ABSTRACT. In this article, we cumulate previous research findings indicating that virtual process optimization requires augmented reality tools, data-driven predictive modeling techniques and maintenance algorithms, and cloud-edge computing systems. We contribute to the literature on digital twin algorithms, smart city technologies, and 3D spatio-temporal simulations in virtual urban environments by showing that urban sensing data necessitate machine learning and digital twin algorithms and convolutional neural networks. Throughout March 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “virtual urban environments” + “digital twin algorithms,” “smart city technologies,” and “3D spatio-temporal simulations.” As we inspected research published in 2022, only 177 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 32, 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, ROBIS, and SRDR.

Keywords: virtual; urban; environment; digital twin; smart city; simulation

How to cite: Zvarikova, K., Horak, J., and Downs, S. (2022). “Digital Twin Algorithms, Smart City Technologies, and 3D Spatio-Temporal Simulations in Virtual Urban Environments,” Geopolitics, History, and International Relations 14(1): 139–154. doi: 10.22381/GHIR14120229.

Received 26 March 2022 • Received in revised form 25 June 2022
Accepted 28 June 2022 • Available online 30 June 2022