Search Results (80)

To advance evidence-based urban design prioritizing non-motorized mobility, this study proposes a street view-enabled explainable machine learning framework that systematically links built environment semantics to non-motorized transportation vitality optimization. By integrating Baidu Street View images with deep learning-based object detection (Faster R-CNN), we quantify fine-grained human-powered and mechanically assisted mobility vitality. These features are fused with multi-source geospatial data encompassing 23 built environment variables into an interpretable machine learning pipeline using SHAP-optimized random forest models. The key findings reveal distinct nonlinear response patterns between HP and MA modes to built environment factors; for instance, a notable promotion in mechanically assisted NMT vitality is observed as enterprise density increases beyond 0.2 facilities per ha. Emergent synergistic and threshold effects are evident from variable interactions requiring multidimensional planning consideration, as demonstrated in phenomena such as the peaking of human-powered NMT vitality occurring at public facility densities of 0.2–0.8 facilities per ha, enterprise densities of 0.6–1 facilities per ha, and spatial heterogeneity patterns identified through Bivariate Local Moran’s I clustering. This research contributes an innovative technical framework combining street view image recognition with explainable AI, while practically informing urban planning through evidence-based mobility zone classification and targeted strategy formulation, enabling more precise optimization of pedestrian-/cyclist-oriented urban spaces. Full article

This study aimed to identify spatial clustering and maternal and birth-related factors associated with the incidence of orofacial clefts in Brazil from 2001 to 2022. A nationwide ecological study was conducted in Brazil using data from 2001 to 2022 obtained from the Brazilian Live Birth Information System (SINASC). The municipality was used as the spatial unit of analysis. Variables included maternal age and education, newborn sex, gestational age, birth weight, and skin color/ethnicity. Univariate and bivariate global and local Moran’s I indices were used to assess spatial autocorrelation. A total of 234 municipalities (4.2%) formed high–high spatial clusters, primarily in the South and Southeast, while 431 municipalities (7.7%) formed low–low clusters, mostly in the Northeast (Moran’s I = 0.121, 95% CI: 0.107 to 0.135). High–high clusters had a lower median proportion of adolescent mothers (≤19 years: 17.4%) and a higher proportion of mothers aged ≥ 35 years (12.9%) compared to low–low clusters (23.5% and 8.7%, respectively; p < 0.001). High–high clusters also had fewer mothers with less than seven years of education (31.0% vs. 45.9%, p < 0.001) and higher rates of preterm births and low birth weight (p < 0.001). The proportion of White newborns was higher in high–high clusters than in low–low clusters (82.8% vs. 13.6%, p < 0.001). These findings suggest that orofacial cleft incidence in Brazil is spatially associated with maternal sociodemographic characteristics, perinatal outcomes, and newborn race/ethnicity. Full article

The match relationship between urbanization and ecosystem services (ESs) is a cornerstone of achieving sustainable development. However, the evolution patterns of urbanization/ecosystem service (UES) synergies under economic polarization in the rapid urbanization process remain poorly understood. This study integrates bivariate local Moran’s index and correlation analysis methods to examine the match relationship between urbanization and three key ESs (water yield, carbon sequestration, and food production) from 2000 to 2020 and explores the impact of intra-city disparities on the match relationship of urbanization and ESs. The findings revealed that urbanization and three ecosystem services showed increasing trends during 2000–2020 simultaneously. The spatial aggregation pattern of urbanization and ecosystem services showed smaller variations from 2000 to 2020. There was a High-High aggregation between urbanization and water yield in urban built-up areas and primarily High-Low aggregations between urbanization, carbon sequestration, and food production. Furthermore, the impact of urbanization on ESs decreased with increasing urban polarization. In particular, the Beijing–Tianjin–Tangshan region still demonstrated pronounced economic polarization, suggesting disparities in economic development within its urban core. This study highlights the importance of mitigating the adverse effects of urban polarization on ESs and fostering resilient and sustainable urban ecosystems in rapidly developing regions. Full article

