Prediction of Underground Space Development Function of Existing Industrial District in City Based on CA-Markov Model

Abstract

Scientific analysis of the spatial evolution of existing industrial areas in cities and prediction of future development needs will help to rationally allocate land resources in existing industrial areas in urban renewal, scientifically and rationally develop underground space, and promote the sustainable development of existing industrial areas. First of all, the development mode and leading function type of the existing industrial zone in the city are sorted out, and its corresponding underground space development function is further sorted out. It is found that the underground space development of the existing industrial zones in the city is closely related to the dominant functions and location of the ground renewal. To scientifically guide the development of underground space in existing industrial areas in cities, this study proposes a method based on the dynamics model and the CA-Markov model to predict the functions of underground space development in existing industrial areas in cities, which will improve the efficiency and Benefits to promote the rational allocation of land resources is of great significance.

AI spatio-temporal prediction of industrial heritage land-use influenced by dynamic passenger flow at metro stations

“…Therefore, the industrial heritage area exhibits signi cant reliance on the subway for various aspects, including regional development planning, cultural and tourism integration for industrial heritage revitalization 3 , and balancing the ow of tourists and citizens during peak and trough periods 4 . These dependencies pose challenges to the regeneration of the old city industrial heritage areas, particularly concerning mixed development and ensuring e cient accessibility [5][6][7] . Simultaneously, due to issues such as property rights and con icting interests, old industrial areas lag behind in regional development, exhibit subjective decisions in functional positioning, rely on a single transformation mode, and lack sustainability.…”