Search Results (242)

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

Jobs–housing balance is a critical concern in urban planning and sustainable economic development. Urban rail transit, as a key determinant of employment and residential location decisions, plays a pivotal role in shaping jobs–housing dynamics. Beijing, the first Chinese city to develop a subway system, offers a comprehensive rail network, making it an ideal case for exploring the effects of transit density on jobs–housing balance. This study utilizes medium-scale panel data from Beijing (2009–2022) and employs a fixed-effects model to systematically examine the impact of rail transit station density on jobs–housing balance and its underlying mechanisms. The results indicate that increasing transit station density tends to aggravate jobs–housing separation overall, with pronounced effects in central and outer suburban areas but negligible effects in near suburban areas. Mechanism analysis reveals two primary pathways: (1) improved accessibility draws employment toward transit-rich areas, reinforcing the attractiveness of central districts; (2) rising housing prices elevate residential thresholds, pushing lower-income populations toward outer suburbs. While enhanced transit density improves commuting convenience, it does not effectively reduce jobs–housing separation. These findings offer important policy implications for optimizing transit planning, improving jobs–housing alignment, and promoting sustainable urban development. Full article

The optimization of land development requires a deep understanding of end-user expectations to ensure that new residential environments are both market-responsive and socially sustainable. This paper presents a novel prioritization-based technique for identifying and ranking property features according to buyer preferences. Using the MoSCoW method in combination with conjoint analysis, the study evaluates the relative importance of various housing attributes, such as layout, number of rooms, access to transportation, and availability of parking or green areas. The results provide structured insights into demand-side priorities and offer actionable guidelines for developers, urban planners, and decision-makers engaged in land use planning. By linking individual housing preferences with broader planning strategies, the proposed framework contributes to the creation of better-aligned, user-centric urban developments. The approach is tested on a local property market, and its potential applications in strategic zoning, infrastructure placement, and residential density modeling are discussed. Full article

Heat waves affect the health and quality of life of older adults, particularly in urban environments. However, there is limited understanding of how extreme temperatures influence their mobility. This research aims to understand the pedestrian mobility patterns of older adults during heat waves in Madrid, analyzing environmental and sociodemographic factors that condition such mobility. Geospatial data from the mobile phones of individuals aged 65 and older were analyzed, along with information on population, housing, urban density, green areas, and facilities during July 2022. Multiple linear regression models and Moran’s I spatial autocorrelation were applied. The results indicate that pedestrian mobility among older adults decreased by 7.3% during the hottest hours, with more pronounced reductions in disadvantaged districts and areas with limited access to urban services. The availability of climate shelters and health centers positively influenced mobility, while areas with a lower coverage of urban services experienced greater declines. At the district level, inequalities in the availability of urban infrastructure may exacerbate the vulnerability of older adults to extreme heat. The findings underscore the need for urban policies that promote equity in access to infrastructure and services that mitigate the effects of extreme heat, especially in disadvantaged areas. Full article

The COVID-19 pandemic has profoundly influenced urban planning and disease management in residential areas. Focusing on Beijing as a case study (3898 communities), this research develops a big data analytics framework integrating anonymized mobile phone signals (China Mobile), location-based services (AMAP.com), and municipal health records to quantify COVID-19 transmission dynamics. Using logistic regression, we analyzed 15 indicators across four dimensions: mobility behavior, host demographics, spatial characteristics, and facility accessibility. Our analysis reveals three key determinants: (1) Population aged 65 and above (OR = 62.8, p < 0.001) and (2) housing density (OR = 9.96, p = 0.026) significantly increase transmission risk, while (3) population density exhibits a paradoxical negative effect (β = −3.98, p < 0.001) attributable to targeted interventions in high-density zones. We further construct a validated risk prediction model (AUC = 0.7; 95.97% accuracy) enabling high-resolution spatial targeting of non-pharmaceutical interventions (NPIs). The framework provides urban planners with actionable strategies—including senior activity scheduling and ventilation retrofits—while advancing scalable methodologies for infectious disease management in global urban contexts. Full article

