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AbstractThis paper examines the spatiotemporal inequality in electricity consumption at the subnational level of Bangladesh using nighttime light (NTL) data. The NTL data, sourced from the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) for the period from 2013 to 2020, reveals persistent variability in electricity consumption among the districts. Notably, the gap between urban and non-urban areas has widened. While within district inequality (measured by NTL Gini) has declined over time, it remains high in several districts. Convergence analysis confirms that while lagging districts are showing a catching up effect, the sub-districts are diverging among themselves (in terms of mean NTL per capita). Interestingly, the rural sub-districts are converging among themselves despite urban sub-district divergence. The study also identifies regions with significant imbalance between NTL, population, and built-up area density values. These findings have implications for policymakers aiming to ensure electricity for all and reduce inequality. [ABSTRACT FROM AUTHOR]

As the world continues to struggle with the effects of the COVID-19 pandemic, there has been much speculation on the impact of the virus on crime rates, especially domestic violence. Researchers have largely adopted a routine activities explanation for increased levels of domestic violence, where disruptions in patterns of daily life caused by lockdowns increased the opportunity for such events to occur. The purpose of this manuscript was to utilise emerging hot spot analysis to explore spatiotemporal changes in the number of calls for service for domestic disturbance from 1 March 2020 – 31 December 2020 in the City of Tampa, FL USA based on critical time frames for restrictions and recovery. As predicted by opportunity theory, there was a statistically significant decline in the counts of calls over the 10-month period. Additionally, sporadic hot spots were found at some locations during times of heightened restrictions. Locations with sporadic hot spots coincided with census tracts reporting lower education levels; higher levels of poverty, unemployment, single parent households; and more minority residents. [ABSTRACT FROM AUTHOR]

Background: Yellow fever (YF) is a zoonotic disease transmitted by mosquitoes among humans and nonhuman primates. Although urban YF is eradicated, the sylvatic YF has reemerged in some areas of Brazil in the twenty-first century. From 2016 to 2019, a sylvatic YF epidemic occurred in Southeast Brazil, where it had been eradicated in the 1940s. Methods: This study's objective was to describe the epidemic in the states of the Southeast region, based on descriptive, cluster, and mobility analyses. Results: Both the descriptive and cluster analyses showed that the YF cases spread from the state of Minas Gerais southward, causing peaks in cases during the summer months. None of the state capitals was included in the clusters, but the connectivity between the municipalities in Greater Metropolitan São Paulo highlighted potential paths of spread. Despite differences in sociodemographic profiles between the Southeast and North of Brazil (the latter region considered endemic), the epidemiological profile was similar, except for patients' occupation, which was not related to rural work in the Southeast. Conclusion: The results contributed to our understanding of the paths by which YF spread across Southeast Brazil and the epidemiological profile in an area that had gone decades without autochthonous cases. [ABSTRACT FROM AUTHOR]

Accurate and fine‐scaled prediction of ozone concentrations across space and time, as well as the assessment of associated human risks, is crucial for protecting public health and promoting environmental conservation. This paper introduces NetGBM, an innovative machine‐learning model designed to comprehensively model ozone levels across China's diverse topography and analyze the spatiotemporal distribution of ozone and exposure. Our model focuses on daily, weekly, and monthly predictions, achieving commendable R2 ${\mathrm{R}}^{2}$ coefficients of 0.83, 0.77, and 0.79, respectively. By constructing a gridded map of ozone and incorporating both land use and meteorological features into each grid, we achieved ozone prediction at a high spatiotemporal resolution, outperforming previous research in terms of performance and scale, particularly in regions with limited monitoring stations. The results can be further improved when applied to regional research using meteorological and ozone data from regional stations. Additionally, our research revealed that temperature is the most significant factor affecting ozone concentrations across China. In health risk assessment, we retrieved a high‐resolution spatial distribution of ozone‐attributed mortality for 5‐COD and daily ozone inhalation distributions during our study period. We concluded that ozone‐attributed mortality is predominantly caused by stroke and IHD, accounting for more than 70% of the total deaths in 2021, with the highest mortality rates in developed urban areas such as the NCP and the YRD. Our experiment demonstrated the potential of NetGBM in robustly modeling ozone across China with high spatiotemporal resolution and its applicability in measuring associated health risks. Plain Language Summary: This study introduces NetGBM, an innovative machine‐learning model designed to forecast ozone levels and assess ozone‐attributed public health risks. This model is crucial for post‐pandemic air quality management, providing high‐resolution predictions that are essential for targeted health interventions and informed environmental policies. By integrating feature engineering with predictive analytics, NetGBM enhances its performance, particularly in regions with limited monitoring. This makes it a robust tool for developing sustainable environmental strategies. Key Points: We introduce NetGBM, a machine‐learning model for high spatiotemporal resolution ozone prediction across ChinaWe perform ozone inhalation and GEMM analysis, which identifies regions facing elevated health risks due to high ozone exposureThe NetGBM model has superior performance compared to prior research and performs robustly in regions with limited monitoring resources [ABSTRACT FROM AUTHOR]

Exocytosis is a fundamental process used by eukaryotes to regulate the composition of the plasma membrane and facilitate cell--cell communication. To investigate exocytosis in neuronalmorphogenesis, previously we developed computational tools with a graphical user interface to enable the automatic detection and analysis of exocytic events from fluorescence timelapse images. Although these tools were useful, we found the codewas brittle and not easily adapted to different experimental conditions. Here, we developed and validated a robust and versatile toolkit, named pHusion, for the analysis of exocytosis, written in ImageTank, a graphical programming language that combines image visualization and numerical methods. We tested pHusion using a variety of imaging modalities and pH-sensitive fluorophores, diverse cell types and various exocytic markers, to generate a flexible and intuitive package. Using this system, we show that VAMP3-mediated exocytosis occurs 30-times more frequently in melanoma cells compared with primary oligodendrocytes, that VAMP2-mediated fusion events inmature rat hippocampal neurons are longer lasting than those in immature murine cortical neurons, and that exocytic events are clustered in space yet randomin time in developing cortical neurons. [ABSTRACT FROM AUTHOR]

Objective The epidemic situation of influenza from 2014 to 2023 in Huanggang City was analyzed to provide reference for prevention and control of influenza in Huanggang City. [WTHZ]Methods [WTBZ]The data of influenza online report, ILI monitoring data and etiological detection data in Huanggang City from 2014 to 2023 were collected and analyzed. [WTHZ]Results [WTBZ]From 2014 to 2023, the annual incidence of influenza in Huanggang City ranged from 5.38/100,000 (2014) to 1,178.19/100,000 (2023), with a trend of first increasing (2014-2019), then decreasing (2020-2022), and reaching a peak in 2023.Retrospective spatiotemporal scanning analysis showed that the onset time was mainly concentrated in December of each year and January and February of the following year. In the epidemic season, Huangzhou, Luotian and Qichun were the main cluster areas of Class I and Class II. The ILI% value of each year in Huanggang City from 2014 to 2023 ranged from 4.41% to 13.17%, and the difference of ILI% each year was statistically significant (χ2=83 652.46, P<0.01), and the annual change trend was similar to that reported by influenza network. The positive rate of influenza virus in ILI case samples showed the characteristics of peak in winter and spring and small peak in summer, and H3N2, H1N1 and BBV were alternately or co-prevalent in each year and within each year. The analysis of correlation results between ILI% and positive rate of influenza virus showed that: except 2014-2016 and 2020-2022, the other 4 years showed positive correlation, and the P-value was less than 0.05, [WTHZ]Conclusion [WTBZ]The prevalence of influenza in Huanggang showed an increasing trend year by year, with obvious seasonal characteristics and temporal and spatial aggregation. The dominant strains, mainly H1N1, H3N2 and BV, alternated or co-circulated within the year and between different years. [ABSTRACT FROM AUTHOR]

