Search Results (73)

The purpose of this study is to explore the spatial impact of short-term rental activity on house prices in the city of Athens, Greece. It is well established that the increasing number of short-term rentals has a number of consequences on the functions and living standards in several cities around the world. An aspect that is not studied very often is the effect of short-term rentals on house prices and, especially, the spatial distribution of this effect. In this paper, spatial regression models are presented, incorporating several of the commonly employed house characteristics, such as structural and locational characteristics, with the addition of the short-term rentals as an explanatory factor. Geographically Weighted Regression (GWR) models, in particular, produce the geographic distribution of regression coefficients, allowing for the study of the short-term rentals’ influence on house prices at the local level. Furthermore, spatial regression models are compared to global linear models. Although global models indicate that short-term rentals have an overall positive contribution on house prices, Geographically Weighted Regression, through the local regression coefficients, reveals spatial patterns with mixed effects, both positive and negative. The greater positive impact is observed in the area around the historical center of the city where short-term rentals’ presence is intense. Full article

Urban heat island (UHI), a significant environmental issue caused by urbanization, is a pressing challenge in modern society. To mitigate it, urban thermal policies have been implemented globally. However, despite differences in topographical and environmental characteristics between cities and within the same city, these policies are largely uniform and fail to reflect contexts, creating notable drawbacks. This study analyzed three cities in Korea with high land surface temperatures (LSTs) to identify factors influencing LST by applying Extreme Gradient Boosting (XGBoost) with Shapley Additive explanations (SHAP) and Geographically Weighted Regression (GWR). Each variable was derived by calculating the average values from May to September 2020. LST was the dependent variable, and the independent variables were chosen based on previous studies: Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), ALBEDO, Population Density (POP_D), Digital Elevation Model (DEM), and SLOPE. XGBoost-SHAP was used to derive the relative importance of the variables, followed by GWR to assess spatial variation in effects. The results indicate that NDBI, reflecting building density, is the primary factor influencing the thermal environment in all three cities. However, the second most influential factor differed by city: SLOPE had a strong effect in Daegu, characterized by surrounding mountains; POP_D had greater influence in Incheon, where population distribution varies due to clustered islands; and DEM was more influential in Seoul, which contains a mix of plains, mountains, and river landscapes. Furthermore, while NDBI and ALBEDO consistently contributed to LST increases across all regions, the effects of the remaining variables were spatially heterogeneous. These findings highlight that urban areas are not homogeneous and that variations in land use, development patterns, and morphology significantly shape heat environments. Therefore, UHI mitigation strategies should prioritize improving urban form while incorporating localized planning tailored to each region’s physical and socio-environmental characteristics. The results can serve as a foundation for developing strategies and policy decisions to mitigate UHI effects. Full article

Guided by the “Healthy China” initiative, understanding the impact of the built environment on running behavior is essential for encouraging regular physical activity and advancing public health. This study addresses a critical gap in healthy city research by examining the spatial heterogeneity in how urban environmental contexts affect residents’ running preferences. Focusing on two contrasting areas of Suzhou, namely the historic Gusu District and the modern Industrial Park District, we developed a 5Ds-based analytical framework (density, accessibility, diversity, design, and visual) that incorporates Suzhou’s unique water networks and street features. Methodologically, we used Strava heatmap data and multi-source environmental indicators to quantify built-environment attributes and examined their relationships with running-space selection. We applied linear regression and interpretable machine learning to reveal overall associations, while geographically weighted regression (GWR) was used to capture spatial variations. Results reveal significant spatial heterogeneity in how the built environment influences running-space selection. While the two districts differ in their urban form, runners in Gusu District prefer dense and compact street networks, whereas those in Industrial Park District favor open, natural spaces with higher levels of human vibrancy. Despite these differences, both districts show consistent preferences for spaces with a more intense land use mix, stronger transportation accessibility, and larger parks and green spaces. The multi-dimensional planning strategies derived from this study can improve the urban running environment and promote the health and well-being of residents. Full article

The construction industry is a major contributor to global resource consumption and waste. This sector extracts over two billion tons of raw materials each year and contributes over 30% of all solid waste generated annually through construction and demolition debris. The movement toward circularity in the built environment aims to replace linear processes of extraction and disposal by promoting policies favoring building preservation and adaptive reuse, as well as the salvage and reuse of building materials. Few North American cities have implemented explicit policies that incentivize circularity to decouple urban growth from resource consumption, and there remain substantial hurdles to adoption. Nonetheless, existing regulatory and planning tools, such as zoning codes and historic preservation policies, may already influence redevelopment in ways that could align with circularity. This article examines spatial patterns in these indirect pathways through a case study of a college town in New York State, assessing how commonly used local planning tools shape urban redevelopment trajectories. Using a three-stage spatial analysis protocol, including exploratory analysis, Geographically Weighted Regressions (GWRs), and Geographic Random Forest (GRF) modeling, the study evaluates the impact of zoning regulations and historic preservation designations on patterns of demolition, reinvestment, and incremental change in the building stock. National historic districts were strongly associated with more building adaptation permits indicating reinvestment in existing buildings. Mixed-use zoning was positively correlated with new construction, while special overlay districts and low-density zoning were mostly negatively correlated with concentrations of building adaptation permits. A key contribution of this paper is a replicable protocol for urban building stock analysis and insights into how land use policies can support or hinder incremental urban change in moves toward the circular city. Further, we provide recommendations for data management strategies in small cities that could help strengthen analysis-driven policies. Full article

