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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.

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.

Continuous evaluation and monitoring of long-term energy usage and carbon emissions are essential for developing, implementing, and assessing regional carbon reduction efforts. This study presents a spatiotemporal analysis of carbon emission trends in the Yangtze River Delta Urban Agglomeration (YRDUA) from 1992 to 2019. Researchers used nighttime light data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) and the National Polar-orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) to assess the evolution of carbon emission patterns. Advanced spatial analysis methods, including geographic autocorrelation, geographical panel modeling, and spatial Markov chains, were applied to explore the spatial impacts, processes, and regional context of these trends. The results show: (1) Carbon emissions in the YRDUA increased by 262.56 %, with high-emission spheres and axial expansion. High-high emission clusters emerged in metropolitan areas, while low-low clusters formed in peripheral mountain regions. (2) Carbon emission types were stable (66.5 %), but 17.6 % showed higher emissions transitioning to lower, particularly in northeast Jiangsu. (3) The broader regional background had a stronger influence on the spatial impacts of carbon emissions than nearest neighbor effects, enhancing both outlier convergence and “club convergence” among similar regions. (4) Spatiotemporal patterns were shaped by the lock-in effect in low-carbon areas and spillover effects in high-carbon areas, with economic scale and industrial structure as key drivers. This study provides crucial insights for regional carbon reduction strategies in the YRDUA.

The changes of land use and climate have significantly impacted urban ecosystem services (ES). Investigating the effects of urbanization and climate change on urban ES is essential for ensuring sustainable urban development and management. Water Yield (WY) and Urban Cooling are key urban ES in response to climate change, this study employs scenario simulation method to set up 9 scenarios, including 3 baseline and 6 simulated scenarios, which are further analyzed using a contribution index to investigate the impacts of climate change and land use on WY and urban cooling.Results showed that 1) From 2000 to 2020, Shanghai exhibited an increase in temperature and precipitation. Land use change from 2010 to 2020 increased by 2.17 % compared to 2000 to 2010. The primary land use type shifted from Cultivated Land to Artificial Surfaces, but both land use type exhibited a downward trend. 2) From 2000 to 2020, the WY from Cultivated Land initially increased and then decreased, with an average magnitude of 193.6Mt every year, while the WY from Artificial Surfaces consistently increased by 1171.5Mt. The Heat Mitigation Index (HMI) of Cultivated Land, Artificial Surfaces, Ocean, and Grassland all declined, while Water Bodies’ HMI increased from 0.35 to 0.38, and Forest’s HMI initially increased then decreased. Overall, Shanghai’s HMI showed a downward trend. 3) From 2000 to 2010, changes in land use led to a reduction in HMI by 0.088 and an increase in WY by 48.73 mm. Climate change caused HMI to decrease by 0.01 and WY to decrease by 7.95 mm. From 2010 to2020, land use change caucused a decrease in HMI by 0.064 and an increase in WY by 13.01 mm. The effect of climate change on HMI is less significant compared to WY, which increased by 388.26 mm. From 2000 to 2020, land use change led to a reduction in HMI by 0.13 and an increase in WY by 61.68 mm, while climate change caused a decrease in HMI by 0.002 and an increase in WY by 280.44 mm. Climate change is the dominate influencing factor for WY, while land use change has stronger effect on HMI. Meanwhile, the influence of climate change on WY is inconsistent, and the impact of land use change and climate change on the HMI exhibits spatial heterogeneity. These findings will assist sustainable urban development in Shanghai.

The quality of the ecological environment directly affects the generation of ecological benefits, and the remote sensing ecological index (RSEI) provides rapid monitoring of regional aboveground ecological environment quality using satellite data. However, it relies on aboveground indicators and cannot fully capture its evolutionary trend. Ecosystem services (ES) indicate the well-being and capacity of ecosystems to benefit humans, and ES directly reflect ecological benefits. They provide a qualitative or quantitative assessment of ecosystem status, but with a delay relative to aboveground environmental changes. In order to quantitatively evaluate ES, ecosystem service index (ESI) was proposed in this paper. The coupling of RSEI and ESI was achieved through the four-quadrant model and the coupling degree model, which compensated for the dependence of RSEI evaluation on aboveground indicators, and the delayed effect of ESI, and provided a more objective spatiotemporal analysis of regional ecological benefits. The example is provided from Fushun City, Liaoning Province, China. Results showed: that ESI could effectively reflect the impact of land use and land cover (LULC) changes on ecosystem quality. The RSEI and ESI showed an overall upward trend in the study area, with the exception of a notable decrease in the central Wanghua District, the southern Dongzhou District, and the northern Fushun County. The quadrant I of the study area showed a continuous decrease, while the rest of the quadrants show a continuous increase. The coupling index significantly decreased in areas impacted by building site expansion and ecological restoration measures implementation. The four-quadrant model and coupling index can reflect the spatiotemporal changes and synergy level of RSEI and ESI, and can provide a more objective spatiotemporal analysis of ecological benefits. Therefore, coupling RSEI and ESI can quickly and comprehensively analyze the ecological environment quality and ecosystem service capacity in time and space, and reflect its evolution trend, which is of great significance for the management, prevention and planning of the regional ecological environment.