Background: Visceral leishmaniasis (VL) remains a significant public health concern in West Pokot County, Kenya, where a large outbreak between 2020 and 2022 emphasised the need for improved VL control strategies. However, these measures are partially hampered by limited insight into the geographical distribution of cases and localised outbreaks of the disease. This study aimed to describe the epidemiology and spatiotemporal patterns of VL in West Pokot between 2018 and 2022, in order to map the spread of VL transmission and identify regions that should be prioritised for control interventions. Methods: VL patient demographics and village of residence were retrieved from admission records of Kacheliba Sub-County Hospital in West Pokot, Kenya. The temporal trend in VL admissions between 2018 and 2022 was analysed using seasonal decomposition analysis. To describe the spatial distribution of VL cases, geographic coordinates of villages of residence were collected from pre-established databases, and VL incidence was mapped at the sub-location level. Hotspot analysis was performed per study year to identify villages with high VL incidence, and scan statistics were applied to detect spatiotemporal clusters of VL cases during the study period. Results: A total of 1948 VL patients were reported between 2018 and 2022. The annual number of cases increased from 245 in 2019 to 598 in 2022, and VL admissions were generally higher at the start of the wet seasons. 70% of the VL cases could be georeferenced, and mapping of VL incidence revealed high case rates in the east of West Pokot during the complete study period. The eastern villages Lotongot and Chepaywat were marked as VL hotspots at a 99% confidence level in all study years. In addition, five significant spatiotemporal clusters were detected in the east and north, suggestive of local VL outbreaks in these regions. Conclusions: The increase in VL hospital admissions during the study period stresses the need for enhanced VL control and outbreak mitigation in West Pokot. These control measures should be focused on the hotspot regions in the east of the county. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2) is a crucial greenhouse gas, and its concentration and spatiotemporal characteristics are among the principal sources of uncertainty in global warming assessments. Satellite remote sensing is a widely adopted, high-accuracy approach for monitoring atmospheric CO 2. However, limited swath width and cloud cover significantly reduce satellite observation coverage. This study addresses the temporal changes in CO 2 concentration and utilizes a machine learning-based fusion of multiple data sources to generate daily, full-coverage, 0.05° spatial resolution column-averaged CO 2 concentration data for China from 2015 to 2020. Ten-fold cross-validation yielded a determination coefficient R2 of 0.97, root mean square error of 0.92 ppm, and mean absolute error of 0.59 ppm. Compared to other datasets, this study's dataset exhibits superior accuracy and spatiotemporal detail. Using the produced CO 2 concentration data in this study, we conducted a spatiotemporal analysis of CO 2 concentrations in China. The results indicate that, in general, the Western region exhibits a higher growth rate in CO 2 concentration than the Eastern and Central regions, with areas of lower CO 2 concentration experiencing higher growth rates while regions with higher CO 2 concentration have lower growth rates. Moreover, the highest increase in CO 2 concentration occurred in 2016, with a substantial decrease in CO 2 concentration growth observed in 2018. Notably, the reduction in CO 2 concentration in the Qinghai-Tibet Plateau region during the summer is considerably smaller than in other regions, possibly due to atmospheric transport from the Indian Peninsula. [ABSTRACT FROM AUTHOR]

Background: Despite being preventable and curable, leprosy remains endemic in some undeveloped regions, including China. Wenshan Zhuang and Miao Autonomous Prefecture (Wenshan prefecture) currently bears the highest leprosy burden in China. In this ecological study, we aimed to analyze the epidemiological characteristics as well as identify and visualize the high-risk townships of Wenshan prefecture using the most updated leprosy data from 2010 to 2022. Methods: Geographical information system combined with spatial scan statistics was used for newly detected leprosy cases abstracted from the Leprosy Management Information System in China. Global Moran's I index was used to uncover the spatial pattern of leprosy at the township level. Spatial scan statistics, encompassing purely temporal, purely spatial, spatial variation in temporal trends, and space-time analysis, were implemented for detecting the risk clusters. Results: Between 2010 and 2022, Wenshan prefecture detected 532 new leprosy cases, comprising 352 (66.17%) males and 180 (33.83%) females. The aggregated time primarily occurred between October 2010 and March 2014. The distribution pattern of newly detected leprosy cases was spatially clustered. We identified four high-risk spatial clusters encompassing 54.51% of the new cases. Furthermore, spatial variation in temporal trends highlighted one cluster as a potential high-risk area. Finally, two space-time clusters were detected, and the most likely cluster was predominantly located in the central and northwest regions of Wenshan prefecture, spanning from January 2010 to September 2013. Conclusions: In this ecology study, we characterized the epidemiological features and temporal and spatial patterns of leprosy in Wenshan prefecture using the most recent leprosy data between 2010 and 2022. Our findings offer scientific insights into the epidemiological profiles and spatiotemporal dynamics of leprosy in Wenshan prefecture. Clinicians and policymakers should pay particular attention to the identified clusters for the prevention and control of leprosy. [ABSTRACT FROM AUTHOR]

Drought is a widespread natural hazard resulting from an extended period of reduced rainfall, with significant socioeconomic and ecological consequences. Drought severity can impact food security globally due to its high spatial and temporal coverage. The primary objective of this paper consists of a comparative spatiotemporal analysis of environmental extremes (drought/wetness) through the estimation of a twelve-month Standardized Precipitation Index (SPI12) between three distinct vulnerable agricultural regions in the Mediterranean basin (i.e., Spain, Lebanon and Tunisia), under a climate change environment in the last 38 years (1982–2020). The added value of this paper lies in the simultaneous estimation of temporal and spatial variability of drought and wetness periodic events, paying special attention to the geographical patterns of these extremes both in annual and interannual (seasonal) time scales. The results indicated that Spain and Tunisia (western Mediterranean) exhibit similar patterns over the studied period, while Lebanon demonstrates contrasting trends. Comparing the two extreme dry hydrological years, the Spanish study area faced the highest drought intensity, areal extent and duration (SPI12 = −1.18; −1.84; 28–78%; 9–12 months), followed by the Lebanese (SPI12 = −1.28; −1.39; 37–50%; 7–12 months) and the Tunisian ones (SPI12 = −1.05; −1.08; 10–34%; 8 months). Concerning the wettest hydrological years, the Lebanese study domain has recorded the highest SPI12 values, areal extent and duration (SPI12 = 1.58; 2.28; 66–83%; 8–11 months), followed by the Tunisian (SPI12 = 1.55; 1.79; 49–73%; 7–10 months) and Spanish one (SPI12 = 1.07; 1.99; 21–73%; 4–11 months). The periodicity of drought/wetness episodes is about 20 years in Spanish area and 10 years in the Lebanese area (for drought events), whereas there seems no periodicity in the Tunisian one. Understanding the spatial distribution of drought is crucial for targeted mitigation strategies in high-risk areas, potentially avoiding broad, resource-intensive measures across entire regions. [ABSTRACT FROM AUTHOR]

This paper complements the existing literature on Bike Share Schemes (BSS) by investigating their use from a spatiotemporal perspective to assess their relationship with public transport (PT). We address these questions through the case of Brussels and its long-standing docked BSS 'Villo!'. Our study analyses comprehensive (consecutive 12 months) and disaggregated (station level) data on rentals and returns and finds that Villo! is used mostly in dense (although not all) districts also well served by PT. However, temporal structures suggest Villo! overtakes PT at night in vibrant districts and possibly in selected districts with lower PT services over weekends. In addition, Villo! stations at key PT hubs usually do not show specific temporal patterns, which suggests intermodality may work at all times during PT operations. There could be an evening peak effect combined with the Brussels' topography, but this needs to be confirmed by on-site surveys. [ABSTRACT FROM AUTHOR]

Although accelerated urbanization has led to economic prosperity, it has also resulted in urban heat island effects. Therefore, identifying methods of using limited urban spaces to alleviate heat islands has become an urgent issue. In this study, we assessed the spatiotemporal evolution of urban heat islands within the central urban area of Fuzhou City, China from 2010 to 2019. This assessment was based on a morphological spatial pattern analysis (MSPA) model and an urban thermal environment spatial network constructed using the minimum cumulative resistance (MCR) model. Optimization measures for the spatial network were proposed to provide a theoretical basis for alleviating urban heat islands. The results show that the heat island area within the study area gradually increased while that of urban cold island area gradually decreased. The core area was the largest of the urban heat island patch landscape elements with a significant impact on other landscape elements, and represented an important factor underlying urban heat island network stability. The thermal environment network revealed a total of 197 thermal environment corridors and 93 heat island sources. These locations were then optimized according to the current land use, which maximized the potential of 1599.83 ha. Optimization based on current land use led to an increase in climate resilience, with effective measures showing reduction in thermal environment spatial network structure and function, contributing to the mitigation of urban heat island. These findings support the use of current land use patterns during urban heat island mitigation measure planning, thus providing an important reference basis for alleviating urban heat island effects. [ABSTRACT FROM AUTHOR]

