Search Results (302)

This study investigates the spatial patterns and driving mechanisms of China’s industrial heritage using nationwide provincial-level geospatial data. It combines multiple spatial analysis techniques to identify distribution characteristics and applies a multi-model framework integrating Multi-Scale Geographically Weighted Regression and machine learning to assess the impacts of demographic, economic, climatic, and topographic factors. Results reveal a pronounced clustered pattern and marked spatial differentiation, with core concentrations in the southeastern coastal and central regions. Industrial layouts across historical periods show a shift from coastal to inland areas, reflecting security-oriented spatial strategies. Economic development has a significant positive influence, whereas temperature and the number of industrial enterprises exert negative effects. Natural environmental conditions—such as slope, vegetation coverage, and water systems—serve as both spatial supports and constraints. At the macro level, the spatial configuration of industrial heritage emerges from the structured interplay of historical path dependence, national strategic regulation, and geographic environmental constraints, rather than short-term interactions among isolated variables. The study elucidates the evolutionary logic of industrial civilization and highlights the synergistic mechanisms linking economic, social, and environmental dimensions. It concludes by advocating a hierarchical and multi-factor balanced framework for spatial governance. Full article

The built environment, serving as the core spatial vehicle for human production and daily activities, constitutes a vital foundation for achieving sustainable urban development and high-quality renewal. However, amidst rapid urbanisation, certain areas continue to grapple with issues such as ageing infrastructure, inefficient land use, and imbalanced spatial structures, hindering the establishment of sustainable urban forms. Consequently, identifying the evolutionary characteristics and influencing mechanisms of the built environment from the perspective of spatial heterogeneity holds critical significance for advancing refined governance and sustainable planning. Taking Kwun Tong District in Hong Kong as a case study, this research constructs an Adaptive-Entropy Multi-Scale Geographically Weighted Regression (MGWR) analytical framework. This systematically reveals the spatial distribution patterns of built environment elements and their multi-scale spatial heterogeneity characteristics. The findings indicate the following: (1) The built environment exhibits significant spatial differentiation and clustering structures across different scales, reflecting complex spatial processes driven by multiple interacting factors (2) Compared with the OLS model at a 1000 m scale and the GWR model at a 500 m scale, the Adaptive-Entropy MGWR model at a 100 m scale demonstrated superior fitting accuracy and explanatory power. It more effectively captured local structural variations and scale effects, thereby offering greater guidance value for sustainable planning. Building upon these findings, this study further proposes pathway recommendations for urban renewal and built environment optimisation in Kwun Tong District, offering an analytical approach and technical framework that may serve as a reference for sustainable development in high-density cities. Full article

Research on the characteristics and functions of ancient Juntun (military tillage) has paid limited attention to the distribution patterns and influencing factors of Juntun in specific regions. This study employs a comprehensive approach integrating GIS technology and the multi-scale geographically weighted regression (MGWR) model to quantitatively analyze the spatial distribution characteristics and influencing factors of Ming Dynasty Juntun in Fujian. The study reveals that Juntun were primarily located in flat areas near water systems, while exhibiting a U-shaped distribution pattern away from garrison forts, reflecting a synergy between agricultural foundations and military defense. MGWR analysis further indicates that fiscal and taxation factors had a stronger influence on their distribution than arable land resources, highlighting their non-purely agriculturally driven nature. This research provides a quantitative basis for understanding the organizational logic and spatial strategy of ancient military settlements, offering valuable insights for the conservation and study of military heritage. Full article

