Spatiotemporal Effects Research Articles

The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment Due to Their Propagation With MHD Modes

AbstractWe investigate how the spatiotemporal structure of induced magnetic fields outside of Callisto is affected by their propagation with the magnetohydrodynamic (MHD) modes. At moons that are surrounded by dense magnetized plasmas like the Galilean moons, low‐frequency induced magnetic fields cannot propagate with the ordinary electromagnetic mode as is implicitly used by standard analytical expressions. Instead, the induced magnetic fields propagate with the MHD modes, which exhibit anisotropic propagation properties and have finite velocities. Using an MHD framework, we model the spatiotemporal effects of the transport on the induced signals and analyze their contribution to Galileo's C03 and C09 flyby observations. We find that the induced magnetic field in Callisto's plasma environment is asymmetric with a pronounced upstream/downstream asymmetry. By neglecting the transport effects, the amplitude of the induced magnetic field is under‐ or overestimated by up to tens of percent, respectively. Additionally, we find that MHD wave and convection velocities are strongly reduced in Callisto's local plasma environment, resulting in an additional temporal delay between the emergence of the induced field at the surface of Callisto or the top of its ionosphere and the measurements at spacecraft location. The associated phase shift depends on the location of the observer and can reach values of several to tens of degrees of the phase of the primary inducing frequency. Transport effects impact the observed induction signals and are consistent with the C03 and C09 magnetic field measurements.

Exploring the spatiotemporal effects of meteorological factors on hand, foot and mouth disease: a multiscale geographically and temporally weighted regression study

The influence of meteorological factors on hand, foot, and mouth disease (HFMD) is not on the same scale, it’s rare for previous studies to measure and recognize the independent regression relationship between each variable in space and time scale. This study used a multiscale geographically and temporally weighted regression (MGTWR) model to explore the relationship between the incidence of HFMD and related meteorological factors in Shandong Province, China, during 2015–2019 and attempted to quantify the influence of meteorological factors on HFMD under different spatiotemporal effects. Meanwhile, we used the Global Moran’s I statistic and Local Moran’s I statistic to test the spatial autocorrelation of the incidence of HFMD. HFMD had spatial autocorrelation at the county level in Shandong Province. The MGTWR model outperformed the OLS and GTWR models in determining the relationship between meteorological factors and HFMD. The study highlights significant spatiotemporal non-stationarity in the relationship between meteorological factors and HFMD. Temperature was predominantly positively correlated with HFMD, especially in the peninsula region during spring and summer. Humidity exhibited a predominantly positive correlation, especially in the Shandong Peninsula. Precipitation also showed a positive correlation with HFMD, particularly in western regions and during the winter months. Wind speed had a predominantly negative correlation with HFMD in the central and southwestern regions. The results might help public health authorities set priorities for targeted prevention and control measures in different regions and weather conditions, and provide guidance for the government to rationally allocate public health resources.

Quantitative Estimation and Analysis of Spatiotemporal Delay Effects in Expressway Traffic Accidents

Expressway traffic accidents often result in severe congestion, with their unpredictable nature complicating timely and effective response measures. This paper presents a comprehensive method for accurately estimating and analyzing the spatiotemporal delay effects of expressway accidents through the integration of multi-source geographic data. The innovation lies in utilizing real-world vehicle trajectory data, combined with a Traffic Performance Index (TPI), to quantitatively assess delay impacts. By applying spatial clustering and hotspot detection techniques, we investigate the distribution patterns of delays and further employ a Spatial Error Model (SEM) to examine the relationships between accident characteristics and associated delay effects. Using expressway accident data and vehicle trajectory records from Hunan Province, the results demonstrate that the TPI-based approach effectively captures the duration, extent, and severity of traffic delays. Moreover, significant correlations are identified between delay impacts and specific accident characteristics, such as accident type, road type, road environment, pre-accident vehicle speed, and secondary accidents. This approach provides traffic management authorities with actionable insights into the overall roadway impact, facilitating targeted emergency response strategies and informing road usage policies tailored to the characteristics of accident impacts, thus helping to mitigate future risks.

