ABSTRACT. The present study systematically reviews the existing research on urban digital twins, machine learning techniques, and 3D city modeling and virtual simulation tools. Our findings clarify that virtual simulation and computer vision algorithms, ambient sound recognition software, and intelligent sensor networks assist Internet of Things-enabled smart cities, and I contribute to the literature by indicating that monitoring and sensing technologies, spatial cognition algorithms, and data visualization tools articulate urban simulated environments and digital twin cities. Throughout June 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “the geopolitics of urban digital twins” + “spatial cognition and predictive modeling algorithms,” “simulation and virtualization technologies,” and “blockchain-enabled cyber-physical systems.” As research published between 2021 and 2022 was inspected, only 169 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 24 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: geopolitics; urban digital twins; spatial cognition and predictive modeling algorithms; simulation and virtualization technologies; blockchain; cyber-physical systems

How to cite: Sullivan, R. (2022). “The Geopolitics of Urban Digital Twins: Spatial Cognition and Predictive Modeling Algorithms, Simulation and Virtualization Technologies, and Blockchain-enabled Cyber-Physical Systems,” Geopolitics, History, and International Relations 14(2): 25–39. doi: 10.22381/GHIR14220222.

Received 27 July 2022 • Received in revised form 22 October 2022
Accepted 28 October 2022 • Available online 30 October 2022

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