Search Results (378)

Ground-level ozone (O₃) has become a major air pollutant in China following PM_2.5, particularly in the southeastern coastal region, where the frequent interaction of typhoons and the subtropical high complicates pollution control. In this paper, spatial autocorrelation and a multiscale geographically weighted regression (MGWR) model were employed to estimate the spatiotemporal heterogeneity and driving mechanisms of O₃ in the Southeast Coastal urban agglomerations from 2015 to 2024. Temporally, the annual average O₃ concentration exhibited a fluctuating trend of an initial increase, followed by a decrease and a subsequent rebound. A bimodal monthly pattern was observed, with peaks in May–June and August–September and minima in winter. Diurnally, the concentration showed a consistent pattern of being higher in the daytime and lower at night, peaking in the afternoon, driven by solar radiation and temperature. Spatially, O₃ exhibited a distinct north–south gradient, with the highest in Jiangsu Province, followed by Shanghai, Zhejiang and Guangdong, and the lowest in Fujian. Significant spatial autocorrelation was detected, with hot spots in the Yangtze River Delta and cold spots in Fujian and adjacent areas. Seasonally, the most severe pollution with the greatest spatial heterogeneity, occurred in summer, contrasting with the uniformly low concentrations in winter. Compared with OLS and GWR, the MGWR demonstrated superior explanatory power. O₃ was jointly influenced by precursors, natural factors, and socioeconomic factors, with the influence intensity ranked as follows: NO₂ > average elevation > population density > annual precipitation> wind speed > built-up area > proportion of the secondary industry in GDP. Notably, the effects of NO₂, annual precipitation, and the proportion of the secondary industry exhibited strong spatial heterogeneity, operating at finer spatial scales. These findings provide scientific support for sustainable air quality management and region-specific O₃ control in southeastern coastal China. Full article

Urban heat stress and deteriorating air quality are environmental risks in semi-arid cities, positioning urban forests as vital nature-based solutions for climate adaptation. Despite growing recognition of urban greening imperatives, South Africa’s (SA) Northern Cape Province remains characterized by sparse vegetation Land Use/Land Cover (LULC) and built environment expansion. The study’s research problem focuses on how vegetation LULC dynamics influence urban forests’ potential in mitigating heat stress and atmospheric pollution in arid urban systems. The study adopts a multi-scale analytical approach, conducting the LULC and NDVI analysis through a multi-temporal Landsat satellite imagery analysis quantifying LULC change from 2004 to 2024. Grounded in the Integrated Spatial Justice-Ecosystem Services (ISJES) Framework, the analysis reveals significant decline in dense vegetation LULC from 9021.77 km² (2.4%) to 1262.10 km² (0.3%), while barren land expanded from 73,417.01 km² (19.7%) to 222,866.82 km² (59.8%) intensifying urban thermal exposure. Built-up areas expanded from 91.06 km² to 357.072 km², further constraining ecological buffers across the province’s urban nodes and undermining urban climate resilience. The Global Moran’s I statistic for the NDVI change surface (I = 0.7843, Z = 443.87, p < 0.0001) confirms spatial clustering of degradation hotspots of NDVI decline affecting 66.5% of the study area. Furthermore, Geographically Weighted Regression (GWR) results confirm that vegetation loss is being driven by the combined and spatially differentiated effects of mining proximity, urban expansion, livestock pressure, declining rainfall, and rising temperatures. In terms of thermal regulation findings, the Getis-Ord Gi hot spot analysis identifies significant NDVI decline covering 23.5% of the study area at the 99% confidence level, expanding to 33.5% and 39.5% at the 95% and 90% confidence levels, respectively; hence, there is a need for urban forest corridors, climate-sensitive spatial planning frameworks, and targeted greening interventions in heat-vulnerable arid geographies. This study provides the first comprehensive, multi-decadal quantification of vegetation loss across SA’s largest province. Full article

