The Geopolitics of Internet of Things-based Smart City Environments: Digital Twin and Image Recognition Technologies, Virtual Simulation and Spatial Data Visualization Tools, and Deep and Machine Learning Algorithms
The Geopolitics of Internet of Things-based Smart City Environments: Digital Twin and Image Recognition Technologies, Virtual Simulation and Spatial Data Visualization Tools, and Deep and Machine Learning Algorithms

Summary/Abstract: The objective of this paper is to systematically review digital twin simulation tools, cognitive computing systems, and urban big data. The findings and analyses highlight that spatial computing and data sharing technologies, virtual simulation algorithms, and visual analytics tools enable smart sustainable city governance. Throughout May 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “the geopolitics of Internet of Things-based smart city environments” + “digital twin and image recognition technologies,” “virtual simulation and spatial data visualization tools,” and “deep and machine learning algorithms.” As research published between 2021 and 2022 was inspected, only 173 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, we selected 31 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.

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