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Geospatial Dashboards for Monitoring Smart City Performance

Author

Listed:
  • Changfeng Jing

    (School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Mingyi Du

    (School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Songnian Li

    (School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
    Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada)

  • Siyuan Liu

    (Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada)

Abstract

Geospatial dashboards have attracted increasing attention from both user communities and academic researchers since the late 1990s. Dashboards can gather, visualize, analyze and advise on urban performance to support sustainable development of smart cities. We conducted a critical review of the research and development of geospatial dashboards, including the integration of maps, spatial data analytics, and geographic visualization for decision support and real-time monitoring of smart city performance. The research about this kind of system has mainly focused on indicators, information models including statistical models and geospatial models, and other related issues. This paper presents an overview of dashboard history and key technologies and applications in smart cities, and summarizes major research progress and representative developments by analyzing their key technical issues. Based on the review, we discuss the visualization model and validity of models for decision support and real-time monitoring that need to be further researched, and recommend some future research directions.

Suggested Citation

  • Changfeng Jing & Mingyi Du & Songnian Li & Siyuan Liu, 2019. "Geospatial Dashboards for Monitoring Smart City Performance," Sustainability, MDPI, vol. 11(20), pages 1-23, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:20:p:5648-:d:276102
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    References listed on IDEAS

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    3. Suhono H. Supangkat & Rohullah Ragajaya & Agustinus Bambang Setyadji, 2023. "Implementation of Digital Geotwin-Based Mobile Crowdsensing to Support Monitoring System in Smart City," Sustainability, MDPI, vol. 15(5), pages 1-27, February.
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    6. Eduardo Graells-Garrido & Vanessa Peña-Araya & Loreto Bravo, 2020. "Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned," Sustainability, MDPI, vol. 12(15), pages 1-17, July.

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