Understanding scale effects is important and indispensable for geography studies. However, spatial and spatiotemporal statistical tools for measuring the operational scales of different processes are rather limited. This article extends the popular geographically and temporally weighted regression (GTWR) model to consider operational scale effects by proposing multiscale GTWR (MGTWR), which offers a flexible and scalable framework for identifying and analysing multiscale processes by specifying flexible bandwidths for various covariates. Then, MGTWR is employed to explore spatiotemporal variations and how influential factors are associated with housing prices in Shenzhen. This article attempts to extend GTWR to MGTWR in consideration of scale effects, thereby highlighting the importance of different levels of spatiotemporal heterogeneity. Furthermore, the empirical results of this study can provide valuable policy implications for real estate development in areas where urban planning should address multiscale effects in both temporal and spatial dimensions.

Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE) to analyse the spatial patterns of check-in spots (or places of interest, POIs) and employ the Getis-Ord method to identify the hot spots for different types of POIs in Shenzhen, China. New indexes are then proposed based on the hot-spot results as measured by check-in data to analyse the effects of these locations on housing prices. This modelling is performed using the hedonic price method (HPM) and the geographically weighted regression (GWR) method. The results show that the degree of clustering of POIs has a significant influence on housing values. Meanwhile, the GWR method has a better interpretive capacity than does the HPM because of the former method’s ability to capture spatial heterogeneity. This article integrates big social media data to expand the scope (new study content) and depth (study scale) of housing price research to an unprecedented degree.

Shenzhen is a city that is highly representative of China’s rapid urbanization process. As the city rapidly expands, there are enormous challenges to the sustainable use of land resources. This paper introduces the evolution of urban land expansion and the sustainable land use policy of the Shenzhen Government since 2005. The policy covers the reduction in rural-to-urban land conversion, the delineation of urban growth boundaries, arable land reclamation and the establishment of farmland protection areas, urban redevelopment, and the investigation and prosecution of illegal construction. This paper considers the aspects of urbanization and land management systems that are unique to China. The current top-down indicative and mandatory mode of control, which relies on the central government, has very limited effects. Good results were achieved in Shenzhen for the following elements: governmental self-restraint, governmental identity change, and policy innovation. Shenzhen’s sustainable land use practices can provide a reference for other cities in China.

Traditional villages are important carriers of traditional cultural heritage, and they have strong historical, cultural, aesthetic and tourism value for all countries and the international community. In China, the number of traditional villages is currently decreasing each year, and the precious material and non-material heritage is at risk of disappearing in the process of urbanization. A comprehensive understanding of the spatiotemporal patterns of traditional villages on multiple scales has important significance in protecting traditional culture, revitalizing traditional villages and achieving sustainable urbanization. Therefore, the spatiotemporal characteristics of traditional villages at the city, province, and geographic zone scales are explored by a series of Geographic Information System(GIS)-based methods in this article. Specifically, the analysis units are multi-scale, the applied methods are multi-variate, and the identified patterns are multi-perspective. The results demonstrate that the distribution of traditional villages in China is unbalanced over space and time. Moreover, the different spatiotemporal distributions of traditional villages are sensitive to scales. These findings clarify differences in the corresponding geographic and environmental factors, the level of economic development and local policy support. We further suggest that exploring the effective and suitable modes of protection and rural development is necessary. The results of this article revealing the unbalanced spatiotemporal distribution of traditional villages can provide valuable suggestions and insights into alleviating regional inequality in China.