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We propose a new strategy for analyzing the evolution of random phenomena over time and space simultaneously based on the high-order multivariate Markov chains. We develop a novel Markov model of order r for m chains consisting of s possible states to gather parsimony with realism. It can capture negative and positive associations among the chains with only a reduced number of parameters, r m 2 s 2 + 2 , remarkably lower than m s r m + 1 required for the full parameterized model. Our model privileges are enhanced by a Monte Carlo simulation experiment, besides application to analyze the spatial–temporal dynamics for the risk level of a recently global pandemic (COVID-19) outbreak in world health organization (WHO) regions for predicting the risk state of epidemiological prevalence and monitoring infection control. [ABSTRACT FROM AUTHOR]

Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. • Analyzed carbon emission trends in the Yangtze River Delta using satellite data. • Carbon emissions in YRDUA increased by 262.56 %, with notable spatial expansion. • High-high emission clusters emerged in urban areas; low-low clusters in mountains. • Regional context more influential than nearest neighbor effects on carbon emissions. • Economic scale and industrial structure were key drivers of spatiotemporal patterns. [ABSTRACT FROM AUTHOR]

Taxi‐demand forecasting and hotspot prediction can be critical in reducing response times and designing a cost effective online taxi‐booking model. Taxi demand in a region can be predicted by considering the past demand accumulated in that region over a span of time. However, other covariates—like neighborhood influence, sociodemographic parameters, and point‐of‐interest data—may also influence the spatiotemporal variation of demand. To study the effects of these covariates, in this paper, we propose three models that consider different covariates in order to select a set of independent variables. These models predict taxi demand in spatial units for a given temporal resolution using linear and ensemble regression. We eventually combine the characteristics (covariates) of each of these models to propose a robust forecasting framework which we call the combined covariates model (CCM). Experimental results show that the CCM performs better than the other models proposed in this paper. [ABSTRACT FROM AUTHOR]

This study provides an overview of the current status of clonorchiasis and cholangiocarcinoma (CCA), and their relationship in Korea during 2012-2020. Data were obtained from the Health Insurance Review & Assessment Service of Korea. Cluster, trend, and correlation analyses were performed. Gyeongsangnam-do and Seoul had the highest average number of cases (1,026 and 4,208) and adjusted rate (306 and 424) for clonorchiasis and CCA, respectively. The most likely clusters (MLC) for clonorchiasis and CCA were Busan/Gyeongsangnam-do/Ulsan/Daegu/Gyeongsangbuk-do (Relative Risk; RR=4.55, Likelihood Ratio; LLR=9,131.115) joint cluster and Seoul (RR=2.29, LLR=7,602.472), respectively. The MLC for clonorchiasis was in the southeastern part of Korea, while that for CCA was in the southern part. Clonorchiasis showed a decreasing trend in the southeastern districts, while increased in the southwestern districts. Cities in the central region had a decreasing trend, while the western districts had an increasing trend. In most adults (30-59), infection rate of clonorchiasis showed a significant decrease until 2018, while thereafter increased, although not significant. CCA showed a sharply decreasing tendency. The incidence of clonorchiasis and CCA were positively correlated. In general, the correlation was weak (r=0.39, P<0.001), but it was strongly positive around the 4 river basins (r=0.74, P<0.001). This study might provide an analytic basis for developing an effective system against clonorchiasis and CCA. [ABSTRACT FROM AUTHOR]

Copyright of Ciência Florestal (01039954) is the property of Ciencia Florestal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Global North and South cities show divergent trends in urban green space (UGS) dynamics. • Global South cities experience more rapid UGS decline than Global North cities. • UGS exposure varies significantly across cities, with disparities having equity implications. • Urbanization drives UGS decline, with higher pressures in Global South cities. • Context-specific urban greening strategies are crucial for equitable and sustainable cities. Urban green space (UGS) plays a vital role in enhancing the resilience and livability of cities. However, the distribution and accessibility of these spaces often vary significantly within and between cities, raising concerns about environmental justice and social equity. This study aims to analyze the spatiotemporal dynamics of UGS and assess population exposure and equality implications across eight diverse cities from the Global North (GN) and Global South (GS) over the past three decades. Employing a multimethod approach combining geospatial analysis, remote sensing, and statistical techniques, this study reveals an alarming global trend of decreasing UGS, with GS cities experiencing more rapid decline than GN cities. The analysis of UGS exposure uncovers distinct trends and variations across cities, with GN cities generally having higher exposure but showing a concerning decline over time, while GS cities display mixed patterns. Arid-climate cities Phoenix and Riyadh have managed to maintain low but stable UGS levels despite their unique climatic challenges. Urbanization emerges as a dominant force driving the decline in UGS, with GS cities facing significantly higher pressures than GN cities. The findings highlight the urgent need for comprehensive urban greening strategies that prioritize UGS protection and expansion, innovative policies, community engagement, and data-driven decision-making to promote equitable and sustainable urban environments. This study contributes to the growing research on urban greening by providing a comparative analysis of UGS trajectories and exposure equality implications across diverse cities, underscoring the importance of context-specific approaches and inclusive planning processes. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVD) stand out as the leading cause of mortality, and the mortality rate attributed to this disease is notably elevated in Iran. Consequently, dedicated studies on CVD become imperative. This cross-sectional study utilized data from the death registration system of the Ministry of Health, Treatment and Medical Education of Iran. In this study, the statistical population of all people who died due to CVD in Iran were18,146, 21,945, and 24,352 individuals in the years 2017, 2018, and 2019, respectively. The primary objective is to conduct a spatiotemporal analysis of CVD mortality spatiotemporally using GIS-based methodologies. To achieve this, CVD mortality data at the township level for the years 2017, 2018, and 2019 in Iran are subjected to spatial statistical tests, including Anselin Local Moran's I and Hot Spot Analysis (Getis-Ord Gi*), as well as analytical techniques such as Mean Center (MC), (SD), and (GIS). The study identified a rising trend in cardiovascular disease-related deaths in Iran, reaching (46.36% females and 53.64 males), (45.39% females and 54.61% males) and (45.67% females and 54.33% males) individuals in the years 2017, 2018, and 2019, respectively. Throughout this period, the mortality rate was higher among men, with the elderly showing the highest mortality. Notably, distinct hotspots of cardiovascular disease mortality emerged in the western, southern, and eastern regions of Iran. These findings emphasize the importance of targeted interventions and further investigation into the contributing factors in these specific geographic areas. Geographic factors are identified as significant contributors to an elevated risk of cardiovascular disease mortality. Our study, shedding light on the spatial dynamics of the disease, offers valuable insights for decision-makers. The findings can contribute to the formulation of effective strategies and policies, aligning with a Holistic Cardiovascular Health Strategy, Gender-Based Healthcare Policies, and Spatial Planning and Environmental Policies. [ABSTRACT FROM AUTHOR]

This study aimed to identify the spatiotemporal characteristics in technical hurdle clearance considering differences in centre of mass (CM) height and sprint ability. Spatiotemporal variables were calculated for 13 male hurdlers by capturing hurdle clearances at the same height as their respective CM and hurdle-free sprints with a high-speed camera. Relationships between each variable and the horizontal velocity during hurdle clearance (HC-v), ratio of HC-v to sprint velocity without hurdles (HC-index), rate of deceleration on takeoff (D-takeoff), and rate of deceleration on landing (D-landing) were examined. Results showed that the support time on takeoff was not significantly correlated with the HC-index, and the correlation coefficient with the release height of CM at landing was significantly higher for the HC-index than for the HC-v. After stepwise multiple regression analysis, the following explanatory variables were selected as promising: clearance time for HC-v, long release distance on takeoff and high release height of CM at landing for HC-index, short touchdown distance on takeoff and long release distance on takeoff for D-takeoff, and takeoff–landing distance ratio for D-landing. These results suggest that the spatiotemporal variables that are important for speed, technicality, and low deceleration on takeoff versus on landing during hurdle clearance are different. [ABSTRACT FROM AUTHOR]