Investigating the impacts of climate variations and armed conflict on drought and vegetation cover in Northeast Syria (2000–2023)

Over the last decades, The Northeast part of Syria (NES) has been significantly affected by multiple drought events, which are exacerbated by armed conflict and climate variations. In this study, the spatiotemporal effects of climate fluctuations on drought episodes and agricultural areas in NES from 2000 to 2023 were examined by utilizing diverse meteorological parameters combined with the normalized difference vegetation index. The relationships between the change in climatic variables and vegetation cover alterations were determined by performing different statistical methods, such as the Pearson correlation coefficient and Mann-Kendall trend analysis. The results indicated a significant decrease in the agricultural area, especially in recent years, accompanied by a notable increase in the precipitation levels. Moreover, there has been a substantial increase in temperatures, particularly in the minimum temperatures. The results also indicate that drought severity and frequency have increased since the armed conflict despite the area receiving higher precipitation amounts, highlighting the role and impacts of violence. Therefore, we recommend further research on how different vegetation species have been affected by climate change and armed conflict, defining specific growing seasons for each vegetation species, and creating land use land cover maps to understand the spatial alteration of these types better.

Spike and Slab Regression for Nonstationary Gaussian Linear Mixed Effects Modeling of Rapid Disease Progression

ABSTRACTSelect measures of social and environmental determinants of health (referred to as “geomarkers”), predict rapid lung function decline in cystic fibrosis (CF), defined as a prolonged decline relative to patient and/or center‐level norms. The extent to which hyper‐localization, defined as increasing the spatiotemporal precision of geomarkers, aids in prediction of rapid lung decline remains unclear. Linear mixed effects (LME) models with specialized covariance functions have been used for predicting rapid lung function decline, but there are few options to properly incorporate spatial correlation into the covariance functions while inducing simultaneous variable selection. Our innovative Bayesian model uses a spike and slab prior for simultaneous variable selection and offers additional advantages when coupled with nonstationary Gaussian LME modeling. This model also incorporates spatial correlation through an additional random effect term that accounts for spatial correlation based on ZIP code distances. We validated the model with simulations and applied it to real CF data from a Midwestern CF Center. We demonstrate how a combination of demographic, clinical, and geomarker variables can be selected as optimal predictors using Bayesian false discovery rate controlling rule. Our results indicate that incorporating spatiotemporal effects and geomarkers into this novel Bayesian stochastic LME model enhances the dynamic prediction of rapid CF disease progression.

High-performance PEI-based nanofiltration membrane by Mxene-regulated interfacial polymerization reaction: Design, Fabrication and Testing

Development of cost-effective materials and technologies for lithium extraction in salt-lake brines is of great importance to meeting rising lithium demands. Nanofiltration (NF) process is a feasible solution, however, it has commonly had operational problems such as low permeability. In this study, an MXene-regulated interfacial polymerization (MRIP) method was developed for fabrication of high-performance polyethyleneimine (PEI)-based polyamide (PA) NF membrane. The MXene nanosheets tiled at the water-oil interface, by which a spatio-temporal confinement effect was created; such structure could nicely regulate diffusion of monomers and precisely control PA nanofilm. The MXene-regulated membrane (PA@M) had thinner thickness, larger pore size, and better hydrophilicity. As a result, the permeance of PA@M membrane was significantly increased by more than 300%. Moreover, the membrane had outstanding cation selectivity with >98% rejection for Mg2+ and <20% rejection for Li+. Meanwhile, the selectivity factor of Li+/Mg2+ reached 31.5. Furthermore, a two-stage nanofiltration process by the membrane could reduce the Mg2+/Li+ ratio from 40 to 0.45, suggesting greater suitability of membrane for lithium extraction. The MRIP method offers a promising strategy and method for development of high-performance PA-NF membranes for resource recovery and water purification applications.

