Search Results (258)

Urban forests, as a form of green infrastructure, play a vital role in enhancing urban resilience, environmental health, and quality of life. However, users perceive and utilize these spaces in diverse ways. This study aims to identify latent perception types among urban forest visitors and analyze their behavioral, demographic, and policy-related characteristics in Incheon Metropolitan City (Republic of Korea). Using latent class analysis, four distinct visitor types were identified: multipurpose recreationists, balanced relaxation seekers, casual forest users, and passive forest visitors. Multipurpose recreationists preferred active physical use and sports facilities, while balanced relaxation seekers emphasized emotional well-being and cultural experiences. Casual users engaged lightly with forest settings, and passive forest visitors exhibited minimal recreational interest. Satisfaction with forest elements such as vegetation, facilities, and management conditions varied across visitor types and age groups, especially among older adults. These findings highlight the need for perception-based green infrastructure planning. Policy recommendations include expanding accessible neighborhood green spaces for aging populations, promoting community-oriented events, and offering participatory forest programs for youth engagement. By integrating user segmentation into urban forest planning and governance, this study contributes to more inclusive, adaptive, and sustainable management of urban green infrastructure. Full article

Spatial association analysis is essential for understanding interdependencies, spatial proximity, and distribution patterns within spatial data. The spatial scale is a key factor that significantly affects the result of spatial association mining. Traditional methods often rely on a fixed distance threshold (bandwidth) to define the scale effect, which can lead to scale sensitivity and discontinuity results. To address these limitations, this study introduces the Fuzzy Geographically Weighted Colocation Quotient (FGWCLQ) method. By integrating fuzzy theory, FGWCLQ replaces binary distance cutoffs with continuous membership functions, providing a more flexible and stable approach to spatial association mining. Using Point of Interest (POI) data from the Beijing urban area, FGWCLQ was applied to explore both intra- and inter-category spatial association patterns among star hotels, transportation facilities, and tourist attractions at different fuzzy neighborhoods. The results indicate that FGWCLQ can reliably discover global prevalent spatial associations among diverse facility types and visualize the spatial heterogeneity at various spatial scales. Compared to the deterministic GWCLQ method, FGWCLQ delivers more stable and robust results across varying spatial scales and generates more continuous association surfaces, which enable clear visualization of hierarchical clustering. Empirical findings provide valuable insights for optimizing the location of star hotels and supporting decision-making in urban planning. The method is available as an open-source Matlab package, providing a practical tool for diverse spatial association investigations. Full article

Detecting leakage using a point cloud acquired by mobile laser scanning (MLS) presents significant challenges, particularly from within three-dimensional space. These challenges primarily arise from the prevalence of noise in tunnel point clouds and the difficulty in accurately capturing the three-dimensional morphological characteristics of leakage patterns. To address these limitations, this study proposes a classification method based on XGBoost classifier, integrating both intensity and geometric features. The proposed methodology comprises the following steps: First, a RANSAC algorithm is employed to filter out noise from tunnel objects, such as facilities, tracks, and bolt holes, which exhibit intensity values similar to leakage. Next, intensity features are extracted to facilitate the initial separation of leakage regions from the tunnel lining. Subsequently, geometric features derived from the k neighborhood are incorporated to complement the intensity features, enabling more effective segmentation of leakage from the lining structures. The optimal neighborhood scale is determined by selecting the scale that yields the highest F_1-score for leakage across various multiple evaluated scales. Finally, the XGBoost classifier is applied to the binary classification to distinguish leakage from tunnel lining. Experimental results demonstrate that the integration of geometric features significantly enhances leakage detection accuracy, achieving an F_1-score of 91.18% and 97.84% on two evaluated datasets, respectively. The consistent performance across four heterogeneous datasets indicates the robust generalization capability of the proposed methodology. Comparative analysis further shows that XGBoost outperforms other classifiers, such as Random Forest, AdaBoost, LightGBM, and CatBoost, in terms of balance of accuracy and computational efficiency. Moreover, compared to deep learning models, including PointNet, PointNet++, and DGCNN, the proposed method demonstrates superior performance in both detection accuracy and computational efficiency. Full article

