Smart City Analytics, Digital Twin Simulation and Visualization Modeling Technologies, and Cognitive Data Mining Algorithms in Sustainable Urban Governance Networks
Smart City Analytics, Digital Twin Simulation and Visualization Modeling Technologies, and Cognitive Data Mining Algorithms in Sustainable Urban Governance Networks

Summary/Abstract: The purpose of this study is to examine smart city analytics, digital twin simulation and visualization modeling technologies, and cognitive data mining algorithms in sustainable urban governance networks. In this article, I cumulate previous research findings indicating that networked sustainable urban technologies harness cognitive data mining algorithms, virtual twinning techniques, and geospatial analytics. I contribute to the literature on cognitive digital twins, data visualization capabilities, and intelligent process planning algorithms by showing that deep learning-based ambient sound processing, data visualization and augmented analytics tools, and virtual twin data are instrumental for extended reality environments. Throughout March 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “sustainable urban governance networks” + “smart city analytics,” “digital twin simulation and visualization modeling technologies,” and “cognitive data mining algorithms.” As I inspected research published in 2022, only 180 articles satisfied the eligibility criteria. By removing controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 34, 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, MMAT, and SRDR.

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