Search Results (39)

Background: Lumpy skin disease (LSD) is major transboundary disease affecting cattle and water buffaloes, indirectly causing huge socio-economic losses. Following its first outbreak in India in 2019, the heterologous Goatpox (Uttarkashi strain) vaccine mitigated LSD. Objective: Due to limited data on the spatiotemporal distribution of the disease, this study investigates its dynamics and presents findings from a field study conducted in Maharashtra, India. This study evaluates the safety, immunogenicity, and duration of immunity provided by a heterologous vaccine. Additionally, it examines post-vaccination responses in relation to factors such as age, gender, and breed. Methods: This study employed spatiotemporal analysis of lumpy skin disease (LSD) outbreaks from 2020 to 2024 using GeoDa (v1.22), incorporating Moran’s I and Getis-Ord Gi* statistics to identify spatial clustering patterns. A randomized field trial was conducted to evaluate vaccine safety and immunogenicity in 657 cattle across seven districts. Humoral immune responses were assessed using the serum neutralization test (SNT) and indirect enzyme-linked immunosorbent assay (ELISA), while cell-mediated immunity was evaluated via Interferon-gamma (IFN-γ) ELISA. For sero-monitoring, a total of 1925 serum samples from 22 districts were analyzed. Additionally, statistical analyses (n = 1925), including the Kappa Index, ANOVA, and logistic regression, were performed using SPSS v27 to investigate the influence of factors such as age, sex, and breed (significance level: p < 0.05). Results: LSD exhibited significant spatial clustering across Maharashtra. The Goatpox vaccine was 100% safe, with no adverse reactions. Protective antibody titers (≥1:8) were observed in 96.9% of vaccinated cattle by 14–21 days post-vaccination (dpv), peaking at 60 dpv before declining at 150 dpv. The cell-mediated immune response peaked at 28 dpv. Clinical monitoring for one year showed that only 2% of vaccinated cattle developed mild LSD symptoms after nine months, with no mortality. At six months post-vaccination, seroconversion was 69.7%, with breed significantly influencing seropositivity. Conclusions: This study confirms the Goatpox vaccine’s safety and strong immunogenicity in cattle, marking its first large-scale evaluation in the Indian subcontinent. Further research is needed to assess long-term immunity and protection against virulent LSD strains. Full article

This study investigates the spatial dynamics of participation in the carbon farming eco-scheme in Poland under the EU CAP for 2023–2027. Addressing the broader context of sustainable agriculture and climate change mitigation, this research explores how farm size and structural characteristics influence the adoption of regenerative practices incentivised through this eco-scheme. Using spatial statistical methods, including the global Moran’s I test and the global spatial cross-correlation index, this study analyses county-level data from 2023 to 2024 on farm size, the number of beneficiaries, and payment levels. The findings reveal distinct spatial clusters in eco-scheme participation, with larger farms showing greater regional concentration and smaller farms displaying stronger local clustering in payment distribution. The findings highlight varied spatial mechanisms that influence adoption and financial support patterns, indicating that both farm size and regional context play a significant role in shaping the uptake of eco-schemes. This study emphasises the significance of comprehensive spatial and socio-economic data in the formulation of effective, evidence-based policies pertaining to sustainable agriculture. It establishes a basis for more precisely targeted interventions and optimal resource allocation, thereby supporting both national and EU climate objectives while simultaneously enhancing the resilience and sustainability of rural regions. Full article

