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Geo-social media data involve various kinds of inhomogeneities. These can concern, amongst others, the users, but also spatial distributions or the fact that the most frequently used hashtags, keywords or emojis often have little relevance in the context under investigation. In order to properly tackle and reduce these inhomogeneities and to strive for a less distorted analysis, normalisation of geo-social media data is expedient. Various measures exist that are frequently used in research for this purpose. This paper presents four of these measures and compares them with each other, both theoretically as well as practically in the form of a demonstration through three exemplary case studies highlighting potentials and limitations of each measure. This comparison involves the relatively new typicality measure, which was developed specifically for this type of data following the dimensions commonly used to describe geo-social media data (temporal, spatial, social and thematic dimension). [ABSTRACT FROM AUTHOR]

Context: Artificial nighttime lights can adversely impact animal behavior, particularly in nocturnal species. Evidence shows that bats can become inactive due to night lights and may avoid trees bathed in such light, consequently decreasing nocturnal pollination frequency. Although numerous biological studies have been conducted to understand the alteration in bat behavior due to artificial night lights, mapping the intrusion of nighttime light into bat habitats remains largely unexplored. Objective: In this study, we aim to visualize the exposure likelihood of bat habitats to nighttime light in the United States over the past 30 years. Methods: We propose a novel method known as the "Emerging Bivariate Cluster" which has the potential to pinpoint locations experiencing the most significant exposure likelihood of bat habitats to nighttime light over time. Results: Our results show that the low count nighttime light value is decreasing for 24.9% of bat habitats in the contiguous United States. Additionally, 1.4% of bat habitats are likely to experience persistent exposure over the years. Our analysis reveals significant disparities in hotspot intervention patterns across different regions, highlighting the probable areas where light pollution is escalating. Conclusion: The insights gleaned from this study illuminate critical areas for future research and conservation efforts, pinpointing specific locations where the impact of artificial nighttime lighting on bat habitats is most likely to be pronounced. By highlighting these areas, our findings offer a roadmap for biological scientists seeking to delve deeper into the effects of light pollution on nocturnal wildlife. Moreover, the identification of these key exposure likelihood zones provides a valuable foundation for developing targeted mitigation strategies, aiming to preserve bat populations and maintain ecological balance. This research not only contributes to our understanding of the spatial–temporal patterns of light pollution but also emphasizes the necessity of implementing informed conservation practices to safeguard the natural nocturnal environment. [ABSTRACT FROM AUTHOR]

This study employs Landsat images from 1995, 2009, and 2022, utilizing Google Earth Engine and Random Forest algorithm in R software for land use and land cover change analysis in Dar es Salaam Metropolitan City. Results show a substantial shift, notably in bushland and forest, with a 14.57% and 2.9% decline, respectively. Drivers of change include urban (14.87%) and agricultural (4.47%) growth. Overall, 64.3% of land cover changed, primarily transitioning from bushland to agriculture (25.7%) and forest to agriculture (9.2%). Qualitative insights underscore unregulated urban expansion, informal settlements, migration, human activities, and inadequate planning as significant contributors, aiding sustainable urban governance. [ABSTRACT FROM AUTHOR]

Numerous large cities’ sustainability and livability are severely hampered by traffic congestion. To create efficient measures to relieve this congestion, it is vital to comprehend the spatiotemporal patterns of this congestion. Our study addresses the scarcity of long-term spatiotemporal analysis for traffic congestion in developing megacities, focusing on Dhaka, the fifth most congested city. Utilizing big data analytics with a sample of 350,400 records from Google Maps over a year, we identify temporal patterns, spatial distribution, and recurrent congestion patterns. Through extensive image processing, peak hours, congestion variations, inter-zone relationships, and causes of extreme congestion are analyzed. Dhaka is divided into ten zones, revealing distinct congestion patterns with variations between weekends and weekdays. These findings offer crucial insights for urban planning, traffic control, and infrastructure development in rapidly expanding megacities, contributing to the alleviation of congestion and enhancement of sustainability and livability.

Introduction: Transportation corridors, as special economic corridors, have a greater impact on land cover and landscape pattern changes. Methods: Therefore, 10 buffer zones were established at 1 km intervals on both sides of the Longhai Railway as the centerline to trace the impact of the railroad corridor on the land use change and regional landscape pattern change of the cities along the line from 1985 to 2020. Result: The results show that: (1) The land cover changes along the railroad corridor during the 35 years are mainly characterized by the conversion between cropland, grassland, and construction land. Compared with 1985, in 2020, the construction land increased by 161.96%, the grassland area decreased by 11.83%, and the cropland area decreased by 15.83%. (2) The fragmentation of land patches and vegetation coverage is negatively and positively correlated with the buffer zone distance, respectively. In the same year, the comprehensive landuse dynamic degree is smaller as it is further away from the railway. The nighttime light index in the buffer zone is significantly correlated with the land aggregation index and average patch area, and the closer to the railroad, the higher the land aggregation index of construction land. (3) In terms of zoning, the intensity of land cover and landscape pattern changes in the eastern section is higher than that in the western section, with a higher degree of land fragmentation and more agglomeration of construction land, and the transportation corridor has a greater impact on the change of integrated land use motives in this region. The results of the study can provide a scientific basis for optimising the spatial pattern of land and improving the ecological environment in the construction of cross-regional transport corridors. [ABSTRACT FROM AUTHOR]