Objective: to analyze the spatial and temporal pattern of mortality by suicide in Northeast Brazil in the period 2008-2018. Method: ecological study that used data from the Mortality Information System, Ministry of Health - Brazil. The temporal trend was assessed by the Joinpoint method. The formation of spatial clusters of suicide was evaluated by the spatial autocorrelation function and purely spatial Scan statistical technique. Results: most deaths occurred among male (79.5%), brown (76.8%), single (59.2%), 20 to 49 years old (61.7%) individuals. Statistically significant growth of suicide was observed in six of the nine northeastern states (p<0.05). According to the spatial autocorrelation function and Scan statistics, the spatial clusters of deaths were located predominantly in Piauí and Ceará. Conclusion: the findings reinforce the need to direct strategies for prevention of the grievance to the municipalities with the highest occurrence. [ABSTRACT FROM AUTHOR]

The number of wintering Great Cormorants Phalacrocorax carbo sinensis in France has strongly increased since the 1970s, mainly due to the protection of the 'continental' sinensis subspecies in countries north of France. This increase has led to the establishment of a pioneering inland breeding colony in western France, while previously only the largely marine 'Atlantic' P. c. carbo subspecies occurred on the Channel coast. The marine subspecies was attracted and bred in this new inland settlement of sinensis, which rapidly became the largest colony in France. This paper analyses the migration pattern of birds from this colony by analysis of the dispersal of colour-ringed birds between 1989 and 2008. Interestingly, besides a classic south-west migration pattern, birds from this colony also displayed a pattern heading north-east, up to countries such as The Netherlands, from where the founders (sinensis) of this colony probably originated. Sightings and recoveries revealed that about 25% of the adults and 19% of the first-year birds headed north-east. Due to this north-east migration direction, the overall annual dispersal point was located only 50 km south-west of the colony, although in December and January this midpoint was located about 320 km south-west of the colony. The birds largely avoided Brittany, presumably to avoid competition with individuals of the carbo subspecies, and the main wintering areas of sinensis from other colonies, both in France (east, centre and south) and in Spain. Over the years 1989–2008, in the breeding period, the mean dispersal distance was shorter for adults than for young birds (54 km vs. 144 km, respectively) but in winter adult birds migrated further than young ones (305 km vs. 221 km, respectively). The mean annual dispersal distance in winter varied from 106 km to 527 km (all age-classes taken together). Migratory distance was not related to mean winter temperature. For adults, dispersal distance correlated with the annual number of breeding pairs in the Grand-Lieu colony between 1990 and 2003, but not between 2004–2008. Emigration (breeding in another colony) was recorded up to 2011 to 11 inland colonies and one coastal colony (founded more recently than Grand-Lieu), nine of them in France, two in Spain and one in The Netherlands. Annual emigration rate was negatively related to colony size in Grand-Lieu. The study points to the existence of density-dependent effects on distribution patterns of Cormorants outside the breeding season but also suggests connectivity and interaction among colonies that are hundreds of kilometres apart. [ABSTRACT FROM AUTHOR]

Background: The clinical presentation of dengue ranges from self-limited mild illness to severe forms, including death. African ancestry is often described as protective against dengue severity. However, in the Latin American context, African ancestry has been associated with increased mortality. This "severity paradox" has been hypothesized as resulting from confounding or heterogeneity by socioeconomic status (SES). However, few systematic analyses have been conducted to investigate the presence and nature of the disparity paradox.Methods: We fit Bayesian hierarchical spatiotemporal models using individual-level surveillance data from Cali, Colombia (2012-2017), to assess the overall morbidity and severity burden of notified dengue. We fitted overall and ethnic-specific models to assess the presence of heterogeneity by SES across and within ethnic groups (Afro-Colombian vs. non-Afro-Colombians), conducting sensitivity analyses to account for potential underreporting.Results: Our study included 65,402 dengue cases and 13,732 (21%) hospitalizations. Overall notified dengue incidence rates did not vary across ethnic groups. Severity risk was higher among Afro-Colombians (risk ratio [RR] = 1.16; 95% Credible Interval [95% CrI] = 1.08, 1.24) but after accounting for underreporting by ethnicity this association was nearly null (RR = 1.02; 95% CrI = 0.97, 1.07). Subsidized health insurance and low-SES were associated with increased overall dengue rates and severity.Conclusion: The paradoxically increased severity among Afro-Colombians can be attributed to differential health-seeking behaviors and reporting among Afro-Colombians. Such differential reporting can be understood as a type of intersectionality between SES, insurance scheme, and ethnicity that requires a quantitative assessment in future studies. [ABSTRACT FROM AUTHOR]

Understanding urban green space (UGS) provision in relation to its proximity, amid rapid urbanization and increasing UGS construction, is crucial for sustainable urban development. This study introduces a population-weighted proximity index to analyze the supply, surplus, and deficits of UGS provision for residents, both inside and outside UGS access radii. We assessed spatiotemporal changes in UGS provision from 2012 to 2021 in Shanghai to identify areas lacking UGSs, planning irrationalities, and needs for new UGS construction. We calculated the priority locations for new UGSs, focusing on residents most in need yet situated outside UGS access radii. Based on these, we accordingly create detailed and specific UGS development proposals. The findings show that the UGS provision on proximity that residents actually obtained in Shanghai citywide has improved at the city, community, and neighborhood levels over the past 10 years. Shanghai has undergone a phase of increasing UGS construction, resulting in more residents, previously outside UGS access radii, now being within these areas. However, despite these developments, many residential locations within UGS access radii still face insufficient UGS provision. At the neighborhood level, large proximity UGS deficits, where residents are located out of an access radius, still need to be solved. At the city and community levels, large UGS deficits within access radii also need to be solved. The proximity deficits in most urgent need to be solved at any level are for residents located outside of a UGS access radius. We also find that many of the newly built UGSs, which were constructed to address the proximity deficits for residents located outside of an access radius at both the city and community levels, were not smartly or efficiently planned. In the future, more UGSs could be newly built at locations with optimal priorities according to the findings of this study to solve efficiently the proximity deficits for residents still located outside of a UGS access radius, and to improve UGS provision on proximity at all three levels. • Assessed changes and developments of proximity to urban green space (UGS) provision. • Spatiotemporal UGS proximity assessment was performed by space time pattern mining. • UGS provision in terms of proximity in Shanghai got better from 2012 to 2021. • The number of residents outside UGS access radii in Shanghai is decreasing. • However, UGS deficits within access radii are increasing. [ABSTRACT FROM AUTHOR]

• Analyzed accessibility changes at UGS locations using diverse indices. • Identified key factors and the mechanisms affecting UGS accessibility. • Assessed accessible UGS provision efficiency based on multi-factors. • Evaluated UGS development by effectiveness, feasibility, and equity. • Shanghai UGS development 2012–2021: generally effective but improvable. This study evaluates accessible urban green space (UGS) provision in Shanghai, China (2012–2021), focusing on accessibility and its influencing factors amidst urbanization. We aim to understand if and how UGS provision addresses urban demands effectively and efficiently on limited conditions. Utilizing an improved n-step floating catchment area method, we conducted a comprehensive assessment of UGS locations' accessibility, integrating comprehensive capacity, population demand, and transport supply. Multidimensional analyses were applied to evaluate changes in UGS accessibility and these three influencing factors. Based on the relationships among these three factors, we investigate the rationality and efficiency of the development of accessible UGS provision. By integrating UGS provision efficiency, the mechanisms driving by the three factors, and the UGSs' effect on UGS equity citywide, new UGS construction and existing UGSs needing improvement are evaluated from the aspects of feasibility, effectiveness, and justice. Our findings demonstrate that Shanghai effectively developed its UGS accessibility from 2012 to 2021, committing to sustainable urban development and improving residents' quality of life. Comprehensive capacity emerged as a key driver in UGS provision, highlighting its importance in future policy and planning. This study provides nuanced insights into UGS provision efficiency and optimization, aiding urban planners in enhancing UGSs worldwide. [ABSTRACT FROM AUTHOR]