Synergistic effects of urban forest on urban heat island-air pollution-carbon stock in mega-urban agglomeration

Urban forests provide multiple ecosystem services from mitigation of urban heat island and air pollution to multi-dimensional human well-beings. However, extensive studies focus on the singular contribution of urban forests to cooling, clean air and increase carbon sequestration. This study combined multi-source data and geostatistical methods to quantify the spatiotemporal effects of urban forests on the spatial interactions between surface urban heat island (SUHI), PM2.5, and carbon stock (i.e., synergistic effects) and its mechanisms in the mega-urban agglomeration of Guangdong–Hong Kong–Macao Greater Bay Area. Results showed that urban forest evolution was spatiotemporally coupled with the patterns of PM2.5, SUHI, and carbon stock in 2000–2020. 80 % of the urban forest landscape indices demonstrated significant positive effects on cooling–clean air–increase carbon sequestration, with much higher (on average 2.98 times) influence than for each single environmental variable. In addition, patch dominance explained an average of 60.55 % of the synergistic effects, and its control zone rapidly expanded spatially by 54.3 %. In contrast, the influence of cover rate diminished, with q value and control zone proportion decreasing by 18.87 % and 26.61 %. Moreover, the contributions of anthropogenic factors to synergistic effects were 1.2–3.8 times higher than the natural factors. This study established an urban ecosystem service framework, thereby providing crucial information and decision support for effective urban forest management.

Citizen science observations shed light on how anthropogenic food sources influence wildlife disease.

Research Highlight: Knutie, S., Bahouth, R., Bertone, M., Webb, C., Mehta, M., Nahom, M., Barta, R., Ghai, S., Love, A., Horan, S., Soldo, A., Cochrane, E., Bartholomew, J., Cowan, E., Bjerke, H., Balenger, S., Butler, M., Cornell, A., Kennedy, A., Rolland, V., Schultz, E., Stanback, M., Taff, C., Albery, G. (2024). Understanding spatiotemporal effects of food supplementation on host-parasite interactions using community-based science. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.14155. Wildlife have become increasingly dependent on anthropogenic food supplementation, resulting in altered nutritional intake and inter- and intraspecific interactions. Subsequently, supplemental feeding can affect both the immunological function of individuals and transmission dynamics among individuals and species. The magnitude of the effect supplemental feeding has on disease is likely to vary across time and space with the nutritional demands of hosts. However, the broad temporal or spatial scale effects of supplementation are poorly understood. Recently, Knutie etal. (2024) introduced their citizen science program, the Nest Parasite Community Project, a broadscale coordinated effort by scientists and the public to monitor box nesting wild birds and their ectoparasites across the eastern United States. The authors amassed an impressive 4-year data set with hundreds of nests spanning the entire US breeding range of Eastern bluebirds (Sialia sialis). In the first study to come from the project, the authors demonstrate that the effects of food supplementation on host-parasite interactions vary across time and space and do not consistently influence host-parasite outcomes, highlighting that host-parasite interactions are often context dependent and influenced by many environmental factors (e.g. weather and habitat quality). The authors also found that supplemental feeding increases host fitness, regardless of parasitism. The study provides strong evidence that citizen science projects can help broaden our understanding of how human food sources influence wildlife disease in various environmental contexts.

Bacterial community dynamics as a result of growth-yield trade-off and multispecies metabolic interactions toward understanding the gut biofilm niche

Bacterial communities are ubiquitous, found in natural ecosystems, such as soil, and within living organisms, like the human microbiome. The dynamics of these communities in diverse environments depend on factors such as spatial features of the microbial niche, biochemical kinetics, and interactions among bacteria. Moreover, in many systems, bacterial communities are influenced by multiple physical mechanisms, such as mass transport and detachment forces. One example is gut mucosal communities, where dense, closely packed communities develop under the concurrent influence of nutrient transport from the lumen and fluid-mediated detachment of bacteria. In this study, we model a mucosal niche through a coupled agent-based and finite-volume modeling approach. This methodology enables us to model bacterial interactions affected by nutrient release from various sources while adjusting individual bacterial kinetics. We explored how the dispersion and abundance of bacteria are influenced by biochemical kinetics in different types of metabolic interactions, with a particular focus on the trade-off between growth rate and yield. Our findings demonstrate that in competitive scenarios, higher growth rates result in a larger share of the niche space. In contrast, growth yield plays a critical role in neutralism, commensalism, and mutualism interactions. When bacteria are introduced sequentially, they cause distinct spatiotemporal effects, such as deeper niche colonization in commensalism and mutualism scenarios driven by species intermixing effects, which are enhanced by high growth yields. Moreover, sub-ecosystem interactions dictate the dynamics of three-species communities, sometimes yielding unexpected outcomes. Competitive, fast-growing bacteria demonstrate robust colonization abilities, yet they face challenges in displacing established mutualistic systems. Bacteria that develop a cooperative relationship with existing species typically obtain niche residence, regardless of their growth rates, although higher growth yields significantly enhance their abundance. Our results underscore the importance of bacterial niche dynamics in shaping community properties and succession, highlighting a new approach to manipulating microbial systems.