As network scale and demand rise, the Uncapacitated Facility Location Problem (UFLP), a classical NP-hard problem widely studied in operations research, becomes increasingly challenging for traditional methods confined to formulation, construction, and benchmarking. This work generalizes the UFLP to network setting in light of demand intensity and network topology. A new initialization technique called Network- and Demand-Weighted Roulette Wheel Initialization (NDWRWI) has been introduced and proved to be a competitive alternative to random (RI) and greedy initializations (GI). Experiments were carried out based on the TRB dataset and compared eight state-of-the-art methods. For instance, in the ultra-large-scale Gold Coast network, the NDWRWI-based Neighborhood Search (NS) achieved a competitive optimal total cost (9,372,502), closely comparable to the best-performing baseline (RI-based: 9,189,353), while delivering superior clustering quality (Silhouette: 0.3859 vs. 0.3833 and 0.3752 for RI- and GI-based NS, respectively) and reducing computational time by nearly an order of magnitude relative to the GI-based baseline. Similarly, NDWRWI-based Variable Neighborhood Search (VNS) improved upon RI-based baseline by reducing the overall cost by approximately 3.67%, increasing clustering quality and achieving a 27% faster runtime. It is found that NDWRWI prioritizes high-demand and centrally located nodes, fostering high-quality initial solutions and robust performance across large-scale and heterogeneous networks. Full article

Evaluating the walkability of embedded community service facilities is crucial for fostering age-friendly neighborhoods. Existing studies predominantly focus on qualitative analyses of single age groups, lacking a quantitative framework that integrates multigenerational demands and dynamically combines accessibility with facility usability. This study proposes a novel age-differentiated walkability evaluation framework through a case study of the Maple Community in Chengdu, China. The framework innovatively integrates facility classification weighting systems, population-specific distance decay functions, and multisource spatial data analysis to reveal intergenerational disparities in facility demand and spatial suitability. Key contributions include the following: (1) developing mobility-attenuated accessibility models tailored to distinct age groups, overcoming the limitations of traditional single-function approaches; (2) establishing a multidimensional evaluation system that bridges accessibility theory and usability metrics; (3) empirically demonstrating the spatial equity improvements through embedded facility optimization. The results highlight that elderly residents prioritize healthcare facilities (weight = 0.22), while adolescents rely heavily on cultural–recreational amenities (weight = 0.32). Post-renewal walkability scores in core areas increased by 52.3%, yet persistent peripheral disparities underscore intergenerational accessibility gaps. The framework provides theoretical and methodological advancements for age-friendly community planning, offering actionable insights for data-driven urban renewal policies. Full article

In aging residential neighborhoods, insufficient public spaces and a weakened sense of belonging have led to declining community vitality. Addressing the widespread idleness of boiler room facilities in cold-region contexts, this study integrates GPS tracking, Wi-Fi probe detection, questionnaire surveys, and field observations to develop a three-dimensional “space–time–behavior” evaluation model comprising five core indicators: activity type, spatial range, duration, frequency, and volatility. Unlike prior studies that rely on single data sources or unidimensional metrics, our multi-source approach enhances spatiotemporal resolution, improves the accuracy of subjective perceptions, and enables cross-validation between objective behavioral trajectories and residents’ self-reports, thereby significantly strengthening the comprehensiveness and reliability of community vitality measurement. The results show that the community service center conversion model maximizes spatial efficiency through functional integration, achieving a vitality score of 3.64—substantially higher than those for recreational renovations (3.16) and non-renovated sites (2.67). This model increases space utilization by 2.2-fold, sustains 12 h daily vitality, reduces residents’ activity radii by 38%, and boosts intergenerational interaction frequency by 43%, effectively bridging age group divides. We identify a “functional hybridization–spatial permeability–usage sustainability” mechanism underlying renovation efficacy and recommend the community service center paradigm as a priority strategy. The quantitative decision support framework established here offers empirical guidance for renewing existing spaces in severe climatic environments. Full article

Why do environmental regulators allow some facilities to discharge more water pollution than similar facilities in other locations? Drawing on general theories of regulatory decisionmaking, this study assesses four possible reasons: (1) variation in governments’ ability to export pollution to other political jurisdictions, (2) variation in the demographic composition of the neighborhood surrounding the facility, (3) variation in local communities’ ability to mobilize for effective collective action, and (4) variation in subnational political context. Analyses of effluent discharge limits imposed by the U.S. states on two common classes of water pollution indicate that state regulators allow greater discharges when the receiving river crosses state lines, and that they are responsive to a variety of other demographic and political factors. The specific factors, however, often vary across the two pollutants assessed. These results suggest that subnational political considerations may shape the conditions imposed in water pollution permits, and, ultimately, water quality outcomes. 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