This article presents a multi‐dimensional spatial pattern analysis of crime events in San Francisco. Our analysis includes the impact of spatial resolution on hotspot identification, temporal effects in crime spatial patterns, and relationships between various crime categories. In this work, crime prediction is viewed as a classification problem. When predictions for a particular category are made, a binary classification‐based model is framed, and when all categories are considered for analysis, a multiclass model is formulated. The proposed crime‐prediction model (HotBlock) utilizes spatiotemporal analysis for predicting crime in a fixed spatial region over a period of time. It is robust under variation of model parameters. HotBlock's results are compared with baseline real‐world crime datasets. It is found that the proposed model outperforms the standard DeepCrime model in most cases. [ABSTRACT FROM AUTHOR]

Background: The rapid growth of opioid abuse and the related mortality across the United States has spurred the development of predictive models for the allocation of public health resources. These models should characterize heterogeneous growth across states using a drug epidemic framework that enables assessments of epidemic onset, rates of growth, and limited capacities for epidemic growth.Methods: We used opioid overdose mortality data for 146 North and South Carolina counties from 2001 through 2014 to compare the retrodictive and predictive performance of a logistic growth model that parameterizes onsets, growth, and carrying capacity within a traditional Bayesian Poisson space-time model.Results: In fitting the models to past data, the performance of the logistic growth model was superior to the standard Bayesian Poisson space-time model (deviance information criterion: 8,088 vs. 8,256), with reduced spatial and independent errors. Predictively, the logistic model more accurately estimated fatality rates 1, 2, and 3 years in the future (root mean squared error medians were lower for 95.7% of counties from 2012 to 2014). Capacity limits were higher in counties with greater population size, percent population age 45-64, and percent white population. Epidemic onset was associated with greater same-year and past-year incidence of overdose hospitalizations.Conclusion: Growth in annual rates of opioid fatalities was capacity limited, heterogeneous across counties, and spatially correlated, requiring spatial epidemic models for the accurate and reliable prediction of future outcomes related to opioid abuse. Indicators of risk are identifiable and can be used to predict future mortality outcomes. [ABSTRACT FROM AUTHOR]

The specific characteristics of the Federal District (DF) favor the introduction, reproduction, dissemination, and permanence of dengue vector and viruses. Here, we aimed to analyze the spatiotemporal patterns of dengue epidemics in the Administrative Regions (RAs) of the DF from January 2007 to December 2017. We used Fourier partial series model to obtain a seasonal signature of the time series, which allowed calculating indicators of permanence (number of epidemic years, number of epidemic months per year, the proportion of epidemic months for the period) and time/moment of epidemics (month of epidemic peak). A total of 82 epidemics were recorded in this period. The RAs with the largest number of epidemic years were Varjão (5 epidemics), Gama, Lago Sul, and Sobradinho (4 epidemics). These last three RAs also had the highest proportions of epidemic months of the entire study period (9 epidemic months). The RAs with urban centrality function had an earlier epidemic peak than the others, in February and March. Epidemics showed high permanence values in RAs with different types of occupations, emphasizing the need to consider the social organization of space processes in dengue distribution studies. [ABSTRACT FROM AUTHOR]

Large-scale long-period urban heat island (UHI) intensity (UHII) prediction with high spatiotemporal resolutions, satisfactory accuracy, and calculation efficiency is crucial and challenging for UHI mitigation studies. This study proposes a framework combining an urban weather generator (UWG), local climate zone (LCZ), deep-learning method, and Python automatic cyclical calculation to obtain hourly UHII throughout one year and over a total of 1920 blocks in Hangzhou City. The spatial-averaged hourly UHII are between −2 °C and 6 °C in more than 96 % time, and those at 0 °C–1 °C contribute to 43.80 % of the total time. Spring gives the most intense nocturnal UHII, while winter has the weakest one. A significant diurnal UCI phenomenon could accompany the strong nocturnal UHI phenomenon. From the synthetic (based on seasonal data) 24-h curves, mean UHII and UCII (average of the positive and negative values, respectively) drop by approximately 25 % in winter compared to spring. Spatially, UHII is higher in the central regions with compact buildings but significantly lower in the high-vegetation-coverage regions. Based on LCZ framework, LCZ 1 (compact high-rise configurations) has the highest UHII, independent of examined periods. UHII relations to building coverage, vegetation ratio, and building height are individually quantified. The building coverage has the highest influence on annual UHII, with a correlation coefficient as high as 0.76. Results indicate that, for UHI mitigation purposes, the percentage of compact high-rise configurations (LCZ 1) in the urban area shall be limited, and the vegetation ratio is better to be greater than 20 %. • Most of the spatial-averaged hourly UHII are between −2 °C and 6 °C. • Spring has the highest seasonal-averaged UHII and winter has the lowest one. • Strong UHII could be accompanied with significant diurnal UCII. • Annual UHII has a correlation coefficient of 0.76 with building coverage. • Vegetation coverage is suggested to be beyond 20 % for UHII mitigation. [ABSTRACT FROM AUTHOR]

This paper examines the secular trends and variations in mean height of 17-year-old students born in 1957–2002 across 47 prefectures in Japan. Mean height is consistently lower in southwest prefectures and greater in prefectures in the Greater Tokyo Area and from the south-central area to the north-western area facing Eurasia in the main island. Both the range and the coefficient of variation stay constant in the cohorts born during the 1970s or later, following rapid increases of mean height in the prefectures that initially have the lowest means. A comprehensive policy framework may be needed to address diverse factors affecting the physical growth of adolescents at the subnational level. • There is a variation across 47 Japanese prefectures in the mean height of Japanese students aged 17 years born between 1957 and 2002. • Mean height of those students increased across the 47 prefectures from the birth cohorts born between the late 1950s and the 1970s. • The increase was largest in the prefectures with the lowest initial height. • The disparities in prefectural mean height decreased rapidly in the cohorts born during the late 1950s and the 1960s. • Substantial inequalities in prefectural mean height still remain, with little change since. [ABSTRACT FROM AUTHOR]

• A quantitative attribution framework to measure policy impacts was developed. • School commuting has a significant impact on NO 2 emissions in megacities. • International Travel contributes to NO 2 emissions more in coastal megacities. • A discernible linkage exists between policy interventions and environmental results. This comparison study deciphered the intricate dynamics of policy impacts on NO 2 air pollution emissions across five populous megacities – Tokyo and Osaka in Japan, Shanghai, Beijing, and Tianjin in China. Utilizing spatiotemporal mapping and time series analysis, we traced pollution trends from 2015 to 2022, underlining the profound influence of rapidly enacted policy measures on these densely populated urban areas. Leveraging daily policy data coupled with NO 2 emission records, we developed a quantitative attribution framework to measure policy impacts. The results showed that 'School Closure' emerged as a dominant factor in four out of five cities. Besides, 'International Travel Controls' played a pivotal role in coastal megacities like Tokyo, Osaka, and Tianjin, revealing their unique emission characteristics. The insights demonstrated the essential need for targeted, city-specific policy interventions, cautioning against the over-simplified approach of one-size-fits-all strategies. [ABSTRACT FROM AUTHOR]