Objective: Recently, the South China Sea has been facing a crisis of depleted fishery resources, primarily due to the impacts of illegal, unreported, and unregulated fishing activities, as well as overfishing. Accurately understanding the fishing activity intensity in the South China Sea holds significant implications for the sustainable management of fisheries resources. Methods: Leveraging the automatic identification system trajectory data from 2018, this paper employs spatial statistical methods and fishing effort indicators to comparatively analyze the spatial variations in fishing intensity between Chinese and Vietnamese fishing vessels. Result: The results of this study show that (1) in 2018, the total fishing effort of Chinese fishing vessels in the South China Sea was 7.65 times that of Vietnamese vessels, but during China's South China Sea fishing moratorium, the fishing effort exerted by Vietnamese vessels surpassed that of China and (2) the top 10 ports in China and Vietnam support approximately 30% and 55.13% of their respective fishing intensities in the South China Sea. Conclusion: The study highlights significant variations in fishing intensity between Chinese and Vietnamese vessels and the substantial support provided by major ports. These findings offer valuable insights for fisheries resource monitoring and maritime spatial planning, contributing to the sustainable management of the South China Sea's fisheries resources. Impact statementThis study sheds light on fishing by Chinese and Vietnamese vessels during the South China Sea fishing ban and the intensity of fishing by vessels supported by ports along the South China Sea. By understanding these patterns, we can better manage fishery resources in the South China Sea and ensure sustainable fishing. [ABSTRACT FROM AUTHOR]

The harvesting of the puerulus of rock, or spiny, lobsters for seed for aquaculture is a large industry throughout the Indo-Pacific region, and an increasing number of puerulus collectors are deployed. Little is known about the 'crowding effect' of the collectors and how catch might be influenced by their placement relative to each other owing to a lack of aggregate statistics for puerulus collectors in this region. The rock lobsters puerulus is also collected for fisheries research, as an index of puerulus catches on collectors can provide an indicator of recruitment strength. Both as a management tool and for commercial purposes, the collectors should act independently, not reducing or influencing catch rates on adjacent or other collectors in the area. We explored the independence of collectors of the puerulus of the southern rock lobster (Jasus edwardsii (Hutton, 1875)) using a long-term-data series from four sites on the east coast of Tasmania, Australia. Arrays of collectors were in grid and cross-shaped configurations, with individual collectors spaced three metres apart. Patterns in catches were examined using generalised least squares (GLS) modelling to determine if collectors filter pueruli as they swim shoreward, testing directional, neighbour, edge, and radial effects. The results revealed that there were no significant differences in puerulus settlement among collectors at this scale of spacing, meaning that arrays with collectors even as close as 3 m apart did not interfere with each other. This finding also suggests that collectors at this spacing only capture a small proportion of the available pueruli, allowing a significant number to pass through collector grids, with no significant reduction in catch of collectors further inshore. Such a result implies that it cannot be assumed that recruitment to a natural reef will be reduced by harvesting of pueruli with this type of collector and configuration. [ABSTRACT FROM AUTHOR]

Existing studies have highlighted the significance of touristic learning; however, few have explored the effectiveness of different learning contexts. This study employs a combination of spatial–temporal behavior analysis and survey research to investigate how activity context (static vs. interactive) and affectional context (positive vs. negative) influence visitors' spatial–temporal behaviors and learning outcomes in zoological theme parks. The results indicated that interactive contexts are more effective than static contexts in extending visitors' duration of stay, increasing the diversity of venues explored, and enhancing both emotion and learning outcomes of visitors. The moderating effect of affectional context is significant, with the positive context notably enhancing emotion and learning outcomes of visitors associated with the interactive context. Additionally, visitors' emotion played a mediating role in the relationship between activity contexts and learning outcomes. These empirical findings offer valuable practical implications for the design of educational experiences within theme parks. [ABSTRACT FROM AUTHOR]

The UK residential sector is energy inefficient and has an overwhelming reliance on natural gas as a heating source. For the UK to meet its 2050 net zero obligations, the sector will need to go through a process of decarbonisation. Previous studies acknowledge the spatial disparities of household energy consumption, but have neglected how consumption varies over time. This paper advances such shortcomings via a sequence and clustering analysis to identify common gas consumption trajectories within neighbourhoods in England and Wales between 2010 and 2020. Four clusters are identified: "Very High to High Consumption"; "High to Medium Consumption"; "Medium to Low Consumption" and "Low to Very Low Consumption". The clusters were contextualised using spatial datasets representing the socio-economic and built environment. Across all clusters, the proportion of energy inefficient dwellings were high, but there was a trend of high consumption associated with lower proportions of energy efficient dwellings. The results provide useful insight to policy makers and practitioners about where best to target electrification and retrofitting measures to facilitate a cleaner and more equitable residential sector. Policy targeting of areas with continual high gas consumption will accelerate the decarbonisation process, whilst targeting areas who continually under consume will likely enhance household health and well-being. [ABSTRACT FROM AUTHOR]

Although many earthquake precursors have been proposed to forecast earthquakes, even in this modern era, short-term earthquake forecasting remains challenging due to the heterogeneous nature of the earthquake's occurrence. This study mainly focused on how the impending earthquake influences pre-earthquake scenarios using minor shocks and further confirmed by atmospheric parameters such as Outgoing Longwave Radiation (OLR). The Himalayan belt is one of the most at-risk areas during a continental-continental collision. The spatiotemporal analysis of the pre-earthquake scenario is carried out to identify the most vulnerable seismic risk zone and to forecast the probable magnitude of the earthquake. From the analysis, it is found that the accumulation of strain energy focussing near the epicenter of the impending earthquake. Furthermore, the study also revealed that abnormal changes in atmospheric parameters observed several days before an earthquake, which could serve as a precursor of seismic activity. On certain days, the anomalous OLR due to the radon gas emanation was observed at the different locations around the epicenter of the impending earthquakes. This phenomenon probably due to the transfer of accumulated strain from one side of the fault to other side of the fault through epicenter of the impending earthquake. This gives vital clue in determining the possible epicenter of the earthquake. The statistical analysis of minor shocks associated with significant earthquakes made it possible to determine the magnitude and depth range of minor shocks that may trigger the nucleation process for major earthquakes. The magnitude and depth ranges of microshocks involved in the nucleation process differed among fault types. This research highlights the importance of monitoring seismic and atmospheric activity to improve earthquake forecasting and preparedness. Hence, it is possible to identify the most vulnerable seismic zone, location of the epicenter and probable magnitude spatio-temporal analysis. [ABSTRACT FROM AUTHOR]