Cropland and rural settlements are core components of rural human–environment systems, and their coordinated development is crucial for regional sustainability, particularly in China’s major agricultural production regions. Taking the Huang-Huai-Hai region as the study area, this study systematically investigates the spatiotemporal evolution of cropland and its coupling relationship with rural settlements using land use data from 1990 to 2020. Grid-based analysis and multiple spatial modeling methods were employed. The results show that: (1) From 1990 to 2020, the cropland in the region decreased by a net total of 21,021.94 km², with annual dynamic degrees ranging from −0.13% to −0.28%. Cropland conversion to other land uses far exceeded conversion from others, with construction land being the primary destination. Among these, rural settlements and urban construction land accounted for 43.75% and 55.58% of the total cropland loss, respectively. (2) The spatial distribution of cropland exhibited a distinct pattern of “hot in the center and south, cold in the periphery and north” (Moran’s I = 0.232, p < 0.001), indicating significant positive spatial autocorrelation. Hot spot areas clustered in the North China Plain and the Huang-Huai Plain, while cold spot areas were distributed in the Yanshan–Taihang mountains and the hilly regions of the Shandong Peninsula, clearly controlled by topography. (3) Cropland change exhibited stage-specific characteristics. The pattern was relatively stable during 1990–2000. During 2000–2010, cropland conversion to other uses intensified, with high-value conversion areas concentrated around urban agglomerations. In the 2010–2020 period, these high-value conversion areas diffused from the core plain areas to urban fringe zones. (4) The spatial coupling between cropland and rural settlements was predominantly characterized by the Moderately Coordinated Type (MCT), accounting for 48.38–58.44% of the area. However, the proportion of Rural Settlement-Dominant Type (RC) increased from 15.51% to 21.58%, indicating a trend toward intensifying human–environment conflicts. Overall, the Huang-Huai-Hai region experienced significant cropland changes. While its spatial pattern remains relatively stable, the coupling relationship between cropland and rural settlements is deteriorating, posing challenges to regional food security and rural sustainable development. Full article

Land-use/cover change threatens the ecological integrity of the Miombo region of south-central Africa. In Angola, Miombo ecosystems are of high ecological and socio-economic importance, providing rural populations with woody and non-timber forest products. Fire plays an important role in regional agricultural and silvicultural land-use systems. This study contextualised Copernicus land-cover classes at the regional level to analyse LULC transition pathways and their association with fire occurrence in Central Angola. LULC change was assessed using a post-classification comparison approach combined with pixel-based trajectory analysis. Fire activity was analysed using MODIS-derived ignition points, burned-area data, and a hexagonal-grid aggregation approach. At the same time, spatial clustering was assessed using hot spot analysis based on the Getis-Ord Gi* statistic. Differences in mean fire size among LULC transition classes were tested using the Kruskal–Wallis test followed by Dunn’s post hoc test. The results indicate a gradual reduction in forest cover and conversion to Cultivated Land, associated with the expansion of agricultural frontiers and urban areas. Fire activity was highest in areas affected by LULC conversion, with seasonal patterns varying notably among classes. Mean fire size differed by more than two orders of magnitude among transition types. Overall, fire activity was strongly associated with areas undergoing land-cover transition, highlighting the need to integrate fire management into sustainable land-use policies for long-term Miombo conservation. Full article

The deep integration of the digital economy and education is a critical pathway to addressing the common challenges in less-developed regions, such as human capital shortages, unequal public service provision, and low developmental inclusiveness during new-type urbanization. Using panel data from 11 prefecture-level cities in Jiangxi Province from 2017 to 2024, this study first constructs a comprehensive index system to measure the integration level of the digital economy and education, as well as the inclusive development level of new-type urbanization. The entropy method is employed for objective weighting and composite score calculation. The spatiotemporal patterns of these two variables are visualized using hot spot analysis. A spatial Durbin model (SDM) with dual fixed effects is then applied to empirically examine the direct effect, spatial spillover effects, and regional heterogeneity of the digital-education integration. The main findings are as follows: (1) Both the integration level of the digital economy and education and the inclusive development of new-type urbanization in Jiangxi Province exhibit a distinct spatial pattern characterized as “high in the north, low in the south, and weak in the central region”, with significant spatiotemporal coupling between the two. (2) The digital-education integration exerts a significant positive direct effect on the local inclusive development of new-type urbanization. The core transmission mechanisms are the inclusive sharing of digital educational resources and the effective enhancement of human capital. (3) The integration generates a positive, albeit relatively weak, spatial spillover effect on neighboring areas. The strength of this spillover effect shows pronounced regional heterogeneity, being strongest in Northern Jiangxi, followed by Southern Jiangxi, and weakest in Central Jiangxi. (4) Economic development and industrial upgrading synergistically drive inclusive development alongside the digital-education integration. However, unequal social security provision remains a significant inhibiting factor for inclusive development. Full article

