Search Results (258)

Urban street canyons in high-density areas exacerbate PM_2.5 accumulation, posing significant public health risks. Through integrated empirical and computational methods—including empirical PM_2.5 and microclimate measurements, multivariate regression analysis, and high-resolution ENVI-met5.1 simulations—this study quantifies the threshold effects of pedestrian-oriented morphological indicators on PM_2.5 exposure in east–west-oriented residential streets. Key findings include the following: (1) the height-to-width ratio (H/W) negatively correlates with exposure, where H/W = 2.0 reduces the peak concentrations by 37–41% relative to H/W = 0.5 through enhanced vertical advection; (2) the Build-To-Line ratio (BTR) exhibits a positive correlation with exposure, with BTR = 63.2% mitigating exposure by 12–15% compared to BTR = 76.8% by reducing aerodynamic stagnation; (3) pollution exposure can be mitigated by enhancing airflow ventilation within street canyons through architectural facade design. These evidence-based morphological thresholds (H/W ≥ 1.5, BTR ≤ 70%) provide actionable strategies for reducing health risks in polluted urban corridors, supporting China to meet its national air quality improvement targets. Full article

The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and spatial dependencies among factors influencing EVCS locations. To address this research gap and better understand the spatial impacts of urban activities on EVCS placement, this study presents a spatially aware machine learning (SAML) method that combines a multi-layer perceptron (MLP) model with a spatial loss function to optimize EVCS sites. Additionally, the method uses the Shapley additive explanation (SHAP) technique to investigate nonlinear relationships embedded in EVCS placement. Using the city of Wuhan as a case study, the SAML method reveals that parking site (PS), road density (RD), population density (PD), and commercial residential (CR) areas are key factors in determining optimal EVCS sites. The SAML model classifies these grid cells into no EVCS demand (0 EVCS), low EVCS demand (from 1 to 3 EVCSs), and high EVCS demand (4+ EVCSs) classes. The model performs well in predicting EVCS demand. Findings from ablation tests also indicate that the inclusion of spatial correlations in the model’s loss function significantly enhances the model’s performance. Additionally, results from case studies validate that the model is effective in predicting EVCSs in other metropolitan cities. Full article

This article presents a spatial analysis of projects selected by city residents and implemented in five successive editions (2015–2019) of the participatory budgeting in Częstochowa, Poland. The study examines the relationship between the type of hard projects (small investments in public infrastructure and landscaping) and the pre-existing characteristics of the land use of each district. Kernel density estimation and Spearman correlation analysis were used. The highest spatial density occurred in projects related to the modernization of roads and sidewalks, recreation, and greenery, indicating a relatively high number of proposals within or near residential areas. Key correlations included the following: (1) greenery projects were more common in districts lacking green areas; (2) recreational infrastructure was more frequently chosen in areas with significant water features; (3) street furniture projects were mostly selected in districts with sparse development, scattered buildings, and postindustrial sites; (4) educational infrastructure was often chosen in low-density, but developing districts. The selected projects often reflect local deficits in specific land use or public infrastructure, but also stress the predestination of the recreational use of waterside areas. Full article

Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to UHI mitigation by integrating Machine Learning (ML) with physical and socio-demographic data for sustainable urban planning. Using high-resolution spatial data across five functional zones (residential, commercial, industrial, official, and downtown), we apply three ML models, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), to predict land surface temperature (LST). The models incorporate both environmental variables, such as imperviousness, Normalized Difference Vegetation Index (NDVI), building area, and solar influx, and social determinants, such as population density, income, education, and age distribution. SVM achieved the highest R² (0.870), while RF yielded the lowest RMSE (0.488 °C), confirming robust predictive performance. Key predictors of elevated LST included imperviousness, building area, solar influx, and NDVI. Our results underscore the need for zone-specific strategies like more greenery, less impervious cover, and improved building design. These findings offer actionable insights for urban planners and policymakers seeking to develop equitable and sustainable UHI mitigation strategies aligned with climate adaptation and environmental justice goals. Full article