• Cities with lower health and continued degradation are concentrated in east-central China. • The impact of urban landscape patterns on ecosystem health varies by time and place. • The spatial configuration of urban patches is becoming more important to ecosystem health than urban area. • Adaptive and differentiated landscape management is key to achieving sustainable cities. Healthy ecosystems are the foundation of sustainable urbanization. While scientists have long understood the ecological benefits of healthy landscapes, there is still limited understanding of how urban landscape patterns affect ecosystem health. To fill this gap, we systematically investigated the spatiotemporal relationships between different urban landscape patterns and ecosystem health in 212 major cities in China from 2000 to 2020 using Geographically and Temporally Weighted Regression. Results show that cities with poor ecosystem health are mainly concentrated in central-eastern China and show a long-term trend of degradation. There is spatial and temporal variability in how landscape patterns drive ecosystem health changes. The size, density, and perimeter of urban patches, along with the degree of interspersion between urban land covers with other land covers, are more important for ecosystem health than other spatial pattern measures, but these patterns also have greater temporal variation. Over the past two decades, the negative impacts of urban area growth have diminished in cities in lower-lying plains areas, but remain a major cause of local ecological degradation. The ecological health of mountain cities is more sensitive to changes in the shape and spatial arrangement of urban patches because of the intertwined distribution of natural ecosystems and urban land that often characterize these cities. This work not only confirms the advantages of spatiotemporal analytical thinking in the study of driving mechanisms of ecosystem health but also provides important guidelines for creating desirable landscape patterns for sustainable urban development. [ABSTRACT FROM AUTHOR]

Abstract Chemical environmental pollution is currently one of the most concerning environmental problem on a global scale, due to the high risks posed to ecological systems and human health. Risk assessment methodologies are valuable tools for preventive management and the mitigation of human health risks. However, the application of these methodological tools involves several steps and the knowledge of many variables, which can hinder its correct implementation. The main objective of this work was the development of the computational code for human health risk assessment: HHRISK (Human Health Risk). This code allows for an agile and accurate risk assessment based on the methodology established by the U.S. Environmental Protection Agency (U.S. EPA). Different from other published methods, the HHRISK code includes a new spatiotemporal matrix for the analysis of the aggregated risk (for multiple exposure pathways) and the cumulative (for exposure to multiple chemicals). HHRISK was applied to two case studies published dealing with the assessment of risk to human health through exposure to toxic metals, obtaining satisfactory results. The concordance between the average results obtained with the HHRISK and those reported by the authors confirm the validity of the implemented model. The inclusion of a greater spatiotemporal detail of the risks allowed to carry out a more accurate analysis and to propose new subsidies for a more efficient risk mitigation management by affected place and period of time. Graphical abstract fx1 Highlights • A computational code for human health risk assessment: the HHRISK is described. • Nineteen routes of human contamination by chemical substances are considered by the code. • A new spatiotemporal matrix is included for the analysis of risks. • The code also provides the uncertainty values of the risk calculations. • The HHRISK code was applied to two cases of studies published. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Salud Pública is the property of Universidad Nacional de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Highlights • Data envelopment analysis (DEA) model and Malmquist productivity index (MPI) model are used to analyze the investment efficiency of rural water conservancy. • Spatiotemporal analysis of investment efficiency of rural water conservancy for the period of the 12th Five-Year Plan from 2011 to 2015 in China are conducted. • Investment efficiency of rural water conservancy in China are evaluated by considering economic,ecological and social outputs comprehensively. Abstract Rural water conservancy is crucial to the development of agriculture which is the foundation of national economy in China. During the period of the 12th Five-Year Plan from 2011 to 2015, Chinese government promoted the rural water conservancy investment vigorously and obtained some achievements. Meanwhile, there were some problems faced in the rural water conservancy such as a weak foundation of rural water conservancy, an uneven distribution of rural water resources by time and space, low investment efficiency. Therefore, the investment efficiency of rural water conservancy needs to be systematically and comprehensively evaluated, and the evaluation results for the period of the 12th Five-Year Plan from 2011 to 2015 can provide reliable references for China's rural water conservancy investment over the period of the 13th Five-Year Plan from 2016 to 2020. Based on the existing study findings, this study conductes the empirical analysis for the investment efficiency of China's rural water conservancy during the period of 2011 to 2015 using the data envelopment analysis (DEA) model and Malmquist productivity index (MPI) model from the spatiotemporal perspective. The statistical data of the 31 provinces over the period of 2011 to 2015 are gathered as inputs and outputs of the DEA model and MPI model. It finds that the average investment efficiency of China's rural water conservancy in each year during the study period is 0.732 and the investment efficiency fluctuates for the same period, which is mainly caused by scale efficiency (SE). The investment efficiency of China's rural water conservancy is decreased by an average of 1.2% from 2011 to 2015, which is principally due to technology change (TC). From the regional aspect, the investment efficiency of rural water conservancy in the eastern, central, northeast and western regions increase successively, which means uneven distribution of investment efficiency among the four regions. Based on the MPI analysis of the four regions, only the investment efficiency of eastern region improved by an average of 4.3% for each year, while the investment efficiency of the other three regions all decreased. At the provincial level, disequilibrium in the investment efficiency of rural water conservancy existed in the 31 provinces. Only the four provinces including Shanghai, Hainan, Tibet and Xinjiang were DEA efficiency for each year during the study period. The improvement of the investment efficiency among the 31 provinces in China mainly relied on the investment expansion and the technology innovation of rural water conservancy. Furthermore, the method provided in this study can be not only used to analyze the investment efficiency of rural water conservancy in other developing countries, but also to evaluate the investment efficiency of the other fields in the time and space dimensions. [ABSTRACT FROM AUTHOR]

Assessment of ecosystem responses to coupled human and environmental impacts is increasingly acknowledged as an important research of environmental informatics. However, current ecological and environmental models are not effective for capturing the coupled influences due to prevalent approaches of separating human interferences from environmental changes, common uses of time-averaged or cumulative data, and the lack of efficient methods integrating environmental observations with socioeconomic statistics that are tabulated over different spatial units. In this paper, we presented an integrated modeling framework to tackle these limitations. We developed data-assimilation techniques to integrate ecological and climate data with socioeconomic statistics into a coherent dataset on the basis of conforming spatial units. These data were used in panel regressions to estimate responses of grassland productivity to coupled climate factors (seven) and socioeconomic indicators (ten) across 37 counties for nine 16-day growing periods each year from 2000 to 2010. We also advanced the analysis of climate impacts by allowing for quadratic rather than linear impacts and by incorporating lagged time effects for the dependent variable. The case study was conducted in Inner Mongolia Autonomous Region of China. Our findings provided strong evidence that the grassland productivity responded significantly to variations in both climate factors and socioeconomic variables; displayed significant seasonal, annual, and regional variation; and revealed cumulative influences from prior climate conditions and extreme climate fluctuations. The assimilation of climatic, ecological and socioeconomic data into a big-data set and the application of multi-spatial-and-temporal panel regression model were much more comprehensive than prior studies. [ABSTRACT FROM AUTHOR]

Abstract Water and gas production and potential water treatment facility requirements for the Marcellus formation are discussed using data analytic methods. These methods aim to handle dataset diversity and scale, and apply data analytics for statistical imputation, estimating future drilling activity, fluids production, and the optimization of water recycling facility locations and size. The objective of this study is to quantify and predict the volumes of produced fluids in the short- and medium-term for the Marcellus shale. The paper accomplishes this objective for the Pennsylvania section comprising 10,000 wells. The application of data analytics to large-scale, data-intensive, low-integrity public environmental databases is illustrated, and challenges of implementation methods are discussed and resolved. In addition, a special class of data analytic tools and workflows for spatiotemporal analysis (spatially correlated variation of parameters with time) is discussed and implemented. The results quantify the prospect of future drilling activity, and water and gas production for all Pennsylvania counties in the Marcellus. Finally, several practical problems of interest on applications of predictive analytics and management support are proposed and solved. The limitations of the proposed workflow are briefly discussed. Highlights • Data analytic methods are used for Marcellus produced water forecasting. • Statistical imputation methods are implemented to data-intensive low-integrity public databases. • Applications of spatiotemporal analysis on field-scale produced water and gas predictions are presented. [ABSTRACT FROM AUTHOR]