The impact of the built environment on urban vitality is multifaceted, yet a holistic understanding that simultaneously considers its scale dependence, spatial heterogeneity, and interactive mechanisms remains limited. To unravel these multi-scalar mechanisms, this study develops an integrated analytical framework. Taking Xi’an, China, as a case study, we first construct a multidimensional built environment indicator system grounded in Jane Jacobs’ theory of vitality. Empirically, we employ the Optimal Parameters-based GeoDetector (OPGD) to objectively identify the optimal spatial scale and detect non-linear and interaction effects. Meanwhile, the Multiscale Geographically Weighted Regression (MGWR) model is used to delineate spatial heterogeneity. Our findings systematically unravel the complex mechanisms: (1) The optimal analysis scale is identified as a 2 km grid; (2) All elements significantly influence vitality, but through distinct linear or non-linear pathways; (3) The effects of attraction density, road network structure, and bus stop density exhibit significant spatial heterogeneity; and (4) Third place density and population density act as key catalysts, non-linearly enhancing the effects of other elements. This research presents a synthesized perspective and nuanced evidence for precision urban regeneration, demonstrating the necessity of integrating scale, heterogeneity, and interaction to understand the drivers of urban vitality. Full article

China’s higher education system is the largest globally but faces significant spatial imbalance issues. While studies have examined the spatial distribution of universities, long-term dynamic analysis, quantitative exploration of influencing factors, and investigation of spatial heterogeneity are lacking. This study investigates the spatiotemporal evolution of China’s regular higher education institutions (HEIs) from 1952 to 2023 by using ArcGIS spatial analysis and integrating the Geographical Detector and Multi-scale Geographically Weighted Regression (MGWR) models. Findings reveal that (1) the spatial distribution of China’s HEIs has become increasingly clustered, transitioning from a “point-like” to a “network-like” and finally to a “surface-like” pattern, with its center shifting southwestward—this evolution reflects the gradual formation of a spatially sustainable layout that adapts to regional development needs. (2) Multiple interacting factors influence distribution—including the number of full-time faculty, regional GDP, national universities’ presence during the Republic of China era, and fiscal expenditure on education—with significant variations in their explanatory power. Regional population size also exerts a notable influence. (3) The impact of these factors exhibits significant spatial heterogeneity, with pronounced local imbalances. Thus, multi-scale processes operating at different geographical levels have shaped HEIs’ spatial pattern and addressing this heterogeneity is a key prerequisite for achieving sustainable and equitable development of higher education. These findings provide critical insights for optimizing higher education resource allocation, promoting balanced regional development, and advancing the construction of a high-quality education system in China. Full article

Evaluating transit-oriented development (TOD) efficiency in metro station areas remains challenging, as the traditional “Node–Place” model gives limited consideration to guiding factors and struggles to account for inter-regional flows under spatial heterogeneity. To address these limitations, this study develops an enhanced “Node–Place–Accessibility” model by introducing an accessibility dimension to better capture station-level connectivity and walkability. DepthmapX and a convex space approach were applied to quantify station-area accessibility, reflecting passengers’ perceived spatial distance during transfers. The model establishes a TOD measurement framework based on spatial coupling and functional connectivity, enabling the identification of factors influencing metro ridership across different spatial scales. Moran’s I was employed to describe spatial agglomeration and a local spatial clustering method integrating both passenger flow and built-environment (BE) characteristics was constructed to reveal differentiated spatial patterns. The Multiscale Geographically Weighted Regression (MGWR) model was further employed to quantify the spatially varying impacts of BE factors on ridership. Results indicate that the improved model provides stronger discriminative power in identifying “balanced stations,” and that BE conditions exert significant impact on metro ridership, particularly in areas with strong coordination among TOD components. Among the BE dimensions, design granularity exerts a more substantial impact on ridership than connectivity, density, and accessibility. This methodology provides large cities with a reliable tool for formulating targeted strategies that promote positive interactions between transportation and land use, thereby supporting sustainable urban development. Full article