Abstract Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R 2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

Abstract Background Taxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.5 among taxi drivers is important for occupational risk prevention, however, it is limited by data availability. Methods This study proposed a rapid assessment of dynamic exposure to PM2.5 among drivers based on satellite-derived information, air quality data from monitoring stations, and GPS-based taxi trajectory data. An empirical study was conducted in Wuhan, China, to examine spatial and temporal variability of dynamic exposure and compare whether drivers’ exposure exceeded the World Health Organization (WHO) and China air quality guideline thresholds. Kernel density estimation was conducted to further explore the relationship between dynamic exposure and taxi drivers’ activities. Results The taxi drivers’ weekday and weekend 24-h PM2.5 exposure was 83.60 μg/m3 and 55.62 μg/m3 respectively, 3.4 and 2.2 times than the WHO’s recommended level of 25 µg/m3. Specifically, drivers with high PM2.5 exposure had a higher average trip distance and smaller activity areas. Although major transportation interchanges/terminals were the common activity hotspots for both taxi drivers with high and low exposure, activity hotspots of drivers with high exposure were mainly located in busy riverside commercial areas within historic and central districts bounded by the “Inner Ring Road”, while hotspots of drivers with low exposure were new commercial areas in the extended urbanized area bounded by the “Third Ring Road”. Conclusion These findings emphasized the need for air quality management and community planning to mitigate the potential health risks of taxi drivers.

Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations. Many studies use bibliometric data to analyze the popularity of certain research topics, well-adopted methodologies, influential authors, and the interrelationships among research disciplines. However, the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging. This study combined a Space-Time Cube (STC) and a 3D glyph to represent the complex multivariate bibliographic data. We further implemented a visual design by developing an interactive interface. The effectiveness, understandability, and engagement of ST-Map are evaluated by seven experts in geovisualization. The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.

Abstract Background Assuring that emergency health care (EHC) is accessible is a key objective for health care planners. Conventional accessibility analysis commonly relies on resident population data. However, the allocation of resources based on stationary population data may lead to erroneous assumptions of population accessibility to EHC. Method Therefore, in this paper, we calculate population accessibility to emergency departments in Sweden with a geographical information system based network analysis. Utilizing static population data and dynamic population data, we investigate spatiotemporal patterns of how static population data over- or underestimates population sizes derived from temporally dynamic population data. Results Our findings show that conventional measures of population accessibility tend to underestimate population sizes particularly in rural areas and in smaller ED’s catchment areas compared to urban, larger ED’s—especially during vacation time in the summer. Conclusions Planning based on static population data may thus lead to inequitable distributions of resources. This study is motivated in light of the ongoing centralization of ED’s in Sweden, which largely depends on population sizes in ED’s catchment areas.

Landsat images combined with the METRIC model have been used in applications such as EEFlux to estimate actual evapotranspiration (ETa) in irrigated areas, with uncertainty as to whether the results are sufficiently accurate at local scales. This work compares temporally and spatially ETa estimates using Landsat imagery and the METRIC model with LOCAL measured weather data and EEFlux for an irrigated area in northern Sinaloa, Mexico, from 1995 to 2018. A regression analysis and error metrics such as coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and slope were used to compare both models. The temporal analysis results confirm that ETa differences are closely related to crop growth and that the daily mean absolute error was 1.17 mm/day. Spatial analysis showed that when using only cropland pixels without non-cropland pixels in EEFlux, the R2 increases from 0.36 to 0.73, and the RMSE decreases from 2.52 to 1.98 mm/day. Therefore, EEFlux should be limited to cropland areas, and it is suggested to exclude Landsat images close to the planting and harvesting season to reduce the differences.

BackgroundPulmonary tuberculosis (PTB) is a major infectious disease that threatens human health. China is a high tuberculosis-burden country and the Hunan Province has a high tuberculosis notification rate. However, no comprehensive analysis has been conducted on the spatiotemporal distribution of PTB in the Hunan Province. Therefore, this study investigated the spatiotemporal distribution of PTB in the Hunan Province to enable targeted control policies for tuberculosis.MethodsWe obtained data about cases of PTB in the Hunan Province notified from January 2014 to December 2022 from the China Information System for Disease Control and Prevention. Time-series analysis was conducted to analyze the trends in PTB case notifications. Spatial autocorrelation analysis was conducted to detect the spatial distribution characteristics of PTB at a county level in Hunan Province. Space-time scan analysis was conducted to confirm specific times and locations of PTB clustering.ResultsA total of 472,826 new cases of PTB were notified in the Hunan Province during the 9-year study period. The mean PTB notification rate showed a gradual, fluctuating downward trend over time. The number of PTB notifications per month showed significant seasonal variation, with an annual peak in notifications in January or March, followed by a fluctuating decline after March, reaching a trough in November or December. Moran’s I index of spatial autocorrelation revealed that the notification rate of PTB by county ranged from 0.117 to 0.317 during the study period, indicating spatial clustering. The hotspot areas of PTB were mainly concentrated in the Xiangxi Autonomous Prefecture, Zhangjiajie City, and Hengyang City. The most likely clustering region was identified in the central-southern part of the province, and a secondary clustering region was identified in the northwest part of the province.ConclusionThis study identified the temporal trend and spatial distribution pattern of tuberculosis in the Hunan Province. PTB clustered mainly in the central-southern and northwestern regions of the province. Disease control programs should focus on strengthening tuberculosis control in these regions.

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.

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 better-applied 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.