• Urban tree canopy (UTC) change studies are currently limited. • Short temporal extents, few time steps, & low categorical resolution hamper inference. • Land cover change sequence tabulation, multinomial regression, & clustering were used. • Increasing homeownership, income, & educational attainment predicted tree persistence. • Opportunities for tree preservation and planting are discussed. Given ambitious tree canopy goals, land cover change analyses in cities are imperative. Urban tree canopy change analyses have been hindered by data with low categorical resolution (e.g., canopy vs. not canopy), over relatively short time horizons (5–10 years), representing only two points in time, and scarce linkages to other temporal datasets (e.g., historical socioeconomic data). In this study, using a land cover change data set with five cover classes spanning 40 years (1970–2010) for Philadelphia, PA (US), we asked: which types of land cover changes are most common, and how do those relate to and co-vary with socioeconomic change? Specifically, we tabulated land cover changes (i.e., transition sequences), applied multinomial logistic regression with socioeconomic variables as predictors of land cover change, and used cluster analyses to characterize neighborhood changes associated with land cover change. Land cover stability dominated the transition sequences: the four most common sequences were stable road (e.g. road-road-road-road-road), stable building, tree canopy, and herbaceous vegetation, collectively accounting for 62.57% of all sequences. Multinomial logistic regression identified that increases in homeownership, income, and educational attainment were associated with a higher probability of tree canopy persistence. Cluster analyses via Affinity Propagation showed that some Census tracts have similar land cover change trajectories, and yet different socioeconomic trends. These findings point towards opportunities to focus on tree preservation alongside the importance of establishing new tree canopy through planting. Our study demonstrates the mix of stability and dynamism in multidecadal urban land cover change, and the importance of connecting land cover changes with socioeconomic changes. [ABSTRACT FROM AUTHOR]

• The United States experienced at least five COVID-19 waves and most cases are related to the first three waves associated with the Alpha variant, followed by Delta and Omicron during the fourth and fifth waves. • Compared to earlier variants such as Alpha, Delta and Omicron were found to be more contagious and had a higher effect on younger populations and affected the white population to a greater extent in terms of incidence and was less associated with traditional vulnerable populations. • Social determinants of health (SDoH) incorporate a complex range of factors (i.e., race, ethnicity, immigration status, and income) to examine the susceptibility of individuals or social groups to adverse health-related outcomes. • The COVID-19 infections show considerable spatial and temporal heterogeneity as the incidence and mortality were not equally distributed across the US counties and shifted from more urban to more rural areas as time progressed. • The burden of COVID-19 cases and deaths is higher in counties with high percentages of smoking, number of preventable hospital stays, primary care physician rate, the average daily density of PM 2.5 and percentages of high proportions of Hispanic residents. The United States experienced at least five COVID-19 waves linked with different mutated SARS-CoV-2 variants including Alpha, Delta and Omicron. In addition to the variants, the intensity, geographical distribution, and risk factors related to those waves also vary within socio-demographic characteristics and timeframes. In this project, we have examined the spatial and temporal pattern of COVID-19 in the USA and its associations with Social Determinants of Health (SDoH) by utilizing the County Health Rankings & Roadmaps (CHRR) dataset. Our epidemiologic investigation at the county level showed that the burden of COVID-19 cases and deaths is higher in counties with high percentages of smoking, number of preventable hospital stays, primary care physician rate, the average daily density of PM 2.5 and percentages of high proportions of Hispanic residents. In addition, the analysis also demonstrated that COVID-19 incidence and mortality had distinct characteristics in their association with SDoH variables. For example, the percentages of the population 65 and older had negative associations with incidence while a significant positive association with mortality. In addition to the elderly population, median household income, unemployment, and number of drug overdose deaths showed a mixed association with COVID-19 incidence and mortality. Our findings validate several influential factors found in the existing social epidemiology literature and highlight temporal associations between SDoH variables and COVID-19 incidence and mortality not yet frequently studied. [ABSTRACT FROM AUTHOR]

The growing amount of social media data is an invaluable and rapidly accessible source of information for flood response and recovery. In this study, a workflow framework is developed to assess urban flood impacts by extracting and analysing social media data, as well as identifying the intensive public response areas, using the case of 2020 China Chengdu rainstorm-induced flooding. A crawler-algorithm is applied to extract and filter the social media data from the commonly used social platforms, namely Weibo (static data) and Tiktok (dynamic data). Based on the spatiotemporal analysis, 232 flood sites with geological locations are identified. The study shows that, social media activities and precipitation are temporally correlated in a significant and positive way. The temporal evolution analysis of social media topics reveals the process of flooding and enables quick determination of severely affected areas. Spatially, social media data can provide spatial flood information and social media activities are typically connected with user demographics. Based on a flood simulation, the framework can generate valuable data sources of urban flooding from social media, which can enhance flood risk modelling with the aid of a hydrodynamic model. This study demonstrates the utility of social media in urban flooding impact assessment. • We develop an optimized workflow to identify flood data from social media dataset. • Temporally, social media activities are positively correlated with precipitation. • Spatially, flood-points data concentration characterizes flood severity. • 232 flood sites are identified and validated by numerical simulation. [ABSTRACT FROM AUTHOR]

During the last decade, Twitter has become a robust platform for distributing messages (tweets) among numerous subscribers worldwide. Tweets tend to increase significantly during and around the occurrence of natural hazards. While Twitter is used for near real-time alerts, processes for extracting reported damage from tweets and resolving their geographical spread in high resolution are under development. In this study, we attempt to examine what was the spatiotemporal distribution of the tweets associated with the November 2016 fire in Haifa (Israel). The acquired tweets were classified and filtered using topic modeling and RCNN (Recurrent Convolutional Neural Network), a portion of them was georeferenced, and their hyperlocal spatiotemporal patterns were examined. It was found that the tweets' sentiment corresponds to the fire's cascading events, while their spatial and temporal distribution is equivalent to most of the actual reports. Despite large uncertainties in the process of examining tweets, the results indicated that Twitter could serve as another layer of near real-time information to assist decision-makers and emergency agencies during and after cascading catastrophes striking a small-scale city. [ABSTRACT FROM AUTHOR]

Background: In walking rehabilitation trials, self-selected walking speed (SSWS) has emerged as the dominant outcome measure to assess walking ability. However, this measure cannot differentiate between recovery of impaired movement and compensation strategies. Spatiotemporal variables and asymmetry ratios are frequently used to quantify gait deviations and are hypothesized markers of recovery. Objectives: The purpose of this review is to investigate spatiotemporal variables and asymmetry ratios as mechanistic recovery measures in physical therapy intervention studies post-stroke. Methods: A systematic literature search was performed to identify physical therapy intervention studies with a statistically significant change in SSWS post intervention and concurrently collected spatiotemporal variables. Methodological quality was assessed using the Cochrane Collaboration's tool. Walking speed, spatiotemporal, and intervention data were extracted. Results: 46 studies met the inclusion criteria, 41 of which reported raw spatiotemporal measures and 19 reported asymmetry ratio calculations. Study interventions included: aerobic training (n = 2), functional electrical stimulation (n = 5), hippotherapy (n = 2), motor dual task training (n = 2), multidimensional rehabilitation (n = 4), robotics (n = 4), sensory stimulation training (n = 8), strength/resistance training (n = 4), task specific locomotor rehabilitation (n = 9), and visually guided training (n = 6). Conclusions: Spatiotemporal variables help describe gait deviations, but scale to speed, so consequently, may not be an independent factor in describing functional recovery and gains. Therefore, these variables are limited in explaining mechanistic changes involved in improving gait speed. Use of asymmetry measures provides additional information regarding the coordinative requirements for gait and can potentially indicate recovery. Additional laboratory-based mechanistic measures may be required to truly understand how walking speed improves. [ABSTRACT FROM AUTHOR]