This study investigates the spatiotemporal dynamics of land surface temperature (LST) across five distinct land use/land cover (LULC) classes through high-resolution unmanned aerial vehicle (UAV) thermal remote sensing. Thermal orthomosaics were systematically captured at four diurnal periods (morning, afternoon, evening, and midnight) over an urban university campus environment. Using stratified random sampling in each class with spatial controls to minimize autocorrelation, we quantified thermal signatures across bare soil, buildings, grassland, paved roads, and water bodies. Statistical analyses incorporating outlier management via the Interquartile Range (IQR) method, spatial autocorrelation assessment using Moran’s I, correlation testing, and Geographically Weighted Regression (GWR) revealed substantial thermal variability across LULC classes, with temperature differentials of up to 17.7 °C between grassland (20.57 ± 5.13 °C) and water bodies (7.10 ± 1.25 °C) during afternoon periods. The Moran’s I analysis indicated notable spatial dependence in land surface temperature, justifying the use of GWR to model these spatial patterns. Impervious surfaces demonstrated pronounced heat retention capabilities, with paved roads maintaining elevated temperatures into evening (13.18 ± 3.49 °C) and midnight (2.25 ± 1.51 °C) periods despite ambient cooling. Water bodies exhibited exceptional thermal stability (SD range: 0.79–2.85 °C across all periods), while grasslands showed efficient nocturnal cooling (ΔT = 23.02 °C from afternoon to midnight). GWR models identified spatially heterogeneous relationships between LST patterns and LULC distribution, with water bodies exerting the strongest localized cooling influence ($R^2$≈ 0.62–0.68 during morning/evening periods). The findings demonstrate that surface material properties significantly modulate diurnal heat flux dynamics, with human-made surfaces contributing to prolonged thermal loading. This research advances urban microclimate monitoring methodologies by integrating high-resolution UAV thermal imagery with robust statistical frameworks, providing empirically-grounded insights for climate-adaptive urban planning and heat mitigation strategies. Future work should incorporate multi-seasonal observations, in situ validation instrumentation, and integration with human thermal comfort indices. Full article

The Mediterranean Diet is a highly sustainable diet, and legumes are among the products that best characterize this concept. This study evaluates the environmental sustainability of the Protected Geographical Indication (PGI) legume Phaseolus vulgaris L. cultivated in the Asturias region, Spain. Employing a multi-indicator approach, the study aims to define and measure certain biodiversity indicators useful for assessing the ecological quality and sustainability of the agroecosystems under consideration. Spatial analyses were conducted with GIS-based methodologies, integrating the Analytic Hierarchy Process (AHP) to generate a Sustainability Index (SI). The study found that a significant positive spatial autocorrelation was observed using Moran’s I test (Moran’s I = 0.74555, p < 0.01), indicating that the SI values were not equally distributed but clustered around particular regions. Furthermore, the Getis-Ord Gi* analysis determined statistically significant hotspots, mainly distributed in the western and southwestern areas, including regions near Cangas del Narcea and Tineo. This paper highlights the importance of integrating spatial analysis for environmental assessments to develop sustainability approaches. Soil quality, water use, biodiversity, and land management are some of the factors that affect sustainability outcomes in the region. The results underscore the role of PGI in promoting sustainable agricultural practices by meeting geographical and quality requirements for local production. Full article

Urban morphology significantly influences residents’ noise perceptions, yet the impact across different spatial and temporal scales remains unclear. This study investigates the scale-dependent relationship between urban morphology and noise perception in New York City using noise complaint rates (NCR) as a proxy for perceived noise levels. A multi-scale analysis framework was applied, including four spatial scales (100 m, 200 m, 500 m, and 1000 m) and three temporal classifications (daytime/nighttime/dawn, weekdays/weekends, and seasonal divisions). Statistical analyses, including Spearman correlation, Moran’s I test, and Geographically Weighted Regression (GWR), examined spatiotemporal heterogeneity. Results show: (1) NCR and urban morphology indicators vary significantly across spatial and temporal aggregations. (2) Correlations between NCR and urban morphology indicators generally strengthen with larger spatial units, revealing a scale effect. Temporal variations, e.g., residential land ratio (RES) and greenery percentage (SVI Green), show stronger correlations with NCR in summer than in winter. (3) The Moran’s I index revealed significant spatial clustering at the 1000 m scale. Multi-temporal GWR analysis revealed spatial variations in urban morphology-noise relationships across different temporal contexts; in residential areas, building density exacerbates complaints more during non-working periods than during working hours. This study enhances understanding of urban sound environments, offering insights required for more precise urban planning policies. Full article

