Identification and Redevelopment of Inefficient Residential Landuse in Urban Areas: A Case Study of Ring Expressway Area in Harbin City of China

The current efficiency of residential land utilization is witnessing a decline, attributable to accelerated urbanization and inefficient resource allocation, thereby presenting unprecedented threats and challenges to the quality of urban living and the pursuit of high-quality urban development. To enhance residents’ satisfaction and well-being, and to effectively activate existing land resources, it is imperative to accurately identify inefficient residential landuse and its driving factors. While the literature on identifying inefficient urban landuse is expanding, research specifically focusing on residential land, which is closely linked to residents’ lives, remains limited. Furthermore, the factors contributing to inefficient land use are relatively inadequate. Therefore, this study employs a “two-step identification method” to comprehensively identify inefficient residential landuse and utilizes standard deviation ellipses and kernel density assessment methods to analyze the spatial distribution characteristics of such land. Subsequently, the study employs the Random Forest (RF) model to quantitatively analyze factors such as building quality, economic, social, and ecological factors, aiming to provide a scientific basis for subsequent redevelopment initiatives. The findings reveal that inefficient residential landuse is primarily concentrated in city centers, particularly in districts such as Nangang and Xiangfang. In relative inefficient residential areas, aside from Nangang District and Xiangfang District, Songbei District also holds a significant proportion. The intensity of these associations with inefficient residential landuse formation varies depending on urban development history and regional development intensity. In areas other than Songbei District, factors such as aging residential neighborhoods and inadequate green spaces are major contributors to inefficient land use efficiency, whereas in Songbei District, insufficient medical and educational facilities are the primary factors. The RF algorithm, distinguished by its flexibility and accuracy, offers novel perspectives and methods for analyzing issues related to inefficient residential landuse. Moreover, it effectively manages nonlinear relationships between the data, avoiding overfitting and generating precise regression and classification results. Thus, the RF algorithm demonstrates significant promise for widespread application in urban land studies.