Search Results (100)

The coexistence of undernutrition and excess weight is an important child public health challenge. This study described temporal trends and departmental spatial configurations of wasting, stunting, overweight, and obesity among children under five years of age in Peru during 2014–2024. Analyses used anthropometric assessment records from the Nutritional Status Information System (SIEN), collected in public healthcare facilities and aggregated by department and year. Annual Percentage Change summarized average temporal trends, while observed prevalence maps, Global Moran’s I, Local Indicators of Spatial Association (LISA), and a median-based bivariate classification were applied independently to 2014, 2019, and 2024. Queen contiguity was the primary spatial specification, along with KNN-4 sensitivity analysis. Wasting and obesity increased on average, stunting decreased, and overweight showed no significant long-term trend. Global autocorrelation was most consistent for wasting, whereas findings for excess-weight indicators were sensitive to the weights matrix. LISA identified local spatial classifications at nominal p < 0.05; however, these findings were exploratory because no multiple-testing correction was applied. The bivariate classification identified two, four, and three departments with concurrent high relative undernutrition and excess-weight scores in 2014, 2019, and 2024, respectively. These record-based findings show territorial heterogeneity within SIEN but do not represent population prevalence, causal geographic effects, or an integrated spatiotemporal process. Full article

Platform-mediated short-term rental (STR) markets concentrate intensely in heritage urban cores, yet the temporal stability of this concentration remains poorly understood. This study examines spatial dynamics of STR concentration in Dubrovnik’s UNESCO-listed historic core across five biennial cross-sections (2017–2025) using complete administrative eVisitor registration data and an H3 hexagonal grid at resolution 11. Global and local spatial autocorrelation (Moran’s I, LISA, Getis-Ord Gi*), Emerging Hot Spot Analysis for spatiotemporal typologies, and bivariate LISA to distinguish capacity saturation from fragmentation were applied. Results demonstrate structural persistence: Moran’s I remained highly significant (p < 0.001) across all periods including COVID-19 disruption (range 0.417–0.467, CV = 4.4%), despite 53% supply growth and only 3.7% spatial expansion. Consecutive hotspots dominated typological classification, indicating active consolidation. Capacity analysis revealed concordant High–High patterns (56.8% of significant cells) with zero High–Low associations, confirming saturation not fragmentation. Findings support spatial lock-in in STR markets: concentration persists because locational advantages are properties of place rather than market volume, requiring spatially differentiated regulation rather than aggregate supply controls. Full article

Land use and land cover (LULC) changes are major drivers of environmental transformation in sensitive regions, such as the Marajó Archipelago in the Brazilian Amazon. This study assessed accumulated anthropization of LULC in the Immediate Geographic Region of Soure–Salvaterra, Eastern Amazon, between the reference years 1985 and 2023, using MapBiomas data and spatial statistical techniques. Bivariate Local Moran’s I (LISA) was applied to evaluate intertemporal spatial associations between areas classified as natural in 1985 and anthropized in 2023. In this approach, High–Low indicates natural areas associated with low anthropization in 2023, whereas High–High indicates areas where natural cover in 1985 was spatially associated with higher anthropization in 2023. The results indicated a strong predominance of High–Low, with values above 94% in all municipalities and up to 99.86% in Santa Cruz do Arari. In contrast, High–High had localized concentrations in Salvaterra (3.53%), Cachoeira do Arari (1.28%), and Soure (1.16%), especially in coastal zones and inland sectors. Low–Low, associated with lower anthropogenic pressure or possible signs of natural regeneration, was extremely low (≤0.0004%). These findings indicate that LISA is useful for identifying local LULC patterns and supporting environmental assessment and territorial planning in tropical regions. Full article