The characteristics of spatial and temporal distribution of carbon emissions from various land use types is one of the hot spots of researches. The carbon emission of Shanxi section of the Yellow River during 2000–2019 were analyzed based on the land use type data and fossil energy consumption data for the same period. Here, we explored the spatial and temporal characteristics of ecological stress using carbon emission, carbon footprint, carbon deficit, carbon ecological carrying capacity and carbon footprint pressure index models. The results indicated that the overall net carbon emissions had increased from 27959.67Gg CO2 equivalents in 2000–97355.36Gg CO2 equivalents in 2019 in the study area. During the study period, construction land was the main carbon sources, while forest land was the main carbon sinks. The carbon footprint of coal energy was the most dominant. The carbon footprint was greater than the ecological carrying capacity, which caused an ecological deficit during 2000–2019. The net carbon emissions and carbon footprint showed a continuous increasing trend, which became slower. The ecological pressure was spatially characterized as "less pressure in the northern and more pressure in the southern". The findings can provide scientific guidance for the land use sustainable development for coal cities. [ABSTRACT FROM AUTHOR]

This study uses complete earthquake catalog data and spatio-temporal analysis to construct a reliable model to forecast the potential seismogenic earthquake or earthquake fault zones. It integrates models developed based on different researchers’ methods and earthquake catalogs from different periods. It constructs and compares models - Model-1, Model-2, and Model-3 - from the complete shallow earthquake catalog between 1963-1999 and 1963-2006. The δAIC is used to evaluate the reliability of the models, with Model-3 emerging as the most reliable in all tests in this study. The model is constructed based on the product of the normalized model of the combined smooth seismicity model of a relatively small to moderate complete earthquake catalog data with a relatively uniform background model and weighted by the normalized seismic moment rate derived from the surface strain rate. It is suggested that a more extended observation period and using a complete, albeit relatively small-to-moderate, earthquake catalog leads to a more reliable and accurate model. Implementation of the Probabilistic Seismic Hazard Function (PSHF) window using the b-value of a 5-year window length with a 1-year sliding window prior to a significant seismic event proved successful, and the methodology demonstrates the importance of the temporal "b-value" in conjunction with the reliable seismicity rate and spatial probabilistic earthquake forecasting models in earthquake forecasting. The results showed large changes in the PSHF prior to giant and large earthquakes and the finding of a correlation between decreased b-value time window length and earthquake magnitude. The results have implications for the implementation of seismic mitigation measures.

In the global challenge of Coronavirus disease 2019 (COVID-19) pandemic, accurate prediction of daily new cases is crucial for epidemic prevention and socioeconomic planning. In contrast to traditional local, one-dimensional time-series data-based infection models, the study introduces an innovative approach by formulating the short-term prediction problem of new cases in a region as multidimensional, gridded time series for both input and prediction targets. A spatial-temporal depth prediction model for COVID-19 (ConvLSTM) is presented, and further ConvLSTM by integrating historical meteorological factors (Meteor-ConvLSTM) is refined, considering the influence of meteorological factors on the propagation of COVID-19. The correlation between 10 meteorological factors and the dynamic progression of COVID-19 was evaluated, employing spatial analysis techniques (spatial autocorrelation analysis, trend surface analysis, etc.) to describe the spatial and temporal characteristics of the epidemic. Leveraging the original ConvLSTM, an artificial neural network layer is introduced to learn how meteorological factors impact the infection spread, providing a 5-day forecast at a 0.01° × 0.01° pixel resolution. Simulation results using real dataset from the 3.15 outbreak in Shanghai demonstrate the efficacy of Meteor-ConvLSTM, with reduced RMSE of 0.110 and increased R2 of 0.125 (original ConvLSTM: RMSE = 0.702, R2 = 0.567; Meteor-ConvLSTM: RMSE = 0.592, R2 = 0.692), showcasing its utility for investigating the epidemiological characteristics, transmission dynamics, and epidemic development.

BackgroundThe coordination and the directional order of ciliary metachronal waves are the major factors that determine the effectiveness of mucociliary clearance (MCC). Even though metachronal waves play an essential part in immune response, clinical diagnostic tools and imaging techniques that can reliably and efficiently capture their spatial distribution and function are currently limited.MethodsWe present label-free high-speed visualization of ciliary metachronal wave propagations in freshly-excised tracheal explants using a spectrally-encoded interferometric microscope over a two-dimensional (2D) plane of 0.5 mm × 0.5 mm at an acquisition rate of 50 frame-per-second. Furthermore, phase-resolved enhanced dynamic (PHRED) analysis of time-series doppler images was performed, where spatial-temporal characteristics of cilia metachronal wave motions are revealed through frequency component analysis and spatial filtering.ResultsThe PHRED analysis of phase-resolved Doppler (PRD) images offers a capability to distinguish the propagation direction of metachronal waves, and quantitatively assess amplitude and dominant frequency of cilia beating at each spatial location. Compared to the raw PRD images, the phase-resolved dynamic wavefront imaging (PRDWI) method showed the direction and coordination of collective cilia movement more distinctively.ConclusionsThe PRDWI technique can have broad application prospects for the diagnosis of human respiratory diseases and evaluation of the curative effect of treatments and open new perspectives in biomedical sciences.