Urban parks are essential public resources that contribute significantly to residents’ well-being. However, disparities in the spatial distribution and social benefits of urban parks remain a pressing issue. This study focuses on the central urban area of Shanghai, a representative high-density megacity, and its findings hold significant reference value for similar cities, systematically evaluating urban park services from the perspectives of accessibility, spatial equity, and social equity. Leveraging multi-source big data and enhanced analytical methods, this study examines disparities and spatial mismatches in park services. By incorporating dynamic data, such as actual visitor attendance and residents’ travel preferences, and improving analytical models, such as an enhanced Gaussian two-step floating catchment area method and spatial lag regression models, this research significantly improves the accuracy and reliability of its findings. Key findings include (1) significant variations in accessibility exist across different types of parks, with regional and city parks offering better accessibility compared to pocket parks and community parks. (2) Park resources are unevenly distributed, with neighborhoods within the inner ring exhibiting relatively low overall accessibility. (3) A spatial mismatch is observed between park accessibility and housing prices, highlighting equity concerns. The dual spatial-social imbalance phenomenon reveals the prevalent contradiction in rapidly urbanizing areas where public service provision lags behind land development. Based on these results, this study proposes targeted recommendations for optimizing urban park layouts, including increasing the supply of small parks in inner-ring areas, enhancing the multifunctionality of parks, and strengthening policy support for disadvantaged communities. These findings contribute new theoretical insights into urban park equity and fine-grained governance while offering valuable references for urban planning and policymaking. Full article

Ride-hailing services have reshaped urban commuting patterns, yet the spatiotemporal mechanisms linking built environment features to ride-hailing demand remain underexplored. Existing studies often overlook the joint effects of origin–destination visual walkability. This study integrates ride-hailing GPS trajectories and geospatial data to quantify mobility patterns and built-environment indicators in Chengdu, China. A dual analytical framework combining global regression and localized modeling was applied to disentangle spatial–temporal influences of urban form and socioeconomic factors. The results reveal that population density, floor–area ratio, and housing prices positively correlate with demand, while road density and distance to city center exhibit negative associations. Visual walkability metrics show divergent effects: psychological greenery and pavement visibility reduce ride-hailing usage, whereas outdoor enclosure enhances it. Temporal analysis identifies time-dependent impacts of built environment variables on main urban area travel. Housing price effects demonstrate spatial globality, while population density and city-center proximity exhibit geographically bounded correlations. Notably, improved visual walkability in specific zones reduces reliance on ride-hailing by facilitating sustainable alternatives. These findings provide empirical support for optimizing urban infrastructure and land-use policies to promote equitable mobility systems. The proposed methodology offers a replicable framework for assessing transportation–land-use interactions, informing targeted interventions to achieve metropolitan sustainability goals through coordinated spatial planning and pedestrian-centric design. Full article

Urban vitality (UV) is a critical indicator for measuring the level of sustainable urban development, closely associated with environmental factors such as population density, economic activity, and spatial utilization efficiency. However, traditional methods face significant limitations in capturing the heterogeneity and nonlinear relationships between urban vitality and its influencing factors. This study suggests an interpretable machine learning framework to address the aforementioned issues. It combines a gradient boosting decision tree (GBDT) model with the SHapley Additive exPlanation (SHAP) framework to examine the urban vitality distribution characteristics and factors that influence them in Beijing’s fifth ring road. The main findings include the following: Urban vitality within Beijing’s fifth ring road exhibits significant spatial clustering and positive correlations, with clear spatial heterogeneity. The plot ratio (PR) exerts a notable positive influence on urban vitality, while green space accessibility (DG) demonstrates the strongest negative impact. The building density (BD), in contrast, shows a strong negative correlation with urban vitality. Variables such as the NDVI, average housing price (AHP), and road network density (RND) contribute significantly to urban vitality, reflecting the combined effects of vegetation coverage, economic conditions, and transportation layout. The findings provide a quantitative analytical tool for urban planning, facilitating resource optimization, improving urban vitality, and supporting scientific and rational decision-making. Full article