Leptospirosis is a worldwide re-emerging zoonotic disease. The study was conducted to estimate the Seroprevalence of canine leptospirosis in a total of 450 dogs, from a total of 97 puppies and 353 adult dogs selected for examination Sampling, started from January to December 2023 in District Kasur in the province Punjab of the country Pakistan. Leptospira IgG ELISA kit manufactured by DRG Instruments GmbH, Germany was used for the screening of canine Leptospira antibodies. Out of 450 tested dogs, 183 dogs (40.67%) were tested positive for Leptospira antibody for the screening of Leptospira antibodies. The estimated Seroprevalence of leptospirosis in various age groups of dogs, were 23.7% (23/97) and 45.3% (160/353), in puppies and adults, respectively (P < 0.05). It was found that out of the sampled dogs, a total of 35/127 (27.6%), 29/100 (29%), 73/130 (56.2%), and 46/93 (49.5%) dogs were tested seropositive for Leptospira antibodies in winter, spring, summer and fall, respectively (P < 0.05). [ABSTRACT FROM AUTHOR]

This study investigates the cooling potential of vegetation in rural landscapes of the Czech Republic to mitigate heat-related issues. Using remote sensing, the Cooling Capacity Index (CCI) is assessed to measure green spaces' ability to lower air temperatures using evapotranspiration and shading. Landsat 8/9 and meteorological data are utilised, with CCI calculated based on vegetation cover, albedo, and evapotranspiration. Our results demonstrate significant variations in cooling capacity across different land use types. Forests exhibited the highest cooling potential, while urban areas, characterised by heat-absorbing materials, displayed the least. We analysed temporal and spatial variations in cooling capacity using various visualisation tools and validated the results against the InVEST software (v3.14.0). This study highlights the effectiveness of remote sensing in quantifying ecosystem functions, particularly the cooling services provided by vegetation. Our findings emphasise the crucial role of vegetation in mitigating urban heat islands and addressing climate change. This research provides valuable insights for developing climate change adaptation strategies in rural landscapes. [ABSTRACT FROM AUTHOR]

One of the reasons cities are attractive is the rich array of urban amenities they provide. In this study, we examine how investments in urban rail transit affect clusters of consumer amenities in China, based on the experiences of six Chinese cities that are at varying stages of rail transit development. A difference-in-differences model is built using geocoded data on wide categories of consumer amenities to estimate the rail transit effects from a sample of 315 new rail transit stations opened between 2015 and 2018. Our results show that the opening of a new station induces an increase of approximately 4% in the total number of consumer amenities in the vicinity of the station within the first year. The impact strengthens substantially after two years. The effect of rail transit varies across cities and among different neighborhoods within a city. In most cities, rail transit effects are stronger in central areas, contributing to agglomeration of retail and dining amenities. When the rail system expands rapidly, however, the opening of a new station tiggers greater growth of daily service amenities in the suburbs, leading to a decentralization effect. Additionally, we find that neighborhood purchasing power and transit-adjacent land supply have limited impacts on amenity growth driven by new rail stations. This study provides important insight into how public amenities interact with private amenities and its findings have significant implications for urban planners and policymakers. [ABSTRACT FROM AUTHOR]

Social sensing, using humans as sensors to collect disaster data, has emerged as a timely, cost-effective, and reliable data source. However, research has focused on the textual data. With advances in information technology, multimodal data such as images and videos are now shared on media platforms, aiding in-depth analysis of social sensing systems. This study proposed an analytical framework to extract disaster-related spatiotemporal information from multimodal social media data. Using a pre-trained multimodal neural network and a location entity recognition model, the framework integrates disaster semantics with spatiotemporal information, enhancing situational awareness. A case study of the April 2024 heavy rain event in Guangdong, China, using Weibo data, demonstrates that multimodal content correlates more strongly with rainfall patterns than textual data alone, offering a dynamic perception of disasters. These findings confirm the utility of multimodal social media data and offer a foundation for future research. The proposed framework offers valuable applications for emergency response, disaster relief, risk assessment, and witness discovery, and presents a viable approach for safety risk monitoring and early warning systems. [ABSTRACT FROM AUTHOR]

Objective: There is a demand to make hospital management information beyond basic key performance indicators (KPIs) accessible for clinicians. Methods: We developed an interactive application (IAPP) in R Shiny to visualize such information. We provided the IAPP source code online. As a use case, we recorded basic KPIs (numbers of patients (NPs), reimbursed valuation ratios (RVRs), mean length of stay (LOS)), main diagnoses (MDGNs), main procedures (MPRCs), and catchment area (CA) by district from April 2022 to March 2024 at the index department in central Germany, where a neurotrauma and spinal surgery service was resumed on 1 April 2022. Case mix indexes (CMIs) were calculated. We retrieved information about online-reported patient satisfaction (ORPS) from an online physician rating platform between January 2022 and March 2024. Information on longitudes and latitudes of the index department and neighbouring hospitals was collected. We calculated car travelling isochrones (CTIs) of the hospitals as a proxy variable for accessibility. Chi-square and Fisher's exact served as statistical tests. Results: During the observation period, the monthly NPs increased from 26 to 43, the RVR showed a 3.96-fold increase, the CMI showed a 2.41-fold increase, and the LOS reached a steady state in the 2nd year after service resumption. CA (p = 0.03), MDGNs, and MPRCs diversified. ORPS trended towards better overall evaluation after service resumption (p = 0.09). CTI mapping identified a unique market position of the index department. Conclusions: The IAPP makes extended hospital management data accessible to clinicians, can inform other stakeholders in healthcare, and can be tailored to local conditions. [ABSTRACT FROM AUTHOR]

Using GIS technology, this study investigated the spatiotemporal distribution pattern of influenza incidence in Xinjiang from 2014 to 2023 based on influenza surveillance data. The study revealed a noticeable fluctuation trend in influenza incidence rates in Xinjiang, particularly notable spikes observed in 2019 and 2023. The results of the 3-year moving average showed a significant long-term upward trend in influenza incidence rates, confirmed by Theil-Sen method (MAD = 2.202, p < 0.01). Global spatial autocorrelation analysis indicated significant positive spatial autocorrelation in influenza incidence rates from 2016 and from 2018 to 2023 (Moran's I > 0, P < 0.05). Local spatial autocorrelation analysis further revealed clustering patterns in different regions, with high-high clustering and low-high clustering predominating in northern Xinjiang, and low-low clustering predominating in southern Xinjiang. Hotspot analysis indicated a progressive rise in the number of influenza incidence hotspots, primarily concentrated in northern Xinjiang, particularly in Urumqi, Ili Kazakh Autonomous Prefecture, and Hotan Prefecture. Standard deviation ellipse analysis and the trajectory of influenza incidence gravity center migration showed that the transmission range of influenza in Xinjiang has been expanding, with the epidemic center gradually moving northward. The spatiotemporal heterogeneity of influenza incidence in Xinjiang highlights the need for differentiated and precise influenza prevention and control strategies in different regions to address the changing trends in influenza prevalence. [ABSTRACT FROM AUTHOR]

App-based ridesharing services (RSSs), exemplified by platforms like Uber, play a pivotal role in modern transportation by offering convenient and on-demand services. The exploration of RSSs necessitates a comprehensive consideration of the inherent spatiotemporal variability within the data. Prior research, however, has tended to analyze the spatial and temporal dimensions separately, with many studies omitting the temporal aspect. This study addresses the gap by using geovisualization techniques to illustrate emerging hot spot analysis in New York City in 2022, derived from space–time data mining. Overall, despite temporal variations in overall RSSs ridership, certain taxi zones maintain distinct ridership patterns. Across the five New York City boroughs (Manhattan, Bronx, Queens, Brooklyn, and Staten Island), Midtown Manhattan and the Brooklyn areas adjacent to Queens exhibit saturated intensifying hot spots, signaling a notable increase in RSSs ridership throughout 2022, surrounded by sporadic hot spots. Conversely, peripheral areas of New York City reveal diminishing cold spots, indicating a decrease in their intensity as cold spots. Furthermore, the study conducts separate spatial and temporal profiling. By presenting the spatiotemporal trends of RSSs, this research complements existing literature and provides valuable insights for more informed interventions. The study also highlights certain limitations that could be addressed in future endeavors. [ABSTRACT FROM AUTHOR]

This study investigates the spatiotemporal variations in ambulance demand in southern Sweden, and how land use factors and sociodemographic factors are associated with variations in demand. A standardized ambulance demand ratio (SADR) indicating whether the risk of requiring an ambulance exceeded the expected was calculated using ambulance dispatch data in southern Sweden in 2018. Spatial analysis using a geographic information system (GIS) and spatial statistical methods underlie the methodology. Findings show that although rural areas are associated with a higher risk of requiring an ambulance (especially in daytime), this risk may be obscured by the more numerous dispatches occurring in urban areas. The increased risk in areas with bars and nightclubs, schools, industrial areas or road crossings reflects temporal variations in people's routine activities. By dictating how and when population's reshuffle, land use factors can help explain variations in risk for requiring an ambulance. Confirming previous research, areas with a higher share of older adults are associated with a higher ambulance demand while areas with a high median income are associated with lower demand. Population size may be an inadequate indicator of ambulance demand. Planning should account for daily changes in population sizes and compositions as this impacts ambulance demand. [ABSTRACT FROM AUTHOR]