Utilizing satellite remote sensing for the assessment and temporal-spatial analysis of Chromophoric Dissolved Organic Matter (CDOM) is vital for overseeing lake water health and devising management plans. This study focused on the saline, turbid and arid Ebinur Lake, located in China’s northwestern region. The Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) model algorithms were compared to select the one with the highest accuracy. It combined Sentinel-2 remote sensing data and in situ measurement data for the quantitative inversion of CDOM. Monthly CDOM distribution maps were generated with a 10 m resolution for the non-frozen months of May to October from 2018 to 2022, followed by a comprehensive analysis of temporal trends. The primary conclusions are: (1) The XGBoost model yielded highly accurate CDOM estimates, with a training set coefficient of determination (R2) of 0.94, a Root Mean Square Error (RMSE) of 0.06 mg/L, Mean Absolute Percentage Error (MAPE) of 6.05 %, Relative Percent Difference (RPD) of 4.07; the test set demonstrated an R2 of 0.41 with an RMSE of 0.22 mg/L, MAPE of 22.74 %, RPD of 1.35; (2) Throughout the study period, the main lake portion displayed variable CDOM spatial patterns and trends. The inversion indicated higher CDOM concentrations in the central part than nearshore areas and decreasing CDOM in tandem with seasonable water-surface shrinkage. The findings offer hints for an accurate evaluation of water color parameters of Ebinur Lake and practical references for monitoring arid-region lake water quality via remote sensing.

Background: Pregnancy termination is a major public health problem, and complications of unsafe abortion are among the proximate and major causes of maternal mortality. Mapping the trend and spatiotemporal variation and identifying factors that are responsible for the changes in pregnancy termination help achieve the sustainable development goal of reducing maternal mortality in Ethiopia by understanding the epidemiology and regional variations. Methods: Data from the 2000–2016 Ethiopian Demographic and Health Survey were analyzed with a total weighted sample of 40,983 women of reproductive age. Variables with a p-value

BackgroundIntestinal infectious diseases are a global concern in terms of morbidity, and they are closely linked to socioeconomic variables such as quality of life, weather and access to healthcare services. Despite progress in spatial analysis tools and geographic information systems in epidemiology, studies in Ecuador that evaluate temporal trends, specific geographic groups, and their correlation with socioeconomic variables are lacking. The absence of such information makes it challenging to formulate public health policies. This study sought to identify the spatial and temporal patterns of these diseases in Ecuador, along with their correlation with socioeconomic variables.MethodsIn Ecuador, the study was carried out in a continental territory, focusing on data related to intestinal infectious diseases collected from the National Institute of Statistics and Census (Instituto Nacional de Estadística y Censos) during the period from 2014 to 2019. This study involved spatial and temporal analyses using tools such as the global Moran’s index and Local Indicators of Spatial Association to identify spatial clustering patterns and autocorrelation. Additionally, correlations between morbidity rates and socioeconomic variables were examined.ResultsDuring the investigated period, Ecuador registered 209,668 cases of these diseases. Notable variations in case numbers were identified, with a 9.2% increase in 2019 compared to the previous year. The most impacted group was children under 5 years old, and the highest rates were centered in the southern and southwestern regions of the country, with Limón Indanza and Chunchi being the cantons with the highest rates, notably showing a significant increase in Limón Indanza. Additionally, there were significant correlations between morbidity rates and socioeconomic variables, school dropout rates, low birth weight, and access to water services.ConclusionThis study emphasizes the importance of considering socioeconomic variables when addressing these diseases in Ecuador. Understanding these correlations and geospatial trends can guide the development of health policies and specific intervention programs to reduce the incidence in identified high-risk areas. More specific research is needed to understand the underlying causes of variability in morbidity and develop effective prevention strategies.

Understanding the spatiotemporal dynamics of climatic conditions within a region is paramount for informed rural planning and decision-making processes, particularly in light of the prevailing challenges posed by climate change and variability. This study undertook an assessment of the spatial and temporal patterns of rainfall trends across various agro-ecological zones (AEZs) within Wolaita, utilizing data collected from ten strategically positioned rain gauge stations. The detection of trends and their magnitudes was facilitated through the application of the Mann–Kendall (MKs) test in conjunction with Sen's slope estimator. Spatial variability and temporal trends of rainfall were further analyzed utilizing ArcGIS10.8 environment and XLSTAT with R programming tools. The outcomes derived from ordinary kriging analyses unveiled notable disparities in the coefficient of variability (CV) for mean annual rainfall across distinct AEZs. Specifically, observations indicated that lowland regions exhibit relatively warmer climates and lower precipitation levels compared to their highland counterparts. Within the lowland AEZs, the majority of stations showcased statistically non-significant positive trends (p > 0.05) in annual rainfall, whereas approximately two-thirds of midland AEZ stations depicted statistically non-significant negative trends. Conversely, over half of the stations situated within highland AEZs displayed statistically non-significant positive trends in annual rainfall. During the rainy season, highland AEZs experienced higher precipitation levels, while the south-central midland areas received a moderate amount of rainfall. In contrast, the northeast and southeast lowland AEZs consistently received diminished rainfall across all seasons compared to other regions. This study underscores the necessity for the climate resilient development and implementation of spatiotemporally informed interventions through implementing region-specific adaptation strategies, such as water conservation measures and crop diversification, to mitigate the potential impact of changing rainfall patterns on agricultural productivity in Wolaita.