The Mayanghe National Nature Reserve, a key habitat for the endangered François’ langur (Trachypithecus francoisi), faces significant anthropogenic disturbances, including extensive distribution of croplands, roads, and settlements. These human-modified features are predominantly concentrated at elevations between 500 and 800 m and on slopes of 10–20°, which notably overlap with the core elevation range utilized by François’ langur. Spatial analysis revealed that langurs primarily occupy areas within the 500–800 m elevation band, which comprises only 33% of the reserve but hosts a high density of human infrastructure—including approximately 4468 residential buildings and the majority of cropland and road networks. Despite slopes >60° representing just 18.52% of the area, langur habitat utilization peaked in these steep regions (exceeding 85.71%), indicating a strong preference for rugged karst terrain, likely due to reduced human interference. Habitat type analysis showed a clear preference for evergreen broadleaf forests (covering 37.19% of utilized areas), followed by shrublands. Landscape pattern metrics revealed high habitat fragmentation, with 457 discrete habitat patches and broadleaf forests displaying the highest edge density and total edge length. Connectivity analyses indicated that distribution areas exhibit a more continuous and aggregated habitat configuration than control areas. These results underscore François’ langur’s reliance on steep, forested karst habitats and highlight the urgent need to mitigate human-induced fragmentation in key elevation and slope zones to ensure the species’ long-term survival. Full article

To date, few studies have been published for cities in Germany that take into account climate change and changing hydrologic patterns due to increases in building density. This study investigates the efficiency of LID for past and future climate in the polycentric agglomeration area Frankfurt, Main (Central Germany) using observed and projected climate (model) data for a standard reference period (1961–1990) and a high emission scenario (RCP 8.5) as well as a climate protection scenario (RCP 2.6), under 40 to 75 percent building density. LID elements included green roofs, permeable pavement and bioretention cells. SWMM₅ was used as model for simulation purposes. A holistic evaluation of simulation results showed that effectiveness increases incrementally with LID implementation percentage and inverse to building density if implemented onto at least 50 percent of available impervious area. Building density had a higher adverse effect on LID efficiency than climate change. The results contribute to the understanding of localized effects of climate change and the implementation of adaption strategies to that end. The results of this study can be helpful for the scientific community regarding future investigations of LID implementation efficiency in dense residential areas and used by local governments to provide suggestions for urban water balance revaluation. Full article

Urban green spaces (UGS) are essential for residents’ well-being, environmental quality, and social cohesion. However, previous studies have typically employed undifferentiated analytical frameworks, overlooking UGS types and failing to adequately measure the structural disparities of different UGS types within residents’ walking distance. To address this, this study integrates Gaussian Two-Step Floating Catchment Area models, Simpson’s index, and the Gini coefficient to construct an accessibility–diversity–equality assessment framework for UGS. This study conducted an analysis of accessibility, diversity, and equity for various types of UGSs under pedestrian conditions, using the high-density city of Shenzhen, China as a case study. Results reveal high inequality in accessibility to most UGS types within 15 min to 30 min walking range, except residential green spaces, which show moderate-high inequality (Gini coefficient: 0.4–0.6). Encouragingly, UGS diversity performs well, with over 80% of residents able to access three or more UGS types within walking distance. These findings highlight the heterogeneous UGS supply and provide actionable insights for optimizing green space allocation to support healthy urban development. Full article

This review examines the effect of geometric properties and the spacing of road humps on vehicle speed and noise, with a particular emphasis on South Asian contexts, especially Malaysia. Road humps are widely used traffic-calming devices designed to reduce vehicle speed and enhance road safety. The effectiveness of these measures is strongly influenced by parameters such as height, width, profile, and placement intervals. While the geometric optimization of humps generally improves speed-reduction outcomes, several studies indicate that braking and acceleration at humps can lead to increased traffic noise, particularly in residential and high-density areas. This review also explores design strategies and material choices (e.g., asphalt use, sinusoidal profiles) that may help mitigate noise impacts. Overall, a balance between speed control and noise management is necessary to ensure both safety and community acceptance. Full article