The ecological and environmental problems of arid zones have become an urgent global concern. Current research on ecological risk is based mainly on the dominant functions of land use, with a primary focus on land use landscape projections and less consideration of potential risks to ecosystems, system resilience and interactions between nature and future sustainable development. In this study, a potential–connectivity–resilience ecological risk assessment model based on the SDGs was constructed using multisource data to spatially quantify indicators at the grid scale in the Turpan and Hami regions of Xinjiang, China. This model was used as a basis for studying ecological risk in arid zones from a production–living–ecological space (PLES) perspective. The results revealed that, during the period 2000–2020, PLESs in the Turpan and Hami regions presented significant spatial similarity, with an increasing trend in overall risk. The production space in the Turpan and Hami regions showed a parabolic trend of increasing and then decreasing, whereas the living space and ecological space in the Hami region showed continuous linear upward trends. The state of ecological security in the Turpan and Hami regions is gradually deteriorating, and comprehensive ecological protection and restoration measures are urgently needed to rationally allocate the structure and layout of the production-–living-–ecological space. The study of ecological risk from a PLES perspective not only helps in fully understanding the development trend of the arid zone; it also provides new ideas and methods for evaluating regional ecological environmental safety and scientific references for formulating regional sustainable development, ecological risk prevention and control and the rational allocation of resources. [ABSTRACT FROM AUTHOR]

The information on land use/land cover (LULC) is indispensable in regional planning, policy formulation and tracking land use/land cover changes (LULCC). The rapid urbanization of hilly terrains, driven by population growth, has significant implications for landslide risk reduction. Recognizing the need for an innovative approach for extracting LULC information, the present study uses a random forest (RF) classifier to develop a novel, pre-trained and universal tool that automatically generates LULC classification maps based on natural colour satellite imagery without any training input from the end-user. The proposed framework with an overall accuracy of 0.75 and an area under the curve (AUC) score of 0.95 in the receiver operating characteristic curve (ROC) approach was used for mapping built-up area expansion in regions susceptible to rainfall-induced landslides in Idukki block panchayat (administrative division), Kerala, India. By comparing the LULC information for the years 2012 and 2022, it was understood that the built-up area in the location has increased from 12.76% of the total area in 2012 to 26.48% in 2022. It is important to consider the rapid increase in built-up area expansion in the 'very high' landslide susceptibility zones in the study area. This clearly demonstrates the need for hazard inclusive planning and tracking of LULCC, for disaster risk reduction. Research Highlights: A pre-trained Land Use/Land Cover (LULC) classification tool is developed using the Random Forest (RF) classifier. Based on natural colour satellite imagery, the tool automatically generates LULC maps for various landscapes worldwide. The tool demonstrates a satisfactory performance, achieving an overall accuracy of 0.75 and an overall ROC AUC score of 0.95. The tool was used to understand the LULC changes in Idukki block panchayat between 2012 and 2022. [ABSTRACT FROM AUTHOR]

Context: The urban landscape plays a crucial role in achieving sustainability goals and aspiring to a zero-emission future. Effective carbon mitigation strategies need a spatially explicit and life-cycle analysis of building emissions, detailed mapping highlights the geographical distribution and specific characteristics of buildings, enabling precise identification and targeted management of emission sources. Objectives: This study aims to create a framework that reveals the spatial and temporal dimensions of building carbon emissions at each stage of their life cycle. Methods: We developed a comprehensive approach for carbon accounting in buildings. This framework captures spatiotemporal carbon emission patterns across various building types, considering material, energy, and waste flows. It also identifies potential mitigation strategies within the urban building landscape. Results: Applying this framework to a case study in Shenzhen, we observed a fluctuating increase in total carbon emissions, peaking at 201.5 Mt in 2016 and subsequently declining. Embodied emissions dominated before the twenty-first century, while operational emissions became significant afterward. Spatially, emissions hotspots concentrated in central urban districts, expanding outward, with residential buildings contributing the most. Scenario analysis revealed that extending building lifetimes is a key strategy for mitigating embodied carbon, while improving energy efficiency and adopting clean energy work well together to reduce operational carbon. Conclusions: Mapping the spatiotemporal patterns of carbon emissions throughout a building’s lifespan can assist urban planners and policymakers in formulating targeted strategies for carbon reduction, thereby enhancing urban sustainability. [ABSTRACT FROM AUTHOR]

Background Leptospirosis is an endemic zoonosis in tropical areas that is mainly related to rural activities; nevertheless, human leptospirosis (HL) outbreaks differ among regions. In Colombia, HL notifications are mandatory. Our objective was to determine the spatiotemporal distribution of HL in Colombia during 2007–2018 and its relationship with the main hydroclimatic variables. Methods We determined the estimated incidence and lethality of HL according to department and year. The Bayesian spatiotemporal analysis of an autoregressive model (STAR) model included HL cases and hydroclimatic factors (average temperature, rainfall and relative humidity) for quarterly periods. Results During the study period, 10 586 HL cases were registered (estimated incidence: 1.75 cases x 105) and 243 deaths by HL (lethality 2.3%). The STAR model found association of HL risk with temperature (RR:6.80; 95% CI 3.57 to 12.48) and space. Quindío and three other Amazonian departments (Guainía, Guaviare and Putumayo) had a positive relationship with a significant number of HL cases, adjusted for quarterly precipitation and humidity. Conclusion Spatial analysis showed a high risk of HL in departments of the western Andean Colombian regions. By contrast, in the spatiotemporal model, a higher HL risk was associated with temperature and departments of the North Colombian Amazon regions and Quindío in the Colombian Andean region. [ABSTRACT FROM AUTHOR]

Exploring the dynamic relationship between land use change and carbon emissions is of great significance in promoting regional low-carbon sustainable development and "dual-carbon". We reveal the characteristics of the evolution of spatial temporal patterns of land use carbon emissions at the county scale in resource-based urban agglomerations over the past 20 years and the analysis of influencing factors. The research results show that: (1) In terms of spatial and temporal characteristics, from 2000 to 2020, net carbon emissions from land use showed an overall upward trend, with construction land being the main source of increased carbon emissions; the spatial distribution pattern of carbon emissions shows a trend of further clustering of centers in the northeast-southwest direction, which mainly occurs in areas rich in coal resources; the economy-contributive coefficient is increasing, but ecological support coefficients are decreasing; (2) In the analysis of influencing factors, land use structure is the most significant factor contributing to the increase of carbon emissions, followed by economic level, while land use intensity per unit of GDP is the most significant factor inhibiting the increase of carbon emissions. The results of the study provide a useful reference for resource-based urban agglomerations to formulate regionally appropriate emission reduction strategies and realize low-carbon sustainable development. [ABSTRACT FROM AUTHOR]

Abstract Background Visceral leishmaniasis (VL) remains a significant public health concern in West Pokot County, Kenya, where a large outbreak between 2020 and 2022 emphasised the need for improved VL control strategies. However, these measures are partially hampered by limited insight into the geographical distribution of cases and localised outbreaks of the disease. This study aimed to describe the epidemiology and spatiotemporal patterns of VL in West Pokot between 2018 and 2022, in order to map the spread of VL transmission and identify regions that should be prioritised for control interventions. Methods VL patient demographics and village of residence were retrieved from admission records of Kacheliba Sub-County Hospital in West Pokot, Kenya. The temporal trend in VL admissions between 2018 and 2022 was analysed using seasonal decomposition analysis. To describe the spatial distribution of VL cases, geographic coordinates of villages of residence were collected from pre-established databases, and VL incidence was mapped at the sub-location level. Hotspot analysis was performed per study year to identify villages with high VL incidence, and scan statistics were applied to detect spatiotemporal clusters of VL cases during the study period. Results A total of 1948 VL patients were reported between 2018 and 2022. The annual number of cases increased from 245 in 2019 to 598 in 2022, and VL admissions were generally higher at the start of the wet seasons. 70% of the VL cases could be georeferenced, and mapping of VL incidence revealed high case rates in the east of West Pokot during the complete study period. The eastern villages Lotongot and Chepaywat were marked as VL hotspots at a 99% confidence level in all study years. In addition, five significant spatiotemporal clusters were detected in the east and north, suggestive of local VL outbreaks in these regions. Conclusions The increase in VL hospital admissions during the study period stresses the need for enhanced VL control and outbreak mitigation in West Pokot. These control measures should be focused on the hotspot regions in the east of the county.

