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A social media analytics perspective for human‐oriented smart city planning and management

Understanding how people engage in daily activities within a region can provide valuable information for smart city planners and strategic partners to use to assist in their decision‐making processes. Such insights may relate to economic activities, sustainable city design, environmental impacts, an...

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Bibliographic Details
Published in:Journal of the American Society for Information Science and Technology 2022-01, Vol.73 (1), p.119-135
Main Authors: Miah, Shah Jahan, Vu, Huy Quan, Alahakoon, Damminda
Format: Article
Language:English
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Summary:Understanding how people engage in daily activities within a region can provide valuable information for smart city planners and strategic partners to use to assist in their decision‐making processes. Such insights may relate to economic activities, sustainable city design, environmental impacts, and responses to climate change, contributing to the improvement in the quality of human life. Considerable attention is recently directed towards smart city initiatives that benefit majority of people, rather than projects that cater to the political, architectural, or vanity needs of a minority. However, understanding citizen requirements, behaviors, and opinions is difficult, and requires the use of technology and appropriate information sources. While social‐media big data have provided opportunities to develop evidence‐based insights into human daily activities, effective analytical methods to harness these opportunities remain in development. We propose a new analytical method to provide a deeper understanding of citizen activities by constructing building blocks in their activity storylines, with analysis of these storylines providing evidence‐based insights into their activities. Results demonstrate the usefulness of our method to smart city planners and strategic partners, providing invaluable insights to assist them in making decisions regarding sustainable smart city development.
ISSN:2330-1635
2330-1643
DOI:10.1002/asi.24550