Using a visualization engine to display the analyze results of power consumption data in a building can provide immediate and informative feedback for energy conservation research. Previous research has demonstrated that change of residents’ behavior can facilitate achieving the net-zero energy goal for a building. This research proposed a system called iARTS (interactive Augmented Reality system for Temporal and Spatial analysis of power consumption data integrated with building information models) that was designed to: (1) integrate building information model data into power consumption data sets in order to visualize the analysis results in Unity, which is a visualization engine originally designed for game development; (2) perform a spatiotemporal analysis mechanism to help residents realize an energy-saving tip, by identifying the appliances to be turned off; (3) perform another spatiotemporal analysis mechanism to identify the appliances that can be used jointly in order to consume all the solar PV-generated electricity at a maximum; (4) provide residents with query forms, scenes retrieval functions, and animations to educate residents as to where and when to implement the aforementioned energy-saving tips. With the use of iARTS, the temporal relationships between power sockets and appliances can be accurately described along with timestamped power consumption data. Residents are expected to be able to identify the electricity usage patterns that are wasteful, as well as to see any potential adjustment plan for using as much generated electricity as possible. [ABSTRACT FROM AUTHOR]

Vanadium is a component of different natural and industrial products and a widely used metal, which, nonetheless, has only garnered attention in recent years owing to its potential risks. Six sampling trips were conducted over different seasons and years, collecting 108 samples from rivers and 232 from the bays and analyzed using high-precision inductively coupled plasma mass spectrometry. This study investigated the sources, spatiotemporal characteristics, and risks of vanadium in the aquatic ecosystems of two typical bays of the Northwest Pacific that have strong links with vanadium-related industries. Likewise, the health and ecological risks were assessed using probabilistic and deterministic approaches. Overall, vanadium concentrations were higher in Jiaozhou Bay (JZB: 0.41–52.7 μg L−1) than in Laizhou Bay (LZB: 0.39–17.27 μg L−1), with concentrations higher than the majority of the worldwide studies. Vanadium-realted industries significantly impacted (p < 0.05) the metal concentrations in the rivers with 54.22% (40.73–150%) and 54.45% (27.66%–68.87%) greater concentrations in JZB and LZB rivers. In addition, vanadium exhibited significant seasonal variation, and higher values were quantified during the monsoon period at LZB owing to the greater catchment area. Impacted by smaller freshwater inputs, the post-monsoon period had substantial impacts on JZB, and vanadium in the rivers and bays was significantly higher during the winter. Despite some concentrations being higher than that indicated in the drinking water guidelines established by China, vanadium presents low to null risks to the population as per both approaches. Last, species with limited resilience are likely to face medium to high risks, with an incidence of 65–93% using the probabilistic method and 52–97% using the deterministic assessment. [Display omitted] • Concentrations in water near vanadium-related industries are up to 54.45% higher. • Seasonal variations in the vanadium concentrations linked with vanadium adsorption capacity. • Vanadium concentrations in both study areas higher than those found across China. • Medium and high ecological risks were estimated for both bays. [ABSTRACT FROM AUTHOR]

• Derived two new sprawl indices to analyze the growth patterns in peri-urban areas. • Linear development along the major road networks, urban cores, and Hooghly River. • Consolidated growth around urban cores and major roads while dispersed elsewhere. • Methodology adopted is fundamentally valuable for urban planning and management. The impacts of uncontrolled development may be catastrophic in areas experiencing fast population expansion and climatic variability, so the South Bengal Region, adjacent to Kolkata Municipal Corporation (KMC), is facing similar threats. It is essential to monitor such Land use and land cover changes (LULC) in a rapidly urbanizing country like India for long-term and efficient resource management. Remote Sensing (RS) and Geographic Information Systems (GIS) were used in this study to examine LULC dynamics in the South 24 Parganas district of the South Bengal Region during the past 30 years (1991-2021). Indices for urban sprawl monitoring, including patch size, density and growth, were determined statistically to analyze the urban typologies. It has also derived decadal growth rate and expansion efficiency metrics to assess the rate of changing peri-urban scenarios along the South 24 Parganas district. This research helps evaluate the quantitative aspects of urban expansion observed in eco-sensitive and cyclone-prone regions in the context of developing nations. It also helps to design guidelines based on the issues resulting from unsustainable urban sprawl. This study's findings depict substantial growth in population and built-up densities of immediate peri-urban areas to the core city by 70% at the expense of green and blue spaces. [ABSTRACT FROM AUTHOR]

Outdoor air pollution damages the climate and causes many diseases, including cardiovascular diseases, respiratory infections, and lung damage. In particular, Particulate Matter (PM 2.5) is considered a hazardous air pollutant to human health. Accurate hourly forecasting of PM 2.5 concentrations is thus of significant importance for public health, helping the citizens to plan the measures to alleviate the harmful effects of air pollution on health. This study analyses and discusses the temporal characteristics of PM 2.5 at different locations in Ho Chi Minh City (HCMC), Vietnam - an economic center and a megacity in a developing country with a population of 8.99 million people. We developed several AI-based one-shot multi-step PM 2.5 forecasting models, with both an hourly forecast granularity (1 h to 24 h) and a 24-h rolling mean. These Machine Learning algorithms include Stochastic Gradient Descent Regressor, hybrid 1D CNN-LSTM, eXtreme Gradient Boosting Regressor, and Prophet. We collected the data from six monitoring stations installed by the HealthyAir project partners at different locations in HCMC, including traffic, residential and industrial areas in the city. In addition, we developed a suitable model training protocol using data from a short period to address the non-stationarity of PM 2.5 time series. Our proposed PM 2.5 forecasting models achieve state-of-the-art accuracy and will be deployed in our HealthyAir mobile app to warn HCMC citizens of air pollution issues in the city. [Display omitted] • The trends and spatiotemporal characteristics of PM 2.5 across Ho Chi Minh City were discussed. • A dataset of PM 2.5 time series was collected from six stations established in different regions across the city. • Fast and competitive models were developed for hourly PM 2.5 forecasting and 24-h rolling mean PM 2.5 forecasting. • A suitable forecasting model training protocol was developed using the data to address the non-stationarity of the PM 2.5. [ABSTRACT FROM AUTHOR]

Data of particulate matter (PM) of particle size 2.5 (PM 2.5) obtained from September 2017 to December 2019 using 247 low-cost PM 2.5 sensors were analyzed to determine the spatiotemporal characteristics of PM 2.5 in Changwon City, South Korea. The highest average PM 2.5 concentration (33 μg/m3) was observed in winter (November to March); the lowest average concentration (18.60 μg/m3) was measured in summer (June to October). The PM 2.5 concentrations were the highest (31.81 μg/m3) during the morning rush hour (06:00 to 09:00 h) and lowest (23.82 μg/m3) in the afternoon (15:00 to 18:00 h). In agricultural areas, the highest average PM 2.5 concentrations (37.26 and 34.01 μg/m3) were observed in February from 06:00 to 09:00 h. High PM 2.5 concentrations were recorded at similar times in agricultural areas. This finding was because activities generating PM 2.5 , such as illegal incineration, occur in these areas. These high concentrations could cause damage to nearby vulnerable groups, such as elderly people. The results of this study could be useful for urban and environmental planning, reducing PM 2.5 concentrations, and promoting sustainable development in urban areas. • Fine particulate matter (PM) is an important factor that hinders sustainable development of cities. • It is necessary to identify spatiotemporal patterns for mitigating PM. • Low-cost sensor can analysis PM concentration in consideration of various spatial characteristics. • High concentration of PM was continuously generated in some times and regions. • Results will can be usefully used for mitigating PM in urban and environmental planning. [ABSTRACT FROM AUTHOR]