Abstract Background Despite being preventable and curable, leprosy remains endemic in some undeveloped regions, including China. Wenshan Zhuang and Miao Autonomous Prefecture (Wenshan prefecture) currently bears the highest leprosy burden in China. In this ecological study, we aimed to analyze the epidemiological characteristics as well as identify and visualize the high-risk townships of Wenshan prefecture using the most updated leprosy data from 2010 to 2022. Methods Geographical information system combined with spatial scan statistics was used for newly detected leprosy cases abstracted from the Leprosy Management Information System in China. Global Moran’s I index was used to uncover the spatial pattern of leprosy at the township level. Spatial scan statistics, encompassing purely temporal, purely spatial, spatial variation in temporal trends, and space-time analysis, were implemented for detecting the risk clusters. Results Between 2010 and 2022, Wenshan prefecture detected 532 new leprosy cases, comprising 352 (66.17%) males and 180 (33.83%) females. The aggregated time primarily occurred between October 2010 and March 2014. The distribution pattern of newly detected leprosy cases was spatially clustered. We identified four high-risk spatial clusters encompassing 54.51% of the new cases. Furthermore, spatial variation in temporal trends highlighted one cluster as a potential high-risk area. Finally, two space-time clusters were detected, and the most likely cluster was predominantly located in the central and northwest regions of Wenshan prefecture, spanning from January 2010 to September 2013. Conclusions In this ecology study, we characterized the epidemiological features and temporal and spatial patterns of leprosy in Wenshan prefecture using the most recent leprosy data between 2010 and 2022. Our findings offer scientific insights into the epidemiological profiles and spatiotemporal dynamics of leprosy in Wenshan prefecture. Clinicians and policymakers should pay particular attention to the identified clusters for the prevention and control of leprosy.

Background & objectives: Dengue poses a considerable public health threat in Saudi Arabia, with escalating outbreaks in Jazan, where seasonal rains create ideal mosquito breeding conditions. Elucidating local epidemiological dynamics is imperative to strengthen evidence-based prevention policies. This study analyzed the spatiotemporal, demographic, and meteorological patterns of dengue in Jazan from 2015-2020. Methods: This retrospective cross-sectional study utilized surveillance records for 3427 confirmed dengue cases. Descriptive analyses characterized geographic, seasonal, age, gender, and nationality distributions. Forecasting models project expected epidemics through 2025. Regression analysis identified climate factors associated with monthly case counts. Results: Dengue exhibited shifting seasonal peaks, transitioning into year-round transmission by 2019, indicating endemic establishment. Cases clustered in different high-burden sectors annually, requiring localized vector control. The majority of affected individuals were young male adults, with gender gaps narrowing over time. Saudi nationals had an escalating incidence, but non-citizens showed a higher risk, signaling importation threats. Seasonal outbreaks were associated with temperature, wind speed, and direction. Interpretation & conclusion: Enhanced surveillance, outbreak forecasting, targeted control activities, and integrated prevention policies grounded in continuous evidence assessment can effectively address endemic dengue transmission in Jazan. This study provides key insights to optimize data-driven decision-making for dengue control in Saudi Arabia.

Abstract Using GIS technology, this study investigated the spatiotemporal distribution pattern of influenza incidence in Xinjiang from 2014 to 2023 based on influenza surveillance data. The study revealed a noticeable fluctuation trend in influenza incidence rates in Xinjiang, particularly notable spikes observed in 2019 and 2023. The results of the 3-year moving average showed a significant long-term upward trend in influenza incidence rates, confirmed by Theil-Sen method (MAD = 2.202, p 0, P

Abstract Mountain landscapes can be fragmented due to various human activities such as tourism, road construction, urbanization, and agriculture. It can also be due to natural factors such as flash floods, glacial lake outbursts, land sliding, and climate change such as rising temperatures, heavy rains, or drought.The study’s objective was to analyze the mountain landscape ecology of Pir Chinasi National Park under anthropogenic influence and investigate the impact of anthropogenic activities on the vegetation. This study observed spatiotemporal changes in vegetation due to human activities and associated climate change for the past 25 years (1995–2020) around Pir Chinasi National Park, Muzaffrabad, Pakistan. A structured questionnaire was distributed to 200 residents to evaluate their perceptions of land use and its effects on local vegetation. The findings reveal that 60% of respondents perceived spatiotemporal pressure on the park. On the other hand, the Landsat-oriented Normalized Difference Vegetation Index (NDVI) was utilized for the less than 10% cloud-covered images of Landsat 5, 7, and 8 to investigate the vegetation degradation trends of the study area. During the entire study period, the mean maximum NDVI was approximately 0.28 in 1995, whereas the mean minimum NDVI was − 2.8 in 2010. QGIS 3.8.2 was used for the data presentation. The impact of temperature on vegetation was also investigated for the study period and increasing temperature trends were observed. The study found that 10.81% (1469.08 km2) of the area experienced substantial deterioration, while 23.57% (3202.39 km2) experienced minor degradation. The total area of degraded lands was 34.38% (or 4671.47 km2). A marginal improvement in plant cover was observed in 24.88% of the regions, while 9.69% of the regions experienced a major improvement. According to the NDVI-Rainfall relationships, the area was found to be significantly impacted by human pressures and activities (r ≤ 0.50) driving vegetation changes covering 24.67% of the total area (3352.03 km2). The area under the influence of climatic variability and change (r ≥ 0.50 ≥ 0.90) accounted for 55.84% (7587.26 km2), and the area under both climatic and human stressors (r ≥ 0.50

Dockless bicycle-sharing system substantially impacts the present docked bicycle-sharing usage in the urban transportation system. This study explores such effects using usage data of docked bicycle-sharing systems in Xi’an before and after launching the dockless bicycle-sharing system. The influence period is divided into the no-influence stage, transition stage, and stabilization stage. The hotspot analysis method is employed to identify the most influenced areas. The geographically weighted regression model is then revised to determine the response of socioeconomic factors considering the launches of the dockless bicycle-sharing system. The results reveal that the critical impact of the dockless bicycle-sharing system on the docked system. Specifically, the weekly usage share of docked bicycle-sharing declined ranged from 0.51 % to 41.4 % in transition stage. Furthermore, population density, job density, per capita income, and per capita GDP are identified as the key contributing factors based on spatial analysis, where for each 1 % increase in the four indicators, docked bicycle-sharing system’s usage decreases by 1.014 %, 1.182 %, 2.021 %, and 0.904 %, respectively.

Urban growth changes spatial uses over time due to different dynamics. These processes cause many physical, environmental, and socioeconomic problems, such as climate change, pollution, and population-related events. Therefore, it is essential to predict future urban expansion to produce effective policies in sustainable urban planning and make long-term plans. Many models, such as dynamic, statistical, and Cellular Automata and Markov Chain (CA-MC) models, are used in geographic information system (GIS) environments to meet the high-performance requirements of land use modeling. This study estimated the growth of settled areas in Eskişehir city center using models developed using two different methods. In this context, settled areas in the city center were examined within the scope of 1990–2018, and the growth areas of settled areas in 2046 were predicted using the CA-Markov method in Model 1: Quantum GIS (QGIS) MOLUSCE plugin and Model 2: IDRISI Selva. While settled areas are continuously increasing, other urban areas are decreasing. Model 1 predicts an increase of 1195 ha in settled areas by 2046, while Model 2 predicts an increase of 45,022 ha. At the same time, it is concluded that settled areas will grow in a central location in Model 1, while they will spread in an east-west extension in Model 2. The study results show that QGIS-based modeling predicts more limited spatial growth than IDRISI Selva. The research interprets growth in terms of the staging of urban services, the population size of neighboring cities, distances, and income levels based on the internal and external dynamics of the city. [ABSTRACT FROM AUTHOR]

Abstract This study aims to quantify the effectiveness of lockdown as a non-pharmacological solution for managing the COVID-19 pandemic. Daily COVID-19 death counts were collected for four states: California, Georgia, New Jersey, and South Carolina. The effectiveness of the lockdown was studied and the number of people saved during 7 days was evaluated. Five neural network models (MLP, FFNN, CFNN, ENN, and NARX) were implemented, and the results indicate that FFNN is the best prediction model. Based on this model, the total number of survivors over a 7-day period is 211, 270, 989, and 60 in California, Georgia, New Jersey, and South Carolina, respectively. The coefficients and weights of the FFNN for each state differ due to various factors, including socio-demographic conditions and the behavior of citizens towards lockdown laws. New Jersey and South Carolina have the most lockdowns and the least.