[Objective] In Shaanxi Province, the intensity of soil erosion in the Qinling-Daba Mountains is next only to that of the Loess Plateau. The study of response relationship between the intensity of summer day sunny and night rainfall (DSNR) and climate warming is of great importance for the control of soil erosion in China’s north-south transition zone. [Methods] Based on daily precipitation data in summer (June to August) of 72 meteorological stations, assisted by B-G segmentation, empirical orthogonal function (EOF) method and Lasso regression model, we analyzed the spatiotemporal variation and influencing factors of the intensity of DSNR at summertime in the north and south of Qinling mountains during the period 1970—2020. [Results] From 1970 to 2020, the intensity of DSNR in summer exhibited a significantly increasing trend, which was more obvious in Hanjiang River Valley [0.30 mm/(d·10 a)]. Spatially, the leading mode of the intensity of summer DSNR can be characterized by “consistent enhancement across the whole region, with a higher degree of anomalies in the valleys than in the mountains”. It indicated that valley-plain areas below 1 000 m were sensitive to variations in the intensity of DSNR at summertime. In terms of influencing factors, the correlation between evolution in the intensity of DSNR at summertime and meteorological factors in the Hanjiang River Valley and southern slopes of Qinling Mountains was higher than that in Guanzhong Plain. In addition, cumulative duration of sunshine was the leading climatic factor driving the changes of summer DSNR intensity in the southern slopes of Qinling Mountains, while the variations of summer DSNR intensity in the Hanjiang River Valley was jointly influenced by cumulative duration of sunshine and average wind speed. [Conclusion] The year 2003 was a mutation year after which the intensity of DSNR at summertime began to be greater in the north and south of Qinling mountains. It informs us that, in the north and south of Qinling mountains, we should put the focus of soil and water conservation on the precipitation type with high temperature in the day and heavy precipitation at night.

Shared micromobility has gained significant attention in the field of transportation engineering in recent years as an environmentally friendly, convenient, and easily accessible transportation mode. Like other medium-sized cities, Birmingham, Alabama implemented a shared micromobility pilot program in 2021 that captured the attention of local travelers. This study examined shared e-scooter usage and associated travel patterns in Birmingham using 2021–2022 field data. From these data, ArcGIS maps were used to showcase trip origins and destinations. To gain a further understanding of e-scooter travel patterns in the study area, zip code and block group densities were calculated. Additionally, a negative binomial regression model was constructed to identify determinants of shared e-scooter trips. The analysis results showed that the usage of shared e-scooters was the highest during the nighttime (9109 trips between 9 p.m. to 10 p.m.), on weekends (20,077 trips on Saturday), and in the fall season (a total of 29,024 trips). Furthermore, the research findings indicated that shared e-scooters experienced their highest utilization rates in areas with a higher proportion of educated and higher-income individuals. These findings suggest that travelers’ mode choice related to the use of micromobility modes is influenced by environmental and demographic factors. Overall, this case study offers valuable contributions to the understanding of the role of shared e-scooters in Birmingham’s transportation landscape and can guide transportation authorities in other medium-sized cities in their efforts to plan for micromobility options.

Many single-family homes built in Germany in the first decades following the Second World War are now occupied by elderly residents. If local conditions are unfavorable, a large number of these buildings may enter the real estate market in a short period of time and put pressure on the local housing market. Planners and decision-makers therefore need detailed spatiotemporal information about these neighborhoods to effectively address and counteract such developments. We present the design and implementation of a planning support system that can generate the required information. The architecture of this newly developed software consists of a composite, multitier framework to perform the complex tasks of data importation, data processing, and visualization. Legally mandated municipal population registers provide the key data for the calculation of indicators as a base for spatiotemporal analyses and visualizations. These registers offer high data quality in terms of completeness, logical consistency, spatial, and temporal and thematic accuracy. We demonstrate the implemented method using population data from a local government in a rural area in southwestern Germany. The results show that the new tool, which relies on open software components, is capable to identify and prioritize areas with particularly high levels of problem pressure. The tool can be used not only for analyses in a local context, but also at a regional level.

The aim of the study was to assess the impact of the COVID-19 pandemic on the notification of new VL cases in Brazil in 2020. It is an ecological and time-series study (2015–2020) with spatial analysis techniques, whose units of analysis were the 5570 Brazilian municipalities. The study population consisted of all new cases of VL recorded between 2015 and 2020. The P-score was calculated to estimate the percentage variation in new VL cases. Global and local univariate Moran’s Indices and retrospective space–time scan statistics were used in spatial and space–time analyses, respectively. It was expected that there would be 3627 new cases of VL in Brazil in 2020, but 1932 cases were reported (−46.73%). All Brazilian regions presented a negative percentage variation in the registration of new VL cases, with the Southeast (−54.70%), North (−49.97%), and Northeast (−44.22%) standing out. There was spatial dependence of the disease nationwide in both periods, before and during the first year of the COVID-19 pandemic. There was a significant reduction in the incidence of new VL cases in Brazil during the first year of the COVID-19 pandemic. These findings reinforce the need for better preparedness of the health system, especially in situations of new epidemics.