Urban expansion has reshaped land-use patterns, altered the provision of ecosystem services, and brought challenges to regional sustainable development. However, studies on urban agglomerations with uneven development remain insufficient. This study takes the core development area of the Central Plains Urban Agglomeration as the study area and explores changes in ecosystem services during multidimensional urbanization from 2000 to 2020. Using the CASA and InVEST models, three ecosystem services, namely net primary productivity (NPP), water yield (WY), and soil conservation (SC), were quantified. Spatial associations and local heterogeneity were analyzed using the bivariate Moran’s I. The results show that regional urbanization exhibited a Zhengzhou-centered monocentric pattern, with rapid growth in GDP density and significant expansion of urban land. The responses of ecosystem services to urbanization showed divergent trends, with NPP increasing slightly, while WY and SC decreased. NPP and SC showed a synergistic effect, whereas WY had trade-off relationships with both services. Due to uneven regional development, urbanization indicators and ecosystem services showed evident spatially heterogeneous relationships. This study provides evidence for ecological conservation, ecosystem-service management, and sustainable spatial governance in developing urban agglomerations where rapid growth and ecological constraints coexist. Full article

As a key component of ecosystems, the synergistic relationship between wetland carbon storage and habitat quality is vital for maintaining ecological functions, and its evolution is profoundly influence by changes in wetlands. This study focuses on wetlands in western Jilin Province. Based on four sets of land use data from 2010 to 2023 and utilizing the InVEST model, combined with methods such as spatial autocorrelation, the Coupled Coordination Degree Model, and the GeoDetector, the study analyzed the co-variation of carbon storage and habitat quality, as well as their response to landscape patterns. The study found that between 2010 and 2023, the wetland area increased by a net 858.13 km², and landscape fragmentation was generally alleviated, although local connectivity continued to degrade. Regional carbon storage increased by 68.1%, totaling 7.43 × 10⁶ Mg, while the habitat quality index exhibited high spatiotemporal stability, fluctuating marginally between 0.609 and 0.621. Spatially, high-value areas remained primarily concentrated within nature reserves. Results of bivariate spatial autocorrelation analysis revealed a strengthening of spatial positive autocorrelation between carbon storage and habitat quality, with Moran’s I increasing from 0.410 to 0.501. The coupled coordination degree model further confirmed that the level of synergy between the two services exhibited a pattern of higher values in the north and lower values in the south, and that areas of high coordination expanded significantly outward following restoration projects. GeoDetector analysis indicates that the largest patch index is the core factor driving the synergistic development of ecosystem services. The results also suggest that the integrity of core wetland patches and a heterogeneous landscape pattern can promote the synergistic improvement of carbon storage and habitat quality through boundary effects and habitat complementarity. Full article

Milltailings deposition areas in abandoned mines are inherently unstable and spatially extensive and heterogeneous, making regional-scale field investigations challenging under intense rainfall. With the advancement of space–airborne remote sensing technologies, large-scale surface deformation monitoring has become feasible. In this study, a 22.02 km² abandoned mine in Lingqiu County, Shanxi Province, was selected as a case site; during the late-July 2023 extreme rainfall event, the site experienced large-scale surface displacements. Surface deformation was interpreted using Sentinel-1 SBAS-InSAR data, combined with differential digital elevation models (DEMs) derived from UAV surveys before and after heavy rainfall. A bivariate spatial autocorrelation analysis was conducted to evaluate the spatial relationship between differential DEMs and InSAR-derived deformation. The results indicate that: (1) SBAS-InSAR revealed significant spatial heterogeneity of ground deformation, with pronounced subsidence observed in the milltailings deposits; (2) the bivariate spatial autocorrelation analysis yielded a Moran’s I value of 0.2, suggesting a weak but positive spatial correlation between the DEM differences and InSAR results, with dispersed correlation patterns; (3) hotspot analysis highlighted notable clustering of deformation, with approximately 27.84% of the study area showing strong deformation responses, while 25.81% represented low–low clusters with limited deformation. Beyond tailings-deposit settings, this workflow is also applicable to the regional investigation of rainfall-responsive deformation and debris-flow-related terrain change on natural slopes under global change, providing technical support for surface investigations and offering insights for disaster early warning and ecological restoration in similar regions. Full article

