Search Results (1,054)

Unsupervised feature selection aims to characterize the cluster structure of original features and select the optimal subset without label guidance. However, existing methods overlook fuzzy information in the data, failing to model cluster structures between data effectively, and rely on squared error for data reconstruction, exacerbating noise impact. Therefore, a robust unsupervised feature selection algorithm based on fuzzy anchor graphs (FWFGFS) is proposed. To address the inaccuracies in neighbor assignments, a fuzzy anchor graph learning mechanism is designed. This mechanism models the association between nodes and clusters using fuzzy membership distributions, effectively capturing potential fuzzy neighborhood relationships between nodes and avoiding rigid assignments to specific clusters. This soft cluster assignment mechanism improves clustering accuracy and the robustness of the graph structure while maintaining low computational costs. Additionally, to mitigate the interference of noise in the feature selection process, an adaptive fuzzy weighting mechanism is presented. This mechanism assigns different weights to features based on their contribution to the error, thereby reducing errors caused by redundant features and noise. Orthogonal tri-factorization is applied to the low-dimensional representation matrix. This guarantees that each center represents only one class of features, resulting in more independent cluster centers. Experimental results on 12 public datasets show that FWFGFS improves the average clustering accuracy by 5.68% to 13.79% compared with the state-of-the-art methods. Full article

Numerous studies have demonstrated that in residential areas of Western cities, both luxury and legacy effects significantly shape tree species diversity dynamics. However, the specific mechanisms driving these diversity patterns in China, where urbanization has progressed at an unprecedented pace, remain poorly understood. In this study we selected 20 residential settlements and 7 key socio-economic properties to investigate the change trend of tree diversity (2006–2021) and its socio-economic driving factors in Beijing. Our results demonstrate significant increases in total, native, and exotic tree species richness between 2006 and 2021 (p < 0.05), with average increases of 36%, 26%, and 55%, respectively. Total and exotic tree Shannon-Wiener indices, as well as exotic tree Simpson’s index, were also significantly higher in 2021 (p < 0.05). Housing prices was the dominant driver shaping total and exotic tree diversity, showing significant positive correlations with both metrics. In contrast, native tree diversity exhibited a strong positive association with neighborhood age. Our findings highlight two dominant mechanisms: legacy effect, where older neighborhoods preserve native diversity through historical planting practices, and luxury effect, where affluent communities drive exotic species proliferation through ornamental landscaping initiatives. These findings elucidate the dual dynamics of legacy conservation and luxury-driven cultivation in urban forest development, revealing how historical contingencies and contemporary socioeconomic forces jointly shape tree diversity patterns in urban ecosystems. Full article

Semantic change detection technology based on remote sensing data holds significant importance for urban and rural planning decisions and the monitoring of ground objects. However, simple convolutional networks are limited by the receptive field, cannot fully capture detailed semantic information, and cannot effectively perceive subtle changes and constrain edge information. Therefore, a dynamic graph reasoning network with layer-by-layer semantic decomposition for semantic change detection in remote sensing data is developed in response to these limitations. This network aims to understand and perceive subtle changes in the semantic content of remote sensing data from the image pixel level. On the one hand, low-level semantic information and cross-scale spatial local feature details are obtained by dividing subspaces and decomposing convolutional layers with significant kernel expansion. Semantic selection aggregation is used to enhance the characterization of global and contextual semantics. Meanwhile, the initial multi-scale local spatial semantics are screened and re-aggregated to improve the characterization of significant features. On the other hand, at the encoding stage, the weight-sharing approach is employed to align the positions of ground objects in the change area and generate more comprehensive encoding information. Meanwhile, the dynamic graph reasoning module is used to decode the encoded semantics layer by layer to investigate the hidden associations between pixels in the neighborhood. In addition, the edge constraint module is used to constrain boundary pixels and reduce semantic ambiguity. The weighted loss function supervises and optimizes each module separately to enable the network to acquire the optimal feature representation. Finally, experimental results on three open-source datasets, such as SECOND, HIUSD, and Landsat-SCD, show that the proposed method achieves good performance, with an SCD score reaching 35.65%, 98.33%, and 67.29%, respectively. 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