Energy transition affordability in China: Disparities and determinants

This paper develops a parametric global-clustering metafrontier approach integrating input-output information to evaluate energy transition decarbonization costs. We then propose a novel and intuitive index for measuring energy transition affordability (ETA) that couples energy transition decarbonization costs with economic development level. Spatial Gini coefficient decomposition and spatial Markov chain are used to investigate the spatiotemporal disparities and long-term trend of ETA. Furthermore, we design a combined lasso, geographically and temporally weighted regression framework to identify the key determinants of ETA and capture their spatiotemporal heterogeneous effects. We apply the methodology to study the disparaities and determinants of energy transition affordability across 283 cities in China. The results indicate a favorable increasing trend, but a significant inequality of ETA across urban groups. Unique impacts of key determinants on ETA are observed for each city and each year. Our findings underscore the necessity of targeted policies and strategies towards more equitable energy transition in developing countries.

Research on key aircraft identification method based on time-effective network

For the problem of identifying key aircraft in air traffic situation, existing research has failed to fully consider the spatiotemporal effects in the actual operation of air traffic. Therefore, a key aircraft identification method based on time-effective network is proposed. The convergence relationship and complexity between aircraft are used to construct a time-effective network model through the neighbor topology overlap coefficient, and the key aircraft is determined based on the eigenvector centrality. A network attack is carried out on the key aircraft nodes to observe the changes in sector complexity, and compared with the attack based on static network indicators, an improved genetic algorithm is used to assign new entry time to the aircraft nodes deleted by the network attack, so as to verify the selection effect of key aircraft. The actual data verification shows that compared with the static network attack, this method is more efficient in reducing the average complexity of the sector when removing key aircraft. The improved genetic algorithm has higher convergence when solving the key aircraft entry time allocation problem, making the sector complexity more stable within a certain period of time. Analysis of the control effect of key aircraft shows that the time-effective network method can more accurately identify aircraft that have a greater impact on sector complexity within a period of time than the static network.

Effects of climate variability on the spatio-temporal distribution of Dengue in Valle del Cauca, Colombia, from 2001 to 2019.

Dengue is a vector-borne disease that has increased over the past two decades, becoming a global public health emergency. The transmission of dengue is contingent upon various factors, among which climate variability plays a significant role. However, there remains substantial uncertainty regarding the underlying mechanisms. This study aims to investigate the spatial and temporal patterns of dengue risk and to quantify the associated risk factors in Valle del Cauca, Colombia, from 2001 to 2019. To achieve this, a spatio-temporal Bayesian hierarchical model was developed, integrating delayed and non-linear effects of climate variables, socio-economic factors, along with spatio-temporal random effects to account for unexplained variability. The results indicate that average temperature is positively associated with dengue risk 0-2 months later, showing a 35% increase in the risk. Similarly, high precipitation levels lead to increased risk approximately 2-3 months later, while relative humidity showed a constant risk within a 6 months-lag. These findings could be valuable for local health authorities interested in developing early warning systems to predict future risks in advance.

Gradient boosting: A computationally efficient alternative to Markov chain Monte Carlo sampling for fitting large Bayesian spatio-temporal binomial regression models

Disease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time. Bayesian spatio-temporal regression models fit with Markov chain Monte Carlo (MCMC) methods are commonly used for such data. When the spatio-temporal support of the model is large, implementing an MCMC algorithm becomes a significant computational burden. This research proposes a computationally efficient gradient boosting algorithm for fitting a Bayesian spatio-temporal mixed effects binomial regression model. We demonstrate our method on a disease forecasting model and compare it to a computationally optimized MCMC approach. Both methods are used to produce monthly forecasts for Lyme disease, anaplasmosis, ehrlichiosis, and heartworm disease in domestic dogs for the contiguous United States. The data have a spatial support of 3108 counties and a temporal support of 108–138 months with 71–135 million test results. The proposed estimation approach is several orders of magnitude faster than the optimized MCMC algorithm, with a similar mean absolute prediction error.