Clarifying how the community-scale built environment shapes the spatial heterogeneity of cardiovascular disease (CVD) prevalence is essential for precision urban health interventions. We integrated CVD prevalence data from the Guangxi Zhuang Autonomous Region Hospital (2020–2022) with 14 built-environment indicators across 77 communities in Xixiangtang District, Nanning, and compared ordinary least squares (OLS), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR). MGWR provided the best model fit (adjusted R² increased by 0.136 and 0.056, respectively; lowest AICc and residual sum of squares) and revealed significant scale-dependent effects. Distance to metro stations, road network density, and the number of transport facilities exhibited pronounced local-scale heterogeneity, while population density, building density, healthy/unhealthy food outlets, facility POI density, and public transport accessibility predominantly exerted global-scale effects. High-risk clusters of CVD were identified in mixed-use, high-density urban communities lacking rapid transit access. The findings highlight the need for place-specific, multi-scale planning measures, such as transit-oriented development and balanced food environments, to reduce the CVD burden and advance precision healthy-city development. Full article

Urban vitality serves as an important indicator for evaluating the level of urban quality development and sustainability. In response to a series of urban challenges arising from rapid urban expansion, enhancing urban quality and fostering urban vitality have become key objectives in contemporary urban planning and development. This study summarizes the spatial distribution patterns of urban vitality at the street and neighborhood levels in the central area of Qingdao, and analyzes their spatial characteristics. A 5D built environment indicator system is constructed, and the effects of the built environment on urban vitality are explored using the Optimal Parameter Geographic Detector (OPGD) and the Multi-Scale Geographically Weighted Regression (MGWR) model. The aim is to propose strategies for enhancing spatial vitality at the street and neighborhood scales in central Qingdao, thereby providing references for the optimal allocation of urban spatial elements in urban regeneration and promoting sustainable urban development. The findings indicate the following: (1) At both the subdistrict and block levels, urban vitality in Qingdao exhibits significant spatial clustering, characterized by a pattern of “weak east-west, strong central, multi-center, cluster-structured,” with vitality cores closely aligned with urban commercial districts; (2) The interaction between the three factors of functional density, commercial facilities accessibility and public facilities accessibility and other factors constitutes the primary determinant influencing urban vitality intensity at both scales; (3) Commercial facilities accessibility and cultural and leisure facilities accessibility and building height exert a positive influence on urban vitality, whereas the resident population density appears to have an inhibitory effect. Additionally, factors such as building height, functional mixing degree and public facilities accessibility contribute positively to enhancing urban vitality at the block scale. (4) Future spatial planning should leverage the spillover effects of high-vitality areas, optimize population distribution, strengthen functional diversity, increase the density of metro stations and promote the coordinated development of the economy and culture. Full article

The development of the Great Wall National Cultural Park has unleashed the potential for integrating cultural and tourism development along the Great Wall. However, ICH along the Great Wall, a key part of its cultural identity, suffers from low recognition and a mismatch between protection and development efforts. This study analyzes provincial-level and above ICH along Hebei’s Great Wall using geospatial tools and the Geographical Detector model to explore distribution patterns and influencing factors, while Geographically Weighted Regression is utilized to reveal spatial heterogeneity. It tests two hypotheses: (H1) ICH shows a clustered pattern; (H2) economic factors have a greater impact than cultural and natural factors. Key findings show: (1) ICH distribution is numerically balanced north–south but spatially uneven, with dense clusters in the south and scattered patterns in the north. (2) ICH and crafts cluster significantly, while dramatic balladry spreads evenly, and other categories are random. (3) Average annual temperature and precipitation have the greatest impact on ICH distribution, with the factors ranked as: natural > cultural > economic. Multidimensional interactions show significant enhancement effects. (4) Influencing factors vary spatially. Population density, transport, temperature, and traditional villages are positively related to ICH. Elevation, precipitation, tourism, and cultural institutions show mixed effects across regions. These insights support targeted ICH conservation and sustainable development in the Great Wall cultural corridor. Full article

