Heritage buildings are a crucial aspect of a country’s cultural heritage, serving as a means of preserving and passing down its history and traditions to future generations. The heritage buildings in southern China possess significant conservation, utilization, and research value. However, research is lacking on the spatial distribution characteristics and subdivision types of these buildings in the region. This study aimed to investigate the spatial agglomeration and distribution characteristics of heritage buildings in southern China, as well as the factors contributing to the formation of these spatial distribution patterns. This article focused on the protection of 981 heritage buildings in southern China since the founding of China. The study examined the buildings’ spatial agglomeration and distribution characteristics from various dynasties and subdivided types. It utilized the average nearest neighbor analysis, unbalance index, and kernel density estimation to analyze this distribution. Additionally, this study also investigated the primary factors influencing the spatial distribution and differentiation of these buildings. The results demonstrated the following: (1) In general, the spatial distribution of heritage buildings in southern China is characterized by unevenness and clustering, with a concentration in the eastern coastal and Sichuan provinces. (2) In terms of temporal dimension, the spatial distribution of heritage buildings exhibits unique characteristics in various dynastic zones. (3) In the type dimension, the number of different types of heritage buildings varies greatly. (4) Further analysis of the distribution and types of heritage buildings indicates that quantitative differences are primarily influenced by natural, human, and socio-economic factors. This research was unique as it explored the geospatial distribution characteristics and determinants of heritage buildings. It offers a valuable perspective on the spatial distribution of heritage buildings and can serve as a reference for future studies on the preservation and protection of such buildings in China. Additionally, the findings can provide guidance for the management and rational use of heritage buildings in southern China.

The traditional methods of estimating housing vacancies rarely use daytime housing exterior images to estimate housing vacancy rates (HVR). In view of this, this study proposed the idea and method of estimating urban housing vacancies based on daytime housing exterior images, taking Guangzhou, China as a case study. Considering residential quarters as the basic evaluation unit, the spatial pattern and its influencing factors were studied by using average nearest neighbor analysis, kernel density estimation, spatial autocorrelation analysis, and geodetector. The results show that: (1) The urban housing vacancy rate can be estimated by the method of daytime housing exterior images, which has the advantage of smaller research scale, simple and easy operation, short time consumption, and less difficulty in data acquisition. (2) Overall, the housing vacancy rate in Guangzhou is low in the core area and urban district, followed by suburban and higher in the outer suburb, showing a spatial pattern of increasing core area–urban district–suburban–outer suburb. Additionally, it has obvious spatial agglomeration characteristics, with low–low value clustered in the inner circle and high–high value clustered in the outer suburb. (3) The residential quarters with low vacancy rates (50%) are distributed in the outer suburb in a multi-core point pattern, both of which have clustering characteristics. (4) The results of the factor detector show that all seven influencing factors have an impact on the housing vacancy rate, but the degree of impact is different; the distance from CBD (Central Business District) has the strongest influence, while subway accessibility has the weakest influence. This study provides new ideas and methods for current research on urban housing vacancies, which can not only provide a reference for residents to purchase houses rationally, but also provide a decision-making basis for housing planning and policy formulation in megacities.

The livability environment is an important aspect of urban sustainable development. The floating population refers to people without local hukou (also called ‘non-hukou migrants’). The floating population distribution is influenced by livability environment, but few studies have investigated this relationship. Especially, the influence of social environment on floating population distribution is rarely studied. Therefore, we study 1054 communities in Guangzhou’s urban district to explore the relationship between livability environment and floating population distribution. The purpose of this article is to study how livability environment affects floating population distribution. We develop a conceptual framework of livability environment, which consists of physical environment, social environment and life convenience. A cross-sectional dataset of the impact of livability environment on the floating population distribution is developed covering the proportion of floating population in the community as the dependent variable, eight factors of livability environment as the explanatory variables, and two factors of architectural characteristics and one factor of location characteristics as the control variables. We use spatial regression models to explore the degree of influence and direction of physical environment, social environment and life convenience on the floating population distribution in livability environment. The results show that the spatial error model is more effective than ordinary least squares and spatial lag model models. The five factors of the livability environment have statistical significance regarding floating population distribution, including four social environment factors (proportion of middle- and high-class occupation population, proportion of highly educated people in the population, proportion of rental households, and unemployment rate) and regarding life convenience factors (work and shopping convenience). The conclusion has value for understanding how the social environment affects the residential choice of the floating population. This study will help city administrators reasonably guide the residential pattern of the floating population and formulate reasonable management policies, thereby improving the city’s livability, attractiveness and sustainable development.

The three-dimensional (3D) spatial structure within cities can reveal more information about land development than the two-dimensional spatial structure. Studying the relationship between the urban 3D spatial structure and the population distribution is a crucial aspect of the relationship between people and land within cities. However, a few relevant studies focus on the differences between employment population and night population distribution in relation to urban 3D spatial structure. Therefore, this study proposes a new concept of 3D space-filling degree (3DSFD), which is applicable to evaluate the city’s 3D spatial structure. We took 439 blocks in Kunming’s Main Urban District as a sample and analyzed the 3D spatial structure based on geographic information data at the scale of a single building. The characteristics and differences of the daytime and night population distribution in Kunming’s Main Urban District were identified using cell phone signaling big data. Accordingly, a cross-sectional dataset of the relationship between the city’s 3D spatial structure and the population distribution was constructed, with the 3D space-filling degree of the block as the dependent variable, two indicators of population distribution (daytime and night population density) as the explanatory variables, and seven indicators of distance from the city center, and building, road, and functional place densities, proportion of undevelopable land area, housing prices, and land use type as the control variables. We used spatial regression models to explore the significance, strength, and direction of the relationship between urban 3D spatial structure and population distribution. We found that the spatial error model (SEM) was the most effective. The results show that only night population distribution is significantly and positively related to 3DSFD. Every 1% increase in night population density in a block will increase the value of 3DSFD by 2.8307%. The night population distribution is the core factor affecting the 3D spatial structure of Kunming’s Main Urban District. The correlation between daytime population distribution and 3DSFD is not significant. This variability has been ignored in previous studies. The findings are informative for further understanding of the relationship between urban 3D space and population distribution, especially the difference between night and daytime populations. This study can help city managers reasonably plan urban land development intensity and construction height, guide the population layout and formulate management policies to improve urban population and space matching, enhancing the livability and attractiveness of cities.

Factors affecting the housing vacancy rate (HVR) vary, but few studies have considered the relationships between the HVR and plot area (PA) and floor area ratio (FAR). This study thus considered 212 residential quarters in the Guangzhou metropolitan area as the research object, and we constructed a regression model of the factors impacting housing vacancies. The model includes two explanatory variables, PA and FAR, and the remaining six impact factors as control variables. In this study, the influences of PA and FAR on the HVR was analyzed by combining the traditional ordinary least squares (OLS) and two spatial regression models: the spatial lag model (SLM) and spatial error model (SEM). The results indicate that (1) the HVR in the Guangzhou metropolitan area shows spatial difference characteristics of the low central area and high edge, and there is spatial autocorrelation. (2) The PA of the residential quarters gradually increases from the central to the edge area, but the spatial pattern of FAR is the opposite. (3) The SLM results indicate that the PA and FAR of the residential quarters have significant positive correlations with HVR; that is, the larger the PA and FAR, the larger the HVR of the residential quarters, which is in accordance with the expected direction of the theory; furthermore, basic education convenience, road density, and waterfront accessibility have significant negative effects on HVR. This conclusion provides a reference for government departments to formulate reasonable and effective housing policies aimed at the current housing vacancy problem and should help alleviate urban housing vacancies.