Comprehensive Evaluation and Planning Optimization of Urban Development Levels in Xinjiang Based on POI Data

Abstract

This study constructs a multidimensional mathematical model for evaluating and optimizing the development level of cities in Xinjiang based on multi-source data. For housing price prediction and existing housing stock estimation, a multi-factor linear prediction model is established. This model comprehensively considers four major factors-economic, service, location, and policy-with corresponding weight coefficients assigned: the economic factor carries a weight of 0.35, and the service factor a weight of 0.25. Model solutions predict housing price ranges for major Xinjiang cities between 5,668-7,308 RMB/m², with an average forecast of 5,379 RMB/m². The total existing housing stock is estimated at approximately 73.32 million units. For quantifying and classifying urban service levels, a multidimensional evaluation system encompassing four core domains—healthcare, education, transportation, and commerce-was constructed. Principal component analysis was applied to reduce the dimensionality of the service level matrix, revealing that the first principal component explained 99.93% of the variance, indicating high correlation among service domains. Subsequently, the K-means clustering algorithm categorized cities into four types: excellent service, good service, weak service, and developing service. For urban resilience assessment and investment demand planning, a four-dimensional resilience evaluation model was established, comprising infrastructure (weight 0.4), economy (weight 0.3), society (weight 0.2), and environment (weight 0.1). Based on the resilience index, an investment demand assessment model and a weakness identification algorithm were constructed. Model results indicate a total investment requirement of 259.8 billion yuan, with Urumqi achieving the highest comprehensive resilience index. Finally, for smart city development planning and benefit assessment, an evaluation model for current and target smart levels was established. Investment allocation ratios were determined across five key domains—smart transportation, smart government services, etc.—projecting over 50% improvement in urban development levels.