Spatio-temporal effects and influence mechanism of digital technology on tourism efficiency in Chinese provinces

Digital technology serves as a new industrial driving force, playing a crucial role in promoting the efficiency of regional tourism. To explore the inherent logic between digital technology and tourism development, the study employed the DEA-BCC model and the entropy-weighted TOPSIS method to measure the tourism efficiency and digital technology development level of 31 provinces (municipalities and districts) in China from 2011 to 2022. The evolutionary characteristics of the relationship between digital technology and tourism efficiency were analyzed from the spatio-temporal dimension, and empirical tests were carried out using the spatial error model (SEM) and the spatial lag model (SLM) to explore the mechanism of the impact of digital technology on tourism efficiency. The results show that: during the study period, the overall trend of digital technology development and tourism efficiency is upward, but there is a certain degree of spatial mismatch between the two; digital technology is not only conducive to the improvement of tourism efficiency in the province, but also acts on neighboring provinces through spatial spillover effects, mechanism of action tests show that digital technology can positively moderate the effects of tourism economic growth and tourism industry structure on tourism efficiency; in western and northeast China, the positive effect of digital technology on tourism efficiency is more obvious. The conclusions provide a new perspective for understanding and analyzing the development of provincial tourism in China, as well as a reference for the rational use of digital technology to promote tourism development.

A spatiotemporal analysis of NATO member states' defense spending: how much do allies actually free ride?

Abstract Concerns over free riding in NATO are widespread. An intuitive approach to analyzing free riding is treating it as a systematic pattern of spatial interdependence between the allies: how does a NATO member's defense spending react to changes in other allies' military expenditures? While recent work has found statistically significant free riding (negative spatial interdependence in the outcomes), it suffers from important limitations. First, this research does not adequately account for temporal dependence. Second, it does not quantify the effect of interest. Accounting directly for temporal dependence provides a meaningfully distinct perspective on the within-alliance dynamics, demonstrating that the spatiotemporal effect of free riding is, in fact, more substantial than its short-run effect, challenging inferences of static spatial models. We discuss the relevant practical and theoretical implications.

The influence of fiscal transparency on expenditure by regional and provincial governments in Vietnam

The intricate relationship between budget transparency and government expenditure has been confirmed, with a particular focus on how these dynamics are influenced by spatial and temporal factors. This study deals with the spatio-temporal effects of transparent communal budgets on local government spending, specifically across the 63 provinces in Vietnam. By analyzing data over time, the study uncovers critical insights into how the disclosure of budget information influences government spending patterns, both in the short term and the long term. The findings reveal a nuanced relationship, where both the direct and indirect effects of budget transparency on expenditure tend to be negative, suggesting that increased transparency may lead to a reduction in government spending. This trend is observed from both a short-term perspective, where immediate changes in budget disclosure affect spending, and a long-term perspective, where the effects of sustained transparency become more evident. The study highlights significant regional disparities in the impact of budget transparency on government expenditure. It finds that the influence of budget disclosure is less pronounced in northern Vietnam compared to the southern regions. The results advocate for policies that support targeted resource allocation at the provincial or regional levels. Such tailored approaches are essential for achieving more effective and equitable government budget management, ensuring that transparency initiatives are adapted to the specific needs and conditions of different regions within Vietnam. Overall, this research contributes to the broader understanding of how transparency in budgetary processes can influence government spending. By highlighting the spatial and temporal dimensions of this relationship, the study provides valuable insights for policymakers seeking to enhance fiscal accountability and optimize public resource allocation in Vietnam.