Municipal activities should not be profitable. Their intention is to provide the highest possible quality of service to citizens and, in this way, contribute to improving their quality of life. For this reason, the evaluation of their performance is very complex and should include several aspects, or criteria. The aim of this study is to quantify the agreement of the financial health assessment of the territorial self-government entities in 2020 with the financial health assessment based on a gradually decreasing number of entry criteria. For this purpose, we use a TOPSIS technique, and a total of 26 combinations of criteria are created with a gradually decreasing number of criteria, i.e., five, four, three, and two criteria used. For a description of the results obtained, we use a wide range of mathematical and statistical methods. The tests used include the Jaccard index, Kolmogorov–Smirnov test, Levene test, Moran index, and others. Our results confirm the fact that the outcome of MCDM analysis is directly and significantly affected by the structure and number of entry criteria. The reduction in the number of criteria resulted in a change in the parameters of the overall results. Full article

Ecosystem service value is crucial for balancing economic growth and ecological preservation in ecologically vulnerable watershed areas. Although Gross Ecosystem Product (GEP) has received significant attention, most existing studies have focused on how to measure it. Few studies have explored spatiotemporal variations in GEP and how land-use changes affect these variations regarding ecological restoration at the river basin level. Additionally, while many studies have examined the relationship between ecosystem service value and economic growth, there is little research on how components of GEP influence economic growth. Analyzing the spatiotemporal structure of GEP and its components could offer new insights into optimizing ecological restoration strategies and promoting sustainable development in vulnerable watershed regions. In this study, we used ArcGIS, InVEST, SPSS, and Python to analyze spatiotemporal variations in GEP in the Yongding River Basin within the Beijing–Tianjin–Hebei Economic Region from 1995 to 2020. Moran’s Index and variance decomposition were applied to analyze the spatiotemporal structure. The grey prediction model forecasted GEP trends from 2025 to 2035. The random forest model was used to assess land-use changes’ impacts on GEP. Paired T-tests were used to compare GEP and GDP, and a dynamic panel model was used to examine how ecosystem service value factors influenced economic growth. The results show the following: (1) Regarding values, GEP accounting and variance decomposition results indicated that ecosystem cultural service value (ECV) and ecosystem regulating service value (ERV) each contributed about half of the total GEP. Ecosystem provisioning service value (EPV) showed an upward trend with fluctuations. Regarding the spatial distribution, Moran’s I analysis showed significant positive spatial correlations for EPV and ERV. The grey prediction model results indicated significant growth in GEP from 2025 to 2035 under current ecological restoration policies, especially for ERV and ECV. (2) In terms of the influence of land-use changes, random forest analysis showed that the forest land area was consistently the most influential factor across GEP, EPV, and ERV. Unused land area was identified as the most significant factor for ECV. (3) Before 2010, GEP was larger than GDP, with significant differences between 1995 and 2000. From 2010 onwards, GDP surpassed GEP, but the differences were not statistically significant. Dynamic panel regression further showed that the water conservation value significantly boosted GDP, whereas the water purification value significantly reduced it. This study highlights the importance of integrating GEP into ecological restoration and economic development to ensure the sustainability of ecologically vulnerable watershed areas. Full article

