Urban Computing Algorithms, Virtual Sensor Networks, and Geospatial Data Visualization in Digital Twin Cities
Urban Computing Algorithms, Virtual Sensor Networks, and Geospatial Data Visualization in Digital Twin Cities

Summary/Abstract: Based on an in-depth survey of the literature, the purpose of the paper is to explore urban computing algorithms, virtual sensor networks, and geospatial data visualization in digital twin cities. In this research, previous findings were cumulated showing that cloud computing technologies, multi-sensor data fusion algorithms, and routing and navigation data configure smart and sustainable urban systems, and I contribute to the literature by indicating that simulation modeling algorithms, spatial computing technology, and multi-sensor remote sensing data enable digital twin cities. Throughout March 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “digital twin cities” + “urban computing algorithms,” “virtual sensor networks,” and “geospatial data visualization.” As research published in 2022 was inspected, only 187 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, I selected 34 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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