Accurate observation of precipitation data is crucial for hydrometeorological applications, requiring temporal and spatial precision. Satellite precipitation products offer a promising solution for obtaining precipitation estimates, facilitating long-term observations from global to local scales. However, assessing their accuracy compared to rain gauge observations is essential. This study aims to assess the accuracy and applicability of precipitation data from CMORPH, IMERG, and PERSIANN CCS in the Rio Grande–San Juan Basin in northeast Mexico. The evaluation of estimated precipitation was assessed using the Pearson and Spearman correlations, RMSE, MAE, and BIAS for both monthly and yearly averages. CMORPH showed minimal errors and low underestimation, while IMERG exhibited high correlations with consistent underestimation. PERSIANN CCS had lower correlations, significant overestimation, and higher errors. The Mann–Kendall (MK) test was used to determinate the precipitation trends of observed and estimated data. The observed data showed a significant positive trend in monthly averages, which is not reflected in the annual trend. Furthermore, negative annual trends were found in at least 10 stations across the basin. The application of satellite precipitation data yielded mixed outcomes, with CMORPH showing the highest level of agreement with the trend analysis results from rain gauge data. This demonstrates its reliability for weather and climate studies and suggests the potential for CMORPH to be used as an input in hydrological modeling. [ABSTRACT FROM AUTHOR]

Simple Summary: This study reviews the development of the cetacean stranding response program in Taiwan from 1994 to 2018, and examines 1320 stranding events involving 1698 animals across at least 27 species. The stranding rates have increased significantly over the years, with a notable rise in annual events from 16 to over 90. Seasonal variation demonstrates higher stranding rates during the northeastern monsoon season. The study highlights the spatial distribution of strandings, with the highest frequency in northern Taiwan and mass strandings predominantly in the southwest. Additionally, live strandings accounted for 29.5% of events and 38.9% of animals. The rescue and rehabilitation of some individuals have been conducted, and 15 individuals have been released since 2000. The findings underscore the diversity and stranding patterns of cetaceans around Taiwanese waters, and provide valuable information for developing conservation strategies in the western Pacific. A national cetacean stranding response program in Taiwan has evolved significantly in the past three decades. Initially co-ordinated by National Taiwan University from 1994, the program transitioned to the Taiwan Cetacean Society in 1999, and local governments took on a more prominent role after 2009. A comprehensive stranding database (1994–2018) has been maintained, which documented 1320 stranding events involving 1698 animals from at least 27 species. The most commonly stranded species include finless porpoises, bottlenose dolphins, Kogia spp., and Risso's dolphins. The stranding rates varied annually and seasonally, with increases noted from an average of 16 events per year for the first 3 years to 44–58 events per year between 1997 and 2015, and a sharp rise to over 90 events per year for the period of the last three years. Seasonal variations were also significant, with higher stranding rates during the northeastern monsoon (NEM, October to next April) than that during southwestern monsoon (SWM, May to September). From the aspect of distribution, more frequent and even strandings occurred along the coast of northern Taiwan, while mass strandings were concentrated in the southwestern counties during NEM. Among all strandings, 390 events (29.5%) and 660 animals (38.9%) were live ones. Under great effort in rescuing and rehabilitating 52 cases, 15 cetacean individuals have been released since 2000. Additionally, there have been 56 mass strandings involving at least 11 species since 1994, predominated by pygmy killer whales, particularly during the NEM season along the southwest coast. This study not only contributes to our understanding of the stranding patterns and diversity of the cetaceans in Taiwan, but also provides valuable insights for future conservation strategies on cetaceans in the western Pacific. [ABSTRACT FROM AUTHOR]

China's first Mars sampling return mission (Tianwen‐3) is designed to launch and retrieve samples around 2030. Three tentative landing areas (TLAs) (Amazonis, Chryse and Utopia Planitiae, i.e., TLA‐A, TLA‐C and TLA‐U) are selected based on elevation <−2,000 m and latitude between 17° and 30°N. As a dominant feature of Martian meteorology, dust storms manifest in all seasons and affect the accuracy and safety of Mars exploration missions. Tianwen‐3's landing, sampling and ascent phases are in the dust storm season. Therefore, analyzing dust storm activity around landing areas is significant for the Tianwen‐3 mission. According to Mars Daily Global Maps taken by Mars Orbiter Camera spanning Martian years 24–28, 2,476 dust storm events around the three TLAs were identified in this research. Dust storm temporal probabilities within TLA‐A, TLA‐C and TLA‐U were calculated as 0%–44.69%, 0%–66.29% and 0%–33.64%, separately. Dust storm spatial probabilities around the TLA‐A, TLA‐C and TLA‐U were computed, with ranges of 0%–10.71%, 0%–14.55% and 0%–19.96% during the T1 period (Ls = 161–309°), and 0%–6.75%, 0%–7.65% and 0%–8.26% during the T2 period (Ls = 342‐55°), respectively. Finally, considering the temporal and spatial distribution of dust storms, we recommend the T2 period as the launch scenario. Three safe periods (Ls = 2–18°, 4–12°, and 356–4°) were assigned for the entry‐descent‐landing (EDL) phase, along with one period (Ls = 45–55°) for the take‐off and ascent phase. Five circular landing zones with dust storm spatial probability <3% were selected for the Tianwen‐3 mission. Plain Language Summary: Tianwen‐3 is the first Mars sampling return mission by China, scheduled to launch and return samples around 2030. Three tentative landing areas (TLAs), Amazonis Planitia (TLA‐A), Chryse Planitia (TLA‐C), and Utopia Planitia (TLA‐U), located at elevations of <−2,000 m and within the latitude range of 17°–30°N. Three TLAs are situated along the trajectories of Martian dust storm sequences. The harsh dusty conditions significantly impact the success of the Mars exploration mission. Therefore, it is meaningful to analyze the dust storm occurrence probability within and around three TLAs. In this study, we utilized Mars Global Surveyor (MGS) Mars Daily Global Maps to (a) detect the dust storm events around three TLAs in five consecutive Mars years (MY 24–28); (b) calculate and analyze the temporal and spatial probabilities of dust storm occurrence; and (c) designate the safe landing areas, the preferred take‐off and ascent and entry‐descent‐landing (EDL) periods for the Tianwen‐3 mission. Key Points: Dust storms within three tentative landing areas exhibit distinct seasonality and heterogeneity throughout a Mars year, with clear solstitial pause periodsDust storm spatial‐temporal analysis reveals that the T2 period is more suitable as the launch scenario for the Tianwen‐3The preferred entry‐descent‐landing period, preferred take‐off and ascent period, and safe landing areas are chosen for the Tianwen‐3 [ABSTRACT FROM AUTHOR]