This study investigates the heavy metal contamination in urban and peri-urban soils of Thessaloniki, Greece, over a two-year period (2023–2024). A total of 208 composite soil samples were systematically collected from 52 sites representing diverse land uses, including high-traffic roadsides, industrial zones, residential neighborhoods, parks, and mixed-use areas, with sampling conducted both after the wet (winter) and dry (summer) seasons. Soil physicochemical properties (pH, electrical conductivity, texture, organic matter, and calcium carbonate content) were analyzed alongside the concentrations of heavy metals such as Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn. A pollution assessment employed the Geoaccumulation Index (I_geo), Contamination Factor (Cf), Pollution Load Index (PLI), and Potential Ecological Risk Index (RI), revealing variable contamination levels across the city, with certain hotspots exhibiting a considerable to very high ecological risk. Multivariate statistical analyses (PCA and HCA) identified distinct anthropogenic and geogenic sources of heavy metals. Health risk assessments, based on USEPA models, evaluated non-carcinogenic and carcinogenic risks for both adults and children via ingestion and dermal contact pathways. The results indicate that while most sites present low to moderate health risks, specific locations, particularly near major transport and industrial areas, pose elevated risks, especially for children. The findings underscore the need for targeted monitoring and remediation strategies to mitigate the ecological and human health risks associated with urban soil pollution in Thessaloniki. Full article

Substance use is a critical public health issue in the U.S., with Black communities, particularly those with criminal justice contact, disproportionately affected. Chronic exposure to stressors can lead to substance use as a coping strategy. This study used data from 1476 Black adults with criminal justice involvement from the National Survey of American Life to examine how psychosocial stress and sleep disturbances relate to lifetime substance use and to determine if there are any sex differences. Sex-separate generalized linear models for a Poisson distribution with a log-link function estimated prevalence ratios and adjusted prevalence ratios (APRs) for lifetime alcohol abuse, lifetime cigarette, and marijuana use. Independent variables include stressors (family, person, neighborhood, financial, and work-related) and sleep problems, with covariates such as age, SES, and marital status. Lifetime alcohol abuse was associated with family stressors (APR = 2.72) and sleep problems (APR = 3.36) for males, and financial stressors (APR = 2.75) and sleep problems (APR = 2.24) for females. Cigarette use was linked to family stressors (APR = 1.73) for males and work stressors (APR = 1.78) for females. Marijuana use was associated with family stressors (APR = 2.31) and sleep problems (APR = 2.07) for males, and neighborhood stressors (APR = 1.72) for females. Lifetime alcohol abuse, as well as lifetime cigarette and marijuana use, was uniquely associated with various psychosocial stressors among Black adult males and females with criminal justice contact. These findings highlight the role of structural inequities in shaping substance use and support using a Social Determinants of Health framework to address addiction in this population. Full article

Among the existing challenges in the field of hyperspectral imaging, the need to optimize memory usage and computational capacity in material detection methods stands out, given the vast amount of data associated with the hundreds of reflectance bands. In line with this, this article proposes a comparative study on the effectiveness and efficiency of five computational methods for detecting composite material asbestos cement (AC) in hyperspectral images: correlation, spectral differential similarity (SDS), Fourier phase similarity (FPS), area under the curve (AUC), and decision trees (DT). The novelty lies in the comparison between the first four methods, which represent the spectral proximity method and a machine learning method, such as DT. Furthermore, SDS and FPS are novel methods proposed in the present document. Given the accuracy that detection methods based on supervised learning have demonstrated in material identification, the results obtained from the DT model were compared with the percentage of AC detected in a hyperspectral image of the Manga neighborhood in the city of Cartagena by the other four methods. Similarly, in terms of computational efficiency, a 20 × 20 pixel region with 380 bands was selected for the execution of multiple repetitions of each of the five computational methods considered, in order to obtain the average processing time of each method and the relative efficiency of the methods with respect to the method with the best effectiveness. The decision tree (DT) model achieved the highest classification accuracy at 99.4%, identifying 11.44% of asbestos cement (AC) pixels in the reference image. However, the correlation method, while detecting a lower percentage of AC pixels (9.72%), showed the most accurate visual performance and had no spectral overlap, with a 1.4% separation between AC and non-AC pixels. The SDS method was the most computationally efficient, running 23.85 times faster than the DT model. The proposed methods and results can be applied to other hyperspectral imaging tasks involving material identification in urban environments, especially when balancing accuracy and computational efficiency is essential. Full article

Form-based design codes have emerged as a planning tool aimed at shaping the physical form of neighborhoods to reinforce local character and enhance sense of place (SoP). However, their effectiveness in delivering these outcomes remains underexplored. This study investigates the extent to which Buffalo’s Green Code—a form-based zoning ordinance—enhances SoP in residential environments, using Elmwood Village as a case study. A multi-scalar analytical framework assesses SoP at the building, street, and neighborhood levels. Empirical data were gathered through an online survey, while the neighborhood was systematically mapped into street segment blocks categorized by Green Code zoning. The study consolidates six Green Code classifications into three overarching categories: mixed-use, residential, and single-family. SoP satisfaction is analyzed through a two-step process: first, comparative assessments are conducted across the three zoning groups; second, k-means clustering is applied to spatially map satisfaction levels and evaluate SoP at different scales. Findings indicate that mixed-use areas are most closely associated with place identity, while residential and single-family zones (as defined by the Buffalo Green Code) yield higher satisfaction overall—though satisfaction varies significantly across spatial scales. These results suggest that while form-based codes can strengthen SoP, their impact is uneven, and more scale-sensitive zoning strategies may be needed to optimize their effectiveness in diverse urban contexts. This research overall offers an empirically grounded, multi-scalar assessment of zoning impacts on lived experience—addressing a notable gap in the planning literature regarding how form-based codes perform in established, rather than newly developed, neighborhoods. Full article