As shared micromobility grows quickly in metropolitan settings, e-scooter safety issues have become more urgent. This paper uses a Bayesian hierarchical model applied to census block groups in several Texas metropolitan areas to construct a spatial risk assessment methodology for e-scooter crashes. Based on crash statistics from 2018 to 2024, we develop a severity-weighted crash risk index and combine it with variables related to land use, transportation, demographics, economics, and other factors. The model comprises a geographically structured random effect based on a Conditional Autoregressive (CAR) model, which accounts for residual spatial clustering after capture. It also includes fixed effects for covariates such as car ownership and nightlife density, as well as regional random intercepts to account for city-level heterogeneity. Markov Chain Monte Carlo is used for model fitting; evaluation reveals robust spatial calibration and predictive ability. The following key predictors are statistically significant: a higher share of working-age residents shows a positive association with crash frequency (incidence rate ratio (IRR): ≈1.55 per +10% population aged 18–64), as does a greater proportion of car-free households (IRR ≈ 1.20). In the built environment, entertainment-related employment density is strongly linked to elevated risk (IRR ≈ 1.37), and high intersection density similarly increases crash risk (IRR ≈ 1.32). In contrast, higher residential housing density has a protective effect (IRR ≈ 0.78), correlating with fewer crashes. Additionally, a sensitivity study reveals that the risk index is responsive to policy scenarios, including reducing car ownership or increasing employment density, and is sensitive to varying crash intensity weights. Results show notable collision hotspots near entertainment venues and central areas, as well as increased baseline risk in car-oriented urban environments. The results provide practical information for targeted initiatives to lower e-scooter collision risk and safety planning. Full article

With the rapid advancement of urbanization, the increasing demand and insufficient supply of public service facilities for children have become urgent problems requiring resolution. This study employs the Shannon diversity index, the location entropy, spatial autocorrelation, and the Geographically Weighted Regression (GWR) to analyze the spatial distribution characteristics and influencing factors of children’s public service facilities in the central urban area of Shenyang. The findings of the study are as follows: (1) There are significant differences in the spatial distribution of children’s public service facilities. Higher quantity distribution and diversity index are observed in the core area and Hunnan District compared to the peripheral areas. The Gini coefficient of various facilities is below the fair threshold of 0.4, but 90.32% of the study units have location entropy values below 1, indicating a supply–demand imbalance. (2) The spatial distribution of various facilities exhibits significant clustering characteristics, with distinct differences between high-value and low-value cluster patterns. (3) The spatial distribution of facilities is shaped by four factors: population, transportation, economy, and environmental quality. Residential area density and commercial service facility density emerge as the primary positive drivers, whereas road density and average housing price act as the main negative inhibitors. (4) The mechanisms of influencing factors exhibit spatial heterogeneity. Positive driving factors exert significant effects on new urban areas and peripheral zones, while negative factors demonstrate pronounced inhibitory effects on old urban areas. Non-linear threshold effects are observed in factors such as subway station density and public transport station density. Full article

China’s rapid urbanization has given rise to the phenomenon of “urban villages”, which are often regarded as chaotic fringe areas in traditional studies. With the rise of the concept of resilient cities, the value of urban villages as potential carriers of sustainable development has been re-examined. This study adopted research methods such as field investigations, in-depth interviews, and conceptual sampling. By analyzing the interlinked governance relationship between Xiamen City and the urban villages in the Bay Area, aspects such as rural housing improvement, environmental governance, residents’ feedback, geographical pattern, and spatial production were evaluated. A field investigation was conducted in six urban villages within the four bays of Xiamen. A total of 45 people in the urban villages were interviewed, and the spatial status of the urban villages was recorded. This research found that following: (1) Different types of urban villages have formed significantly differentiated role positionings under the framework of regional governance. Residential community types XA and WL provide long-term and stable living spaces for migrant workers in Xiamen; tourism development types DS, HX, BZ, and HT allow the undertaking of short-term stay tourists and provide tourism services. (2) These urban villages achieve the construction of their resilience through resisting risks, absorbing policy resources, catering to the expansion of urban needs, and co-construction in coordination with planning. The multi-cultural inclusiveness of urban villages and their transformation led by cultural shifts have become the driving force for their sustainable development. Through the above mechanisms, urban villages have become the source of resilience and sustainability of healthy cities and provide a model reference for high-density urban construction. Full article

Rapid urbanization has exacerbated the urban heat island effect, posing a significant threat to human health and urban ecosystems. While numerous studies have demonstrated that urban morphology significantly influences land surface temperatures (LSTs), few have systematically explored the impact and contribution of urban morphology on LST across different functional zones. Therefore, this study takes Xi’an as a case and employs an interpretable CatBoost-SHAP machine learning model to evaluate the nonlinear influence of building landscape features on LST in different functional zones during summer. The results indicate the following: (1) The highest LST in the study area reached 52.68 °C, while the lowest was 21.68 °C. High-temperature areas were predominantly concentrated in the urban center and industrial zones with dense buildings, whereas areas around water bodies and green spaces exhibited relatively lower temperatures. (2) SHAP analysis revealed that landscape indicators exerted the most substantial impact across all functional zones, with green space zones contributing up to 62%. Among these, fractional vegetation coverage (FVC), as a core landscape factor, served as the primary cooling factor in all six functional zones and consistently demonstrated a negative effect. (3) Population density (POP) exhibited a generally high SHAP contribution across all functional zones, showing a positive correlation. Its effect was most pronounced in commercial zones, accounting for 16%. When POP ranged between 0 and 250 people, the warming effect was particularly prominent. (4) The mean building height (MBH) constituted a major influencing factor in most functional zones, especially in residential zones, where the SHAP value reached 0.7643. Within the range of 10–20 m, the SHAP value increased sharply, indicating a significant warming effect. (5) This study proposes targeted cooling strategies tailored to six functional zones, providing a scientific basis for formulating targeted mitigation strategies for different functional zones to alleviate the urban heat island effect. Full article