Ship pollution emissions have attracted increasing attention in the maritime field due to the massive growth of maritime traffic activities. It is important to identify the ship emissions (SEs) magnitude and corresponding spatial and temporal distributions for the purposes of developing appropriate strategies to mitigate environment pollution. The aim of this study was to estimate ship pollution emissions with various typical merchant ship types under different sailing conditions. We estimated the emission variation with a ship traffic emission assessment model (STEAM2), and then the ship pollution emission distribution was further visualized using ArcGIS. We collected data from the automatic identification system (AIS) for ships in New York Harbor and further analyzed the spatiotemporal distribution of pollutant emissions from ships. The experimental results demonstrate that the ship pollutant emission volume in the New York Harbor area in 2022 was 3340 t, while the pollution in terms of CO, SO2, CXHX, PM10, NOX, and PM2.5 was 136, 1421, 66, 185, 1384, and 148 t, respectively. The overall SEs from container ships, passenger ships, and tankers account for a large amount of pollution discharge. The pollutant emissions of container ships are significantly greater than that of their counterparts. Moreover, the spatiotemporal distributions of ship pollutant discharge can vary significantly among different ship types and sailing conditions. [ABSTRACT FROM AUTHOR]

In system processing, video inevitably suffers from distortion, which leads to quality degradation and affects the user experience. Therefore, it is of great importance to design an accurate and effective objective video quality assessment (VQA) method. In this paper, by considering the multi-dimensional characteristics for video and visual perceptual mechanism, a two-stream convolutional network for VQA is proposed based on spatial–temporal analysis, named TSCNN-VQA. Specifically, for feature extraction, TSCNN-VQA first extracts spatial and temporal features by two different convolutional neural network branches, respectively. After that, the spatial–temporal joint feature fusion is constructed to obtain the joint spatial–temporal features. Meanwhile, the TSCNN-VQA also integrates an attention module to guarantee that the process conforms to the mechanism that the visual system perceives video information. Finally, the overall quality is obtained by non-linear regression. The experimental results in both the LIVE and CSIQ VQA datasets show that the performance indicators obtained by TSCNN-VQA are higher than those of existing VQA methods, which demonstrates that TSCNN-VQA can accurately evaluate video quality and has better consistency with the human visual system. [ABSTRACT FROM AUTHOR]

This study aims to comprehensively examine the dynamics of river morphology changes influenced by the occurrence of two distinct lahar floods which occurred in the years 1975 and 2010 respectively In order to achieve the stated objectives, temporal images representing the conditions of the study area several years after the occurrence of the lahar floods were obtained and used to examine river morphology changes. Furthermore, temporal data from remote sensing and UAV were collected and used to analyze morphology changes of river valleys in the study area. The analysis was carried out using three parameters namely river slope, riverbank morphology, and river curvature. River slope was 0.74%, showing a relatively flat gradient, and this condition resulted in a meandering river morphology Morphology of riverbank was observed to have the "U" shape, providing more surface area for river water to drain· Additionally, the sinuosity ratio (SR) during the two lahar flood periods were found to be dynamic. During the 2010 lahar flood period, the SR pre-lahar flood was 1.92, which increased significantly to 2.65 eight months after the event, and decreased drastically to L60 one year later. It is important to comprehend that the main factor responsible for these changes during the period was flood. The obtained results showed that the SR value for the next decade, starting from 2010, experienced consistent fluctuations. These fluctuations provided evidence that river morphology was always experiencing changes even without major triggers. For the analysis of the 1975 lahar flood period, the SR value was obtained from the PBB maps three years before the event, five years post-event, and from an analysis of Google Earth images ten years after the disaster. Similarly, the SR in this period was observed to fluctuate significantl; and the meander became simpler in the tenth year after the occurrence of flood. The results of this study provide evidence that spatial analysis with temporal images is more efficient and economical for studying the dynamics of river morphology. [ABSTRACT FROM AUTHOR]

COVID-19 has profoundly impacted global daily life, emphasizing the need for effective virus suppression strategies. In response, China has established numerous nucleic acid testing sites to facilitate rapid testing and curb outbreaks. However, these sites often experience congestion, increasing transmission risks and reducing testing efficiency. This study focuses on the spatial–temporal analysis of testing site distribution and associated infection risks in Shenzhen, China. Data from all Shenzhen testing sites were analyzed for the week of October 24–30, 2022, noting the percentage of busy hours per site and incorporating a population size factor by district to assess regional infection risks. Findings indicate three daily peak testing times—primarily in the evening—with the highest risk of transmission in Longgang District, followed by Yantian and Luohu, and the lowest in Futian. The risk coefficient varied from 0.040 to 0.349, with most areas showing stable risk levels between 0.06 and 0.20. This research underlines the necessity for policymakers to alleviate congestion at testing sites and suggests increasing site availability in Longgang District to mitigate COVID-19 spread, offering methodological guidance for managing infection risks in other major Chinese cities.