Under the influence of global climate change, the climatic conditions of China's major agricultural regions have changed significantly over the last half-century, affecting regional grain production levels. With its favorable conditions for agricultural activities, China's central region has been a strategic location for grain production since ancient times and has assumed an essential responsibility for maintaining national grain security. However, the key concerns of this study are whether the national grain security pattern is stable and whether it might be affected by global climate change (especially climate instability and increased risks in recent years). Therefore, the present study collected grain production data and used descriptive statistical and geospatial analyses to reveal the trend and spatiotemporal pattern of grain production in China's central region from 2010 to 2020. Then, a further analysis was conducted by combining meteorological data with a geographically weighted regression (GWR) model to investigate the relationship between spatial differences in the output per unit of the grain sown area (OPUGSA). The findings were as follows: (1) The overall development trend of grain production in China's central region from 2010 to 2020 revealed a positive overall trend in grain production, with notable differences in growth rates between northern and southern provinces. (2) Most regions in the southern part of the central region from 2015 to 2020 showed varying degrees of total output of grain (TOG) and OPUGSA reduction, possibly affected by the effects of the anomalies for global climate change and a strong El Niño effect in 2015. (3) Low-low (L-L) clusters of TOG and OPUGSA indicators were consistently in the northwest part (Shanxi) of the central region, and high-high (H-H) clusters of TOG were consistently in the central part (Henan and Anhui) of the central region, but H-H clusters of OPUGSA were not stably distributed. (4) The fitting results of the GWR model showed a better fit compared to the ordinary least squares (OLS) model; it was found that the annual average temperature (AAT) had the greatest impact on OPUGSA, followed by annual sunshine hours (ASH) and annual precipitation (AP) last. The spatiotemporal analysis identified distinct clusters of productivity indicators. It suggested an expanding range of climate impact possibilities, particularly in exploring climate-resilient models of grain production, emphasizing the need for targeted adaptation strategies to bolster resilience and ensure agricultural security.

Numerous studies have presented compelling findings regarding the favorable influence exerted by the construction of metro systems on the development of land use along the routes. However, the effects of metro network structure on land use change, especially the long-term dynamic effects, remain uncertain. To address this gap, this research utilizes land use coverage data and metro network data from Wuhan, China, spanning the period from 2014 to 2019. A complex network model is employed to analyze changes in the metro network structure, and the GTWR model is employed to investigate the impact of the of metro network structure on land use change. The results indicate that metro construction effectively stimulates land use change along the routes, predominantly leading to the conversion of non-construction land into construction land. Global accessibility, degree centrality, betweenness centrality, and eigenvector centrality, within the metro network structure attributes demonstrate significant positive effects on land use change, and these effects exhibit considerable spatiotemporal non-stationarity. Additionally, built environment factors surrounding metro stations, such as population density, plot ratio, street intersection density, and the quantity of bus stops, also exhibit notably spatiotemporal effects on land use change. These findings indicate that government agencies and urban planners should concentrate on optimizing the distribution of the urban metro network to improve its overall structural efficiency. When planning the construction of the metro network, careful attention should be paid to the built environment near the stations to effectively facilitate land use development in the vicinity.

This study aims to quantify the effectiveness of lockdown as a non-pharmacological solution for managing the COVID-19 pandemic. Daily COVID-19 death counts were collected for four states: California, Georgia, New Jersey, and South Carolina. The effectiveness of the lockdown was studied and the number of people saved during 7 days was evaluated. Five neural network models (MLP, FFNN, CFNN, ENN, and NARX) were implemented, and the results indicate that FFNN is the best prediction model. Based on this model, the total number of survivors over a 7-day period is 211, 270, 989, and 60 in California, Georgia, New Jersey, and South Carolina, respectively. The coefficients and weights of the FFNN for each state differ due to various factors, including socio-demographic conditions and the behavior of citizens towards lockdown laws. New Jersey and South Carolina have the most lockdowns and the least. [ABSTRACT FROM AUTHOR]

Introduction: The study proposes a method for evaluating stress distribution in high-altitude Tibetan Plateau railway tunnels using high-precision radar satellite time-series interferometric synthetic aperture radar technology. Methods: To effectively monitor and prevent geological hazards during the construction process, this method i employed, as it serves as a component of advanced geological prediction and surrounding rock deformation monitoring technology for high-altitude tunnels, particularly in the Dongelu Tunnel of the China-Tibet Railway. The study utilizes time-series interferometric synthetic aperture radar to obtain deformation information for Dongelu Tunnel area between 2022 and 2023 from Sentinel-1A orbit images. This quantitatively investigates the upper mountain body and line-of-sight direction along the tunnel. The deformation characteristics are correlated with high-frequency and high-precision automated vertical displacement monitoring results, determining the spatiotemporal distribution of tunnel deformation. In this study, a model that determines the vertical stress state of the Dongelu Tunnel under loading near the entrance and evaluates its health status was established. Results: The results show that the surface deformation of the mountain above the tunnel axis develops slowly and is relatively small, with a maximum vertical deformation rate of 1-3 mm/year. The average stress on the tunnel arch is 5.54 MPa, with a fluctuation range of 0.01 MPa. Temporal Q9 changes in various parts of the tunnel are periodic, with maximum fluctuations observed in December 2022. The study reveals inconsistent surface settlement of the tunnel arch and mountain above it, causing minor vertical stress changes. As the tunnel construction progresses, vertical stress variation shows periodicity because of an initial imbalance in internal stress within the mountain. Stress fluctuations near the tunnel entrance occur during the initial excavation phase, gradually diminishing as the project progresses and internal stress stabilizes. Discussion: The proposed tunnel monitoring and stability assessment method can reduce its impact on engineering construction and provide guidance for advanced geological prediction. [ABSTRACT FROM AUTHOR]

Player movement is a fundamental component of evaluating performance in most team sports. Movement can be evaluated across multiple scales, referring to the function of anatomical structures through various planes of motion or an individual regulating their field position based on the movement of opposition players. Developments in commercially available tracking systems have afforded end users the ability to investigate the spatiotemporal features of movement in fine detail. These advancements, in conjunction with overlaid contextual information, have provided insights into the strategies adopted by players in relation to their movement. Understanding movement beyond its semantic value allows practitioners to make informed decisions surrounding performance evaluation and training design. This investigation proposes a framework to guide the analysis of player movement within team sports environments. The framework describes how operational standards for assessing movement can be designed in reference to theory and a set training philosophy. Such practice allows for the spatial and temporal complexities within team sports to be described and could potentially lead to betterapplied outcomes through greater interdisciplinary collaboration and an improved holistic understanding of movement. To inform its development, this study evaluates the current research and identifies several open questions to guide future investigations. [ABSTRACT FROM AUTHOR]

Virus diseases represent important economic threats to seed potato production worldwide, yet relatively little is known of their epidemiology at the landscape‐scale. In this study, data was compiled from the Scottish national seed potato classification scheme on the incidence of 10 different potato viruses for the years 2009–2022. A co‐occurrence analysis identified that 12 virus species pairs occurred together more often than expected by chance, and potato blackleg was positively associated with eight potato viruses. ArcGIS was used to investigate spatial and spatiotemporal variation in incidence rates of the three most prevalent viruses (potato virus Y, potato leaf roll virus and potato virus A), and this revealed prominent geographic differences in long‐term disease outcomes. Focusing on potato virus Y as the most commonly occurring single infection, interpretable machine‐learning techniques were used to investigate the influence of key crop, management and environmental factors on patterns of incidence in space and time. The results showed that health characteristics of seed stocks were among the most important predictors of incidence, along with blackleg infection, several management features, cultivar resistance, distance to the nearest seed and ware crop, temperature variables and several soil features. This approach provides a comprehensive overview of potato viruses in Scotland, a deeper understanding of epidemiological risk factors at the landscape‐scale and a forecast model that could serve as the basis of a decision support tool for improved management of potato virus Y. [ABSTRACT FROM AUTHOR]

Food Security (FS) is a major concern in West Africa, particularly in Burkina Faso, which has been the epicenter of a humanitarian crisis since the beginning of this century. Early warning systems for FS and famines rely mainly on numerical data for their analyses, whereas textual data, which are more complex to process, are rarely used. However, this data is easy to access and represents a source of relevant information that is complementary to commonly used data sources. This study explores methods for obtaining the explanatory context associated with FS from textual data. Based on a corpus of local newspaper articles, we analyze FS over the last ten years in Burkina Faso. We propose an original and dedicated pipeline that combines different textual analysis approaches to obtain an explanatory model evaluated on real-world and large-scale data. The results of our analyses have proven how our approach provides significant results that offer distinct and complementary qualitative information on food security and its spatial and temporal characteristics. [ABSTRACT FROM AUTHOR]