Objective: There are currently no integrated data on the trend of dental caries amongst distinct age groups in Iran. We aimed to assess the national and subnational trend of dental caries of permanent teeth in Iran from 1990 to 2017. Methods: A literature search about dental caries and the decayed-missing-filled teeth index (DMFT) was performed in PubMed, Web of Science, Scopus, and 3 national databases (in Persian). All eligible national oral health surveys in these 28 years were included. We categorised and aggregated the DMFT values and their components based on age (5-year-based groups from 5 to 9 to 60+ years), sex, year, and province. The data for missing spots were estimated using the spatiotemporal Bayesian hierarchical model. We used the bootstrap method in multilevel models to predict the uncertainty interval (UI) of the modelled results. Results: Nationally, the all-ages mean DMFT increased by nearly 58.0% (6.8 [95% UI, 4.1–10.5] in 1990 to 10.8 [95% UI, 7.5–14.5] in 2017). Decayed teeth (DT) and missing teeth (MT) rose by 84.5% and 31.6% during this period, respectively. Filled teeth (FT) showed almost a 2.6-fold increase in the same period from 0.6 (95% UI, 0.01–1.6) in 1990 to 1.7 (95% UI, 0.6–2.8) in 2017. The proportion of DT and FT continuously increased in both sexes. In 2017, the highest DT, MT, and FT were estimated in the 25–29 (4.9 [95% UI, 2.5–7.2]), 60+ (21.5 [95% UI, 17.5–25.4]), and 35–39 (2.6 [95% UI, 1.3–4.0]) year age groups. Conclusions: Caries of permanent dentition levies a growing burden on the Iranian population. Considering the continuous increase in caries during the 1990–2017 period, Iranian policymakers should pay heed to these findings and react more proactively to mitigate this perpetuating issue. Implementing nationwide interventions such as sugar consumption management should be encouraged to achieve sustainable outcomes in this regards.

Goal: This study introduces a novel approach to examine the temporal-spatial information derived from High-Density surface Electromyography (HD-sEMG). By integrating and adapting postural control parameters into a framework for the analysis of myoelectrical activity, new metrics to evaluate muscle fatigue progression were proposed, investigating their ability to predict endurance time. Methods: Nine subjects performed a fatiguing isometric contraction of the lumbar erector spinae. Topographical amplitude maps were generated from two HD-sEMG grids. Once identified the coordinates of the muscle activity, novel metrics for quantifying the muscle spatial distribution over time were calculated. Results: Spatial metrics showed significant differences from beginning to end of the contraction, highlighting their ability of characterizing the neuromuscular adaptations in presence of fatigue. Additionally, linear regression models revealed strong correlations between these spatial metrics and endurance time. Conclusions: These innovative metrics can characterize the spatial distribution of muscle activity and predict the time of task failure.

Forest fires are one of the most common natural disasters and public crisis events, presenting a serious threat to the ecosystem and human security. The traditional forest fire monitoring is time-consuming and inaccurate, but nighttime light remote sensing is enhancing the efficiency and speed of forest fire detection. Thus, this study focused on forest fire detection in Southwest China that was affected by fires in 2021 and 2022 using daily remote sensing nighttime light data from the black marble product VNP46A2 of the Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite. Multiple features were developed based on the temporal features of day-level nighttime light radiances, which were divided into black pixels, stable light pixels, and forest fire pixels using the random forest classification model. To verify the accuracy, a confusion matrix was constructed to calculate the accuracy of the forest fire identification results by combining multisource data, and the results were compared with the results of different machine learning models and fire products. Moreover, the spatiotemporal distribution of forest fires was analyzed using trend analysis and nearest-neighbor analysis. Results show that the overall accuracy of pixel classification was over 92% in both years, with forest fire pixels classified with user's accuracy exceeding 99%. The forest fires were mainly dispersed and concentrated around the stable light pixels 400–700 m, and the distribution in different regions showed obvious spatial heterogeneity. Compared with 2021, the distribution of forest fires decreased significantly in 2022. This study can support the future management and protection of forest resources and the prediction of fire disasters. The dataset (2021 and 2022) of forest fires in Southwest China is available free of charge.1

Urbanization is an important symbol of regional economic development and social progress. Studying the urbanization level is greatly important for the scientific development of towns and cities, and for the efficient decision-making of governments. The nighttime light data, which encompasses valuable insights into human activities, economic progress, and social development, effectively addresses the limitations associated with the indicator method in terms of uncertainty and lag issues. Those data can also provide a more intuitive reflection of the level of urbanization and offer novel insights for research on urbanization. In this study, based on the 2000–2020 EANTLI_Like nighttime light remote sensing data, the characteristics of the spatial and temporal dynamic changes in the land urbanization level in China were measured. Results were analyzed at four scales: national, regional, provincial, and municipal. Finally, the main economic indicators affecting urbanization were selected as variables to simply analyze the driving force of land urbanization through spatial autocorrelation and spatiotemporal geographically weighted regression model. Results indicated that China's land urbanization level continued to grow from 2000 to 2020, with an average annual growth rate of 2.51%. The spatial distribution of land urbanization varied significantly across regions. The level of land urbanization in the eastern coastal region was significantly higher than that in the inland and western provinces, at the agricultural and provincial scales. Hong Kong, Macao, and Shanghai ranked high in terms of land urbanization, and the Pearl River Delta and Yangtze River Delta regions in which they were located also belong to the cluster of high land urbanization levels. Meanwhile, the goodness of fit among GDP, the proportion of output of secondary and tertiary industries, freight volume, and land urbanization level were higher than 0.86. Among them, GDP and the output of secondary and tertiary industries had a more obvious role in promoting land urbanization. The freight volume exhibited positive effects on land urbanization level in the western region, Heilongjiang, and Jilin provinces. This study is very important for understanding the law of urban construction land expansion in China, the regional differences in land urbanization level, and the optimization of land use layout.