Hyperspectral sparse unmixing (SU) networks have recently received considerable attention due to their model hyperspectral images (HSIs) with a priori spectral libraries and to capture nonlinear features through deep networks. This method effectively avoids errors associated with endmember extraction, and enhances the unmixing performance via nonlinear modeling. However, two major challenges remain: the use of large spectral libraries with high coherence leads to computational redundancy and performance degradation; moreover, certain feature extraction models, such as Transformer, while exhibiting strong representational capabilities, suffer from high computational complexity. To address these limitations, this paper proposes a hyperspectral unmixing dual-branch network integrating an adaptive hop-aware GCN and neighborhood offset Mamba that is termed DGMNet. Specifically, DGMNet consists of two parallel branches. The first branch employs the adaptive hop-neighborhood-aware GCN (AHNAGC) module to model global spatial features. The second branch utilizes the neighborhood spatial offset Mamba (NSOM) module to capture fine-grained local spatial structures. Subsequently, the designed Mamba-enhanced dual-stream feature fusion (MEDFF) module fuses the global and local spatial features extracted from the two branches and performs spectral feature learning through a spectral attention mechanism. Moreover, DGMNet innovatively incorporates a spectral-library-pruning mechanism into the SU network and designs a new pruning strategy that accounts for the contribution of small-target endmembers, thereby enabling the dynamic selection of valid endmembers and reducing the computational redundancy. Finally, an improved ESS-Loss is proposed, which combines an enhanced total variation (ETV) with an $l_{1/2}$ sparsity constraint to effectively refine the model performance. The experimental results on two synthetic and five real datasets demonstrate the effectiveness and superiority of the proposed method compared with the state-of-the-art methods. Notably, experiments on the Shahu dataset from the Gaofen-5 satellite further demonstrated DGMNet’s robustness and generalization. Full article

Property crime is a pressing issue in maintaining social order and urban sustainability, particularly in regions marked by pronounced socioeconomic disparity. While the link between socioeconomic stress and crime is well established, regional variations in Thailand have not been fully examined. Therefore, the purpose of this research was to examine spatial patterns of property crime and identify the potential associations between property crime and socioeconomic environment across Thailand. Using nationally compiled property-crime data from official sources across all provinces of Thailand, we employed geographic information system (GIS) tools to conduct a spatial cluster analysis at the sub-national level across 76 provinces. Both global and local statistical techniques were applied to identify spatial associations between property-crime rates and neighborhood-level socioeconomic conditions. The results revealed that property-crime clusters are primarily concentrated in the south, while low-crime areas dominate parts of the north and northeast regions. To analyze the spatial dynamics of property crime, we used geospatial statistical models to investigate the influence of socioeconomic variables across provinces. We found that property-crime rates were significantly associated with monthly income, areas experiencing high levels of household debt, migrant populations, working-age populations, an uneducated labor force, and population density. Identifying associated factors and mapping geographic regions with significant spatial clusters is an effective approach for determining where issues concentrate and for deepening understanding of the underlying patterns and drivers of property crime. This study offers actionable insights for enhancing safety, resilience, and urban sustainability in Thailand’s diverse regional contexts by highlighting geographies of vulnerability. Full article

Objectives: The aim of this systematic review is to analyze the research journals, sample characteristics and research methodology used in the studies about school neighborhood built environment (SNBE) and the physical health of children and adolescents. Methods: Using 124 key terms across four databases (Web of Science, PubMed, Sportdiscus and Transportation Research Board), 8837 studies were identified, and 55 were selected. The research question and evidence search were guided by the “Population, Intervention, Comparison, Outcomes” (PICO) framework. Results: Most studies were published in health-related research journals (67.3%) and conducted in 16 countries, primarily urban contexts (44.4%). Cross-sectional designs dominated (89.1%), with participation ranging from a minimum of 7 schools and 94 students to a maximum of 6362 schools and 979,119 students. Street network distances are often defined by 1000 or 800 m. The SNBE variables (135 total) were often measured via GIS (67.2%). In contrast, 70.6% of the 45 physical health measures relied on self-reports. Conclusions: This systematic review highlights the diverse approaches, gaps, and common patterns in studying the association between the SNBE and the physical health of children and adolescents. Therefore, this manuscript may serve as a valuable resource to examine the current landscape of knowledge and to guide future research on this topic. Full article