Autonomous mobile vehicles are becoming commoner in outdoor scenarios for agricultural applications. They must be equipped with a robot navigation system for sensing, mapping, localization, path planning, and obstacle avoidance. In autonomous vehicles, safety becomes a major challenge where unexpected obstacles in the working area must be conveniently addressed. Of particular interest are, people or animals crossing in front of the vehicle or fixed/moving uncatalogued elements in specific positions. Detection of unexpected obstacles or elements on video sequences acquired with a machine vision system on-board a tractor moving in cornfields makes the main contribution to this research. We propose a new strategy for automatic video analysis to detect static/dynamic obstacles in agricultural environments via spatial-temporal analysis. At a first stage obstacles are detected by using spatial information based on spectral colour analysis and texture data. At a second stage temporal information is used to detect moving objects/obstacles at the scene, which is of particular interest in camouflaged elements within the environment. A main feature of our method is that it does not require any training process. Another feature of our approach consists in the spatial analysis to obtain an initial segmentation of interesting objects; afterwards, temporal information is used for discriminating between moving and static objects. To the best of our knowledge in the field of agricultural image analysis, classical approaches make use of either spatial or temporal information, but not both at the same time, making an important contribution. Our method shows favourable results when tested in different outdoor scenarios in agricultural environments, which are really complex, mainly due to the high variability in the illumination conditions, causing undesired effects such as shadows and alternating lighted and dark areas. Dynamic background, camera vibrations and static and dynamic objects are also factors complicating the situation. The results are comparable to those obtained with other state-of-art techniques reported in literature. [ABSTRACT FROM AUTHOR]

The rapid decline in urban green (UGS) and blue space (UBS) in developing countries has led to a widespread degradation of available ecosystem services (ES). However, impacts of UGS and UBS changes on ES tend to vary over space and time, and to date these impacts have not been studied in sufficient detail in emerging economies. By comparing UGS and UBS change patterns with multitemporal Landsat data recorded during the past 30 years (1991–2021), this study has examined the impact of several factors on ES in some of the world's climate hotspots. Although obtaining relevant and accurate information on ES is difficult in many parts of the developing world, this work has developed baseline data suitable for assessing ES loss over five densely populated cities in Bangladesh – Dhaka, Chattogram, Khulna, Rajshahi, and Sylhet. ES loss was quantified in monetary terms using adjusted value coefficients. The topographic and anthropogenic factors driving spatial differences in ES degradation in these cities were analyzed with a geographical detector. The results indicated that the cities experienced a combined monetary loss of USD 628.58 million as a result of specific ES degradation, primarily due to the decline of UGS and UBS. The value of ES loss was notably higher in Dhaka and Chattogram than in the other cities due to marked differences in anthropogenic activities. Population growth, extensive urban sprawl, and the development of dense road networks were identified as the major causes of urban green and blue space loss and consequent reduction of ES. The findings of this study provide important insights which can be used to support the formulation of public policies and management plans aimed at restoring and maintaining sustainable urban ecosystems. • Spatiotemporal pattern of urban green-blue space is mapped in five populated cities. • Providing baseline information on ESs loss due to increased human activities. • Pattern of blue space change is highly random than that of green space change. • Degradation of urban green-blue space caused ESV loss of USD 628.58 m. • Spatial heterogeneity of factors affected loss of ESs in large cities. [ABSTRACT FROM AUTHOR]

This paper presents a control chart approach for monitoring, diagnostics, and prognostics to support condition-based maintenance (CBM) using condition data on linear railway infrastructure assets. The condition data were obtained from regular inspections conducted using a railway track measurement wagon. The condition data were statistically analysed using two control charts to evaluate the possibility of improved detection of derailment-hazardous faults using both temporal and spatial information. The study indicates that that the proposed control chart approach can be used for the condition assessment of track, providing valuable decision support for CBM. The control chart for condition information in the temporal domain supports diagnostics, while the control chart for condition information in the spatiotemporal domain supports prognostics as well. The two proposed control charts give earlier fault warnings than does the traditional approach. This facilitates decisions regarding CBM actions with an extended planning horizon and permits the increased operational availability of track. [ABSTRACT FROM AUTHOR]

Childhood cancers are among the leading causes of child mortality worldwide. We aimed to analyze the spatiotemporal incidence patterns of five common cancer types in Iran. A total of 17155 incident malignant 0-14 years old cases during 2005-2013 recorded by the Iran National Cancer Registry were included. An adaptive spatiotemporal smoothing model was applied to explore spatiotemporal variations of the age-standardized incidence rate (ASIR). The highest overall ASIR was estimated at 137.9 per million person-years in 2011-2013. Most of the five common cancers had an increasing trend in most provinces for both males and females during this time. Increasing ASIR and risk of cancers were observed during the study period, which follows the trend of childhood cancers incidence worldwide. The health system should take this rise as a serious alarm and provide appropriate prevention plans. [ABSTRACT FROM AUTHOR]

Summary: Background: Healthcare-associated infections (HCAIs) cause significant morbidity and mortality worldwide, and outbreaks are often only identified after they reach high levels. A wide range of data is collected within healthcare settings; however, the extent to which this information is used to understand HCAI dynamics has not been quantified. Aim: To examine the use of spatiotemporal analyses to identify and prevent HCAI transmission in healthcare settings, and to provide recommendations for expanding the use of these techniques. Methods: A systematic review of the literature was undertaken, focusing on spatiotemporal examination of infectious diseases in healthcare settings. Abstracts and full-text articles were reviewed independently by two authors to determine inclusion. Findings: In total, 146 studies met the inclusion criteria. There was considerable variation in the use of data, with surprisingly few studies (N = 22) using spatiotemporal-specific analyses to extend knowledge of HCAI transmission dynamics. The remaining 124 studies were descriptive. A modest increase in the application of statistical analyses has occurred in recent years. Conclusion: The incorporation of spatiotemporal analysis has been limited in healthcare settings, with only 15% of studies including any such analysis. Analytical studies provided greater data on transmission dynamics and effective control interventions than studies without spatiotemporal analyses. This indicates the need for greater integration of spatiotemporal techniques into HCAI investigations, as even simple analyses provide significant improvements in the understanding of prevention over simple descriptive summaries. [Copyright &y& Elsevier]

• Explores the CO 2 mitigation and air pollution control synergy degree (CASD) in China. • Explores the factors that influence CASD from multiple perspectives. • The CASD was low in general, with obvious spatial differences. • Higher levels of urbanization and innovation can help increase the CASD. Mitigating carbon dioxide (CO 2) emission while controlling air pollution is a critical challenge. However, few studies have explored how to achieve synergy between them from a spatiotemporal perspective. In this study, we constructed index system for CO 2 mitigation and air pollution control performances based on China's 30 provinces. Then, we calculated the CO 2 mitigation and air pollution control synergy degree (CASD) of each province from 2005 to 2018 using a synergy model, and based on its output, we further analyzed the spatiotemporal evolution of CASD at national, regional and local levels. Finally, we identified the key factors that affect CASD and their spatial spillover effects. We found that the CASD was low (no synergy or low synergy) and fluctuated from 2005 to 2018. CASD showed significant spatial variation: the high values appeared mainly in the southern region, and the northeastern region had the worst synergy. Furthermore, the CASD is influenced by many factors, including urbanization, industry, economy, energy, innovation and neighboring areas, driving temporal patterns and regional differences. Therefore, transdepartment and transregional synergistic governance should be implemented. Our findings provide policy references for central and local government to achieve this synergy and thereby support efforts to achieve sustainable development. [ABSTRACT FROM AUTHOR]