Abstract This article delves into the evolving landscape of ethnobiology, mainly focusing on integrating quantitative approaches and the hypothetical-deductive method. We highlight the challenges associated with adopting quantitative tools, such as the potential for oversimplification and lack of critical reflection on the principles guiding these methods. We also examine ecological diversity indices, cultural importance indices, sample sufficiency, replication in studies, and the importance of spatial-temporal context in ethnobiological research. We conclude with practical tips to enhance research's validity, reliability, and generalizability, proposing a path forward for the discipline that respects its essence while adapting to the demands of scientific evolution. While this paper broadly addresses ethnobiology, it primarily focuses on Ethnobotany literature, highlighting the field's rapid advancements and its relevance to other areas within ethnobiology.

Objectives: To investigate the effect of scheduled school break on the circulation of influenza in young children, school-aged population, and adults. Methods: In a spatial-temporal analysis using influenza activity, school break dates, and meteorological covariates across mainland China during 2015-2018, we estimated age-specific, province-specific, and overall relative risk (RR) and effectiveness of school break on influenza. Results: We included data in 24, 25, and 17 provinces for individuals aged 0-4 years, 5-19 years and 20+ years. We estimated a RR meta-estimate of 0.34 (95% confidence interval 0.29-0.40) and an effectiveness of 66% for school break in those aged 5-19 years. School break showed a lagged and smaller mitigation effect in those aged 0-4 years (RR meta-estimate: 0.73, 0.68-0.79) and 20+ years (RR meta-estimate: 0.89, 0.78-1.01) versus those aged 5-19 years. Conclusion: The results show heterogeneous effects of school break between population subgroups, a pattern likely to hold for other respiratory infectious diseases. Our study highlights the importance of anticipating age-specific effects of implementing school closure interventions and provides evidence for rational use of school closure interventions in future epidemics.

Evapotranspiration (ET) is a key link between atmospheric processes and land surface hydrological processes. With the impact of global warming and human activities, research on ET has become a hot topic. Supported by a total of 1,222 Chinese and English literatures from China National Knowledge Infrastructure and the Web of Science Core Collection from 2013 to 2022, this paper adopts the bibliometric visualization method to review the current research progress and future trend of ET with respect to the time of publication, countries, institutions, journals, and research hotspots. The results show that the number of related research articles is increasing rapidly and the journals with high citations are Journal of Hydrology, Agricultural and Forest Meteorology and Agricultural Water Management. The research hotspots have been focused on prototype observation, remote sensing inversion, mechanism equation, model simulation, spatial–temporal analysis, and attribution identification. In the future, there is an urgent need to integrate algorithms such as machine learning and artificial intelligence, to develop higher resolution remote sensing products, to improve the mechanism equations based on precise observations, and to clarify the impact of synergistic effects on ET among the driving factors. HIGHLIGHTS This study adopts the bibliometric method to systematically sort out the domestic and international articles on evapotranspiration.; The mechanism equations and remote sensing products of evapotranspiration were systematically summarized.; Spatial–temporal analysis and attribution identification methods of evapotranspiration have been summarized.; This study can be a guideline for future research on evapotranspiration.;

Promotional strategies for electric vehicle (EV) adoption need to be tailored to the public's perception of these vehicles. However, there is a notable gap in understanding how public perception of EVs differs between adoption pioneers and those slower to adopt ("adoption laggards") in terms of the percentage of registered EVs. This understanding is vital for the development of effective promotional strategies, especially for adoption laggards. To address this research gap, this study compares various public perception of EVs among adoption pioneers and laggards in U.S. states by analyzing historical Twitter discussions using Natural Language Processing (NLP) techniques, including topic and sentiment analysis. The findings reveal distinct patterns in online EV discussions between the two groups, with adoption laggards being concerned more about affordability and gas prices, and adoption pioneers consistently discussing various topics covering EV and battery manufacturing, charging infrastructure, government EV initiatives, and the EV market. Adoption pioneers exhibit stable, positive attitudes, while adoption laggards display frequent attitude fluctuations, including more negative ones recently. These differences emphasize the importance of EV affordability for adoption laggards and indicate a possible widening gap in EV adoption due to ongoing debates among adoption laggards surrounding the value of transportation electrification. By addressing adoption laggards' concerns and promoting the benefits of EVs, policymakers and stakeholders can enhance perceptions toward EVs, bridging the gap between adoption pioneers and laggards, and promoting widespread adoption of cleaner transportation options. • Examines EV perception via social media among adoption pioneers and laggards. • Merges social media data with geography to show spatial differences. • Highlights diverse EV discussion topics by adoption pioneers and laggards. • Notes distinct time-based attitudes of adoption pioneers and laggards towards EVs. [ABSTRACT FROM AUTHOR]

The foundation for ensuring the sustainable utilization of natural resources and human well-being lies in achieving a harmonious balance between nature and humans. In the Yellow River basin (YRB), numerous water crises, including floods, droughts, soil erosion, and water pollution, threaten its crucial role as a significant economic belt and ecological barrier. Unfortunately, less comprehension regarding the complex human–water relationship in this region has impeded watershed water management decision-makers from identifying key priorities for intervention. Here, we selected 29 evaluation indicators, including water resources, environment, ecology, society, economy, and science and technology from three dimensions: healthy water systems, sustainable human systems, and synergy of human–water system. We applied the entropy weight method, hierarchical analysis, and Single index quantification, multiple index synthesis, and poly-criteria integration (SMI-P) methods to quantify the spatial–temporal variation of the human–water harmony degree (HWHD) in nine provinces of the YRB from 2002 to 2021. We observed a consistent increase in the HWHD across all provinces in the YRB in the past two decades. Notably, five provinces have transitioned from Complete disharmony (0 ≤ HWHD ≤ 0.2) to Nearly complete disharmony (0.2 < HWHD ≤ 0.4). Additionally, the average growth rate of the downstream provinces is faster compared to those upstream. By 2021, the HWHD of upstream provinces like Sichuan and Ningxia, constrained by slower growth, became the two lowest provinces of the YRB, at 0.19 and 0.12 respectively. These findings offer valuable guidance for the region and similar areas grappling with the complex challenges of human–water conflicts, providing insights to navigate and address such dilemmas effectively. [ABSTRACT FROM AUTHOR]