Ecological environment quality reflects the overall condition and health of the environment. Analyzing the spatiotemporal dynamics and driving factors of ecological environment quality across large regions is crucial for environmental protection and policy-making. This study utilized the Google Earth Engine (GEE) platform to efficiently process large-scale remote sensing data and construct a multi-scale Remote Sensing Ecological Index (RSEI) based on Landsat and Sentinel data. This approach overcomes the limitations of traditional single-scale analyses, enabling a comprehensive assessment of ecological environment quality changes across provincial, municipal, and county levels in Fujian Province. Through the Mann–Kendall mutation test and Sen + Mann–Kendall trend analysis, the study identified significant change points in the RSEI for Fujian Province and revealed the temporal dynamics of ecological quality from 1987 to 2023. Additionally, Moran’s I statistic and Geodetector were employed to explore the spatial correlation and driving factors of ecological quality, with a particular focus on the complex interactions between natural factors. The results indicated that: (1) the integration of Landsat and Sentinel data significantly improved the accuracy of RSEI construction; (2) the RSEI showed a consistent upward trend across different scales, validating the effectiveness of the multi-scale analysis approach; (3) the ecological environment quality in Fujian Province experienced significant changes over the past 37 years, showing a trend of initial decline followed by recovery; (4) Moran’s I analysis demonstrated strong spatial clustering of ecological environment quality in Fujian Province, closely linked to human activities; and (5) the interaction between topography and natural factors had a significant impact on the spatial patterns of RSEI, especially in areas with complex terrain. This study not only provides new insights into the dynamic changes in ecological environment quality in Fujian Province over the past 37 years, but also offers a scientific basis for future environmental restoration and management strategies in coastal areas. By leveraging the efficient data processing capabilities of the GEE platform and constructing multi-scale RSEIs, this study significantly enhances the precision and depth of ecological quality assessment, providing robust technical support for long-term monitoring and policy-making in complex ecosystems. Full article

Identifying spatial clusters of rabies in animals aids policymakers in allocating resources for rabies prevention and control. This study aimed to investigate spatial patterns and hotspots of rabies in different animal species at the county level in New Jersey. Data on animal rabies cases from January 1989 to December 2023 were obtained from the New Jersey Department of Health and aggregated by county. Global Moran’s index (I) statistics were computed for each species to detect global spatial clustering (GeoDa version 1.22). Local Moran’s indicators of spatial association (LISA) were computed to identify local clusters of rabies. The results from the LISA analysis were mapped using ArcGIS Pro to pinpoint cluster locations. A total of 9637 rabies cases were analyzed among raccoons (n = 6308), skunks (n = 1225), bats (n = 1072), cats (n = 597), foxes (n = 225), and groundhogs (n = 210). A global Moran’s test indicated significant global spatial clustering in raccoons (I = 0.32, p = 0.012), foxes (I = 0.29, p = 0.011), and groundhogs (I = 0.37, p = 0.005). The LISA results revealed significant spatial clustering of rabies in raccoons and foxes in southeastern New Jersey and in groundhogs in northern New Jersey. These findings could guide the development of targeted oral rabies vaccination programs in high-risk New Jersey counties, reducing rabies exposure among domestic animals and humans. Full article

Leptospirosis is a zoonotic disease caused by the spirochete bacteria Leptospira spp. From December 2017 to December 2023, a total of 34 canine leptospirosis cases were reported in urban Sydney, Australia. During the same spatio-temporal frame, one locally acquired human case was also reported. As it was hypothesised that human residents and companion dogs might both be exposed to pathogenic Leptospira in community green spaces in Sydney, an environmental survey was conducted from December 2023 to January 2024 to detect the presence of pathogenic Leptospira DNA in multipurpose, recreational public parks in the council areas of the Inner West and City of Sydney, Australia. A total of 75 environmental samples were collected from 20 public parks that were easily accessible by human and canine visitors. Quantitative PCR (qPCR) testing targeting pathogenic and intermediate Leptospira spp. was performed, and differences in detection of Leptospira spp. between dog-allowed and dog-prohibited areas were statistically examined. The global Moran’s Index was calculated to identify any spatial autocorrelation in the qPCR results. Pathogenic leptospires were detected in all 20 parks, either in water or soil samples (35/75 samples). Cycle threshold (Ct) values were slightly lower for water samples (Ct 28.52–39.10) compared to soil samples (Ct 33.78–39.77). The chi-squared test and Fisher’s exact test results were statistically non-significant (p > 0.05 for both water and soil samples), and there was no spatial autocorrelation detected in the qPCR results (p > 0.05 for both sample types). Although further research is now required, our preliminary results indicate the presence of pathogenic Leptospira DNA and its potential ubiquity in recreational parks in Sydney. Full article

