Identification of Inefficient Urban Land for Urban Regeneration Considering Land Use Differentiation

Abstract

Accurately identifying inefficient urban land is essential for urban regeneration and mining underutilized assets. Previous studies have primarily focused on examining the overall efficiency of land use without adequately considering the heterogeneity of urban land use types and comprehensive characteristics of urban quality. As a result, the spatial accuracy and precision of research findings have been relatively low. To address this gap, we developed a comprehensive method to identify inefficient urban lands for residential, commercial, and industrial use. The method integrated multi-source geographic data to quantitatively characterize the efficiency of different land use types considering six key dimensions, including building attribute, urban service, transportation condition, environmental quality, business performance, and production efficiency, utilized principal component analysis to reduce the multicollinearity and the dimensionality of the data, and identified land clusters with similar features that were inefficiently used by means of hierarchical clustering. By applying the method to Changsha, China, we validated its effectiveness. The results demonstrate that the method can accurately identify inefficient residential, commercial, and industrial land, with kappa coefficients of 0.71, 0.77, and 0.68, respectively. The identification results reveal the spatial distribution patterns of different types of inefficient land. Inefficient residential land is concentrated towards the city center, particularly in central areas. Inefficient commercial land is relatively evenly distributed, mainly outside the core commercial regions. Inefficient industrial land clusters towards the periphery, forming several agglomeration areas centered around industrial parks. By precisely identifying inefficient urban land and focusing on the key influencing factors, the proposed method enables the site selection of urban regeneration, site redevelopment evaluation, and optimization of urban resources.

Keywords

• inefficient urban land
• urban regeneration
• multi-source geographic data
• hierarchical clustering
• Changsha City