Several factors contribute to the lack of long-term care (LTC) insurance in Taiwan, insufficient data and an absence of unified definitions of LTC are two of them. In this study, we use LTC-related catastrophic illness (CI) as the assessment criteria to investigate the demand for LTC insurance. We selected 13 categories of CI and explored the spatial–temporal properties of LTC incidence rates and mortality rates from the National Health Insurance Research Database. The study shows that the incidence rates did not change much, while mortality rates decreased significantly. Taiwan's LTC population, which was 0.29 million in 2013, is accordingly expected to triple before 2040 based on the proposed cohort change ratio approach. Currently, Taiwan's government has planned to fund LTC insurance via a pay-as-you-go system. Furthermore, the increasing LTC population indicates that commercial insurance can play a vital role as a supplement to social LTC insurance. [ABSTRACT FROM AUTHOR]

The response of vegetation phenology to urbanization has become a growing concern. As impervious surfaces change as urbanization advances, the variation in vegetation phenology at the dynamic urbanization level was analyzed to significantly quantify the impact of urbanization processes on vegetation phenology. Based on the MOD13Q1 vegetation index product from 2001 to 2020, vegetation phenology parameters, including the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (GSL), were extracted, and the spatial–temporal variation in vegetation phenology, as well as its response to urbanization, was comprehensively analyzed. The results reveal that (1) from 2001 to 2020, the average rates of change for the SOS, EOS, and GSL were 0.41, 0.16, and 0.57 days, respectively. (2) The vegetation phenology changes showed significant spatial–temporal differences at the urbanization level. With each 10% increase in the urbanization level, the SOS and EOS were advanced and delayed by 0.38 and 0.34 days, respectively. (3) The urban thermal environment was a major factor in the impact of urbanization on the SOS and EOS. Overall, this study elucidated the dynamic reflection of urbanization in phenology and revealed the complex effects of urbanization on vegetation phenology, thus helping policymakers to develop effective strategies to improve urban ecological management. [ABSTRACT FROM AUTHOR]

Soil moisture is a key environmental variable. There is a lack of software to facilitate non-specialists in estimating and analyzing soil moisture at the field scale. This study presents a new open-sourced R package mlhrsm, which can be used to generate Machine Learning-based high-resolution (30 to 500 m, daily to monthly) soil moisture maps and uncertainty estimates at selected sites across the contiguous USA at 0–5 cm and 0–1 m. The model is based on the quantile random forest algorithm, integrating in situ soil sensors, satellite-derived land surface parameters (vegetation, terrain, and soil), and satellite-based models of surface and rootzone soil moisture. It also provides functions for spatial and temporal analysis of the produced soil moisture maps. A case study is provided to demonstrate the functionality to generate 30 m daily to weekly soil moisture maps across a 70-ha crop field, followed by a spatial–temporal analysis. [ABSTRACT FROM AUTHOR]

• A spatial-temporal analysis on mitigation effect of school break on influenza. • School break substantially reduces influenza in school-aged population. • School break has a lagged and smaller effect in young children and adults. • The age-specific pattern is likely to hold for other airborne infectious diseases. • Geographical variation in the impact of school break was observed. To investigate the effect of scheduled school break on the circulation of influenza in young children, school-aged population, and adults. In a spatial-temporal analysis using influenza activity, school break dates, and meteorological covariates across mainland China during 2015-2018, we estimated age-specific, province-specific, and overall relative risk (RR) and effectiveness of school break on influenza. We included data in 24, 25, and 17 provinces for individuals aged 0-4 years, 5-19 years and 20+ years. We estimated a RR meta-estimate of 0.34 (95% confidence interval 0.29-0.40) and an effectiveness of 66% for school break in those aged 5-19 years. School break showed a lagged and smaller mitigation effect in those aged 0-4 years (RR meta-estimate: 0.73, 0.68-0.79) and 20+ years (RR meta-estimate: 0.89, 0.78-1.01) versus those aged 5-19 years. The results show heterogeneous effects of school break between population subgroups, a pattern likely to hold for other respiratory infectious diseases. Our study highlights the importance of anticipating age-specific effects of implementing school closure interventions and provides evidence for rational use of school closure interventions in future epidemics. [ABSTRACT FROM AUTHOR]

The Three-River Headstreams Region (TRHR) serves as the cradle of China's three major rivers—the Yangtze, Yellow, and Lancang—rendering its water conservation (WC) capacity quintessentially significant for Asian water resource security. This study integrated the InVEST model and random forest model to holistically elucidate the spatiotemporal characteristics and factors influencing WC in the TRHR from 1980 to 2018. The results revealed that the WC growth rate was 5.65 mm/10a in the TRHR during the study period, yet pronounced regional disparities were observed among different basins, especially with the Lancang River Basin (LRB), which exhibited a decrease at a rate of 5.08 mm per decade despite having the highest WC. Through Theil–Sen trend analysis, the Mann–Kendall abrupt change test, and the cumulative deviation method, meteorological, vegetative, and land use abrupt changes in approximately 2000 were identified as the primary drivers for the abrupt surge in WC within the TRHR. Furthermore, precipitation and the aridity index were the core feature variables affecting WC. However, a positive transition in land use patterns post-2000 was also revealed, and its favorable effect on WC was not as significant as the abrupt climatic changes. This study offers new perspectives on managing multidimensional spatiotemporal data and contributes to laying the groundwork for machine learning applications in water conservation. Additionally, it potentially provides useful references for decision-making processes related to ecosystem security. [ABSTRACT FROM AUTHOR]

