ABSTRACT. The present study systematically reviews the existing research on digital twins of smart cities in relation to spatial data visualization tools, monitoring and sensing technologies, and virtual simulation modeling. My findings indicate that interconnected sensor networks, smart city software systems, and data-driven planning technologies enable digital twin cities. I contribute to the literature by clarifying that simulation-based digital twins require big data analytical tools, monitoring and sensing technologies, and computer vision techniques. Throughout April 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “digital twins + smart cities” + “spatial data visualization tools,” “monitoring and sensing technologies,” and “virtual simulation modeling.” As research published in 2022 was inspected, only 179 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, I selected 35 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.

Keywords: digital twin; smart city; spatial data; virtual; simulation; sensor

How to cite: Rice, L. (2022). “Digital Twins of Smart Cities: Spatial Data Visualization Tools, Monitoring and Sensing Technologies, and Virtual Simulation Modeling,” Geopolitics, History, and International Relations 14(1): 26–42. doi: 10.22381/GHIR14120222.

Received 29 April 2022 • Received in revised form 23 June 2022
Accepted 27 June 2022 • Available online 30 June 2022

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