Pervasive impacts of railway edge effects on edaphic parameters and vegetation distribution patterns

Addressing the ecological impacts of transport corridors while planning any transport network is paramount for a better of understanding ecological processes, biodiversity distribution, and ecosystem resilience. Here, we evaluated the impacts of railway edges on edaphic parameters, plant diversity, and composition in the northwestern Himalaya as a function of perpendicular distance from the railway track. In 2014 and 2017, we sampled vegetation plots across 31 sites distributed along 119 km of the Kashmir railway to test the effect of railway tracks on edaphic and vegetative parameters in short-term periods. T-shaped plots were laid comprising four sub-plots, one parallel to the track and the other three perpendiculars to it. We adopted the Mountain Invasion Research Network road survey methodology for data collection. We found (i) an increasing trend in all soil parameters (electrical conductivity, salinity, organic carbon, available nitrogen, and available phosphorus), except pH, as the distance from the railway track increased; (ii) significant railway edge effects on the importance value index of plant growth forms, life span, nativity and taxonomic group across space (distance of plots from the track) and time (2014–2017); and (iii) a significant spatiotemporal railway edge effects on plant species composition. Our results are pioneering in showing that railway tracks crossing through different ecosystems alter the edaphic conditions, resulting in direct and indirect edge effects on key abiotic and biotic factors, which in turn impact the vegetation of the surrounding natural habitats on both sides of the railway track. Our study contributes toward a better understanding of the role of anthropogenic edges like railways on edaphic parameters and vegetation distribution.

Lessons learned from spatiotemporal effect of COVID on the population density in the CBD

Alonso in 1964 developed the land use and transport interaction theory based on the bid and rent concept. It tells that the central core part of urban areas has the highest housing rental prices, and it gradually decays as it is further away from the centre. The city’s district is divided based on the distribution of land prices according to distance from the city centre. This fundamental relationship provides a basic insight into how people behave rationally subject to their living budgets. Based on this relationship, this paper aims to investigate the temporal and spatial changes in the de facto population of Seoul caused by coronavirus disease 2019 (COVID-19). The de facto population has declined significantly since 2020. A major decrease has been found in areas within 2.5 km of travel distance from the city centre. The de facto population decreased the most during the night hours (18:00–24:00) on weekdays and the daytime (10:00–18:00) on weekends. These findings could be postulated to foresee how people might select their housing locations in terms of transport costs in the post–COVID-19 era.

Converging functional phenotyping with systems mapping to illuminate the genotype-phenotype associations.

Illuminating the phenotype-genotype black box under complex traits is an ambitious goal for researchers. The generation of temporally or spatially phenotypic data today has far outpaced its interpretation, due to their highly dynamic nature depending on the environment and developmental stages. Here, we propose an integrated enviro-pheno-geno functional approach to pinpoint the major challenges of decomposing physiological traits. The strategy first features high-throughput functional physiological phenotyping (FPP) to efficiently acquire phenotypic and environmental data. It then features functional mapping (FM) and the extended systems mapping (SM) to tackle trait dynamics. FM, by modeling traits as continuous functions, can increase the power and efficiency in dissecting the spatiotemporal effects of QTLs. SM could enable reconstruction of a genotype-phenotype map from developmental pathways. We present a recent case study that combines FPP and SM to dissect complex physiological traits. This integrated approach will be an important engine to drive the translation of phenomic big data into genetic gain.

Bayesian spatio-temporal modeling to assess the effect of land-use changes on the incidence of Cutaneous Leishmaniasis in the Brazilian Amazon

Cutaneous Leishmaniasis (CL) is a vector-borne disease caused by a protozoan of the genus Leishmania and is considered one of the most important neglected tropical diseases. The Brazilian Amazon Forest harbors one of the highest diversity of Leishmania parasites and vectors and is one of the main focuses of the disease in the Americas. Previous studies showed that some types of anthropogenic disturbances have affected the abundance and distribution of CL vectors and hosts; however, few studies have thoroughly investigated the influence of different classes of land cover and land-use changes on the disease transmission risk. Here, we quantify the effect of land use and land-cover changes on the incidence of CL in all municipalities within the Brazilian Amazon Forest, from 2001 to 2017. We used a structured spatiotemporal Bayesian model to assess the effect of forest cover, agriculture, livestock, extractivism, and- deforestation on CL incidence, accounting for confounding variables such as population, climate, socioeconomic, and spatiotemporal random effects. We found that the increased risk of CL was associated with deforestation, especially modulated by a positive interaction between forest cover and livestock. Landscapes with ongoing deforestation for extensive cattle ranching are typically found in municipalities within the Amazon Frontier, where a high relative risk for CL was also identified. These findings provide valuable insights into developing effective public health policies and land-use planning to ensure healthier landscapes for people.