Accurately assessing the accessibility and equity of urban public sports facilities is essential for improving public service provision and enhancing residents’ well-being. However, most existing studies rely on administrative units such as subdistricts and communities, often overlooking the multi-level structure of such facilities and failing to reflect their distribution within the spatial scope of residents’ daily activities. To address this gap, this study adopted the residential neighborhood as the basic unit of analysis and developed an integrated methodological framework combining the average nearest neighbor index, kernel density estimation, a Gaussian-based two-step floating catchment area method, the Gini coefficient, and location quotient analysis. When applied to Shanghai, the framework revealed distinct spatial patterns across facility levels, exhibiting scale-dependent characteristics. Community-level and residential-level sports facilities were found to be relatively accessible, whereas city-level and subdistrict-level sports facilities showed limited accessibility, particularly in peripheral suburbs. All facility levels exhibited varying degrees of spatial inequality, highlighting persistent issues of spatial justice. These findings provide empirical evidence to inform the spatial optimization of public sports facilities and to promote more equitable access to urban public services. Full article

Neighborhood facilities received global attention in sustainability implementation because they meet the daily needs of residents and represent the relationship between them, neighborhoods, and cities. This study aims to reveal the factors influencing sustainable facilities in Chinese neighborhoods under rapid urbanization through a systematic review of relevant empirical studies and the Chinese context. This review looks through the Scopus and WoS databases for 59 papers published between 1981 and 2025 on the implementation of sustainable neighborhood facilities; additional evidence was collected from neighborhood sustainability assessments (NSAs) conducted around the world and China’s current relevant regulations in neighborhood facilities for the literature review. The findings indicate that the influencing factors of sustainable neighborhood facilities are an intersubjective structure of providers, recipients, and coordinating mediators. This study extends the current knowledge and literature on sustainable neighborhood facilities and deepens our understanding of how sustainability implementation standards and decision-making influence these facilities to improve livability in China. Meanwhile, it also provides a perspective on four aspects, including the compilation of influencing factors, the development of assessment tools, the categorization assessment, and the development of a framework of sustainable neighborhood facilities in China. In addition, it further enriches sustainable cities and neighborhoods in applications worldwide. Full article

The rise in population and housing mobility presents significant challenges for fostering social cohesion. This study, grounded in consistency theory, leverages survey data collected from various Tianjin regions and adopts a combination of quantitative and qualitative research methods to examine the multilevel influence of public spaces, community facilities, and individual motivation on community participation intention (CPI). We assess regional variations and distinguish between public spaces and the accessibility and usability of service facilities. Logistic regression results reveal that (1) age, family type, community types, the usability of facilities, residents’ needs, and neighborhood attachment have significant effects on CPI; (2) residents’ requirements for education, healthcare, and elderly services are strongly correlated with CPI, and high-quality public spaces and facilities significantly increase place attachment; and (3) the usage of some facilities negatively affects CPI under the influence of contextual factors. The results indicate the relationship between the built environment and CPI. This research contributes to the theoretical framework linking community participation and person–environment fit (P–E fit). It provides evidence-based insights for policymakers to consider inclusive urban development in community renewal and grassroots governance. Full article

Modern and smart cities prioritize providing sufficient facilities for inclusive and bicycle-friendly streets. Several methods have been developed to assess city bicycle environments at street, neighborhood, and city levels. However, the importance of micro-level indicators and bicyclists’ perceptions cannot be neglected when developing a bikeability index (BI). Therefore, this paper proposes a new BI method for evaluating and providing suggestions for improving city streets, focusing on bicycle infrastructure facilities. The proposed BI is an analytical system aggregating multiple bikeability indicators into a structured index using weighed coefficients and scores. In addition, the study introduces bicycle infrastructure indicators using five bicycle design principles acknowledged in the literature, experts, and city authorities worldwide. A questionnaire was used to collect data from cyclists to find the weights and scores of the indicators. The survey of 383 participants showed a balanced gender distribution and a predominantly younger population, with most respondents holding bachelor’s or master’s degrees and 57.4% being students. Most participants travel 2–5 km per day and cycle 3 to 5 days per week. Among the criteria, respondents graded safety as the most important, followed by comfort on bicycle paths. Confirmatory factor analysis (CFA) is used to estimate weights of the bikeability indicators, with the values of the resultant factor loadings used as their weights. The highest-weight indicator was the presence of bicycle infrastructure (0.753), while the lowest-weight indicator was slope (0.302). The proposed BI was applied to various bike lanes and streets in Hasselt, Belgium. The developed BI is a useful tool for urban planners to identify existing problems in bicycle streets and provide potential improvements. Full article