Enormous land exploitation is triggering strong urban growth, and this phenomenon is exacerbating the already existing problem of rising land surface temperatures. This leads to increased human activities and a disruption of the balance of natural ecosystems. The application of thermal remote sensing techniques is, in this context, helpful in learning about the condition of the earth’s surface and monitoring how it changes over time. This paper utilizes thermal data from 2000, 2010 and 2020, with supplementary data from 2024, to assess current trends in two different seasonal conditions (rainy period and low rainy period). Two different areas (urban and rural) of the central Vietnamese Province of Thua Thien-Hue have been analyzed to compare them. Processing Landsat-5 TM, Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Sentinel-2 satellite images, a heat map of the study area was defined, considering hot spots and cold spots. As support for this analysis, spectral indexes have been developed for a better comprehension of the land cover change over the years and to provide a validation of the thermal analysis. This paper aims to assess the threat posed by the intensification of the urban heat island effect on cultural heritage sites. The case studies are represented by areas where there are urban growing and cultural heritage sites to be preserved, such as the UNESCO-listed Hue Citadel. Full article

This study analyzes domestic tourism mobility in Spain using aggregated and anonymized mobile phone data, with a particular focus on the outbound market of the municipality of Madrid and its territorial redistribution between September 2019 and September 2025. Using experimental statistics from the National Institute of Statistics (INE), a monthly series of origin–destination flows to all Spanish municipalities was constructed, harmonizing the municipal database and incorporating intensive indicators to improve inter-territorial comparability. The spatiotemporal dynamics were integrated into a Space–Time Cube (monthly resolution), and Emerging Hot Spot Analysis (EHSA) was applied to classify the persistence, intensification, or attenuation of high- and low-intensity clusters. Additionally, the grouping of time series allowed for the identification of seasonal patterns associated with coastal, urban, and nearby inland destinations. The results show: (i) a synchronous disruption in the spring of 2020 linked to COVID-19; (ii) a staggered recovery beginning in 2021, consolidating in 2023–2025; and (iii) a dual structural pattern, with a strong concentration of volumes in large urban and coastal hubs, along with high relative intensities in small municipalities in the ring surrounding Madrid. EHSA identifies intensifying hotspots in established coastal systems (Costa del Sol and Costa Blanca) and cooling or attenuated dynamics in parts of the inland region, consistent with the reconfiguration of the “tourism radius” following the pandemic. Limitations arising from statistical confidentiality and the representativeness of the source are discussed, and future research directions are proposed based on the integration of the information with expenditure and transportation data and on spatiotemporal modeling to support destination planning and management. Full article

In the context of accelerating technological and industrial transformation, innovation-driven productivity has garnered significant attention. Based on multi-source data, this study employs the entropy method and spatial pattern analysis to delve into the spatial pattern of innovation-driven productivity. The results are as follows: (1) there is a huge difference in innovation-driven productivity at the street and township level in Shanghai; (2) innovation-driven productivity exhibits global spatial autocorrelation in Shanghai; (3) innovation-driven productivity shows a circle structure with high–high agglomeration at the center and low–low agglomeration at the periphery; (4) innovation-driven productivity hot spots are concentrated in the central region, while cold spots are distributed in a southeast–northwest trend around them. This study is of great significance for Shanghai to achieve an accurate allocation of resources, a coordinated development of industries, and an improvement of urban functions. Full article