The spatial morphology of traditional villages stems from prolonged interactions between socio-economic conditions and the regional natural environment under specific historical contexts. Over time, these settlements have acquired distinct spatial patterns through continuous adaptation to their surrounding ecosystems. Nevertheless, accelerated urbanization now exerts dual pressures—disrupting the spatial order and degrading natural ecosystems. In this context, an integrated analysis of the relationship between village spatial patterns and ecological conditions is essential for elucidating their formative mechanisms. The Xiangjiang River Basin is Hunan’s cultural core, and the spatial distribution of traditional villages is directly related to environmental variables. This study uses bivariate spatial autocorrelation and geographically weighted regression to investigate the relationship between the spatial distribution of traditional villages and ecological environmental appropriateness. The findings indicate the following: (1) The spatial distribution density of traditional villages in the Xiangjiang River Basin exhibits a negative correlation with the Ecological Environment Index (EEI), as evidenced by a Moran’s I value of −0.228. This suggests that traditional villages tend to be less concentrated in areas with a higher ecological suitability. (2) Among natural factors, the Relief Degree of Land Surface (RDLS), the Temperature Humidity Index (THI), and the Land Cover Index (LCI) display positive correlations with village density, with regression coefficients of 0.865, 0.003, and 11.599, respectively. In contrast, the Water Resource Index (WRI) shows a negative correlation, with a coefficient of −6.448, and (3) the impact of ecological suitability factors on village distribution is spatially heterogeneous: microtopographic variation is the primary driver in flat terrains, whereas the ecological carrying capacity exerts a greater influence in mountainous areas. These findings clarify the role of ecological suitability in shaping the spatial characteristics of traditional villages and provide a scientific basis for developing protection strategies that integrate ecological sustainability with cultural–heritage preservation. Full article

Amid rapid urbanization, cities are becoming increasingly compact, leading to intensified land resource constraints and environmental pressures. As a result, urban parks and green spaces have emerged as critical areas for providing cultural ecosystem services (CESs). However, the spatial distribution of CES supply and demand within urban green spaces remains significantly unbalanced, necessitating precise identification and quantification of CES supply–demand patterns to enhance ecosystem service efficiency. This study uses Nanjing, China, as a case study to develop an indicator framework for urban green space CES supply and demand, leveraging multi-source data. By employing spatial autocorrelation analysis (Bivariate Moran’s I) and a coupling coordination model, this research systematically assesses the spatial patterns of CESs in urban parks and green spaces. The results indicate that the overall CES supply–demand coordination in Nanjing exhibits a “high in the city center, low at the edges, and mismatched in the suburbs” pattern. Specifically, while 9.71% of the areas demonstrate well-matched CES supply and demand, 4.14% of the areas experience insufficient CES demand, and 3.66% face CES supply shortages, primarily in the urban outskirts, leading to a mismatch in green space distribution. This study further reveals the spatial heterogeneity of CES supply–demand matching across different urban districts. Based on these findings, this research proposes optimization strategies to improve CES allocation, providing a scientific basis for urban green space ecosystem service management and promoting the sustainable development of cities. Full article

