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ABSTRACT. The aim of this systematic review is to synthesize and analyze networked and integrated urban technologies, digital twin modeling tools, and spatio-temporal fusion algorithms. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout May 2022, with search terms including “urban geopolitics and digital twin cities” + “virtual modeling and remote sensing technologies,” “spatial cognition and neural network algorithms,” and “visual analytics tools.” As we analyzed research published between 2021 and 2022, only 166 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 27, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR.

Keywords: virtual modeling and remote sensing technologies; spatial cognition and neural network algorithms; visual analytics tools; urban geopolitics; digital twin cities

How to cite: Valaskova, E., Oláh, J., Popp, J., and Lăzăroiu, G. (2022). “Virtual Modeling and Remote Sensing Technologies, Spatial Cognition and Neural Network Algorithms, and Visual Analytics Tools in Urban Geopolitics and Digital Twin Cities,” Geopolitics, History, and International Relations 14(2): 9–24. doi: 10.22381/GHIR14220221.

Received 20 June 2022 • Received in revised form 21 October 2022
Accepted 24 October 2022 • Available online 30 October 2022

1Faculty of Operation and Economics of Transport and Communications, Department of Economics, University of Zilina, Zilina, Slovak Republic, This email address is being protected from spambots. You need JavaScript enabled to view it..
2John von Neumann University, Hungarian National Bank–Research Center, Hungary; College of Business and Economics, University of Johannesburg, South Africa, This email address is being protected from spambots. You need JavaScript enabled to view it..
2John von Neumann University, Hungarian National Bank–Research Center, Hungary; College of Business and Economics, University of Johannesburg, South Africa, This email address is being protected from spambots. You need JavaScript enabled to view it..
3The Institute of Smart Big Data Analytics, New York, NY, USA; Spiru Haret University, Bucharest, Romania, This email address is being protected from spambots. You need JavaScript enabled to view it. (corresponding author).

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