Against the background of smart city construction and the increasing application of big data in the field of planning, a method is proposed to effectively improve the objectivity, scientificity, and global nature of urban park siting, taking Guangzhou and its current urban park layout as an example. The proposed approach entails integrating POI data and innovatively applying machine learning algorithms to construct a decision tree model to make predictions for urban park siting. The results show that (1) the current layout of urban parks in Guangzhou is significantly imbalanced and has blind zones, and with an expansion of the search radius, the distribution becomes more concentrated; high-density areas decrease from the center outward in a circle, which manifests as a pattern of high aggregation at the core and low dispersion at the edge. (2) Urban park areas with a service pressure of level 3 have the largest coverage and should be prioritized for construction as much as possible; there are fewer areas at levels 4 and 5, which are mainly concentrated in the central city, and unreasonable resource allocation is a problem that needs to be solved urgently. (3) There was a preliminary prediction of 6825 sites suitable for planning, and the fit with existing city parks was 93.7%. The prediction results were reasonable, and the method was feasible. After further screening through the coupling and superposition of the service pressure and the layout status quo, 1537 locations for priority planning were finally obtained. (4) Using the ID3 machine learning algorithm to predict urban park sites is conducive to the development of an overall optimal layout, and subjectivity in site selection can be avoided, providing a methodological reference for the planning and construction of other infrastructure or the optimization of layouts.

Population aging is a global issue. China is facing the same challenge, especially in its megacities, with more than 10 million permanent urban residents. These densely populated cities urgently need the scientific planning and optimization of the layout of service facilities for older people. Taking Guangzhou, a megacity in China, as an example, this study uses point-of-interest (POI) data and the ID3 machine learning decision tree algorithm to train a site selection model for service facilities for older people. The model can help to select appropriate locations for new service facilities for older people more scientifically and accurately, and it can provide targeted suggestions to optimize the layout of the service facilities for older people in Guangzhou. First, Guangzhou city is divided into 29,793 grids of 500 m × 500 m based on the range of activities of older people, and 985 grids are found to contain service facilities for older people. Then, the POI data of the grid are fed into the ID3 algorithm for training to obtain a prediction model for the selection of sites for service facilities for older people. The effective prediction rate of the model reaches 87.54%. Then, we apply the site selection model to predict the whole city of Guangzhou, and 4534 grids are suitable for service facilities for older people. In addition, considering the degree of concentration of the elderly population in each street, we further filter out 1066 priority grids as the final site selection. Finally, taking into account the situation of the streets in different districts, we propose several strategies to optimize the layout of the construction of service facilities for older people.

In order to alleviate the increased habitat fragmentation caused by the accelerating urbanization and ecological deterioration, constructing ecological networks is an effective way to improve ecological connectivity, facilitate regional energy flow, and promote biodiversity enhancement. In this study, Yongtai County was taken as the research object, and the morphological spatial pattern analysis (MSPA) method was used to analyze the landscape pattern, identify the ecological source sites, classify the ecological source sites according to the importance degree by possible connectivity index (PC) and the Delta values for probability index of connectivity (dPC), and then construct the potential ecological corridors with the help of the minimum cumulative resistance (MCR) model to generate the ecological network, and then put forward the optimization strategy according to the current situation. The results show that (1) the core area of Yongtai County is 1071.06 km², the largest among all landscape types, with a fragmented distribution, high degree of fragmentation, and poor connectivity, mainly in the east and southwest, and sparser in the middle. (2) The area of highest resistance value is mainly located in the built-up areas of towns and rural settlements in the central and northwestern parts of the country; the lowest value is distributed in the southwest and southeast, and the land use mode is mainly expressed as woodland. (3) The ecological network consists of 13 ecological sources and 78 potential ecological corridors. The ecological sources are mainly located in the east and southwest, with high connectivity; the potential ecological corridors are distributed in the form of a network, with fewer in the center, resulting in the phenomenon of ecological disconnection. (4) Lack of ecological sources and corridors, serious landscape fragmentation, and optimization of ecological network by adding and protecting ecological sources, repairing ecological breakpoints and building stepping stones. This study is of guiding significance for urban green space system planning, biodiversity protection, and ecosystem function enhancement in Yongtai County, and also provides reference for ecological protection and optimization in other mountainous cities.

Under the dual pressures of rapid urbanization and intense human socioeconomic activities, habitat fragmentation and poor landscape connectivity have become critical issues in cities. Constructing ecological networks is essential for maintaining urban ecosystem health and promoting sustainable environmental development. It represents an effective approach to balancing regional economic growth with ecological conservation. This study focused on the Shenzhen Special Economic Zone. Ecological sources were identified using Morphological Spatial Pattern Analysis (MSPA) and landscape connectivity assessment. Circuit theory was applied to extract ecological corridors, ecological pinch points, and ecological barriers. The importance levels of ecological corridors were classified to form an ecological network. The network was optimized by adding ecological sources, stepping stones, and restoring breakpoints. Its structure and functionality were evaluated before and after optimization. The results indicate the following: (1) The core area in Shenzhen City Area covers 426.67 km², the largest proportion among landscape types. It exhibits high fragmentation, low connectivity, and a spatial pattern characterized as “dense in the east and west, sparse in the center.” (2) Seventeen ecological sources were identified, consisting of 8 key sources, 5 important sources, and 4 general sources, accounting for 17.62% of the total area. Key sources are mainly distributed in forested regions such as Wutong Mountain, Maluan Mountain, Paiya Mountain, and Qiniang Mountain in the southeast. (3) Twenty-six ecological corridors form a woven network, with a total length of 127.44 km. Among these, 13 key corridors are concentrated in the eastern region, while 7 important corridors and 6 general corridors are distributed in the western and central parts. Few corridors exist in the southwest and southeast, leading to ecological flow interruption. (4) The optimized ecological network includes 12 newly added ecological source areas, 20 optimized ecological corridors, 120 ecological pinch points, and 26 ecological barriers. The maximum current value increased from 10.60 to 20.51, indicating significantly enhanced connectivity. The results provide important guidance for green space planning, biodiversity conservation, and ecosystem functionality enhancement in Shenzhen City Area.