Climate regulation ecosystem services (CRESs) play a crucial role in maintaining ecological balance and promoting regional sustainability. Previous studies have primarily focused on the total volume or per-unit-area quantity of CRESs, with limited attention given to their underlying driving mechanisms. This neglect overlooks their multidimensional attributes and dynamic complexity. Such simplifications often overlook the multidimensional attributes and dynamic complexity inherent in these services. Therefore, this study introduces a multidimensional evaluation framework to reveal the characteristic of the spatiotemporal evolution of CRESs. By integrating a multiscale geographically weighted regression (MGWR) model, the intensity and effective distance of theireffects are quantitatively identified, thereby providing a scientific and refined cognitive foundation for regional sustainable development. The results showed the following: (1) Between 2002 and 2022, CRESs in Guizhou Province showed an upward trend, with 64% of counties experiencing positive trends, whereas 51% of counties remained below average in terms of output and efficiency. (2) The spatial pattern of CRESs varied significantly, with stabilization in hotspots, improvement in coldspots, and the highest proportion of “A progress zones” in the east (45%). (3) Vegetation cover and annual precipitation were the two mainpositive factors that most strongly influenced the intensity of the CRESs, with values of 1.494 and 1.196, respectively; GDP had the most significant negative effect, with a value of −0.189; and population density had the largest range of effects, with a bandwidth of 1629. (4) Except for annual rainfall and aspect, the remaining eight influencingfactors, including population density, GDP, altitude, NPP, vegetation cover, annual temperature, and annual humidity, had positive and negative bidirectional effects on CRESs. Overall, this study emphasizes the need for differentiated, sustainability-oriented management strategies to better integrate ecosystem service evaluations into regional planning and sustainable policy development. Full article

Urban park accessibility is often planned with fixed service radii, that is, circular walking catchments around each park defined by a maximum walking distance of about 1500 m, roughly a 15–20 min walk in this study, yet real visitation is uneven and dynamic, leaving persistent gaps between normative coverage and where people actually originate. We propose an interpretable discovery-to-parameter workflow that converts behavior evidence into localized accessibility and actionable planning guidance. Monthly Origin–Destination (OD) and heatmap samples are fused to construct visitation intensity on a 200 m grid and derive empirical park service boundaries. Multiscale Geographically Weighted Regression (MGWR) then quantifies spatial heterogeneity, and its local coefficients are embedded into the enhanced two-step floating catchment area (E2SFCA) model as location-specific supply weights and distance-decay bandwidths. Compared with network isochrones and uncalibrated E2SFCA, the MGWR–E2SFCA achieves higher Jaccard overlap and lower population-weighted error, while maintaining balanced coverage–precision across districts and day types. A $\Delta$-surface lens decomposes gains into corridor correction and envelope contraction, revealing where conventional radii over- or under-serve residents. We further demonstrate an event-sensitivity switch, in which temporary adjustments of demand and decay parameters can accommodate short-term inflows during events such as festivals without contaminating the planning baseline. Together, the framework offers a transparent toolset for diagnosing mismatches between normative standards and observed use, prioritizing upgrades in under-served neighborhoods, and stress-testing park systems under recurring demand shocks. For land planning, it pinpoints where barriers to access should be reduced and where targeted connectivity improvements, public realm upgrades, and park capacity interventions can most effectively improve urban park accessibility. Full article

To clarify the multifunctional supply–demand relationship of cultivated land in the Yellow River Basin of Henan Province, and to provide decision-making support for strengthening cultivated land protection and promoting sustainable agricultural and rural utilisation within this basin, this study employs the entropy value method, hierarchical demand theory, and geographically weighted regression (GWR) models. Analyses were conducted at three scales—functional zoning, municipal, and county—to reveal the spatiotemporal evolution of supply and demand for the productive, ecological, social, and landscape functions of cultivated land from 2013 to 2023. This comprehensive assessment evaluates the supply and demand levels of multifunctional cultivated land within the study area and analyses the risks associated with shortages in multifunctional supply and demand. Results indicate: A significant spatial negative correlation exists between the supply and demand levels of multifunctional agricultural land in the Yellow River Basin of Henan Province. The supply level was in the range of [0.08–0.65], exhibiting an overall slight decreasing trend and a spatial pattern of higher values in the east and lower values in the west. The demand level was in the range of [0.11–0.82], showing an overall increasing trend and a spatial pattern of higher values at both ends and lower values in the middle. Between 2013 and 2023, the severity of multifunctional supply–demand scarcity risk gradually improved, exhibiting an overall spatial distribution pattern characterised by scarcity in core and expansion zones, surplus in coordination zones. Risk severity values ranged from −0.08 to 0.02 in core zones, 0.03 to 0.11 in expansion zones, and 0.08 to 0.16 in coordination zones. To optimise the multifunctional supply–demand structure of cultivated land in Henan’s Yellow River Basin, high-risk areas require targeted management and optimisation to mitigate supply–demand risks. The balance between multifunctional supply and demand for cultivated land should be achieved through tailored approaches, such as standardising cross-regional allocation of multifunctional cultivated land resources and establishing a multi-scale, integrated compensation mechanism for protecting cultivated land functions. Full article