Vegetation serves as a critical ecological barrier on tropical islands, but conventional assessments often emphasize greening magnitude while overlooking persistence, structural quality, and scale-dependent attribution. In this study, we reconstructed a high-precision fractional vegetation cover (FVC) dataset for Hainan Island, China, covering the period from 2000 to 2024 using Google Earth Engine (GEE). We then combined trend analysis, emerging hot spot analysis (EHSA), the coupling coordination degree model (CCDM), RESTREND, and Geodetector to examine vegetation change from complementary perspectives. The results show that FVC increased overall and gradually shifted toward a more stable state over time. EHSA further revealed a distinct core-periphery pattern, with persistent hot spots concentrated in the central mountainous region, persistent cold spots distributed along the coastal urban belt, and oscillating hot spots occurring within agricultural transition zones. Regarding quantity-structure coupling, FVC and the aggregation index (AI) generally improved together across the island; however, some agricultural ecotones exhibited weaker structural improvement despite increasing vegetation cover, suggesting potential risks of homogenization and structural simplification. In the broad attribution analysis, vegetation recovery was primarily associated with the combined influence of climatic and human-related improvement. In the factor-specific analysis, land cover and slope showed the strongest explanatory power, and their interactions with other variables further enhanced spatial differentiation. These results demonstrate that greening magnitude alone is insufficient for evaluating vegetation change on tropical islands. Structural coordination and scale-dependent attribution should also be considered when interpreting ecological improvement and related management implications. Full article

Urban heat islands present a significant obstacle to climate adaptation strategies, yet the interplay between surface and atmospheric thermal elements is not fully understood. This research investigates the spatial relationship between land surface temperature (LST) and near-surface air temperature (TAIR) across Zagreb’s 218 local councils during the summer of 2024, assessing the premise that these constitute separate thermal dimensions with varying land cover correlations. Landsat 8/9-derived LST and CERRA-derived TAIR, temporally aligned to the Landsat overpass slot (09:00 UTC), were examined through spatial autocorrelation (Moran’s I, Getis–Ord Gi*), correlation analysis, and Fisher’s z-tests to compare the effects of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The findings indicated partial coupling (r = 0.537, R² = 0.288), with 71.2% of the variance remaining unexplained, suggesting considerable surface-atmospheric decoupling. Furthermore, hot spot overlap analysis revealed limited convergence (11.9% of neighborhoods), while 44.5% displayed divergent thermal extremes. Land cover showed much stronger connections with LST (NDVI: r = −0.970, R² = 0.941; NDBI: r = +0.973, R² = 0.947) than with TAIR (NDVI: r = −0.478; NDBI: r = +0.496), representing reductions in explained variance of 63–64% (p < 0.001). These findings suggest that surface and atmospheric urban heat are related but distinct thermal aspects. Full article

Equitable access to rehabilitation services is essential for individuals living with a disability, yet geographic disparities in outpatient rehabilitation care remain understudied. This study examined spatial accessibility to outpatient physical and occupational therapy services across Texas to identify regional inequities and inform workforce and policy planning. A descriptive cross-sectional geospatial analysis was conducted using outpatient clinic location data from the Texas Health and Human Services database (2022) and population data from the 2020 U.S. Census. Clinic addresses were verified and geocoded. Accessibility was measured using an origin–destination cost matrix to estimate the travel time to the nearest clinic, and the two-step floating catchment area (2SFCA) method to calculate an accessibility index. Spatial clustering of access was assessed using the Getis-Ord Gi* statistic to identify hot and cold spots. The analysis included 2255 outpatient rehabilitation clinics across 6896 census tracts. Travel times varied substantially, with rural areas experiencing the longest travel burdens. The 2SFCA analysis revealed pronounced disparities, with low-accessibility clusters concentrated in rural and border regions and high-accessibility clusters in urban metropolitan areas. These findings demonstrate persistent geographic disparities in outpatient rehabilitation access across Texas, suggesting the need for targeted workforce placement, transportation investment, and policy interventions to improve equitable access. Full article

National modern agricultural industrial parks are the core carriers for promoting agricultural modernization. Clarifying their spatial differentiation patterns is of great significance for revealing the efficiency of resource allocation and promoting coordinated regional development. Based on the data from 338 national modern agricultural industrial parks in China, this study uses methods such as the nearest neighbor index, Voronoi spatial statistics, and spatial autocorrelation to identify their spatial distribution characteristics, and adopts the XGBoost–SHAP model to explore the nonlinear effects of driving factors. The research found the following: (1) The parks exhibit a distinct “sparse west–concentrated middle–dense east” agglomeration pattern aligned with China’s Hu Huanyong Line agro–economic divide. (2) At the municipal level, four high-density cores emerged in central-eastern regions with “dual hot spots–gradient diffusion” characteristics. (3) Farmers’ professional cooperatives and transportation accessibility are the most consistent fundamental driving elements, reflecting the transition of the development momentum of contemporary agriculture from “resource dependency” to “circulation dependence.” Heterogeneity analysis shows elevation, cooperatives and rural income differentially drive agglomeration across regions, with elevation constituting a universal constraint. (4) While regional development and mechanization show adaptive synergy, excessive urbanization generates a distinct “non–agriculturalization” crowding–out effect on agricultural development. Full article