Accurate drought forecasting is necessary for effective agricultural and water resource management and for early risk warning. Various machine learning models have been developed for drought forecasting. This work developed and tested a fusion-based ensemble model, namely, the stacking (ST) model, that integrates extreme gradient boosting (XGBoost), random forecast (RF), and light gradient boosting machine (LightGBM) for drought forecasting. Additionally, the ST model employs the SHapley Additive exPlanations (SHAP) algorithm to interpret the relationship between variables and forecasting results. Multi-source data that encompass meteorological, vegetation, anthropogenic, landcover, climate teleconnection patterns, and topological characteristics were incorporated in the proposed ST model. The ST model forecasts the one-month lead standardized precipitation evapotranspiration index (SPEI) at a 12 month scale. The proposed ST model was applied and tested in the German federal states of Brandenburg and Berlin. The results show that the ST model outperformed the reference persistence model, XGBboost, RF, and LightGBM, achieving an average coefficient of determination (R²) value of 0.845 in each month in 2018. The spatiotemporal Moran’s I method indicates that the ST model captures non-stationarity in modeling the statistical association between predictors and the meteorological drought index and outperforms the other three models (i.e., XGBoost, RF, and LightGBM). Global sensitivity analysis indicates that the ST model is influenced by a combination of environmental variables, with the most sensitive being the preceding drought indices. The accuracy and versatility of the ST model indicate that this is a promising approach for forecasting drought and other environmental phenomena. Full article

Recent GIS technologies are shaping the direction of Precision Agriculture and Viticulture. Sentinel-2 satellites and UAVs are key resources for multi-spectral analyses of vegetation. Despite being extensively adopted in numerous applications and scenarios, the pros and cons of both platforms are still debated. Researchers have currently investigated different aspects of these sources, mainly comparing different vegetation indexes and exploring potential relationships with agronomic variables. However, due to the costs and limitations of such an approach, a standardized methodology for agronomic purposes is still missing. This study aims to fill such a methodology gap by overcoming the potential flaws or shortages of previous works. To achieve this, an image acquisition campaign covering 6 months and over 17 hectares was carried out, followed by an NDVI comparison between Sentinel-2 and UAV to eventually explore relationships with agronomic variables. Comparative analyses were performed by using both classical (Ordinary Least Squares regression and Pearson Correlation) and spatial (Moran’s Index) statistical approaches: here, 90% of cases show r and MI scores above 0.6 for plain images, with these scores expectedly lowering to 72% and 52% when considering segmented images. Moreover, NDVI thematic maps were classified into clusters and validated by the Chi-squared test. Finally, the relationship and distribution of agronomic variables within NDVI and clustered maps were consistently validated through the ANOVA test. The proposed open-source pipeline allows to strengthen existing UAV and satellite applications in Precision Agriculture by integrating more agronomic variables. Full article

Background: This study aimed at examining the prevalence and variations in undernutrition among children below five years of age in Uganda while considering the influence of space and time factors. Various studies conducted in Uganda analyzed the undernutrition phenomenon among children below five years of age with a focus on the risk factors and spatial variations; however, no study has ever integrated the elements of time in examining the problem of undernutrition in Uganda. The approach of spatial and spatiotemporal analysis is essential in identifying cluster patterns, hotspots, trends, and emerging hotspots, which is crucial in making timely and location-specific interventions. Methods: Data from the six Uganda Demographic and Health Surveys spanning from 1990 to 2016 were used, with the main outcome variable being undernutrition among children below five years of age. A Composite Index of Anthropometric Failure was derived from the three undernutrition outcomes and subsequently used as a proxy of undernutrition in this study. All data that were relevant to this study were retrieved from the survey datasets and combined with the 2014 shape files of Uganda to enable spatial and spatiotemporal analysis. Spatial maps with the spatial distribution of the prevalence of undernutrition, both in space and time, were generated using ArcGIS Pro version 2.8. Moran’s I, an index of spatial autocorrelation, was used to test the hypothesis of no spatial autocorrelation, while the Getis–Ord (Gi*) statistic was used to examine hot and cold spot areas. Furthermore, space-time cubes were generated to establish the trend in undernutrition as well as to mirror its variations over time and across the country. Moreover, emerging hot spot analysis was done to help in identifying the patterns of undernutrition over time. Results: The national prevalence of undernutrition among children below five years of age was 31.96 percent, with significant spatial variations both in space across Uganda and in the time since 1989. The index of spatial autocorrelation (Moran’s I) confirmed spatial clustered patterns as opposed to random distributions of undernutrition prevalence. Four hot spot areas, namely, the Karamoja, the Sebei, the West Nile, and the Toro regions, were significantly evident. Most of the central parts of Uganda were identified as cold spot clusters, while most of Western Uganda, the Acholi, and the Lango regions had no statistically significant spatial patterns by the year 2016. The spatio-temporal analysis identified the Karamoja and Sebei regions as clusters of persistent, consecutive, and intensifying hot spots, West Nile region was identified as a sporadic hotspot area, while the Toro region was identified with both sporadic and emerging hotspots. In conclusions, undernutrition is a silent pandemic that calls for immediate and stringent measures. At 31.96 percent, the prevalence is still very high and unpleasant. To reduce the prevalence of undernutrition and to achieve SDG goal 2, policymakers, as well as implementers, should consider the spatial effects and spatial and spatiotemporal variations across the country and prioritize interventions to hot spot areas. This would ensure efficient, timely, and region-specific interventions. Full article