Modern cities are facing increasing challenges such as traffic congestion, high energy consumption, and poor air quality, making rail transit systems, known for their high capacity and low emissions, essential components of sustainable urban infrastructure. While numerous studies have examined how the built environment impacts transit ridership, the complex interactions among these factors warrant further investigation. Recent advancements in the reasoning capabilities of large language models (LLMs) offer a robust methodological foundation for analyzing the complex joint influence of multiple built environment factors. LLMs not only can comprehend the physical meaning of variables but also exhibit strong non-linear modeling and logical reasoning capabilities. This study introduces an LLM-based framework to examine how built environment factors and station characteristics shape the transit ridership dynamics by utilizing DeepSeek-R1. We develop a 4D + N variable system for a more nuanced description of the built environment of the station area which includes density, diversity, design, destination accessibility, and station characteristics, leveraging multi-source data such as points of interest (POIs), road network data, housing prices, and population data. Then, the proposed approach is validated using data from Qingdao, China, examining both single-factor and multi-factor effects on transit peak-hour ridership at the macro level (across all stations) and the meso level (specific station types). First, the variables that have a substantial effect on peak-hour transit ridership at both the macro and meso levels are discussed. Second, key and latent factor combinations are identified. Notably, some factors may appear to have limited importance at the macro level, yet they can substantially influence the peak-hour ridership when interacting with other factors. Our findings enable policymakers to formulate a balanced mix of soft and hard policies, such as integrating a flexitime policy with enhancements in active travel infrastructure to increase the attractiveness of public transit. The proposed analytical framework is adaptable across regions and applicable to various transportation modes. These insights can guide transportation managers and policymakers while optimizing Transit-Oriented Development (TOD) strategies to enhance the sustainability of the entire transportation system. Full article

Urban regeneration is pivotal to sustainable development, requiring innovative strategies that align social dynamics with spatial configurations. Traditional paradigms increasingly fail to tackle systemic challenges—neighborhood alienation, social fragmentation, and resource inequality—due to their inability to integrate human-centered spatial governance. This study addresses these shortcomings with a novel multidimensional framework that merges social perception (life satisfaction) analytics with spatial quality (GIS-based) assessment. At its core, we utilize geospatial and machine learning models, deploying an ensemble of Gradient Boosted Decision Trees (GBDT), Random Forest (RF), and multiscale geographically weighted regression (MGWR) to decode nonlinear socio-spatial interactions within Suzhou’s community environmental matrix. Our findings reveal critical intersections where residential density thresholds interact with commercial accessibility patterns and transport network configurations. Notably, we highlight the scale-dependent influence of educational proximity and healthcare distribution on community satisfaction, challenging conventional planning doctrines that rely on static buffer-zone models. Through rigorous spatial econometric modeling, this research uncovers three transformative insights: (1) Urban environment exerts a dominant influence on life satisfaction, accounting for 52.61% of the variance. Air quality emerges as a critical determinant, while factors such as proximity to educational institutions, healthcare facilities, and public landmarks exhibit nonlinear effects across spatial scales. (2) Housing price growth in Suzhou displays significant spatial clustering, with a Moran’s I of 0.130. Green space coverage positively correlates with price appreciation (β = 21.6919 ***), whereas floor area ratio exerts a negative impact (β = −4.1197 ***), highlighting the trade-offs between density and property value. (3) The MGWR model outperforms OLS in explaining housing price dynamics, achieving an R² of 0.5564 and an AICc of 11,601.1674. This suggests that MGWR captures 55.64% of pre- and post-pandemic price variations while better reflecting spatial heterogeneity. By merging community-expressed sentiment mapping with morphometric urban analysis, this interdisciplinary research pioneers a protocol for socio-spatial integrated urban transitions—one where algorithmic urbanism meets human-scale needs, not technological determinism. These findings recalibrate urban regeneration paradigms, demonstrating that data-driven socio-spatial integration is not a theoretical aspiration but an achievable governance reality. Full article

