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Characterizing and Forecasting Urban Vibrancy Evolution: A Multi-View Graph Mining Perspective

Published: 28 February 2023 Publication History

Abstract

Urban vibrancy describes the prosperity, diversity, and accessibility of urban areas, which is vital to a city’s socio-economic development and sustainability. While many efforts have been made for statically measuring and evaluating urban vibrancy, there are few studies on the evolutionary process of urban vibrancy, yet we know little about the relationship between urban vibrancy evolution and sophisticated spatiotemporal dynamics. In this article, we make use of multi-sourced urban data to develop a data-driven framework, U-Evolve, to investigate urban vibrancy evolution. Specifically, we first exploit the spatiotemporal characteristics of urban areas to create multi-view time-dependent graphs. Then, we analyze the contextual features and graph patterns of multi-view time-dependent graphs in terms of informing future urban vibrancy variations. Our analysis validates the informativeness of multi-view time-dependent graphs for characterizing and informing future urban vibrancy evolution. After that, we construct a feature based model to forecast future urban vibrancy evolution and quantify each feature’s importance. Moreover, to further enhance the forecasting effectiveness, we propose a graph learning based model to capture spatiotemporal autocorrelation of urban areas based on multi-view time-dependent graphs in an end-to-end manner. Finally, extensive experiments on two metropolises, Beijing and Shanghai, demonstrate the effectiveness of our forecasting models. The U-Evolve framework has also been deployed in the production environment to deliver real-world urban development and planning insights for various cities in China.

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  • (2024)Deep learning for cross-domain data fusion in urban computingInformation Fusion10.1016/j.inffus.2024.102606113:COnline publication date: 21-Nov-2024
  • (2024)Graph neural networks for multi-view learning: a taxonomic reviewArtificial Intelligence Review10.1007/s10462-024-10990-157:12Online publication date: 21-Oct-2024
  • (2023)Uncovering the Socioeconomic Structure of Spatial and Social Interactions in CitiesUrban Science10.3390/urbansci70100157:1(15)Online publication date: 30-Jan-2023

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  1. Characterizing and Forecasting Urban Vibrancy Evolution: A Multi-View Graph Mining Perspective

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        cover image ACM Transactions on Knowledge Discovery from Data
        ACM Transactions on Knowledge Discovery from Data  Volume 17, Issue 5
        June 2023
        386 pages
        ISSN:1556-4681
        EISSN:1556-472X
        DOI:10.1145/3583066
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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 28 February 2023
        Online AM: 30 November 2022
        Accepted: 03 October 2022
        Revised: 23 May 2022
        Received: 29 December 2021
        Published in TKDD Volume 17, Issue 5

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        Author Tags

        1. Urban vibrancy forecasting
        2. spatiotemporal data mining
        3. graph neural network

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        • National Natural Science Foundation of China
        • Foshan HKUST Projects

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        • (2024)Deep learning for cross-domain data fusion in urban computingInformation Fusion10.1016/j.inffus.2024.102606113:COnline publication date: 21-Nov-2024
        • (2024)Graph neural networks for multi-view learning: a taxonomic reviewArtificial Intelligence Review10.1007/s10462-024-10990-157:12Online publication date: 21-Oct-2024
        • (2023)Uncovering the Socioeconomic Structure of Spatial and Social Interactions in CitiesUrban Science10.3390/urbansci70100157:1(15)Online publication date: 30-Jan-2023

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