A major portion of Bangladesh is currently experiencing a scarcity of safe drinking water because of arsenic contamination, high salinity and human-induced pollution. The objectives of this study were to identify locations with a high scarcity of drinking water and suitability of harvesting rainwater. Kriging interpolation algorithms of Geographical Information System (GIS) was employed to identify the probable water scarce zones as well as suitable zones of harvesting rain water from the available data of secondary sources. Statistical methods were employed to cluster, correlate, and regress variables such as rainfall, salinity, and As. The results showed that groundwater quality in the southwestern parts of Bangladesh is saline with high concentration (>10000 μS/cm). On the other hand, the northeastern and southwestern parts of Bangladesh are also vulnerable to arsenic contamination (60 %–97 % of tubewells), compared to other regions. The rainfall zonation map, covering the years 1951–2022, indicated that the Sylhet division had the highest potential for rainfall (ranging from 2600 to 3900 mm). From this study it was demonstrated that Sylhet, Noakhali, Bhola, Barishall, Patuakhali, Bagerhat, and Khulna were identified as suitable places for sustainable rainwater harvesting (RWH). The findings of this study may play significant role towards achieving sustainable potable water supply in vulnerable zones, if they receive attention from policymakers.

Optimizing the pattern of territorial space utilization is one of the key tasks to achieve the sustainable development goals. With the accelerating rate of global urbanization, the understanding of territorial space utilization efficiency, role and potential is a prerequisite for alleviating contradictions in urban and rural space distribution. The city cluster is the main form of organization for urban development in future, so the study attempted to explore the urban and rural space utilization efficiency (URSUE) in Chengdu-Chongqing urban agglomeration (CCEC) from coupling coordination degree (CCD) perspective. Considering the gradual increase in the trend of remote interactions between URSUE, we further introduced the Local and Tele-coupling coordination (LTCCD) model that takes into account interactive development relationship between different systems. The results of the study show that: In CCEC, the more economically developed cities indicated that urban spatial utilization efficiency lags behind rural spatial utilization efficiency; The LTCCD in the geographic center region will indicate a higher level but the LTCCD in the economic core cities is higher compared with their CCD level, especially in Chengdu City. This suggests that the LTCCD model is better able to take into account regional development correlations and spatial spillovers effect. This study attempts to explore several key issues of urban-rural spatial allocation in the process of urbanization development and to provide guidance for the territorial space utilization planning in urban agglomerations.

Abstract China's first Mars sampling return mission (Tianwen‐3) is designed to launch and retrieve samples around 2030. Three tentative landing areas (TLAs) (Amazonis, Chryse and Utopia Planitiae, i.e., TLA‐A, TLA‐C and TLA‐U) are selected based on elevation

Abstract Background In many areas of China, over 30% of tuberculosis cases occur among the elderly. We aimed to investigate the spatial distribution and environmental factors that predicted the occurence of tuberculosis in this group. Methods Data were collected on notified pulmonary tuberculosis (PTB) cases aged ≥ 65 years in Zhejiang Province from 2010 to 2021. We performed spatial autocorrelation and spatial-temporal scan statistics to determine the clusters of epidemics. Spatial Durbin Model (SDM) analysis was used to identify significant environmental factors and their spatial spillover effects. Results 77,405 cases of PTB among the elderly were notified, showing a decreasing trend in the notification rate. Spatial-temporal analysis showed clustering of epidemics in the western area of Zhejiang Province. The results of the SDM indicated that a one-unit increase in PM2.5 led to a 0.396% increase in the local notification rate. The annual mean temperature and precipitation had direct effects and spatial spillover effects on the rate, while complexity of the shape of the greenspace (SHAPE_AM) and SO2 had negative spatial spillover effects. Conclusion Targeted interventions among the elderly in Western Zhejiang may be more efficient than broad, province-wide interventions. Low annual mean temperature and high annual mean precipitation in local and neighboring areas tend to have higher PTB onset among the elderly.

As new forms of industries emerge in marine spaces due to global environmental protection trends and the promotion of recreational activities, traditional navigation areas for vessels are also being affected. To ensure safe vessel passage and effective vessel traffic management in response to evolving maritime environments, navigational routes for ships must be exclusively established. Numerous studies have attempted to derive shipping routes from historical vessel-traffic data; however, the final forms of polygon shapes representing routes have not been produced. Hence, this study aimed to extract polygonal routes based on densely trafficked maritime areas. Data collected from the automatic identification system (AIS) onboard maritime vessels were utilized to analyze dense navigational areas, which were then divided into 1 km grids. Through a spatial-temporal analysis method utilized from The European Marine Observation and Data Network (EMODnet), the occupancy time of the vessels in each grid was calculated to extract dense traffic areas. The dense traffic areas extracted in grid form were processed using the geographical identification system to create polygonal routes by smoothing and simplification. The resulting polygons exhibited different shapes depending on the analysis period. To extract a unique representative route polygon, the CRiteria Importance Through Intercriteria Correlation (CRITIC) method was employed to calculate the shape similarity based on the centroid, shape index, and overlapping area ratio of the polygons. The extracted representative polygon demonstrated the highest shape similarity compared with the other polygons and was utilized by 95.93% of the vessels navigating the analyzed area. The study results can contribute to identifying essential areas for vessels in maritime zones by proposing representative shipping routes.

Air Pollution is constantly causing a severe effect on the environment and public health. Prediction of air quality is widespread and has become a challenging issue owing to the enormous environmental data with time-space nonlinearity and multi-dimensional feature interaction. There is a need to bring out the spatial and temporal factors that are influencing the prediction. The present study concentrates on the correlation prediction of spatial and temporal relations. A Deep learning technique has been proposed for forecasting the accurate prediction. The proposed Bi_ST model is evaluated for 17 cities in India and China. The predicted results are evaluated with the performance metrics of RMSE, MAE, and MAPE. Experimental results demonstrate that our method Bi_ST accredits more accurate forecasts than all baseline RNN and LSTM models by reducing the error rate. The accuracy of the model obtained is 94%.