An ecological restoration assessment aims to evaluate whether ecological restoration projects (ERPs) have achieved predefined ecological objectives, such as improving fractional vegetation cover (FVC) and enhancing ecosystem services (ESs), as well as to optimize restoration strategies based on assessment outcomes. Despite recent advancements, current studies still fall short of fully capturing the trade-offs among ESs and identifying the underlying drivers of different vegetation trends. To address these challenges, we applied the Theil–Sen method to delineate vegetation change zones in the Qilian Mountain National Park (QLMNP) between 2000 and 2020, employed bivariate Moran’s I statistics to analyze the trade-offs and synergies among four ESs within these zones, including carbon sequestration (CS), soil conservation (SC), water conservation (WC), and biodiversity maintenance (BIO), and utilized a spatial random forest (SRF) model to explore the main socio-ecological driving factors of vegetation trends and their spatial distribution. Our results revealed significant vegetation recovery in the QLMNP between 2000 and 2020, particularly in regions with initially low FVC. Positive trends in the CS, SC, and BIO highlighted the success of restoration efforts, primarily driven by land conversion to forests and increased precipitation. However, 8.82% of the QLMNP exhibited stagnation or degradation due to rising temperatures and overgrazing, leading to declines in the SC and BIO. Notably, vegetation restoration introduced trade-offs among the ESs, especially in the high FVC areas, where a strong trade-off emerged between FVC and WC. These findings highlight the need for refining restoration strategies to balance water resource allocation. Finally, we integrated vegetation trends, ES relationships, and driving factors to propose grid-based zonal governance plans for the QLMNP, prioritizing WC and FVC enhancement as critical components of future ecological planning. This study serves as a foundation for optimizing restoration strategies in the QLMNP, maintaining and enhancing ESs, while offering actionable insights for fine-grained restoration evaluation and sustainable development planning in other regions. Full article

Landslides pose a significant threat to the safety and stability of settlements in karst regions worldwide. The long-standing tight balance state of settlement funding and infrastructure makes it difficult to allocate disaster prevention resources effectively against landslide impacts. There is an urgent need to fully leverage the landscape resources of karst settlements and develop landslide risk prevention strategies that balance economic viability with local landscape adaptability. However, limited research has explored the differential resilience characteristics and patterns of landslide disaster risk and settlement landscapes from a spatial coupling perspective. This study, based on landslide disaster and disaster-adaptive landscape data from a typical karst province in China, employs the frequency ratio-random forest model and weighted variance method to construct landslide disaster risk (LDR) and disaster-adaptive landscape (DAL) base maps. The spatial characteristics of urban, urban–rural transition zones, and rural settlements were analyzed, and the resilience differentiation and driving factors of the LDR–DAL coupling relationship were assessed using bivariate spatial autocorrelation and geographical detector models. The key findings are as follows: (1) Urban and peri-urban settlements exhibit a high degree of spatial congruence in the differentiation of LDR and DAL, whereas rural settlements exhibit distinct divergence; (2) the Moran’s I index for LDR and DAL is 0.0818, indicating that urban and peri-urban settlements predominantly cluster in H-L and L-L types, whereas rural settlements primarily exhibit H-H and L-H patterns; (3) slope, soil organic matter, and profile curvature are key determinants of LDR–DAL coupling, with respective influence strengths of 0.568, 0.555, and 0.384; (4) in karst settlement development, augmenting local vegetation in residual mountain areas and parks can help maintain forest ecosystem stability, effectively mitigating landslide risks and enhancing disaster-adaptive capacity by 6.77%. This study helps alleviate the contradiction between high LDR and weak disaster-adaptive resources in the karst region of Southwest China, providing strategic references for global karst settlements to enhance localized landscape adaptation to landslide disasters. Full article

Chagas disease is a serious public health problem worldwide. In Brazil, the state of Pará has the largest number of reported cases. This article analyzes the spatial distribution of this disease and its relationship with socioeconomic, environmental, and public policy health variables in three mesoregions in the Pará state from 2013 to 2022. This ecological study used secondary data obtained from official Brazilian agencies. Spatial analysis was carried out using the flow, kernel, and bivariate global Moran techniques expressed in thematic maps. A total of 3664 cases of the disease were confirmed, with the highest number of cases being reported in the northeast of Pará. A seasonal pattern of the disease, an epidemiological profile similar to other diseases in the Amazon region, and the spatial dependence between the disease prevalence and socioeconomic indicators were observed. The most intense movement of patients for treatment was to the Belém metropolitan mesoregion, which has the majority of the health services and professionals. The disease showed an inhomogeneous pattern of cases in terms of the spatial distribution, with a direct relationship between areas with a higher number of cases and those with human clusters. The socioenvironmental origins of the disease transcend mesoregion boundaries and stem from the historically unsustainable development model in the Amazon. Full article

