Municipal and Urban Renewal Development Index System: A Data-Driven Digital Analysis Framework

Abstract

Urban renewal planning and development are vital for enhancing the living quality of city residents. However, such improvement activities are often expensive, time-consuming, and in need of standardization. The convergence of remote sensing technologies, social big data, and artificial intelligence solutions has created unprecedented opportunities for comprehensive digital planning and analysis in urban renewal development and management. However, fast interdisciplinary development imposes some challenges because the data collected and the solutions built are defined piece by piece and require further fusion and integration of knowledge, evaluation standards, systematic analyses, and new methodologies. To address these challenges, we propose a municipal and urban renewal development index (MUDI) system with data modeling and mathematical analysis models. The MUDI system is applied and studied in three circumstances: (1) at regional level, 337 cities are selected in China to demonstrate the MUDI system’s comparable analysis capabilities on a large scale across cities; (2) at city level, 285 residential communities are selected in Xiamen to demonstrate the use of remote sensing data as key MUDIs for a temporal urban land change analysis; and (3) at the level of residential neighborhoods’ urban renewal practices, Xiamen’s Yingping District is selected to demonstrate the MUDI system’s project management capabilities. We find that the MUDI system is highly effective in municipal and urban data model building through the abstraction and summation of grid-based satellite and social big data. Secondly, the MUDI system enables comprehension of the high dimensionality and complexity of multisource datasets for municipal and urban renewal development. Thirdly, the system is applied to enable the use of the newly developed UMAP algorithm, a model based on Riemannian geometry and algebraic topology, and the carrying out of a principal component analysis for the key dimensions and an index correlation analysis. Fourthly, various artificial intelligence-driven algorithms can be developed for urban renewal analyses based on the MUDIs. The MUDI system is a new and effective method for urban renewal planning and management that can be flexibly extended and applied to various cities and urban districts.