ABSTRACT. This paper provides a systematic literature review of studies investigating algorithm-driven sensing devices, urban analytics and data visualization tools, and geospatial mapping and deep neural network technologies. The analysis highlights that big geospatial data and real-time predictive analytics leverages digital twin simulation modeling, cognitive computing systems, and Internet of Things-based connected devices. Throughout May 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “big data-driven urban geopolitics” + “digital twin simulation and modeling tools,” “deep learning-based sensing and technologies,” and “computer vision algorithms.” 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 30, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, MMAT, and ROBIS.

Keywords: digital twin simulation and modeling tools; deep learning-based sensing and technologies; computer vision algorithms; big data-driven urban geopolitics

How to cite: Woodward, L. (2022). “Digital Twin Simulation and Modeling Tools, Deep Learning-based Sensing and Technologies, and Computer Vision Algorithms in Big Data-driven Urban Geopolitics,” Geopolitics, History, and International Relations 14(2): 40–55. doi: 10.22381/GHIR14220223.

Received 25 June 2022 • Received in revised form 24 October 2022
Accepted 29 October 2022 • Available online 30 October 2022

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