Global climate change and rapid urbanization have increased the frequency of flooding, making urban flood resilience a critical objective. This article introduces a methodology for assessing urban flood resilience, utilizing a social-ecological synthesis index that integrates geographical and temporal data with Geographic Information System (GIS). The study focuses on ten administrative subdistricts in Wuhua District, Kunming City, China, and selects 18 social-ecological indicators. These indicators, chosen from social and ecological perspectives, are weighted using the entropy weight method to determine their significance in the assessment system. By combining scores for each subdistrict, the study quantifies flood resilience and creates a spatial distribution map using ArcGIS. Key findings reveal that out of the ten administrative subdistricts, five in Wuhua District, particularly in the core urban area of Kunming, demonstrate strong overall flood resilience. Influenced by social-ecological indicators, there is significant spatial differentiation in flood resilience within Wuhua District, with a decreasing trend radiating from the city center to areas farther from the urban core. The research indicates that regions with well-established transportation infrastructure, a wide distribution of government institutions, improved water management facilities, and a substantial population with higher education levels contribute significantly to enhancing urban flood resilience. HIGHLIGHTS Analyzing urban flood resistance capacity from the perspective of resilience.; Considering the performance of both social and ecological dimensions in urban flood resilience.; Quantifying urban flood resilience through comprehensive index method.; Using geographic information technology to analyse the spatial characteristics of urban flood resilience.;

The aim of this review was to assess the peculiarities of distribution and epidemiological risk of rabies in the extreme northeast of Asia. The systematic review was prepared through synthesizing publications on rabies over 1860–2022 (n=22) and previously unpublished data for 2009–2023. From the late 19th century until the 1980s, rabies epizootics were consistently observed in Chukotka and Kamchatka. A correlation was found between the time of occurrence of rabies in those territories with a lag period of 1–2 years (r=0.349; p=0.054). In 2009–2023, 24 animal rabies cases were confirmed in Chukotka; rabies has not been registered in Kamchatka since 1981. Until 1982, 5 confirmed human rabies cases were described in Chukotka, as well as 4 lethal cases presumably due to rabies (Chukotka – 3, Kamchatka – 1). The similarity of the spatial distribution of epizootics in different periods of time was established. Rabies was detected mainly in the area of the permanent polar fox (Vulpes lagopus) habitation, in the coastal tundra from the mouth of the Kolyma River to the Anadyr Gulf. Outside this territory (the valleys of the Anadyr and Penzhina Rivers, the Kamchatka Peninsula), rabies was reported in red foxes (Vulpes vulpes). At present, the importance of the polar fox in the spread of rabies in Chukotka has decreased, while the significance of the red fox has increased substantially. Due to vaccination, rabies among dogs is recorded sporadically. Rabies virus isolates from Chukotka belong to the Arctic genetic lineage (Arctic-3 group), which has a circumpolar distribution. The possibility of independent circulation of the rabies virus in the Kamchatka Peninsula is doubtful because of the limited size of the red fox population. Based on the mapping, possible directions for the introduction of rabies to Kamchatka from Chukotka have been identified. Barrier oral rabies vaccination of foxes is recommended during the years of high abundance of red and polar foxes in Chukotka.

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.

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.

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.

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.

IntroductionThis 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.MethodBy 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.DiscussionTo 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.

Urban Flood (UF) and Urban Heat Island (UHI) become prevalent “urban diseases” currently. Previous studies usually regarded UF and UHI as separate issues that requires respective concentration; however, UF and UHI are inextricably connected so that they should be considered together. Hence, this paper proposes a spatiotemporal framework to link UF and UHI by developing an Urban Flood Heat Island (UFHI) index, taking the Greater Bay Area (GBA) in China as the study case. Random Forest (RF) classifier and Urban-Rural Dichotomy were selected to compute the spatiotemporal distribution of UF susceptibility (UFS) and UHI intensity (UHII), respectively. Theil-Sen Median Slope, Mann-Kendall Test, and Hurst analysis were used to explore the spatiotemporal variation pattern of the UF-UHI joint risk. Spatial Durbin Model was used to compute the correlation between UFS and UHII. RF regressor was employed to quantify the contribution of the common driving factors. We found: (1) UFS and UHII are spatiotemporally correlated, thus UHII can be used as a control variable for the refined UFS assessments, (2) the areas with the UF-UHI joint risk of persistently significant increase (∼7.9 %) majorly distributed in the central region, (3) according to the contributions of factors, UF and UHI in the GBA could be effectively alleviated by breaking the continuous impervious surfaces by introducing blue-green structures. This study offers a new perspective for the effective alleviation of UF and UHI, which would be helpful for the sustainable development of cities in developing countries where human and material sources are limited.