Black pepper (Piper nigrum L.) is a crop of significant economic importance in the Amazon, especially in the state of Pará, where intensive production systems predominate. Understanding the spatial variability of soil attributes and their relationship with plant vigor is essential to optimize agricultural practices and input use. Geotechnology-based approaches enable the generation of more precise management zones, contributing to efficient resource use and increased profitability. This study aimed to delimit potential management zones in black pepper crops based on the spatial analysis of soil bulk density (BD) integrated with the NDVI (Normalized Difference Vegetation Index), evaluated using the Bivariate Moran’s Index. The research was conducted in a production area in the municipality of Baião, Pará, Brazil, using soil samples to determine bulk density and UAV images for NDVI calculation. Data were interpolated by kriging and analyzed to identify spatial associations between soil compaction and NDVI. Soil bulk density ranged from 1.14 to 1.80 Mg m⁻³, while NDVI values ranged from 0.07 to 0.91, revealing a clear inverse spatial relationship between soil compaction and vegetative vigor. The integration of BD and NDVI allowed the delineation of site-specific management zones, supporting more efficient decision-making in precision agriculture. Full article

Since the start of the U.S. Conservation Reserve Program (CRP) in 1985, producers have enrolled environmentally sensitive land in exchange for annual rental payments, supporting multiple dimensions of sustainability through reduced soil loss, improved water quality, enhanced habitat provision, and strengthened climate resilience through land stewardship. Recent declines in enrollment raise concerns about whether participation remains spatially aligned with local environmental need and economic incentives. This study examines regional variation in CRP participation and its sustainability implications by identifying spatial patterns in participation and key drivers using exploratory spatial data analysis (ESDA). We analyze county-level CRP participation rates alongside three key drivers (CRP rental rates, soil erosion risk on cultivated cropland, and farm income) and assess spatial dependence using Global Moran’s I, univariate Local Indicators of Spatial Association (LISA), and bivariate LISA (BiLISA). Framed as an assessment of agri-environmental policy effectiveness for sustainable land management, the framework is applied to counties in the U.S. Midwest, a region with historically substantial CRP enrollment. Global Moran’s I statistics indicate significant positive spatial autocorrelation for CRP participation (I = 0.491), CRP rental rates (I = 0.892), and soil erosion (I = 0.503), confirming pronounced regional clustering across Midwestern counties. LISA results further show that more than 60% of counties fall into high–high (HH) or low–low (LL) clusters for CRP rental rates, while BiLISA results indicate that 22.9% of counties form HH clusters between CRP participation and soil erosion, suggesting only partial alignment between CRP participation and the environmental need. These findings indicate that the environmental benefits of CRP may vary across the region depending on where participation occurs. Overall, the findings support a shift toward a data-driven, spatially explicit CRP strategy that integrates environmental risk, economic incentives, and regional context to strengthen sustainability outcomes and enhance environmental effectiveness, economic efficiency, and the spatial equity of conservation benefits in the United States. Full article

Against the backdrop of rapid development in megacities, urban park green spaces serve as essential public resources whose accessibility and equity directly affect residents’ quality of life and broader social justice. This study addresses the imbalance between the spatial distribution of green space resources and the socio-demographic characteristics of different population groups in megacities. It takes the six central districts of Beijing as the study area and integrates data from 457 urban parks. The research applies the Gaussian two-step floating catchment area (G2SFCA) method and bivariate spatial autocorrelation analysis (Moran’s I) to systematically evaluate the equity of urban park green space provision across multiple social dimensions, including economic status, educational attainment, and vulnerable groups. The results indicate that urban park green spaces in Beijing’s six central districts exhibit a pronounced central and northern advantage, with significant deficits in southern and peripheral areas. High accessibility and greater per capita green space are concentrated in core and high-housing-price districts, overlapping with high-income and highly educated populations. In contrast, vulnerable groups and migrant workers are more likely to reside in green-space-deficient areas, facing a structural “high population density–low green space provision” disadvantage, reflecting clear social inequities. In addition, inequity is more pronounced at the walking scale than at the cycling scale. The study reveals a dual mismatch in green space provision across both spatial and social dimensions within a megacity context. The findings suggest that future urban planning should shift from quantitative expansion to the optimization of existing green space resources. Planning strategies should prioritize vulnerable groups and adopt a people-oriented approach. Policymakers should allocate greater support to southern and peripheral areas, increase the provision of pocket parks, and improve slow-mobility systems. These measures can more precisely safeguard equitable access to green space for disadvantaged populations and promote the realization of spatial justice. Full article