INTRODUCTION Cancer has historically been a main cause of death in Cuba, with lung cancer the number one cause of cancer death in both sexes. Cancer morbidity and mortality rates are the basic measures of cancer impact in the community. Cancer mortality has been one of the major applications of geographic analysis and has made important progress in recent decades thanks to access to mortality statistics and to development and availability of geographic information systems. Cuba does not have a strong tradition of etiologic research using spatial analysis. High levels of lung cancer morbidity and mortality in Villa Clara and growing interest in spatial analysis as an epidemiologic tool motivated this study. OBJECTIVE To identify spatial and/or spatiotemporal clusters of lung cancer morbidity and case fatality in the province of Villa Clara, and to demonstrate the value of cluster analysis as an epidemiologic tool. METHODS Descriptive observational study based on administrative data, using the technique of space-time scan statistics. The study focused on new cases diagnosed in 2004 and case-fatality for those cases through 2009. Variables used were: cases diagnosed, deaths, date of diagnosis, date of death, municipality and Cartesian geocoding for each municipality. RESULTS The study identified significant spatial and spatiotemporal clusters of greater than expected lung cancer incidence (municipalities of Encrucijada, Camajuaní, Cifuentes, Sagua la Grande, Caibarién and Santa Clara) and case fatality (Encrucijada, Camajuaní, Cifuentes, Sagua la Grande, Caibarién, Santa Clara, Placetas and Manicaragua). CONCLUSIONS Although the results are not explanatory, the spatial and spatiotemporal patterns of excess lung cancer risk and case-fatality can support hypothesis generation for research and eventual interventions for targeted prevention and management. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes the problem of modeling video sequences of dynamic swarms (DSs). We define a DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal interdependency and stationarity, i.e., the motions are similar in any small spatiotemporal neighborhood. Examples of DS abound in nature, e.g., herds of animals and flocks of birds. To capture the local spatiotemporal properties of the DS, we present a probabilistic model that learns both the spatial layout of swarm elements (based on low-level image segmentation) and their joint dynamics that are modeled as linear transformations. To this end, a spatiotemporal neighborhood is associated with each swarm element, in which local stationarity is enforced both spatially and temporally. We assume that the prior on the swarm dynamics is distributed according to an MRF in both space and time. Embedding this model in a MAP framework, we iterate between learning the spatial layout of the swarm and its dynamics. We learn the swarm transformations using ICM, which iterates between estimating these transformations and updating their distribution in the spatiotemporal neighborhoods. We demonstrate the validity of our method by conducting experiments on real and synthetic video sequences. Real sequences of birds, geese, robot swarms, and pedestrians evaluate the applicability of our model to real world data. [Copyright &y& Elsevier]

Abstract: Detection of motion patterns in video data can be significantly simplified by abstracting away from pixel intensity values towards representations that explicitly and compactly capture movement across space and time. A novel representation that captures the spatiotemporal distributions of motion across regions of interest, called the “Direction Map,” abstracts video data by assigning a two-dimensional vector, representative of local direction of motion, to quantized regions in space-time. Methods are presented for recovering direction maps from video, constructing direction map templates (defining target motion patterns of interest) and comparing templates to newly acquired video (for pattern detection and localization). These methods have been successfully implemented and tested (with real-time considerations) on over 6300 frames across seven surveillance/traffic videos, detecting potential targets of interest as they traverse the scene in specific ways. Results show an overall recognition rate of approximately 91% hits vs 8% false positives. [Copyright &y& Elsevier]

Assessing green development performance is of great significance for guiding cities to realize coordinated urban development. Considering that green development is a complex system that couples social economy and resource environment, an index system that contains 26 indicators for measuring the green development performance of cities in the Yangtze River Economic Belt (YREB) was constructed from three perspectives: green growth degree, green carrying capacity and green guarantee capability. The green development performance of 110 prefecture cities in the YREB during 2011–2017 were assessed based on the entropy weight method, and then the driving factors of cities' green development performance were analyzed by using the obstacle degree model. The results show that the green development performance of cities in the YREB had increased from 2011 to 2017 with an average growth rate of 19.16%, but overall was not high. Especially, green guarantee capability of most cities was low and didn't improve much during 2011–2017. The green development performance of cities in the YREB overall presented high in the east and low in the west, with the cities of medium-high and high values mostly concentrated in the economically developed areas, such as the lower reach of the YREB and the provincial capital cities. Specifically, cities in the lower reach of the YREB, especially Shanghai, Zhejiang and Jiangsu, had the highest level of green development, but with relatively low growth rate. The cities in the upper and middle reaches of the YREB had relatively low level of green development, but the overall growth rate was high, especially the Guizhou province has the highest growth rate of 35%. The obstacle factors analysis revealed that insufficient economic development, poor green life guarantee capability and incomplete ecological protection were key factors affecting the green development performance of cities in the YREB. Based on these findings, we suggest that to further strengthen the ability of economic growth and enhance public infrastructure service and ecological and environmental protection are important for promoting the level of green development in the YREB. • Green development performance in the YREB increased, but overall was not high. • High green development performance concentrated in economically developed cities. • Green guarantee capability of most cities was low and didn't improve much. • Cities need to enhance public infrastructure service and advocate green lifestyle. [ABSTRACT FROM AUTHOR]

Abstract: A suite of simple metrics that can be used to analyse three-dimensional data sets is presented. We show how these metrics can be applied to raster-based, ecological mosaics sampled over uniform time intervals, such as might be obtained from a series of photographs or from repeated spatial sampling in the field. In these analyses, the concept of a 2D landscape “patch” is replaced by a 3D space–time “blob”. The structure of a dataset can be analysed via the characterisation of blobs, using a number of simple composition and configuration metrics. The use of different metrics, including modified versions of some common landscape metrics such as contagion, that describe the distribution of blobs in space and time, is demonstrated using both model and empirical data. With the increasing availability of spatiotemporal data sets in ecology, such three-dimensional metrics may be indispensable tools for the detection and characterization of landscape change in the context of human and naturally caused disturbances. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach of spatiotemporally visualizing the simulation output of migratory insect dynamics and resultant vegetation changes in real-time. The visualization is capable of displaying simulated ecological phenomena in an intuitive manner, which allows research results to be easily understood by a wide range of users. In order to design a fast and efficient visualization technique, a simplified mathematical model is applied to intelligibly represent migrating groups of insects. In addition, impostors are used to accelerate rendering processes. The presented visualization method is implemented in an integrated spatiotemporal analysis system, which models, simulates and analyzes ecological phenomena such as insect migration through time at a variety of spatial resolutions. [Copyright &y& Elsevier]

The article discusses the impact of changes in land management practices towards vegetation patterns in the Greater Yellowstone Ecosystem. It mentions the application of vegetation model to determine how patterns in vegetation cover type and structure have changed through different periods of management. It notes the transition from a fire-driven mosaic of grassland, shrubland, broadleaf forest and mixed forest communities to a conifer-dominated landscape.

Trace metals in atmospheric particulate matter (PM) are a serious threat to public health. Although pollution from toxic metals has been investigated in many Chinese cities, the spatial and temporal patterns in PM 2.5 remain largely unknown. Long-term PM 2.5 field sampling in 11 cities, combined with a systemic literature survey covering 51 cities, provides the first comprehensive database of 21 PM 2.5 -bound trace metals in China. Our results revealed that PM 2.5 elemental compositions varied greatly, with generally higher levels in North China, especially for crustal elements. Pollution with Cr, As, and Cd was most serious, with 61, 38, and 16 sites, respectively, surpassing national standards, including some in rural areas. Local emissions, particularly from metallurgical industries, were the dominant factors driving the distribution in polluted cities such as Hengyang, Yuncheng, and Baiyin, which are mainly in North and Central China. Elevated As, Cd, and Cr levels in Yunnan, Guizhou Province within Southwest China were attributed to the high metal content of local coal. Diverse temporal trends of various elements that differed among regions indicated the complexity of emission patterns across the country. The results demonstrated high non-carcinogenic risks for those exposed to trace metals, especially for children and residents of heavily cities highly polluted with As, Pb, or Mn. The estimated carcinogenic risks ranged from 6.61 × 10−6 to 1.92 × 10−4 throughout China, with As being the highest priority element for control, followed by Cr and Cd. Regional diversity in major toxic metals was also revealed, highlighting the need for regional mitigation policies to protect vulnerable populations. Image 1 • The pollution of 21 PM 2.5 -bound trace metals over China was evaluated. • PM 2.5 elemental composition showed large spatial variability. • Divergent temporal trends were identified for various species. • The most polluted cities posed high health risks with As, Mn and Cr(VI) were the top risk contributors. A comprehensive overview of 21 PM 2.5 -bound trace metals in China revealed that As, Cr and Cd pollution were most serious. Industrial emission patterns largely determined the spatial variations of heavy metals in China, with the associated health risks elevated mainly in North and Central China. [ABSTRACT FROM AUTHOR]