Determining the optimal vegetation cover threshold in a region for facilitating both high levels of ecosystem services (ESs) supply and synergistic sustainable development among different ESs is crucial. This study delineated the nonlinear relationship between the fractional vegetation cover (FVC) and the integrated synergy–supply capacity of ESs in Ji County, on China’s Loess Plateau (2000–2023). The FVC was quantified using Landsat remote sensing data. Assessments of carbon storage, soil conservation, water conservation, and habitat quality were conducted based on multi-source remote sensing datasets and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, which subsequently informed the evaluation of the integrated synergy–supply capacity of ESs. Spatial–temporal distribution characteristics were assessed via trend analysis methods and the spatial correlation relationship was assessed via bivariate local spatial autocorrelation analysis. The constraint line analysis and the restricted cubic spline method were combined to analyze the nonlinear relationship between the two and to quantify the FVC threshold. The results revealed that the spatial distribution of both the FVC and the integrated synergy–supply capacity of ESs was higher in the north, with a growth trend observed respectively. A highly significant positive spatial correlation existed between the two (Moran’s I > 0.6520, p < 0.01), dominated by the High–High agglomeration type (55.71%). The relationship between the regional FVC and the ISSC of ESs, the forest land FVC and the ISSC of ESs, and the grassland FVC and the ISSC of ESs all exhibited a positive convex function constraint line. The regional FVC threshold was 0.5, the forest land FVC threshold was 0.28, and the grassland FVC threshold was 0.77. When the FVC value was above the threshold, its facilitating effect on the ISSC of ESs diminished. This study advances vegetation threshold research by integrating the supply levels and synergy degrees of multiple ESs, providing a scientific foundation for formulating strategies for regional ecological restoration and adaptive management, and offering a reference for high-quality vegetation restoration in global arid, semi-arid, and erosion-prone regions. Full article

Land use conflicts represent an increasing challenge to sustainable development, particularly in regions undergoing rapid urbanization. This study investigated the spatiotemporal dynamics of land use conflicts and their ecological implications in Tianshui City from 1980 to 2020. The main objectives were to identify patterns of spatial heterogeneity, explore the driving factors behind these conflicts, and analyze their relationship with the ecological risks. The results indicate the following findings. In terms of spatiotemporal heterogeneity, early land use changes were primarily driven by structural factors, such as topography and climate, with a Nugget/Still ratio of <0.30 observed from 1980 to 2000. After 2000, however, stochastic factors, including an average annual urbanization rate increase of 5.2% and a GDP growth rate of 9.1%, emerged as the dominant drivers, as reflected in a Nugget/Still ratio > 0.36. Regarding conflict intensity, high-conflict areas expanded by approximately 1110 square kilometers between 1980 and 2020, predominantly concentrated in fertile agricultural regions such as the Weihe River Basin and urban core areas. Conversely, non-conflict zones decreased by 38.7%. In terms of ecological risk correlation, bivariate LISA cluster analysis revealed a significant spatial autocorrelation between severe land use conflicts and ecological risks (Moran’s I = 0.62, p < 0.01). High-risk clusters in areas transitioning from arable land to built-up land increased by 23% after 2000. Predictions based on the future land-use simulation (FLUS) model suggest that by 2030, high-intensity conflict areas will expand by an additional 16%, leading to intensified competition for land resources. Therefore, incorporating ecological safety thresholds into land spatial planning policies is essential for reconciling the conflicts between development and conservation, thereby promoting sustainable land use transitions. Full article