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

Accurate estimation of forest aboveground biomass (AGB) and understanding its ecological drivers are vital for carbon monitoring and sustainable forest management. However, AGB estimation using remote sensing is hindered by signal saturation in high-biomass areas and insufficient attention to ecological structural factors. Focusing on Guangdong Province, this study proposes a novel approach that spatially extrapolates airborne LiDAR-derived Forest structural parameters and integrates them with Sentinel-1/2 data to construct an AGB prediction model. Results show that incorporating structural parameters significantly reduces saturation effects, improving prediction accuracy and AGB maximum range in high-AGB regions (R² from 0.724 to 0.811; RMSE = 10.64 Mg/ha; max AGB > 180 Mg/ha). Using multi-scale geographically weighted regression (MGWR), we further examined the spatial influence of forest type, age structure, and species mixture. Forest age showed a strong positive correlation with AGB in over 95% of the area, particularly in mountainous and hilly regions (coefficients up to 1.23). Species mixture had positive effects in 87.7% of the region, especially in the north and parts of the south. Natural forests consistently exhibited higher AGB than plantations, with differences amplifying at later successional stages. Highly mixed natural forests showed faster biomass accumulation and higher steady-state AGB, highlighting the regulatory role of structural complexity and successional maturity. This study not only mitigates remote sensing saturation issues but also deepens understanding of spatial and ecological drivers of AGB, offering theoretical and technical support for targeted carbon stock assessment and forest management strategies. Full article

Urban vitality, a critical emergent property of complex urban systems, is pivotal for sustainable, human-oriented urbanization. While land use mix (LUM) is recognized as a key strategy for shaping these systems, the systemic mechanisms through which its multifaceted dimensions influence urban vitality across spatio-temporal scales remain underexplored. This study examines the complex and spatially heterogeneous impacts of land use mix on 24 h urban vitality in Ningbo, China, conceptualizing the city as a dynamic socio-spatial system. By integrating multi-source data (Baidu Maps, POI, and OSM) and employing OLS and geographically weighted regression (GWR) models, we unravel these systemic relationships. Key findings include the following: (1) LUM significantly enhances urban vitality, acting as a crucial urban system configuration for both daytime and nighttime activity. (2) The efficacy of LUM stems more from systemic interconnections—convenient access to adjacent spaces (proximity) and functional coordination among diverse land uses—than mere compositional diversity, emphasizing the importance of interrelated elements within the urban fabric. (3) The system’s response to LUM exhibits significant spatial and temporal heterogeneity; proximity’s impact is most variable, while diversity and coordination effects are more stable, underscoring the dynamic and context-dependent nature of these interactions. (4) System-adaptive strategies are crucial: newly developed urban areas benefit from foundational infrastructure and land use diversity (system inputs), while revitalizing older towns requires optimizing spatial accessibility and functional coordination (enhancing existing system linkages). These findings advance the theoretical systems-based theoretical understanding of the LUM–vitality nexus while offering practical insights for urban planners and policymakers. Full article

Bike-sharing has been widely recognized for addressing the “last-mile” problem and improving commuting efficiency. While prior studies emphasize how the built environment shapes feeder trips, the effects of station types and spatial heterogeneity on bike-sharing and metro integration remain insufficiently explored. Taking the urban core area of Shenzhen as a case study, this paper examines how the built environment influences such integration during morning peak hours and how these impacts differ across station types. First, we proposed a “3Cs” (convenience, comfort, and caution) framework to capture key built environment factors. Metro stations were classified into commercial, residential, and office types via K-means clustering. Subsequently, the ordinary least squares (OLS) regression model and the multiscale geographically weighted regression (MGWR) model were employed to identify significant factors and explore the spatial heterogeneity of these effects. Results reveal that factors influencing bike-sharing–metro integration vary by station type. While land-use mix and enclosure affect bike-sharing usage across all stations, employment and intersection density are only significant for commercial stations. Furthermore, these influences exhibit spatial heterogeneity. For instance, at office-oriented stations, population shows both positive and negative effects across areas, while residential density has a generally negative impact. These findings enhance our understanding of how the built environment shapes bike-sharing–metro integration patterns and support more targeted planning interventions. Full article

The built environment is the key to creating vibrant urban spaces that contribute to the health and sustainability of cities. Studies have demonstrated that a reasonable built environment helps to stimulate urban vitality. Nevertheless, there are limitations to the understanding that three-dimensional (3D) built environment indicators from the ‘human perspective’ can substantially affect urban vitality. This study provides an empirical analysis of Xi’an, a city with both traditional historical blocks and a modern city landscape. By applying the ordinary least square model and the geographically weighted regression model, this study explores the impacts of the two-dimensional (2D) and 3D built environments on urban vitality. Results show: (1) the urban vitality exhibits significant spatial and temporal difference characteristics; (2) the 3D built environment exerts a greater influence on urban vitality than 2D; (3) taking weekdays for instance, the indicators of green space and road space (e.g., normalized difference vegetation index (−0.092), green view index (−0.104), road density (−0.021), and enclosure (−0.089)) are negatively correlated with urban vitality, while the indicators of building space and mixed function (e.g., building density, floor area ratio, points of interest (POI) mixing degree, and 3D mixing degree) present a positive effect. To improve urban vitality, the study provides suggestions from the perspective of 3D and human perception, which will contribute to the meticulous practice of urban design. Full article