The hinterland of the Qinghai-Xizang （Tibet） Plateau is affected by two major circulation systems， Westerly Wind and Indian Ocean monsoon.The average altitude of the region is high， and the terrain is complex and changeable.It is extremely complicated that the temperature and precipitation conditions in this region as a comparison to other areas of the Qinghai-Xizang （Tibet） Plateau.In order to accurately obtain the temporal and spatial changes of temperature and precipitation in this region and predict future temperature and precipitation changes， based on the CN05.1 observation dataset， the ability of CMIP6 data to simulate temperature and precipitation in the hinterland of the Qinghai-Xizang （Tibet） Plateau was evaluated.CMIP6 was corrected using Spatial Disaggregation and Equidistant Cumulative Distribution Functions Method Temperature and precipitation conditions of 5 climate models and 7 scenarios in 2015-2100 were estimated.The results show that： （1） In the historical period （from 1961 to 2014）， the temperature and precipitation observation values of CMIP6 data have little deviation from the simulation values， and have strong space-time correlation.（2） In the future （from 2021 to 2100）， the annual average temperature and precipitation will show an overall upward trend.The percentage of temperature anomaly and precipitation anomaly in 2021-2100 of SSP3-7.0 and SSP5-8.5 scenarios increased significantly.The high value of temperatures anomaly is concentrated in the Qaidam Basin， and the high value of precipitation anomaly is located at the source of the Lancang River in the southeast.（3） In the future， the temperature will continue to increase in the four seasons， the precipitation will also show an overall trend of rise in four seasons.However， the degree of precipitation increase is distinct in different seasons and different scenarios.In the four seasons， the temperature increase of SSP5-8.5 scenario is the largest.The temperature of SSP5-8.5 scenario increases fastest in autumn； The precipitation of SSP3-7.0 scenario increases fastest in summer and winter， while that of SSP5-8.5 scenario increases fastest in spring and autumn.（4） Except for the SSP1-1.9 scenario， the temperature of each scenario from the recent period to the end of the period shows strong temporal and spatial similarity.Against to the historical period， the spatial distribution of temperature in spring and winter showed a consistently rising tendency is similar， and that in summer and autumn is similar.The precipitation increase is the largest in summer and the smallest in winter.Compared with the historical period， the spatial distribution of precipitation anomaly percentage shows a strong seasonality and regional feature.The high value area is mainly distributed in the southeast of the study area.

Drought is one of the most serious problems for human societies and ecosystems. Climate change has begun to affect the whole world, but regional climate change can have different effects. Precipitation is frequently used to determine drought; besides, temperature has started to be used in recent years. The effect of temperature on the occurrence of drought was investigated for the Marmara region, located in the northeast of Turkey. The spatiotemporal variability of drought in the Marmara region was analyzed by using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) for 3-, 6-, and 12-month series. Results show that SPEI droughts occur with longer duration and increased magnitude, which can be attributed to the rise in potential evapotranspiration rates with an increase in temperature. The correlation between SPI and SPEI is strong for the same time series, and the number of extreme drought values for SPI is higher than SPEI, while the number of severe and moderate drought values is lower compared to SPEI. The years 1989, 1990, 2001, 2007, and 2014 were characterized as the highest drought years throughout the region. The study concludes that temperature should be considered in future studies. HIGHLIGHTS This study evaluates drought in terms of short- and long-time periods in the Marmara region.; The drought has been analyzed temporally and spatially.; The study gives an idea about the importance of considering the temperature in the determination of drought.; The drought duration and intensity were analyzed according to the short- and long-time periods.; Standardized precipitation and evapotranspiration index droughts were found to last longer and be more severe than standardized precipitation index.;

Objective: Despite significant advancements in understanding risk factors and treatment strategies, ischemic heart disease (IHD) remains the leading cause of mortality worldwide, particularly within specific regions in Brazil, where the disease is a burden. Therefore, the aim of this study was to estimate the risk of hospitalization and mortality from IHD in the state of Paraná (Brazil), using spatial analysis to identify areas with higher risk based on socioeconomic, demographic and health variables. Methods: This is an ecological study based on secondary and retrospective IHD hospitalization and mortality data obtained from the Brazilian Hospitalization and Mortality Information Systems during the 2010–2021 period. Data were analyzed for 399 municipalities and 22 health regions in the state of Paraná. To assess the spatial patterns of the disease and identify relative risk (RR) areas, we constructed a risk model by Bayesian inference using the R-INLA and SpatialEpi packages in R software. Results: A total of 333,229 hospitalizations and 73,221 deaths occurred in the analyzed period, and elevated RR of hospitalization (RR = 27.412, CI 21.801; 34.466) and mortality (RR = 15.673, CI 2.148; 114.319) from IHD occurred in small-sized municipalities. In addition, medium-sized municipalities also presented elevated RR of hospitalization (RR = 6.533, CI 1.748; 2.006) and mortality (RR = 6.092, CI 1.451; 2.163) from IHD. Hospitalization and mortality rates were higher in white men aged 40–59 years. A negative association was found between Municipal Performance Index (IPDM) and IHD hospitalization and mortality. Conclusion: Areas with increased risk of hospitalization and mortality from IHD were found in small and medium-sized municipalities in the state of Paraná, Brazil. These results suggest a deficit in health care attention for IHD cases in these areas, potentially due to a low distribution of health care resources.

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.

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.

The spread of visceral leishmaniasis (VL), a serious global zoonotic parasitic disease, is mostly under control; however, several cases have been reported in recent decades in Xinjiang, China. This study aimed to analyze the epidemiological status and spatiotemporal clustering characteristics of VL in Xinjiang, China, between 2004 and 2021 to provide a basis for the development and implementation of surveillance and response measures. Data on VL incidence during 2004–2021 were collected from the National Diseases Reporting Information System of China. Global spatial autocorrelation analysis, identification of local indicators of spatial association, and spatial–temporal clustering analysis were conducted to identify the distribution and high-risk areas. A total of 2034 VL cases were reported, with a mean annual incidence of 0.50 per 100,000. There was a general decreasing trend in the incidence of VL during our study period. The majority of the cases were reported from October to February of the following year, and fewer cases were reported from April to July. Spatial autocorrelation analysis revealed that the incidence of VL was spatially clustered within a few counties. Significant differences were observed during the study period (Moran’s I = 0.74, Z = 4.900, p < 0.05). The male-to-female ratio was 1.37:1, and most patients were in the age group 0–3 years. Cases were primarily distributed in seven regions and two autonomous prefectures, and Kashgar reported the highest number of cases (1688, 82.98%). Spatial analysis revealed that the aggregation of VL was predominantly observed in southwest Xinjiang. This was in alignment with the high-risk areas identified by spatiotemporal clustering analysis. The H-H clustering region was primarily observed in Gashi, Atushi, Shufu, Injisha, Kashgar, Yepuhu, and Bachu. These findings indicate that integrated control measures must be taken in different endemic areas to strengthen the VL control program in Xinjiang, China.