The dynamic changes of sea ice exhibit spatial clustering, and this clustering has characteristics extending from its origin, through its development, and to its dissipation. Current research on sea ice change primarily focuses on spatiotemporal variation trends and remote correlation analysis, and lacks an analysis of spatiotemporal evolution characteristics. This study utilized monthly sea ice concentration (SIC) data from the National Snow and Ice Data Center (NSIDC) for the period from 1979 to 2022, utilizing classical spatiotemporal clustering algorithms to analyze the clustering patterns and evolutionary characteristics of SIC anomalies in key Arctic regions. The results revealed that the central-western region of the Barents Sea was a critical area where SIC anomaly evolutionary behaviors were concentrated and persisted for longer durations. The relationship between the intensity and duration of SIC anomaly events was nonlinear. A positive correlation was observed for shorter durations, while a negative correlation was noted for longer durations. Anomalies predominantly occurred in December, with complex evolution happening in April and May of the following year, and concluded in July. Evolutionary state transitions mainly occurred in the Barents Sea. These transitions included shifts from the origin state in the northwestern margin to the dissipation state in the central-north Barents Sea, from the origin state in the central-north to the dissipation state in the central-south, and from the origin state in the northeastern to the dissipation state in the central-south Barents Sea and southeastern Kara Sea. Various evolutionary states were observed in the same area on the southwest edge of the Barents Sea. These findings provide insights into the evolutionary mechanism of sea ice anomalies. [ABSTRACT FROM AUTHOR]

Objective To analyze the epidemiological characteristics of reported cases of dengue fever in mainland China from 2019 to 2023 and provide a scientific basis for dengue prevention and control efforts. Methods The temporal, spatial, and population distribution characteristics, as well as the trends of dengue fever cases reported in the National Notifiable Disease Reporting System (NNDRS) of China were analyzed using software such as Joinpoint regression, SaTScan, and ArcGIS. Results From 2019 to 2023, a total of 43 095 cases were reported in China, with an average incidence rate of 61/100 000. The annual reported incidence rate fluctuated significantly (AAPC=-3.16%, 95%CI:-54.16%-91.47%), mainly concentrated in 2019 and 2023, accounting for 96.83% of the total reported cases over the past five years. All age groups were susceptible to the disease, with the 30 to <40 age group comprising 22.6% of cases. Cases were reported throughout the year, with a rapid increase starting in June and a peak period concentrated between August and October (n=34 780, RR=12.44, LLR=29 262.52, P<0.05). The epidemic was primarily characterized by imported cases leading to autochthonous transmission, with the majority of imported cases originating from Southeast Asia (86.56%), particularly from Cambodia (46.11%, 3 284 cases) and Myanmar (25.99%, 1 851 cases). The epidemic characteristics varied significantly across different years, with high risks of autochthonous transmission epidemics in Southwest, South, and Southeast China. High-risk local transmission were mainly distributed in Yunnan and Guangdong. The duration of the peak period of the epidemic was affected by the timeliness of early detection of local transmission, and the peak value of the epidemic was related to the effectiveness of prevention and control measures. The scale of the epidemic was influenced by the pressure of by cross-border transmission and the ability to implement local measures for dengue prevention and control. Conclusions With the increasing trend of global dengue transmission, the risk of autochthonous transmission of dengue in China is rising. It is recommended to strengthen proactive early outbreak surveillance, along with well preparation of personnel, technology, and resources for dengue prevention and control.According to autochthonous conditions, reasonable management and risk assessment of cross-border travelers should be explored to effectively. [ABSTRACT FROM AUTHOR]

The emission of carbon dioxide (CO2) is considered one of the main factors responsible for one of the greatest challenges faced by the world today: climate change. On the other hand, with the increase in energy demand due to the increase in population and industrialization, the emission of CO2 has increased rapidly in the past few decades. However, the world's leaders, including the United Nations, are now taking serious action on how to minimize the emission of CO2 into the atmosphere. Towards this end, accurate modeling and monitoring of historical CO2 can help in the development of rational policies. This study aims to analyze the carbon emitted by the Group Twenty (G20) countries for the period 1971–2021. The datasets include CO2 emissions, nonrenewable energy (NREN), renewable energy (REN), Gross Domestic Product (GDP), and Urbanization (URB). Various regression-based models, including multiple linear regression models, quantile regression models, and panel data models with different variants, were used to quantify the influence of independent variables on the response variable. In this study, CO2 is a response variable, and the other variables are covariates. The ultimate objective was to choose the best model among the competing models. It is noted that the USA, Canada, and Australia produced the highest amount of CO2 consistently for the entire duration; however, in the last decade (2011–2021) it has decreased to 12.63–17.95 metric tons per capita as compared to the duration of 1971–1980 (14.33–22.16 metric tons per capita). In contrast, CO2 emissions have increased in Saudi Arabia and China recently. For modeling purposes, the duration of the data has been divided into two independent, equal parts: 1971–1995 and 1996–2021. The panel fixed effect model (PFEM) and panel mixed effect model (PMEM) outperformed the other competing models using model selection and model prediction criteria. Different models provide different insights into the relationship between CO2 emissions and independent variables. In the later duration, all models show that REN has negative impacts on CO2 emissions, except the quantile regression model with tau = 0.25. In contrast, NREN has strong positive impacts on CO2 emissions. URB has significantly negative impacts on CO2 emissions globally. The findings of this study hold the potential to provide valuable information to policymakers on carbon emissions and monitoring globally. In addition, results can help in addressing some of the sustainable development goals of the United Nation Development Programme. [ABSTRACT FROM AUTHOR]

Urban air pollution is a critical global environmental issue, necessitating an analysis of the spatiotemporal characteristics of air quality and its driving factors for sustainable urban development. However, the traditional urban air quality assessment system ignores the impact of internal urban spatial structures. Therefore, this paper proposes an assessment system that integrates natural, socio-economic, and urban layout factors by utilizing the air quality index (AQI) and 14 types of multi-source geographic data in the main urban area of Beijing from 2016 to 2020 and constructs geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR) models for spatiotemporal analysis. Our findings revealed an annual improvement in air quality, with a U-shaped seasonal pattern and significant spatial clustering (Global Moran's I = 0.922). The MGWR model provided a superior fit over the GWR, capturing spatial variability more effectively. Variables such as NDVI, economic output (GDP), and humidity space adjustment capability (HSAC) showed significant positive spatial impacts on air quality, while population density (POP), temperature (TEMP), and road density (RD) exhibited negative effects. These results explain the changes in air quality in the main urban area of Beijing from a spatiotemporal perspective and provide planning input for urban air quality regulations. [ABSTRACT FROM AUTHOR]

Background and objectives: Land use and land cover change (LULCC) directly affects the human living environment and economic society by influencing ecosystem services. This study explores the complex relationship between LULCC and ecosystem services in the Dongting Lake region of China. Methods: The analysis incorporates the transfer matrix analysis of LULCC, ecosystem service value (ESV) accounting, Mann-Kendall mutation test, gray correlation, and path analysis. This study explored the spatial and temporal characteristics of LULCC and ecosystem services and revealed the comprehensive impacts of LULCC on ecosystem services from the perspective of factor interactions. Findings: The findings reveal that open water (OW) area and farmable land are the dominant land use types transferring in and out of the study area, with significant interconversion observed among open water area, wetland (WL), arable land (AL), and forest land (FL). Water conservation, waste disposal, climate regulation, and biodiversity conservation emerge as the most crucial ecosystem services in the study area. The study identifies consistent patterns of abrupt changes in LULCC and ESV across six analyzed time periods. Forest land exhibits the most substantial direct influence on climate regulation, gas exchange, soil formation and protection, conservation of biodiversity, rawmaterials, and recreation and leisure. Additionally, forest land, wetland, arable land, and openwater area contribute significantly to the total ESV, with forest land being the dominant factor influencing the spatial differentiation of ecosystem services in the Dongting Lake region. Conclusion: The research reveals the consistency of ecosystem services and LULCC trends in the Dongting Lake area. LULCC is a complex process influenced by multiple factors, in which the conversion between ecological and nonecological land use has a significant impact, and the complex coupling and coordination among LULCC drivers together influence ecosystem services. Furthermore, LULCC alters the structure and scale of ecosystems, resulting in alterations in the ESV. [ABSTRACT FROM AUTHOR]