Urban heat islands (UHIs) present a growing environmental issue in swiftly urbanizing regions, where impermeable surfaces and a lack of vegetation increase local temperatures. This research analyzes the spatial distribution of urban heat islands in Zagreb, Croatia, utilizing remote sensing data, urban planning metrics, and spatial-statistical analysis. Composite rasters of land surface temperature (LST) and the Normalized Difference Vegetation Index (NDVI) were generated from four cloud-free Landsat 9 images obtained in the summer of 2024. The data were consolidated into regulatory planning units through zonal statistics, facilitating the evaluation of the impact of built-up density and designated green space on surface temperatures. A composite UHI index was developed by combining normalized land surface temperature (LST) and normalized difference vegetation index (NDVI) measurements, while spatial clustering was examined with Local Moran’s I and Getis-Ord Gi*. The results validate spatial patterns of heat intensity, with high temperatures centered in densely built residential areas. This research addresses the gap in past UHI studies by providing a reproducible approach for detecting thermal stress zones, linking satellite data with spatial planning variables. The results support the development of localized climate adaptation methods and highlight the importance of integrating green infrastructure into urban planning methodologies. Full article

As global climate change intensifies and energy crises deepen, photovoltaic (PV) applications in cities are increasingly garnering attention worldwide. In this context, retrofitting existing high-density residential areas with PV applications is becoming a focus of urban low-carbon development. As the most densely populated city in Western China, Chengdu is characterized by rapid development and high energy consumption. The widespread application of photovoltaic (PV) systems could significantly alleviate its energy consumption issues. This research investigated the PV application potentials of 27 non-residential areas in high-density residential areas in Chengdu, Sichuan Province from a design perspective and proposed design recommendations for PV applications in these spaces. In addition, this study analyzed urban morphological factors affecting the PV generation potential in non-residential areas through a Pearson correlation. The key factors influencing the PV application potential in these areas were building density (BD), non-residential area perimeter-to-area ratio (NBPAR), and maximum building height (H_max). This research aims to provide new strategies and methods for the low-carbon transformation of future urban high-density residential areas. Full article

Extreme rainfall events driven by climate change are increasing flood risks. Addressing flood mitigation solely from either a hydraulic engineering or urban planning perspective may overlook both feasibility and effectiveness. This study focuses on Tainan City and the Tainan Science Park in Taiwan, applying the NbS framework to assess flood mitigation strategies. From an urban planning perspective, Agricultural Development Zone Type II (Agri-DZII), parks, green spaces, and Taiwan Sugar Corporation (TSC) land were selected as flood detention sites. Hydraulic modeling was used to evaluate their effectiveness under both current and climate-change-induced rainfall conditions. Simulation results show that under current rainfall conditions, flood mitigation measures reduced inundated areas with depths exceeding 2.0 m by up to 7.8% citywide and 20.8% within the Tainan Science Park Special District Plan Area. However, under climate change scenarios, the reduction effects declined significantly, with maximum reductions of only 1.6% and 17.8%, respectively. Results indicate that, even when utilizing all available detention areas, the overall flood reduction in Tainan City remains limited. However, TSC agri-land within the Tainan Science Park overlaps with high-flood-risk zones, demonstrating significant local flood mitigation potential. This study recommends integrating hydrological analysis into urban planning to prevent high-density residential and economic zones from being designated in flood-prone areas. Additionally, policymakers should consider reserving appropriate land for flood detention to enhance climate resilience. By combining urban planning and hydraulic engineering perspectives, this study highlights the flexibility of NbS in disaster management, advocating for the integration of Natural Water Detention Measures into flood adaptation strategies to improve urban water management and climate adaptability. Full article