Understanding remote sensing imagery is vital for object observation and planning. However, the acquisition of optical images is inevitably affected by shadows and occlusions, resulting in local discrepancies in object representation. To address these challenges, this paper proposes AFNE-Net, a general network architecture for remote sensing image segmentation. First, the model introduces an attention-based feature fusion module. Through the use of weighted fusion of multi-resolution features, this effectively expands the receptive field and enhances semantic associations between categories. Subsequently, a feature enhancement module based on the consistency of neighborhood semantic representation is introduced. This aims to improve the feature representation and reduce segmentation errors caused by local perturbations. Finally, evaluations are conducted on the ISPRS Potsdam, UAVid, and LoveDA datasets to verify the effectiveness of the proposed model. Full article

Urban vacant land can provide important habitat for birds, especially in cities with high concentrations of residential vacancy. Understanding which vegetation features best support urban biodiversity can inform greening strategies that benefit both wildlife and residents. This study addressed two questions: (1) How does bird species composition reflect the potential conservation value of these neighborhoods? (2) Which vegetation structures predict bird abundance across a fine-grained urban landscape? To answer these questions, we conducted avian and vegetation surveys across 100 one-hectare plots in St. Louis, Missouri, USA. These surveys showed that species richness was positively associated with canopy cover (β = 0.32, p = 0.003). Canopy cover was also the strongest predictor of American Robin (Turdus migratorius) and Northern Cardinal (Cardinalis cardinalis) abundance (β = 1.9 for both species). In contrast, impervious surfaces and abandoned buildings were associated with generalist species. European Starling (Sturnus vulgaris) abundance was strongly and positively correlated with NMS Axis 1 (r = 0.878), while Chimney Swift (Chaetura pelagica) abundance was negatively correlated (r = −0.728). These findings underscore the significance of strategic habitat management in promoting urban biodiversity and addressing ecological challenges within urban landscapes. They also emphasize the importance of integrating biodiversity goals into urban planning policies to ensure sustainable and equitable development. Full article

Background: Suicidal ideation among adolescents remains a major public health challenge. Exposure to neighborhood violence is associated with increased risk of mental health distress and school-related vulnerabilities. This study investigates the predictors of suicidal ideation among U.S. adolescents who have witnessed neighborhood violence. Methods: Data were drawn from the 2023 Youth Risk Behavior Survey (YRBS), a nationally representative survey of high school students in the United States. A subsample of 3495 adolescents who reported witnessing neighborhood violence was analyzed. Key variables included sociodemographic characteristics, mental health symptoms, perceived school safety, and experiences of victimization. Multivariable logistic regression was used to identify factors associated with suicidal ideation, defined as seriously considering suicide in the past year. Analyses were conducted using Jamovi (version 2.6), with statistical significance set at p < 0.05. Results: The prevalence of suicidal ideation in the sample was 34.2%. Bisexual adolescents had significantly higher odds of suicidal ideation compared to heterosexual peers (OR = 2.34, p < 0.001). Depressive symptoms were the strongest predictor (OR = 7.51, p < 0.001). Both perceived lack of safety at school and differences in ethnic and population backgrounds were significant. Black and Hispanic/Latino adolescents had lower odds compared to White peers. Conclusions: Findings highlight sexual identity, depressive symptoms, school safety concerns, and ethnic and population background differences as key correlates of suicidal ideation. Culturally responsive, trauma-informed interventions are urgently needed for youth exposed to community violence. Full article

Most health studies use cross-sectional data to examine neighborhood context because of the difficulty of collecting and analyzing longitudinal data; this prevents an examination of historical trends that may influence health outcomes. Using the Neighborhood Change Database, we categorized longitudinal (1990–2010) poverty and White concentration trajectories (long-term low, long-term moderate, long-term high, increasing, or decreasing) for Texas census tracts and linked them to tract-level health-related characteristics (social determinants of health [SDOH] in 2010, health risk and preventive behaviors [HRPB] in 2017, and health status/outcomes [HSO] in 2017) from multiple sources (N = 2961 tracts). We conducted univariate and bivariate descriptive analyses, followed by linear regressions adjusted for population density. SDOH, HRPB, and HSO measures varied widely across census tracts. Both poverty and White concentration trajectories were strongly and consistently associated with a wide range of SDOH. Long-term high-poverty and low-White tracts showed the greatest disadvantages, while long-term low-poverty and high-White tracts had the most advantages. Neighborhoods undergoing changes in poverty or White concentrations, either increasing or decreasing, had less advantageous SDOH compared with long-term low-poverty or long-term high-White neighborhoods. While associations between poverty, White concentration trajectories, and SDOH were consistent, those with HRPB and HSO were less so. Understanding impact of the relationships between longitudinal neighborhood poverty and racial/ethnic composition on health can benefit stakeholders designing policy proposals and intervention strategies. Full article