Under the influence of global climate change, the climatic conditions of China's major agricultural regions have changed significantly over the last half-century, affecting regional grain production levels. With its favorable conditions for agricultural activities, China's central region has been a strategic location for grain production since ancient times and has assumed an essential responsibility for maintaining national grain security. However, the key concerns of this study are whether the national grain security pattern is stable and whether it might be affected by global climate change (especially climate instability and increased risks in recent years). Therefore, the present study collected grain production data and used descriptive statistical and geospatial analyses to reveal the trend and spatiotemporal pattern of grain production in China's central region from 2010 to 2020. Then, a further analysis was conducted by combining meteorological data with a geographically weighted regression (GWR) model to investigate the relationship between spatial differences in the output per unit of the grain sown area (OPUGSA). The findings were as follows: (1) The overall development trend of grain production in China's central region from 2010 to 2020 revealed a positive overall trend in grain production, with notable differences in growth rates between northern and southern provinces. (2) Most regions in the southern part of the central region from 2015 to 2020 showed varying degrees of total output of grain (TOG) and OPUGSA reduction, possibly affected by the effects of the anomalies for global climate change and a strong El Niño effect in 2015. (3) Low-low (L-L) clusters of TOG and OPUGSA indicators were consistently in the northwest part (Shanxi) of the central region, and high-high (H-H) clusters of TOG were consistently in the central part (Henan and Anhui) of the central region, but H-H clusters of OPUGSA were not stably distributed. (4) The fitting results of the GWR model showed a better fit compared to the ordinary least squares (OLS) model; it was found that the annual average temperature (AAT) had the greatest impact on OPUGSA, followed by annual sunshine hours (ASH) and annual precipitation (AP) last. The spatiotemporal analysis identified distinct clusters of productivity indicators. It suggested an expanding range of climate impact possibilities, particularly in exploring climate-resilient models of grain production, emphasizing the need for targeted adaptation strategies to bolster resilience and ensure agricultural security. [ABSTRACT FROM AUTHOR]

The division and evaluation of data series used in monitoring drought into different time intervals is a practical approach to detecting the spatial and temporal extent of drought spread. This study aimed to determine meteorological drought's spatial and temporal distribution using overlapping and consecutive periods and cycles of the standardized precipitation index (SPI) time series in the Mediterranean region, Turkey. In the scope of the research, SPI values for the SPI12, SPI6 (1), and SPI6 (2) seasons were calculated for consecutive and overlapping hydrological years (1978–1998/21 years, 1978–2008/31 years, and 1978–2018/41 years) at 28 meteorological stations. Autocorrelation, Mann–Kendall, and Sen slope trend tests were applied at a 5% significance level for each season (SPI12, SPI6 (1), and SPI6 (2)) and different time scales (21, 31, and 41 years). For each season and period, maps of the SPI drought class, average formation of drought class, Mann–Kendall (MK) trend, and Sen's slope (SS) trend test statistics for the Mediterranean region were obtained, and the spatial distribution rate of trends was determined by drawing hypsometric curves. Changes in drought occurrence at different time scales were thoroughly evaluated with the changing length of data recording. Consequently, it was determined that the mild wet (MIW) and mild drought (MID) classes dominate the study area in the Mediterranean region. Significant and nonstationary changes detected in extreme wet and drought occurrences (extreme wet, EW; severe wet, SW; extreme drought, ED; severe drought, SD) were found to pose a risk in the study area. It was observed that there were spatially and temporally insignificant decreasing drought trends in the Mediterranean basin, considering that the time scales of these trends slowed down. Despite a nonsignificant trend from the MID drought class to the MIW drought class, it is predicted that the MIW and MID classes will maintain their dominance in the Mediterranean region. The central part of the study area (central Mediterranean basin) is the region with the highest drought risk. [ABSTRACT FROM AUTHOR]

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Understanding vegetation dynamics and its response to climate change and human activities provides essential ecological and environmental resource management information. This study extracted fractional vegetation coverage (FVC) data from 2000 to 2019 using the Google Earth Engine platform and Landsat satellite images, employing trend analysis and other methods to examine spatiotemporal changes in vegetation in the SRB. Additionally, we used partial correlation and residual analyses to explore the response of FVC to climate change and human activities. The main results were: (1) The regional average FVC in the SRB showed a significant upward trend from 2000 to 2019, increasing by 1.3 × 10−3 a–1. The area within 1 km of roads experienced a higher increase of 3 × 10−3 a–1, while the roadless areas experienced a lower increase of 1.1 × 10−3 a–1. The FVC spatial heterogeneity in the SRB is significant. (2) Partial correlation analysis shows that the FVC correlates positively with precipitation and surface water area, with correlation coefficients of 0.575 and 0.744, respectively. A weak negative correlation exists between the FVC and land surface temperature (LST). FVC changes are more influenced by precipitation than by LST. (3) The contributions of climate change to vegetation recovery are increasing. Human activities, particularly agricultural practices, infrastructure development, and the conversion of farmland to grassland, significantly influence vegetation changes in densely populated areas. (4) The area changes of different land types are closely related to climate factors and human activities. Increased construction, agricultural activity, and converting farmland back to grassland have led to an increase in the area proportions of "impervious surfaces", "cropland", and "grassland". Climate changes, such as increased rainfall, have resulted in larger areas of "wetlands" and "sparse vegetation". These results provide valuable information for ecosystem restoration and environmental protection in the SRB. [ABSTRACT FROM AUTHOR]

Introduction: This study delves into the intricate dynamics between fiscal policies supporting agriculture and the non-linear influence of agricultural science and technology innovation on enhancing agricultural resilience. We conducted research across 31 provinces (including autonomous regions and municipalities) in China from 2007 to 2021. Method: By constructing the evaluation index system of agricultural resilience, the entropy value method is used to measure the value of agricultural resilience, and then standard deviation ellipse and center of gravity migration analysis, benchmark regression model, heterogeneity analysis, threshold regression model are used to analyze the relationship between agricultural science and technology innovation, fiscal policies supporting agriculture and agricultural resilience. Result: (1) The analysis of the spatio-temporal evolution trend shows that the overall development of China's agricultural resilience is relatively stable, the resilience range is expanding, and the geographical area with the southeast as the center of gravity presents a stronger pulling effect; (2) The benchmark regression model shows that agricultural science and technology innovation has a significant positive effect on agricultural resilience; (3) Agricultural science and technology innovation plays a nonlinear role in increasing agricultural resilience when fiscal policies supporting agriculture are used as a threshold variable. (4) Heterogeneity analysis highlights stronger promotion of agricultural resilience through science and technology innovation in non-main producing areas and economically underdeveloped regions. Discussion: To address this, policymakers should leverage the resilience of the Southeast, boost innovation capacity, tailor innovation to local needs, and reinforce fiscal policies supporting agriculture. These insights provide valuable direction for policymakers in crafting effective measures to enhance agricultural resilience. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity and temporal variability of traffic in urban environments make traffic emissions inference challenging. To address this challenge, this study introduces a novel geographical context-based approach utilizing high-resolution taxi GPS data, incorporating multidimensional contextual factors such as road data, points of interest (POI), weather data, and population density. The proposed method can enhance the precision of traffic emissions inference compared to conventional macroscopic estimation techniques. To overcome the issue of missing data in traffic emissions inference from taxi data, three ensemble machine learning algorithms—Random Forest, Gradient Boosting Decision Trees (GBDT), and eXtreme Gradient Boosting (XGBoost)—are employed. These algorithms efficiently handle a substantial volume of taxi GPS data, achieving reduced computational time and model complexity. The proposed framework establishes localized models for each road segment, taking into consideration both geographical and external features that characterize the urban environment. This localized modeling contributes significantly to a more profound understanding of traffic dynamics. A thorough comparative analysis is conducted to assess the performance of the proposed method. Results indicate that incorporating multidimensional urban features is advantageous for traffic speed inference. Among the ensemble learning models, Random Forest outperforms others when dealing with a small missing rate or limited sample size, while XGBoost exhibits superior performance for larger missing rates or substantial sample sizes. Additionally, an analysis of the feature importance in traffic speed highlights that road network features are the most significant factors, followed by temporal characteristics, spatial attributes, POI data, and weather information. Finally, leveraging inferred traffic speed and volume information, emissions from large-scale urban road traffic are inferred based on the COPERT model. In contrast to methods relying on complex, multi-source data for emission estimation, our approach utilizes simple and easily accessible data, enabling precise estimation of emissions on a large-scale spatiotemporal basis. [ABSTRACT FROM AUTHOR]