Rapid urbanization is causing ecological and environmental issues to worsen. The stability of the ecosystem function of the farming–pastoral ecotone (FPE) in Inner Mongolia is essential to ensuring the sustained growth of the nearby cities, acting as a vital ecological safeguard in China’s northern regions. This study used the “production–living–ecological” spaces (PLES) spatial dynamics, the rate of change index, and the standard deviation ellipse to examine the spatial and temporal evolution of the PLES in the FPE in Inner Mongolia. This study constructed a spatial conflict index model based on the theory of landscape ecology, and evaluated the ecosystem service value (ESV) of the region and visualized the results of the analysis using the micro-scale of the grid. Finally, the relationship between the ESV and PLES spatial conflicts was determined using a bivariate spatial autocorrelation model. The findings show that: (1) During the 20 years, the maximum ecological spatial change rate reached 0.43%, with the cumulative spatial dynamics of PLES totaling 2.49%. Notably, industrial production space activities experienced the most significant increase, amounting to 277.09%. (2) Regional spatial conflict intensity shows an upward trend from 2000 to 2020, with the average conflict level increasing from 0.53 to 0.56, and high conflict values being concentrated in the east. (3) The ESV pattern in the FPE in Inner Mongolia is characterized by “high ESV in the east and low ESV in the central and western regions”, with an overall trend of increasing and then decreasing. A notable negative correlation was observed between ESV and PLES spatial conflicts in the region, with Moran’s I indicating values of−0.196, −0.293, and−0.163, respectively. Specifically, low-value–high-conflict zones were predominantly found in other ecological spaces, high-value–low-conflict zones was concentrated in forest ecological spaces, and high-value–high-conflict zones were predominantly concentrated in aquatic ecological spaces. The research findings serve as a crucial scientific foundation for the development of ecological civilization and the sustainable advancement of the FPE in Inner Mongolia. Full article

With the acceleration of urbanization and due to the impact of climate warming, economic losses caused by urban waterlogging have become increasingly severe. To reduce urban waterlogging losses under the constraints of limited economic and time resources, it is essential to identify key waterlogging-prone areas for focused governance. Previous studies have often overlooked the spatial heterogeneity in the distribution of value and risk. Therefore, identifying the spatial distribution of land value and risk, and analyzing their spatial overlay effects, is crucial. This study constructs a “Waterlogging-Value-Loss” spatial analysis framework based on the hydrological and value attributes of land use. By developing a 1D–2D coupled hydrodynamic model, the study determines waterlogging risk distributions for different return periods. Combining these results with disaster loss curves, it evaluates land-use values and employs the bivariate local Moran’s I index to comprehensively assess waterlogging risk and land value, thereby identifying key areas. Finally, the SHAP method is used to quantify the contribution of water depth and value to waterlogging losses, and a Birch-K-means combined clustering algorithm is applied to identify dominant factors at the street scale. Using the central urban area of Beijing as a case study, the results reveal significant spatial heterogeneity in the distribution of urban waterlogging risks and values. Compared to traditional assessment methods that only consider waterlogging risk, the bivariate spatial correlation analysis method places greater emphasis on high-value areas, while reducing excessive attention to low-value, high-risk areas, significantly improving the accuracy of identifying key waterlogging-prone areas. Furthermore, the Birch-K-means combined clustering algorithm classifies streets into three types based on dominant factors of loss: water depth-dominated (W), value-dominated (V), and combined-dominated (WV). The study finds that as the return period increases, the dominant factors for 22.23% of streets change, with the proportion of W-type streets rising from 29% to 38%. This study provides a novel analytical framework that enhances the precision of urban flood prevention and disaster mitigation efforts. It helps decision-makers formulate more effective measures to prevent and reduce urban waterlogging disasters. Full article

