- Title
- A social media analytics perspective for human-oriented smart city planning and management
- Creator
- Miah, Shah Jahan; Vu, Huy Quan; Alahakoon, Damminda
- Relation
- Journal of the Association for Information Science and Technology Vol. 73, Issue 1, p. 119-135
- Publisher Link
- http://dx.doi.org/10.1002/asi.24550
- Publisher
- John Wiley & Sons
- Resource Type
- journal article
- Date
- 2022
- Description
- 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.
- Subject
- social media; smart city planning; human daily activities; citizen activities; SDG 8; SDG 11; SDG 13; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1463230
- Identifier
- uon:46675
- Identifier
- ISSN:2330-1635
- Language
- eng
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