Traditional villages in the Chaoshan region serve as living repositories of local cultural heritage. Their concentrated and coordinated conservation and utilization can transcend administrative boundaries, enabling the integrated allocation of regional resources and the enhancement of cultural synergy. Currently, conservation practices for traditional villages are largely limited to piecemeal rescue efforts focused on individual villages. There is a lack of systematic understanding from a regional perspective and an explanation of the mechanisms underlying the formation of local landscapes, which hinders the realization of economies of scale in conservation and the development of cultural synergy. To explore effective approaches for the cluster-based conservation of traditional villages in China’s Lingnan coastal region, as well as the characteristics of human–land relationships and their adaptive mechanisms, this study focuses on 115 national and provincial-level traditional villages in the Chaoshan region. By introducing methods of single-factor and multi-factor cluster identification, the study innovatively constructs a four-dimensional cluster identification framework comprising “spatial proximity, geomorphological similarity, cultural convergence, and residential isomorphism,” and, utilizing the ArcGIS platform for coupled analysis, kernel density analysis, cluster identification, and field surveys, systematically analyzed the diverse typologies and landscape-specific characteristics of traditional village clusters in the Chaoshan region. The results indicate the following: (1) The identification of Chaozhou–Shantou traditional village clusters reveals three diverse types—comprehensive, distinctive, and potential—reflecting the richness and diversity of these clusters in the region. (2) Spatially proximate clusters exhibit a single-core, multi-point distribution, topographically similar clusters show differentiated distributions across plains and river valleys, culturally convergent clusters are significantly correlated with cultural carriers such as postal routes, water transport, and trade, and residential distributions are significantly correlated with topography and landforms, collectively constituting the unique character of Chaozhou–Shantou traditional village clusters. (3) Traditional villages in Chaoshan exhibit significant coupling with the natural environment, forming diverse spatial siting patterns in relation to mountains, water, forests, fields, and the sea, reflecting differentiated adaptation to and ingenious utilization of the natural environment. (4) The adaptive mechanism of the landscape of traditional Chaozhou–Shantou villages can be distilled into a three-tiered structure, natural adaptation as the foundation, social adaptation as the framework, and cultural adaptation as the soul, revealing the spatial planning wisdom of the Chaozhou–Shantou people in complex mountain and coastal environments. This study not only deepens our understanding of the human–land relationship in traditional villages of the Chaoshan region but also provides scientific evidence and theoretical support for the holistic preservation of cultural heritage and regional coordinated development. It holds significant practical value for promoting the protection and sustainable development of rural cultural heritage in the Lingnan coastal region.

Northeastern Guizhou, situated at the confluence of the Wuling Mountains and the Miaoling Range in southwestern China, constitutes a multi-ethnic integration zone where Gelao, Tujia, Miao, Dong, and Han peoples have coexisted for centuries. While existing comparative studies of vernacular dwellings in ethnic minority regions have predominantly adopted a unidirectional “cultural diffusion” framework, the complex spatial interactions among multiple ethnic groups remain insufficiently understood. This study examines the widely distributed linear-type traditional dwellings across this region, employing Space Syntax to quantitatively characterize the hall–room spatial configurations of six representative floor-plan typologies. Four key indicators (connectivity, mean depth, Real Relative Asymmetry (RRA), and control value) were calculated from justified graphs to reveal configurational differences in the tangwu (ancestral hall), fire pit/fire-bed, bedrooms, and transitional spaces. Three principal findings emerged: (1) all ethnic dwellings share an underlying “hall–fire” dual-core spatial system, which manifests in three distinct control modes (parallel dual-core, offset dual-core, and single-core-dominant), reflecting divergent cultural strategies during inter-ethnic integration; (2) spatial depth hierarchies differentiate clearly at their two ends, from the undivided rear space of the Songtao Shu Miao dwelling to the deeply recessed bedroom of the Han dwelling, whereas the relative ordering of the intermediate ethnic groups cannot be reliably distinguished; (3) convergence does not follow a single linear trajectory but proceeds along multiple independent culturally self-sustaining paths, producing a reticular fusion landscape characterized by “multi-linear differentiation with localized convergence.” These findings support a “cultural inter-embedding” rather than “cultural replacement” interpretation of spatial evolution in multi-ethnic regions, offering a new analytical framework for comparative morphological research on vernacular dwellings.