The increasing frequency and severity of forest fires globally highlight the critical need to understand their environmental impacts. This study applies spatial autocorrelation techniques to analyze the dispersion patterns of carbon monoxide (CO) and nitrogen dioxide (NO₂) emissions during the 2021 Manavgat forest fires in Türkiye, using Sentinel-5P satellite data. Univariate (UV) Global Moran’s I values indicated strong spatial autocorrelation for CO (0.84–0.93) and NO₂ (0.90–0.94), while Bivariate (BV) Global Moran’s I (0.69–0.84) demonstrated significant spatial correlations between the two gases. UV Local Moran’s I analysis identified distinct UV High-High (UV-HH) and UV Low-Low (UV-LL) clusters, with CO concentrations exceeding 0.10000 mol/m² and exhibiting wide dispersion, while NO₂ concentrations, above 0.00020 mol/m², remained localized near intense fire zones due to its shorter atmospheric lifetime. BV Local Moran’s I analysis revealed overlapping BV-HH (high CO, high NO₂) and BV-LL (low CO, low NO₂) clusters, influenced by topography and meteorological factors. These findings enhance the understanding of gas emission dynamics during forest fires and provide critical insights into the influence of environmental and combustion processes on pollutant dispersion. Full article

Accurate regional carbon sequestration estimates are essential for China’s emission reduction and carbon sink enhancement efforts to address climate change. Enhancing the spatial precision of vegetation carbon sink estimates is crucial for a deeper understanding of the underlying response mechanisms, yet this remains a significant challenge. In this study, the Beijing–Tianjin–Hebei (BTH) region was selected as the study area. We employed the GF-SG (Gap filling and Savitzky–Golay filtering) model to fuse Landsat and MODIS data, generating high-resolution imagery to enhance the accuracy of NPP (Net Primary Productivity) and NEP (Net Ecosystem Productivity) estimates for this region. Subsequently, the Sen+MK model was used to analyze the spatiotemporal variations in carbon sinks. Finally, the land use intensity index, which reflects human activity disturbances, was applied, and the bivariate Moran’s spatial autocorrelation method was used to analyze the response mechanisms of carbon sinks. The results indicate that the fused GF-SG NDVI (Normalized Difference Vegetation Index) data provided highly accurate 30 m resolution imagery for estimating NPP and NEP. The spatial distribution of carbon sinks in the study area showed higher values in the northeastern forest regions, relatively high values in the southeastern plains, and lower values in the northwestern plateau and central urban areas. Additionally, 58.71% of the area exhibited an increasing trend, with 11.73% showing significant or strongly significant growth. A generally negative spatial correlation was observed between land use intensity and carbon sinks, with the impact of land use intensity on carbon sinks exceeding 0.3 in 2010. This study provides methodological insights for obtaining vegetation monitoring data and estimating carbon sinks in large urban agglomerations and offers scientific support for developing ecological and carbon reduction strategies in the BTH region. Full article

Hospitalizations due to primary care–sensitive conditions (PCSCs) can be considered a proxy for the effectiveness of primary healthcare (PHC), especially diabetes mellitus (DM). The aim of this study was to analyze the temporal, spatial, and space–time patterns of PCSCs associated with DM in a state in Northeast Brazil from 2008 to 2022. An ecological and time–series study that included all records related to PCSCs–DM from the 75 municipalities of Sergipe was conducted. Segmented linear regression, global (I) and local (LISA) Moran indices, spatial scanning, Spearman correlation tests, bivariate I, and LISA were used in our analysis to examine the temporal trends and clusters of high spatial risk. Overall, 14,390 PCSCs–DM were recorded between 2008 and 2022. There was a higher prevalence of PCSCs–DM among women (53.75%) and individuals over 70 years old (57.60%). Temporal trends in PCSCs–DM were increasing with regard to the overall rate (AAPC: 2.39); males (AAPC: 3.15); age groups ≤ 19 years (AAPC: 6.13), 20–39 years (AAPC: 4.50), and 40–59 years (AAPC: 2.56); and 3 out of the 7 health regions. There was a positive spatial correlation between per capita income (I = −0.23; p = 0.004) and diabetic foot examination being performed by a nurse in a PHC (I = −0.18; p = 0.019) setting. The heterogeneous spatial distribution of DM hospitalizations demonstrated that this condition is a persistent public health problem in Sergipe. Full article