This research investigates the mechanism of urban village redevelopment in south China. Through a revised typology of place entrepreneurs based on the growth machine thesis and a case study of Liede village in central Guangzhou, it illustrates how land-based interests embedded in an imbalanced power relationship can (de-)activate urban village redevelopment. The study reveals that while urban villagers, as represented by the village collective, have entrenched interests in the redevelopment process, the city government – as monopolistic land manager and place entrepreneur – plays the deciding role in forging and halting a growth machine geared towards urban village redevelopment. Although developers are also part of the process, the (de-)activation of redevelopment growth machine/coalition in Guangzhou has largely been dominated by the city government. With a comparative view on the original growth machine model, it is hoped that this study would furnish both theoretical and practical thoughts for future research.

Mapping new and old buildings are of great significance for understanding socio-economic development in rural areas. In recent years, deep neural networks have achieved remarkable building segmentation results in high-resolution remote sensing images. However, the scarce training data and the varying geographical environments have posed challenges for scalable building segmentation. This study proposes a novel framework based on Mask R-CNN, named Histogram Thresholding Mask Region-Based Convolutional Neural Network (HTMask R-CNN), to extract new and old rural buildings even when the label is scarce. The framework adopts the result of single-object instance segmentation from the orthodox Mask R-CNN. Further, it classifies the rural buildings into new and old ones based on a dynamic grayscale threshold inferred from the result of a two-object instance segmentation task where training data is scarce. We found that the framework can extract more buildings and achieve a much higher mean Average Precision (mAP) than the orthodox Mask R-CNN model. We tested the novel framework’s performance with increasing training data and found that it converged even when the training samples were limited. This framework’s main contribution is to allow scalable segmentation by using significantly fewer training samples than traditional machine learning practices. That makes mapping China’s new and old rural buildings viable.

Imbalances in allocating public service resources are a universal problem worldwide, especially in urban and rural areas. As a developing country with a significant imbalance between urban and rural areas, China is representative of the unbalanced allocation of public service resources. Presently, China has entered the county urbanization stage. Therefore, this study can provide a new way to realize the equalization of urban and rural public services with the county as the basic unit. Taking counties as the primary study area, this paper analyzes the new trends of population mobility in China’s counties. It combines large-scale questionnaires and field surveys to investigate the new demand of rural residents for public services and the shortcomings of public service resource allocation. First, the county seat attracts a concentration of the county’s rural residents and returning population, whose high expectations for the county seat’s education and medical services have not yet been met. Second, the township assumes the vital function of elementary school education and medical services in rural areas, and the rural children have a great demand for elementary school education services in the township. However, there are still apparent shortcomings in support of teaching facilities and the quality of education services. Third, the problem of aging and hollowing out in rural areas is serious, and the education, medical and elderly service needs of left-behind children and the elderly are difficult to be guaranteed. Finally, this paper proposes targeted planning strategies and policy recommendations for allocating county public service resources at three levels based on the “county–town–village” hierarchy.

Traditional Chinese villages, recognized as national heritage sites, are invaluable cultural assets. Since 2012, efforts have focused on their preservation, though issues remain such as prioritizing authenticity over usability and material over cultural elements. This paper adopts the Historic Urban Landscape (HUL) approach, emphasizing the integration of physical heritage with daily life to shift value assessment. The fundamental argument of this paper is that the HUL method can serve as a new tool for preserving the historical heritage and landscape of rural areas, particularly in the context of assessing and formulating preservation frameworks for traditional Chinese villages. Through the case study of Tangfang village’s new conservation plan, the research demonstrates that the value of traditional villages lies in the continuous interaction between daily activities and physical space, including landscape, functional, and spiritual dimensions. The study concludes that a preservation framework based on local residents’ daily lives ensures more effective heritage conservation and landscape preservation by addressing both material and human values.

There are two opposing trends in urban development: one is the exponential growth of cities and the other is shrinking cities,” which are characterized by a decrease in number of residents. Since the 2008 financial crisis, there have been great changes in the Pearl River Delta (PRD), where growing and shrinking areas coexist. In this paper, we analyze cities' growth and shrinkage in respect of urban construction and economic vitality in the PRD in spatial term. Thus, we define land use changes in urban construction to characterize growth by using Landsat Thematic Mapper data on urban built-up areas of the PRD region in 2000, 2009, and 2013; and we characterize economic activities for vitality by using Defense Meteorological Satellite Program/Operational Linescan System nighttime light data in various years after time continuous correction and vegetation correction, in combination with population data. Four different types of space are identified in this region, namely continuous growth, transformational growth, stationary, and shrinkage. This paper provides policy recommendations and planning strategies for the future development of shrinking cities in China. Shrinkage is considered to be only a short-term stage in the urbanization process faced by most cities in China and abroad. Technological progress and industrial transformation are likely to be critical factors affecting the direction of urban development, where cities can resume growth from shrinkage by revitalizing manufacturing industry and economic activities through education, innovation, and technology.