Understanding the spatial heterogeneity of traffic congestion drivers is crucial for data-informed urban planning in tourist cities. This study investigates the spatiotemporal relationship between built environment characteristics and traffic congestion in the central urban area of a major northern Chinese tourist city. We apply a Multiscale Geographically Weighted Regression (MGWR) model to geospatial data across four typical peak periods and benchmark the results against Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). The MGWR model demonstrates superior capability in capturing spatial non-stationarity and multiscale effects. The results reveal strong spatiotemporal heterogeneity in the effects of built environment factors on congestion. Intersection density demonstrates a stronger mitigating effect during weekday evening peaks. Catering facilities significantly exacerbate congestion in tourist hotspots. Tourism-related facilities such as hotels and attractions intensify congestion during weekend peaks. Parking availability shows dual impacts, with peripheral parking reducing pressure and central clustering worsening congestion. Our geospatially disaggregated results provide empirical evidence for location-sensitive and temporally adaptive traffic management and urban design strategies. This study highlights the value of MGWR-based spatial modeling in supporting geoinformation-driven urban mobility planning. Full article

The unbalanced allocation of educational resources in kindergartens across China has attracted increasing attention from scholars and the public. However, few studies have examined their spatially imbalanced distribution and its influencing factors. Based on point-of-interest data, this study systematically analyzes the spatially imbalanced distribution characteristics of kindergartens in China from a multiscale perspective using the spatial analysis and spatial regression model to identify the factors influencing its formation pattern. The results reveal that the distribution pattern is “more in the southeast and fewer in the northwest,” with the Hu Huanyong Line serving as the boundary. Kernel density analysis revealed that areas with a density greater than 0.34 individual/km² were primarily concentrated in provincial capitals and major metropolitan areas, exhibiting a gradual decrease outward from these core zones. It also reveals a “large dispersion and small aggregation”, with a concentration around mega-cities, urban agglomerations, and provincial capitals. Significant spatial auto-correlations were found at all administrative levels, with hotspots distributed in northeast, north, and southeast China. The spatial determinants of kindergartens distribution in China exhibited significant spatial heterogeneity. The findings provide a reference in improving the spatial pattern and the state of unbalanced development of kindergarten education in China, as well as scientific suggestions to optimize resource allocation. Full article

The sustainable development of cities urgently requires an understanding of the interaction between social equity and ecological quality, especially in the peri-urban areas that traditional environmental justice research has paid less attention to. Taking Outer London as an example in this study, the Comprehensive Social Equity Index (CSEI) and the Remote Sensing Ecological Index (RSEI) were constructed to explore the social–ecological coupling relationship and spatial heterogeneity. Four types of socio-ecological coupling were identified through the four-quadrant model, ordinary least squares (OLS), and multi-scale geographically weighted regression (MGWR). The results reveal the characteristics of nonlinear coupling: in addition to the dual disadvantages and advantages of society and ecology, there are also regional patterns where social conditions are advantageous, but ecology is degraded, and where society is weak, but ecology is rich. This indicates that there is a complex spatial dislocation relationship between society and ecology in the peri-urban. The research proposes a scale-sensitive governance strategy based on location, emphasizing the coordinated countermeasures of social reinvestment and ecological restoration, providing a new perspective for environmental justice and sustainable planning in the peri-urban areas of the UK. Full article