The present research examines the spatial dynamics and impact of Short-Term Rentals (STRs) in highly touristic and spatially constrained insular territories, using the island of Santorini (Thira), Greece, as a case study. Unlike large metropolitan areas, where tourism pressure can diffuse across space, islands present limited land availability and infrastructure capacity, thereby intensifying the potential externalities of uncontrolled STR expansion. The study examines how STRs are spatially distributed, their interactions with formal accommodation facilities, and the extent to which they have penetrated residential, rural, and other legally protected areas. The analysis integrates Airbnb listings with land use, regulatory, environmental, and other datasets, applying specific metrics, including the Average Nearest Neighbor Index, Kernel Density Estimation, Standard Deviation Ellipses, Global Bivariate Moran’s I, and Local Collocation Quotient. The results reveal increased spatial clustering in high-amenity areas, a high degree of professionalization, and increased presence of STRs outside formal settlements. STRs exhibit colocation with formal accommodation in high-tourism zones, as well as a spillover effect in areas where hotel activity is prohibited. This underscores the need for an island-specific spatial analysis to support integrated planning and regulatory approaches that address STR-related impacts on tourism-dependent islands. Full article

Population ageing is intensifying pressure on elderly-care provision in megacity suburbs, but spatially explicit evidence on who benefits and where gaps persist remains limited. Using Daxing District, Beijing, as a case study, under the 15-min community living circle framework, we integrate cleaned elderly-care facility POIs from the municipal government portal (209 points), census-calibrated age-stratified WorldPop 100 m grids, and an OpenStreetMap road network to evaluate walking-based supply–demand matching. Kernel density estimation (KDE) characterizes facility agglomeration; the Gaussian Two-Step Floating Catchment Area (Ga2SFCA) method (1 km threshold) measures accessibility for two cohorts (60–80 and 80+); and global Moran’s I with bivariate LISA identifies spatial coupling between accessibility and elderly population density. The results indicate the following: (1) pronounced spatial imbalance—facilities are concentrated in the northwest and east but remain sparse in central and southern areas, while elderly population density follows a center–periphery gradient, peaking at 12,000 persons/km² in core areas (e.g., Jiugong and Huangcun); (2) clear accessibility stratification—overall accessibility is low and spatially clustered, yet the 80+ cohort (13.6% of the elderly population) exhibits markedly higher accessibility than the 60–80 cohort; and (3) differentiated coupling types—global bivariate Moran’s I = 0.773143 (p < 0.01), with LISA dominated by low-demand–low-accessibility (LL) areas and additional high-demand–low-accessibility (HL) shortage zones and low-demand–high-accessibility (LH) potential redundancy zones, while HH areas are scarce. These diagnostics support zone-specific gap filling to mitigate spatial inequities and age–structural mismatches. Full article

Investigating multiple source apportionment methods and quantitatively characterizing heavy metal contamination in soils are of critical importance for effective pollution control and prevention. This study systematically investigates multiple source apportionment methods for soil heavy metals, with quantitative characterization of contamination features crucial for effective pollution control. Taking Jingxi City in Guangxi, China, as a case study, we conducted a comprehensive analysis of 8816 soil samples using multi-source big data integration. By synergistically applying machine learning algorithms, the potential ecological risk index, and bivariate local Moran’s index, we achieved dual objectives: quantitative inversion of eight heavy metal concentrations and simultaneous ecological risk assessment with pollution source identification. Through comparative model evaluation, the XGBoost algorithm demonstrated optimal predictive performance. Contribution analyses revealed that soil properties (Fe₂O₃, Al₂O₃, and phosphorus content), road distribution, and elevation significantly regulate heavy metal accumulation. Spatial risk mapping identified cadmium, mercury, and arsenic contamination hotspots as critical environmental threat zones. The bivariate local Moran’s index model elucidated spatial coupling characteristics between ecological risks and environmental drivers, providing spatially explicit decision-making support for precision environmental management. Our multidimensional analytical framework incorporates spatial visualization of heavy metal distribution, hierarchical ecological risk assessment, and pollution source contribution analysis, ultimately establishing a scientific decision-making system for land safety utilization and pollution risk management. This integrated approach offers methodological references for regional heavy metal pollution control in karst environments. Full article

