ABSTRACT. Despite the relevance of smart sustainable city governance and immersive virtual worlds, only limited research has been conducted on this topic. I contribute to the literature on big data-driven urban analytics, data mining and fusion technology, and urban digital twins by showing that data modeling and simulation tools, multi-sensor environment data fusion, and modeling and simulation algorithms further smart urban governance and digital twin cities. Throughout May 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “urban geopolitics and Internet of Things-enabled smart cities” + “deep learning-based sensing technologies,” “data modeling and simulation tools,” and “3D virtual equipment monitoring and immersive visualization systems.” As I inspected research published between 2021 and 2022, only 172 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 31, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, Distiller SR, and MMAT.

Keywords: deep learning-based sensing technologies; data modeling and simulation tools; 3D virtual equipment monitoring and immersive visualization systems; urban geopolitics; Internet of Things-enabled smart cities

How to cite: Townsend, D. (2022). “Deep Learning-based Sensing Technologies, Data Modeling and Simulation Tools, and 3D Virtual Equipment Monitoring and Immersive Visualization Systems in Urban Geopolitics and Internet of Things-enabled Smart Cities,” Geopolitics, History, and International Relations 14(2): 136–151. doi: 10.22381/GHIR14220229.

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

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