The aim of this study is to visualize the historical changes in wheat and corn cropping patterns in the Texas High Plains from the perspective of geographical concentration and spatial autocorrelation. Historical county-level agricultural census data were collected from the United States Department of Agriculture and the National Agricultural Statistics Service from 1978 to 2017. Exploratory data analysis techniques were employed to examine the geographical concentration and spatial dependence of crop production among nearby locations. The results of temporal changes indicate that the harvested acres of corn and wheat tended to decrease throughout the study period. Total and irrigated harvested corn and wheat acreages were concentrated in a smaller number of counties over time while wheat production was mainly concentrated in the northern part of the region. The Moran’s I test statistic for total and irrigated areas of cropland suggest that there was spatial dependence among the neighboring counties in crop production in this region. In summary, there was a spatiotemporal change in cropping patterns in the Texas High Plains over the study period. Based on the results of the spatiotemporal changes in cropping patterns in the Texas High Plains, policy makers should promote and support non-irrigated varieties of crops in order to decrease the dependence on irrigation water from the Ogallala Aquifer. Full article

The development and functional perfection of urban areas have led to increasingly severe fire risks in recent decades. Previous urban fire risk assessment methods relied on subjective judgment, rough data collection, simple linear statistical methods, etc. These drawbacks can lead to low robustness of evaluation and inadequate generalization ability. To resolve these problems, this paper selects the indicator and regression models based on the high-resolution data of the spatial distribution characteristics of Longquanyi distinct in Chengdu, China. and proposes an integrated machine learning algorithm for fire risk assessment. Firstly, the kernel density analysis is used to map the fourteen urban characteristics related to fire risks. The contributions of these indicators (characteristics) to fire risk and its corresponding index are determined by Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost). Then, the spatial correlation of fire risks is determined through Moran’s I, and the spatial distribution pattern of indicator weights is clarified through the raster coefficient space analysis. Finally, with these selected indicators, we test the regression performance with a backpropagation neural network (BPNN) algorithm and a geographically weighted regression (GWR) model. The results indicate that numerical variables are more suitable than dummy variables for estimating micro-scale fire risks. The main factors with a high contribution are all numerical variables, including roads, gas pipelines, GDP, hazardous chemical enterprises, petrol and charging stations, cultural heritage protection units, assembly occupancies, and high-rise buildings. The machine learning algorithm integrating RF and BPNN shows the best performance (R² = 0.97), followed by the RF-GWR integrated algorithm (R² = 0.87). Compared with previous methods, this algorithm reduces the subjectivity of the traditional assessment models and shows the ability to automatically obtain the key indicators of urban fire risks. Hence, this new approach provides us with a more robust tool for assessing the future fire safety level in urban areas. Full article