The introduction of blockchain sharding technology in IIoT (Industrial Internet of Things) can leverage the scalability, decentralization, and immutability of blockchain to significantly enhance security. However, existing IIoT operations are highly correlated with business and geographical locations, as well as network conditions, making traditional random sharding schemes unsuitable for such scenarios. To address this issue, we propose the FLPShard model, a method that transforms the sharding problem into a single-source capacitated facility location problem (SSCFLP). FLPShard can handle multiple constraints simultaneously and allows multiple nodes to be bundled and allocated as a whole, meeting the demands of IIoT. By constructing neighborhoods during the dynamic adjustment process, we achieve dynamic incremental updates and automatic splitting of blockchain shards. We evaluated FLPShard by building a system prototype, and the results show that, compared to random sharding algorithms, FLPShard significantly increases system throughput and greatly reduces transaction latency. Full article

This study selected green spaces from three residential areas in Hefei as the research subjects, combining behavioral observation methods and a natural experiment to collect behavioral data from 2010 and 2024. The data were then compared using Poisson regression models. Additionally, home visits were conducted to gather residents’ perceptions of the factors contributing to the decline in vitality. Based on the survey data, multilevel regression analysis was performed to explore the decline in RQGS usage vitality and its influencing factors in the context of rapid urbanization. This study found a significant decline in green space visits, particularly during the afternoon (16:00–18:00) and in areas adjacent to roadways. The main influencing factors include emerging leisure choices (such as taking the subway to large parks or preferring indoor activities) and residents’ satisfaction with RQGS characteristics (such as functional zoning, noise pollution, and neighborhood familiarity). Notably, there was no significant correlation between “disposable leisure time” and visit frequency. These findings suggest that, despite the inherent advantages of proximity, the vitality of RQGS faces increasing challenges due to emerging diverse leisure demands and growing environmental disturbances. In contrast to the traditional emphasis on accessibility, this study recommends that future RQGS planning prioritize functional zoning (e.g., dog-walking areas, sports zones), address the needs of vulnerable groups, and focus on mitigating vehicle noise and air pollution rather than merely expanding parking facilities. Interventions should be scheduled for the afternoon and emphasize strengthening community interaction and cohesion to enhance user experience. This research provides valuable scientific evidence and practical guidance for urban planners and policymakers to optimize residential green spaces in the context of rapid urbanization, offering new perspectives for the empirical evaluation of RQGS upgrades. Full article

Safety, a critical component of sustainable development, necessitates an integrated approach in which urban planning assumes a central role. This study investigates the relationship between urban form and crime incidents in public spaces within the center of the city. This study was conducted in the city of Łódź, located in central Poland. Through geolocated data, this research explores crime incidents that, while not the most severe, disrupt public order and impact the overall quality of life. This study fills a gap in the existing literature by analyzing spatial variables such as urban vibrancy and the presence of alcohol outlets, alongside other urban elements. The analysis incorporates a variety of urban form variables, including land development indices, the functional layout of the urban neighborhood, pedestrian infrastructure, public space amenities, and facilities. Urban vibrancy, represented by the density of human activity, is also assessed in relation to crime incidents. The results indicate significant correlations between certain urban features and the occurrence of crime incidents, particularly the presence of public amenities and small businesses. While these findings suggest that urban design can influence crime rates, further panel and time-series regression analysis is needed to confirm these dynamics. Aligned with the 11th Sustainable Development Goal, this study provides insights that could inform urban planning strategies, offering recommendations to enhance both the functionality and safety of city centers. By understanding how urban design elements contribute to public safety, policymakers can develop more effective and targeted spatial planning strategies that promote not only aesthetics and functionality but also the well-being and security of residents. Full article