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.

Coral reef resilience is eroding at multiple spatial scales globally, with broad implications for coastal communities, and is thus a critical challenge for managing marine social-ecological systems (SESs). Many researchers believe that external stressors will cause key coral reefs to die by the end of the 21st century, virtually eliminating essential ecological and societal benefits. Here, we propose the use of resilience-based approaches to understand the dynamics of coral reef SESs and subsequent drivers of coral reef decline. Previous research has demonstrated the effectiveness of these methods, not only for tracking environmental change, but also for providing warning in advance of transitions, possibly allowing time for management interventions. The flexibility and utility of these methods make them ideal for assessing complex systems; however, they have not been used to study aquatic ecosystem dynamics at the global scale. Here, we evaluate these methods for examining spatiotemporal change in coral reef SESs across the global seascape and assess the subsequent impacts on coral reef resilience. We found that while univariate indicators failed to provide clear signals, multivariate resilience-based approaches effectively captured coral reef SES dynamics, unveiling distinctive patterns of variation throughout the global coral reef seascape. Additionally, our findings highlight global spatiotemporal variation, indicating patterns of degraded resilience. This degradation was reflected regionally, particularly in the Pacific Ocean and Indian Ocean SESs. These results underscore the utility of resilience-based approaches in assessing environmental change in SESs, detecting spatiotemporal variation at the global and regional scales, and facilitating more effective monitoring and management of coral reef SESs.

Air pollution has inevitably come along with the economic development of human society. How to balance economic growth with a sustainable environment has been a global concern. The ambient PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) is particularly life-threatening because these tiny aerosols could be inhaled into the human respiration system and cause millions of premature deaths every year. The focus of most relevant research has been placed on apportionment of pollutants and the forecast of PM2.5 concentration measures. However, the spatiotemporal variations of pollution regions and their relationships to local factors are not much contemplated in the literature. These local factors include, at least, land terrain, meteorological conditions and anthropogenic activities. In this paper, we propose an interactive analysis platform for spatiotemporal retrieval and feature analysis of air pollution episodes. A domain expert can interact with the platform by specifying the episode analysis intention considering various local factors to reach the analysis goals. The analysis platform consists of two main components. The first component offers a query-by-sketch function where the domain expert can search similar pollution episodes by sketching the spatial relationship between the pollution regions and the land objects. The second component helps the domain expert choose a retrieved episode to conduct spatiotemporal feature analysis in a time span. The integrated platform automatically searches the episodes most resembling the domain expert's original sketch and detects when and where the episode emerges and diminishes. These functions are helpful for domain experts to infer insights into how local factors result in particular pollution episodes.

Abstract Background The Democratic Republic of the Congo (DRC) implemented the first strategic Multisectoral Cholera Elimination Plan (MCEP) in 2008–2012. Two subsequent MCEPs have since been implemented covering the periods 2013–2017 and 2018–2021. The current study aimed to assess the spatiotemporal dynamics of cholera over the recent 22-year period to determine the impact of the MCEPs on cholera epidemics, establish lessons learned and provide an evidence-based foundation to improve the implementation of the next MCEP (2023–2027). Methods In this cross-sectional study, secondary weekly epidemiological cholera data covering the 2000–2021 period was extracted from the DRC Ministry of Health surveillance databases. The data series was divided into four periods: pre-MCEP 2003–2007 (pre-MCEP), first MCEP (MCEP-1), second MCEP (MCEP-2) and third MCEP (MCEP-3). For each period, we assessed the overall cholera profiles and seasonal patterns. We analyzed the spatial dynamics and identified cholera risk clusters at the province level. We also assessed the evolution of cholera sanctuary zones identified during each period. Results During the 2000–2021 period, the DRC recorded 520,024 suspected cases and 12,561 deaths. The endemic provinces remain the most affected with more than 75% of cases, five of the six endemic provinces were identified as risk clusters during each MCEP period (North Kivu, South Kivu, Tanganyika, Haut-Lomami and Haut-Katanga). Several health zones were identified as cholera sanctuary zones during the study period: 14 health zones during MCEP-1, 14 health zones during MCEP-2 and 29 health zones during MCEP-3. Over the course of the study period, seasonal cholera patterns remained constant, with one peak during the dry season and one peak during the rainy season. Conclusion Despite the implementation of three MCEPs, the cholera context in the DRC remains largely unchanged since the pre-MCEP period. To better orient cholera elimination activities, the method used to classify priority health zones should be optimized by analyzing epidemiological; water, sanitation and hygiene; socio-economic; environmental and health indicators at the local level. Improvements should also be made regarding the implementation of the MCEP, reporting of funded activities and surveillance of cholera cases. Additional studies should aim to identify specific bottlenecks and gaps in the coordination and strategic efforts of cholera elimination interventions at the local, national and international levels.