Urban water bodies serve as biodiversity hot spots in a human-influenced landscape. We studied the backwater “Alte Donau” (Vienna, Austria), which has been the subject of ongoing management and restoration efforts. We aimed to capture short-term variations in the planktonic and benthic algal community during a vegetation period with a specific focus on Gloeotaenium loitlesbergerianum with its primary distribution in tropical regions. In total, 196 algal taxa were identified, indicating a high and balanced species diversity. Although the waterbody is shallow and densely colonized by macrophytes, phytoplankton and microphytobenthos exhibited significant differences in composition, particularly in spring. Less pronounced differences during summer were probably caused by macrophyte harvesting combined with recreational activities. We found a clear seasonal pattern with spring characterized by blooms of Ochrophyta, followed by a shift towards green algae, Dinophyta, and Cyanobacteria during summer and autumn. We found high variability in spring samples, whereas summer and autumn samples showed increasing similarity. Temperature, silicate, and alkalinity were the primary environmental factors structuring algal community composition. G. loitlesbergerianum was detected during warmer months from May through October across a temperature range of 14 to 28 °C, with highest abundances >20 °C. Warmer water and altered nutrient regimes not only stress native populations but also promote the establishment of new species such as G. loitlesbergerianum, accelerating community shifts. Therefore, sustained monitoring, targeted macrophyte restoration, and effective nutrient management are crucial for preserving both water quality and biodiversity in such systems. Full article

Land conversion due to deforestation and urbanization tends to change oasis ecosystem service in arid and semiarid regions. In this context, this paper examines the impacts of land use/land cover change (LULC) on the degradation of ecosystem service values (ESVs) in AlUla cultural oasis, northwestern Saudi Arabia, using Landsat images of the years 1984, 1992, 2010, and 2023, cross-validated with field surveys and high-resolution data. Different approaches were used for the purpose of this study, including support vector machine (SVM), hot-spot analysis, and cluster and outlier analysis (local Moran’s I). However, to compute and evaluate the ESV, we used the benefit transfer approach (BTM). The results indicated a significant change in the built-up area between 1984 and 2023, which increased by 12.53 km². This transformation led to a wide variation in all ESVs each year, with an increase of ESV by USD 44.78 million during 1984 to 1992. In the following decade, however, the AlUla oasis witnessed a loss in its ESV by approximately USD 0.73 million and USD 36.70 million during the periods 1992 to 2010 and 2010 and 2023, respectively. Moreover, the spatial distribution patterns of ESVs varied considerably, especially for provisioning service (PS) and supporting service (SS), while it was more clustering for regulating service (RS) and cultural service (CS). This study indicates that urban development is among the important factors behind changes and losses in the ESV in this arid oasis. Full article

The paper aims to provide spatial evidence on where to prioritize place-based support for women’s entrepreneurship by linking the geography of female founders to the proximity of formal support infrastructure in Germany. To pursue this objective, we assemble nationwide venture microdata covering founding cohorts 2015–2021 and geocoded locations of support infrastructure. Using GIS-based (Geographic Information System) spatial analysis, in particular kernel density and hot/cold-spot mapping and bivariate correlations, we quantify proximity-entrepreneurship associations and identify policy-relevant hot- and cold-spots. Our findings reveal female entrepreneurship clusters in major urban corridors. Proximity to support infrastructure is positively associated with women founders. At 10 km buffers, correlations reach r = 0.26 for women and r = 0.31 for men. Effects attenuate at 20 km (r = 0.15 and r = 0.14). We map actionable cold-spots, i.e., places with sparse infrastructure and low female-founder presence, alongside high-performing hot-spots. As a practical implication, we propose a spatial targeting logic: resources should be concentrated in identified cold-spots via women-focused hubs, mobile advisory, and improved last-mile accessibility. Progress through a compact KPI set should be monitored. Targeted spatial support can advance gender equity in entrepreneurship while strengthening regional cohesion and efficient public spending. Full article