ГЕОПРОСТРАНСТВЕННЫЙ АНАЛИЗ И ВИЗУАЛИЗАЦИЯ ДАННЫХ ПРОГРАММЫ РЕНОВАЦИИ ЖИЛОГО ФОНДА (НА ПРИМЕРЕ Г. МОСКВЫ)

Abstract

Today, Moscow serves as a key testing ground for the most complex urban planning mechanisms. This article presents an in-depth analysis of data on residential real estate renovation in Moscow. To this end, a proprietary dataset was compiled from open sources. A key step in the process was data normalization, which standardized building identification characteristics (address, year of construction, number of floors) into a unified format for subsequent machine processing. The work places particular emphasis on the analytical visualization of property data. Using Python programming language tools and specialized libraries, a visual model was created that clearly depicts the spatial distribution of properties that do not meet modern requirements for a high-quality urban environment. Interactive maps make it possible not only to identify residential properties but also to compare their locations with the socioeconomic indicators of the city's districts. Adapting similar methods developed for Moscow projects serves as the foundation for the creation of a universal visual model applicable to other major cities. Integrating accumulated experience with artificial intelligence algorithms will enable rational decision-making on development planning in the future, accurately predicting the long-term development of renovation zones, and minimizing urban development risks in new territories.