Traditional Japanese architecture is known for its open, ambiguous spatial boundaries (“kyokai”), which integrate nature and dwelling through Zen/Shinto philosophies. Yet modern urban housing, driven by high-density minimalism, flattens spatial hierarchies and erodes these rich boundary concepts. This study aims to explore how Japanese architect Yoshiji Takehara reinterprets traditional spatial principles to reconstruct the interior–exterior relationships in modern housing through a mixed-methods approach—including a literature review, case studies, and semi-structured interviews—verifying the hypothesis that he achieves the modern translation of traditional “kyokai” through strategies of boundary expansion and ambiguity. Analyzing 78 independent residential projects by Takehara and incorporating his interview texts, the research employs spatial typology and statistical methods to quantify the characteristics of boundary configurations, such as building contour morphology, opening orientations, and transitional space types, to reveal the internal logic of his design strategies. This study identifies two core strategies through which Takehara redefines spatial boundaries: firstly, clustered building layouts, multi-directional openings, and visual connections between courtyards and private functional spaces extend interface areas, enhancing interactions between nature and daily life; secondly, in-between spaces like corridors and doma (earthen-floored transitional zones), double-layered fixtures, and floor-level variations blur physical and psychological boundaries, creating multilayered permeability. Case studies demonstrate that his designs not only inherit traditional elements such as indented plans and semi-outdoor buffers but also revitalize the essence of “dwelling” through contemporary expressions, achieving dynamic visual experiences and poetic inhabitation within limited sites via complex boundary configurations and fluid thresholds. This research provides reusable boundary design strategies for high-density urban housing, such as multi-directional openings and buffer space typologies, and fills a research gap in the systematic translation of traditional “kyokai” theory into modern architecture, offering new insights for reconstructing the natural connection in residential spaces. Full article

In an increasingly urbanized world, biodiversity, and more specifically, birdlife located in urbanized ecosystems, faces several threats. Among these, noise pollution has proven to be one of the most significant, as it affects the effectiveness and efficiency of acoustic communication. We studied the relationship between noise and the diversity and abundance of birds breeding in urban areas in the central region of the Iberian Peninsula (Spain). We analyzed how species diversity and density varied across three levels of noise pollution (high, medium, and low). Species diversity decreased in areas with high noise pollution as compared to sites with medium and low levels of noise. We analyzed the density of the most frequent species found within each category. We identified eight additional noise-tolerant species whose density had significantly increased in environments with high levels of noise (e.g., Blackbird, Eurasian Tree Sparrow, and the Coal Tit). The ten most sensitive species, such as the Common Linnet, House Sparrow, and the European Greenfinch, had significantly decreased densities when the level of noise increased. Identifying the sensitivity (the effect) of urban bird species to acoustic pollution is vital for effective conservation management measures and for the sustainable planning and management of cities. Full article

This work studies the presence and evolution of wood construction in urban environments, using Santiago province in Chile as a relevant comparative case. The first part of the study analyzes the spatial and temporal distribution of wood-based structures in Santiago, showing that although wood has historically been used in low-rise housing, its presence has declined significantly due to increasing urban densification and the widespread adoption of materials like concrete for taller buildings. Currently, only 5.4% of Santiago’s buildings use wood structures, with their presence notably decreasing in the high-density municipalities of the city. Recent construction trends in Santiago show that the average building height is 12 stories, with timber buildings not exceeding 6 stories, despite the absence of specific restrictions in the building code for tall timber structures. The second part of this study contrasts these trends with the global development of tall timber buildings (six stories or more), which total approximately 300 worldwide as of 2024. The leading cities include Paris (with over 35 buildings) and London (over 17), followed by Zürich, Vancouver, and Portland. This study highlights the pivotal role of wood promotion policies in enabling this global expansion. Finally, a five-phase classification is proposed to evaluate the evolution of tall timber construction in a given city, emphasizing the role of public policy in enabling large-scale adoption, especially for cities such as Santiago. Full article