Digital Twin Cities: Data Availability and Systematic Data Collection

Recent studies have shown potential benefits of digital twin cities for smart urban management. However, a major challenge for full-scale implementation of this concept is data availability. A comprehensive digital twin model that represents urban systems, their functionality, and interdependencies requires a wide range of data. However, little is known about systematic approaches for large-scale urban data collection. This study aims to address these gaps in knowledge in two steps to provide a more accurate depiction of available data. First, we conduct a synthesis study using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify current data collection methods in smart cities from technical articles. Second, we review publicly available databases to identify the types of available data that potentially can be used for digital twin cities models. This study identified and classified various types of available data including datasets related to infrastructure, healthcare, education, government, and environment.