocalRec 2021 Workshop Report: The Fifth ACM SIGSPATIAL Workshop on Location-Based Recommendations, Geosocial Networks and GeoadvertisingSIGSPATIAL Special10.1145/3578484.357848613:3(1-5)Online publication date: 23-Dec-2022
https://dl.acm.org/doi/10.1145/3578484.3578486

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