Abstract
Accurate information about real estate in the city, and about residential buildings in particular, is the most important resource for managing the development of the urban environment. Information about residential buildings, for example, the number of residents, is used in the inventory and digitization of the urban economy and subsequently becomes the basis of digital platforms for managing urban processes. Inventory of urban property can be carried out independently by different departments within the framework of official functions, which leads to the problem of conflicting information and missing values in urban data, in building data in particular. These problems are especially pronounced when information from different sources is combined to create centralized repositories and digital twins of the city. This leads to the need to develop approaches to filling missing values and correcting distorted information about residential buildings. As part of this work, the authors propose an approach to data imputation of residential buildings, including additional information about the environment. The analysis of the effectiveness of the approach is based on data collected for St. Petersburg (Russia).
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Khrulkov, A.A., Mishina, M.E., Mityagin, S.A. (2022). Approach to Imputation Multivariate Missing Data of Urban Buildings by Chained Equations Based on Geospatial Information. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13352. Springer, Cham. https://doi.org/10.1007/978-3-031-08757-8_21
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