Although access to quality public spaces encourages physical activity (PA), their unequal distribution can exacerbate social inequalities. This study examined the relationship between the availability of spaces for PA and social marginalization in urban Basic Geostatistical Areas (Spanish acronym AGEB) in Mexico, using national databases on urban facilities and demographics. AGEB calculated space densities for PA, and the bivariate Moran’s I and LISA methodology were followed to identify global and local patterns. A weak negative spatial correlation was detected (I = −0.006) at the national level, with clusters of AGEBs with low marginalization and low density of spaces for PA. Contrasts were observed among the three most populous metropolitan areas: Mexico City and Guadalajara showed significant positive correlations, while Monterrey exhibited a different pattern. The urban furniture earmarked for PA is insufficient and its distribution reproduces socio-spatial inequalities. The dynamics differ across metropolises, underscoring the need for localized policies that will prioritize the provision of public spaces in marginalized communities. Full article

Amid rapid urbanization, Chinese cities face mounting ecological pressure, making it critical to balance environmental protection with public well-being. As visual perception accounts for over 80% of environmental information acquisition, it plays a key role in shaping experiences and evaluations of ecological space. However, current ecological planning often overlooks public perception, leading to increasing mismatches between ecological conditions and spatial experiences. While previous studies have attempted to introduce public perspectives, a systematic framework for analyzing the spatial relationship between ecological and visual sensitivity remains lacking. This study takes 56,210 street-level points in Tianjin’s central urban area to construct a coordinated analysis framework of ecological and perceptual sensitivity. Visual sensitivity is derived from social media sentiment analysis (via GPT-4o) and street-view image semantic features extracted using the ADE20K semantic segmentation model, and subsequently processed through a Multilayer Perceptron (MLP) model. Ecological sensitivity is calculated using the Analytic Hierarchy Process (AHP)—based model integrating elevation, slope, normalized difference vegetation index (NDVI), land use, and nighttime light data. A coupling coordination model and bivariate Moran’s I are employed to examine spatial synergy and mismatches between the two dimensions. Results indicate that while 72.82% of points show good coupling, spatial mismatches are widespread. The dominant types include “HL” (high visual–low ecological) areas (e.g., Wudadao) with high visual attention but low ecological resilience, and “LH” (low visual–high ecological) areas (e.g., Huaiyuanli) with strong ecological value but low public perception. This study provides a systematic path for analyzing the spatial divergence between ecological and perceptual sensitivity, offering insights into ecological landscape optimization and perception-driven street design. Full article

Intensive human interference has severely disrupted the natural and ecological environments of coastal areas, threatening ecosystem services (ESs). Meanwhile, the relationships between ESs exhibit certain variations across different spatial scales. Therefore, identifying the scale effects of interrelationships among ESs and their underlying driving mechanisms will better support scientific decision-making for the hierarchical and sustainable management of coastal ecosystems. Therefore, employing the Integrated Valuation of ESs and Tradeoffs (InVEST) model combined with GIS spatial visualization techniques, this investigation systematically examined the spatiotemporal distribution of four ESs across three scales (grid, county, and city) during 2000–2020. Complementary statistical approaches (Spearman’s correlation analysis and bivariate Moran’s I) were integrated to systematically quantify evolving ES trade-off/synergy patterns and reveal their spatial self-correlation characteristics. The geographical detector model (GeoDetector) was used to identify the main driving factors affecting ESs at different scales, and combined with bivariate Moran’s I to further visualize the spatial differentiation patterns of these key drivers. The results indicated that: (1) ESs (except for Water yield) generally increased from coastal regions to inland areas, and their spatial distribution tended to become more clustered as the scale increased. (2) Relationships between ESs became stronger at larger scales across all three study levels. These ESs connections showed stronger links at the middle scale (county). (3) Natural factors had the greatest impact on ESs than anthropogenic factors, with both demonstrating increased explanatory power as the scale enlarges. The interactions between factors of the same type generally yield stronger explanatory power than any single factor alone. (4) The spatial aggregation patterns of ESs with different driving factors varied significantly, while the spatial aggregation patterns of ESs with the same driving factor were highly similar across different spatial scales. These findings confirm that natural and social factors exhibit scale dependency and spatial heterogeneity, emphasizing the need for policies to be tailored to specific scales and adapted to local conditions. It provides a basis for future research on multi-scale and region-specific precision regulation of ecosystems. Full article