Effective governance of informal commercial spaces is a common challenge faced by cities globally. To break through the superficial governance mindset of traditional spatial regulation, this study focuses on clarifying the spatial distribution characteristics and influencing factors of such spaces. By integrating the theory of “The right to the city” with the “7D” principles of New Urbanism, and focusing on the Jinxiang Street area in Harbin, a representative zone combining traditional industrial and modern residential communities, this study constructed a multidimensional indicator framework including population factors, functional diversity of facilities, accessibility of the built environment, spatial suitability, and intensity of community management, extracting 17 significant variables. Through spatial autocorrelation analysis (Moran’s I), multiscale geographically weighted regression (MGWR), and geographic detector analysis, the results show that informal commercial spaces exhibit clustered yet uneven characteristics between aging and upscale communities; the MGWR model reveals significant spatial heterogeneity in influencing factors; and geographic detector analysis shows that the interaction between public service facilities’ proximity to main roads and enhanced community management has the most significant explanatory power for heterogeneity (q = 0.85). These findings inform differentiated governance strategies and provide scientific support for sustainable governance of informal commercial spaces. Full article

The spatial heterogeneity of the electric bike-sharing (EBS) travel demand in small and medium-sized cities is influenced by a combination of the built environment, socio-economic gradients, transportation accessibility, and residents’ travel behavior patterns, and is significantly different from the shared travel characteristics of developed cities. In order to explore the influencing mechanisms of the EBS travel demand under different travel distance scales in small and medium-sized cities, this paper utilizes multi-source data from Tongxiang, Zhejiang Province, including operational data of EBS and built environment data. This paper analyzes the impact of the built environment on the EBS travel demand and its spatial heterogeneity across various distance scales from a local perspective. The results demonstrate that the fit of the multiscale geographically weighted regression (MGWR) model is superior to that of the geographically weighted regression (GWR) and the ordinary least squares (OLS) model. The explanatory variables exhibit significant spatial heterogeneity in their influence on the demand for EBS trips across different distance scenarios. The density of primary roads demonstrates a positive correlation with EBS travel demand in the western urban core area, but it is negatively correlated with travel demand in the eastern urban core area. Accommodation services show a negative correlation with long-distance EBS travel demand in the urban core area and the northern city, but they are positively correlated with short-distance EBS travel demand in the urban core area. There is competition between long-distance EBS and public transportation in city centers. However, short-distance EBS and public transportation exhibit a complementary relationship in the urban periphery. The research findings are beneficial for gaining a deeper understanding of the patterns of change in the EBS travel demand and promoting the refined and sustainable development of shared transportation. Full article

The study focuses on the multidimensional attributes of rural marginalization and their differentiated impact on rural development levels. Based on a systematic review and summary of research findings, this study elucidates the conceptual implications of rural development levels and rural marginalization and deconstructs rural marginalization into five dimensions. Considering Linhai City in Zhejiang Province, China, as the research object, a measurement model for rural development levels was constructed comprising basic and enhancement factors. An influencing factor set was established based on the perspective of marginalization. Spatial autocorrelation and multiscale geographically weighted regression models were comprehensively employed to measure and analyze rural development levels and their influencing factors. The main findings are as follows: (1) the concept of rural marginalization is defined from five dimensions: spatial, technological, policy, social, and infrastructural, and a quantitative evaluation system is established; (2) through quantitative analysis using Linhai City as an example, it is found that the influence of marginalization across different dimensions on rural development exhibits significant spatial variability, meaning that the impact of marginalization on rural development levels is influenced by multiple factors. These findings suggest that, while formulating rural development policies, we should fully consider the actual and external circumstances of different villages, adopt tailored strategies based on local conditions, and avoid implementing one-size-fits-all policies. Full article