Balancing regional disparities in non-grainization is vital for stable grain production and sustainable urbanization. This study employs geospatial analysis to examine the spatiotemporal patterns and driving factors of non-grainization in Jiangsu Province from 2001 to 2020. By integrating geospatial data from 77 county-level units and employing spatial autocorrelation analysis, multiple linear regression, and mixed geographically weighted regression (MGWR), this study reveals the spatial heterogeneity and key driving factors of non-grainization. The results indicate strong spatial dependence, with persistent high–high clusters in economically developed southern/coastal areas and low–low clusters in northern regions. Furthermore, the driving mechanism shifted significantly over the two decades. Early constraints from natural endowments (e.g., elevation’s positive impact significantly weakened post 2010) and individual economics diminished with technological progress, while macroeconomic development became dominant, influencing both scale and structure. Infrastructure improvements (reflected by rural electricity use) consistently limited non-grainization; some factors showed phased effects, and annual mean precipitation emerged as a significant influence in 2020. MGWR revealed substantial, dynamic spatial heterogeneity in these drivers’ impacts across different periods. These findings highlight the importance of geoinformation tools in managing regional disparities. Integrating spatial and socio-economic analysis offers practical insights for policymakers to develop targeted strategies that balance food security with agricultural diversification. Full article

Background: The post-acute impact of SARS-CoV-2 infections on chronic conditions remains poorly understood, particularly in general populations. Objectives: Our primary aim was to assess the association between SARS-CoV-2 infections and new diagnoses of chronic conditions. Our two secondary aims were to explore geographic variations in this association and to assess the association between SARS-CoV-2 infections and the exacerbation of pre-existing conditions. Methods: This longitudinal study used data from 8086 participants of the Specchio-COVID-19 cohort in the canton of Geneva, Switzerland (2021–2023). Mixed-effects logistic regressions and geographically weighted regressions adjusted for sociodemographic, socioeconomic, and healthcare access covariates were used to analyze self-reported SARS-CoV-2 infections, new diagnoses of chronic conditions, and the exacerbation of pre-existing ones. Results: Participants reporting a SARS-CoV-2 infection were more likely to be diagnosed with a new chronic condition compared to those who did not report an infection (adjusted odds ratio [aOR] = 2.15, 95% CI 1.43–3.23, adjusted p-value = 0.002). Notable geographic variations were identified in the association between SARS-CoV-2 infections and new diagnoses. While a positive association was initially observed between SARS-CoV-2 infections and exacerbation of pre-existing chronic conditions, this association did not remain significant after adjusting p-values for multiple comparisons. Conclusions: These findings contribute to understanding COVID-19’s post-acute impact on chronic conditions, highlighting the need for targeted health management approaches and calling for tailored public health strategies to address the pandemic’s long-term effects. Full article

Despite Indonesia’s significant economic progress, certain regions, such as West Manggarai Regency in East Nusa Tenggara, continue to face persistent poverty challenges. While strategic tourism initiatives in Labuan Bajo have spurred regional development, the benefits have not reached local communities equitably, highlighting a disconnect between economic growth and community well-being. Addressing this gap requires an integrated approach that links social protection, disaster risk reduction, climate adaptation, and economic diversification. This paper proposes an adaptive social protection (ASP) framework that aims to increase the resilience of vulnerable populations by integrating social protection systems with disaster preparedness and sustainable economic strategies. The research critically examines the Regional Medium-Term Development Plan (RPJMD) of Kabupaten Manggarai Barat (2021–2026), identifying existing policy gaps and opportunities for improvement. Using a mixed-methods approach, this study used cluster mapping and geographically weighted regression analysis to model and visualise poverty distribution alongside infrastructure conditions. These findings will inform the design of a targeted ASP programme to reduce poverty and build resilience to economic and environmental shocks. By aligning with sustainable development principles, the proposed framework addresses the dual goals of poverty reduction and disaster risk reduction. This study provides actionable recommendations for local governments to strengthen social protection mechanisms, promote inclusive economic growth, and ensure equitable distribution of tourism benefits. The findings provide a policy blueprint for promoting sustainable and inclusive development in West Manggarai Regency, with implications for similar contexts in other regions. Full article