Spatiotemporal Perspective Research Articles

Public Opinions on COVID-19 Vaccines-A Spatiotemporal Perspective on Races and Topics Using a Bayesian-Based Method.

The COVID-19 pandemic has been sweeping across the United States of America since early 2020. The whole world was waiting for vaccination to end this pandemic. Since the approval of the first vaccine by the U.S. CDC on 9 November 2020, nearly 67.5% of the US population have been fully vaccinated by 10 July 2022. While quite successful in controlling the spreading of COVID-19, there were voices against vaccines. Therefore, this research utilizes geo-tweets and Bayesian-based method to investigate public opinions towards vaccines based on (1) the spatiotemporal changes in public engagement and public sentiment; (2) how the public engagement and sentiment react to different vaccine-related topics; (3) how various races behave differently. We connected the phenomenon observed to real-time and historical events. We found that in general the public is positive towards COVID-19 vaccines. Public sentiment positivity went up as more people were vaccinated. Public sentiment on specific topics varied in different periods. African Americans’ sentiment toward vaccines was relatively lower than other races.

Ictal direct current shifts contribute to defining the core ictal focus in epilepsy surgery.

Identifying the minimal and optimal epileptogenic area to resect and cure is the goal of epilepsy surgery. To achieve this, EEG analysis is recognized as the most direct way to detect epileptogenic lesions from spatiotemporal perspectives. Although ictal direct-current shifts (below 1 Hz) and ictal high-frequency oscillations (above 80 Hz) have received increasing attention as good indicators that can add more specific information to the conventionally defined seizure-onset zone, large cohort studies on postoperative outcomes are still lacking. This work aimed to clarify whether this additional information, particularly ictal direct-current shifts which is assumed to reflect extracellular potassium concentration, really improve postoperative outcomes. To assess the usefulness in epilepsy surgery, we collected unique EEG data sets recorded with a longer time constant of 10 s using an alternate current amplifier. Sixty-one patients (15 with mesial temporal lobe epilepsy and 46 with neocortical epilepsy) who had undergone invasive presurgical evaluation for medically refractory seizures at five institutes in Japan were retrospectively enrolled in this study. Among intracranially implanted electrodes, the two core electrodes of both ictal direct-current shifts and ictal high-frequency oscillations were independently identified by board-certified clinicians based on unified methods. The occurrence patterns, such as their onset time, duration, and amplitude (power) were evaluated to extract the features of both ictal direct-current shifts and ictal high-frequency oscillations. Additionally, we examined whether the resection ratio of the core electrodes of ictal direct-current shifts and ictal high-frequency oscillations independently correlated with favourable outcomes. A total of 53 patients with 327 seizures were analyzed for wide-band EEG analysis, and 49 patients were analyzed for outcome analysis. Ictal direct-current shifts were detected in the seizure-onset zone more frequently than ictal high-frequency oscillations among both patients (92% versus 71%) and seizures (86% versus 62%). Additionally, ictal direct-current shifts significantly preceded ictal high-frequency oscillations in patients exhibiting both biomarkers, and ictal direct-current shifts occurred more frequently in neocortical epilepsy patients than in mesial temporal lobe epilepsy patients. Finally, although a low corresponding rate was observed for ictal direct-current shifts and ictal high-frequency oscillations (39%) at the electrode level, complete resection of the core area of ictal direct-current shifts significantly correlated with favourable outcomes, similar to ictal high-frequency oscillation outcomes. Our results provide a proof of concept that the independent significance of ictal direct-current shifts from ictal high-frequency oscillations should be considered as reliable biomarkers to achieve favourable outcomes in epilepsy surgery. Moreover, the different distribution of the core areas of ictal direct-current shifts and ictal high-frequency oscillations may provide new insights into the underlying mechanisms of epilepsy, in which not only neurons but also glial cells may be actively involved via extracellular potassium levels.

Geoscience Knowledge Graph (GeoKG): Development, construction and challenges

AbstractBig earth data is a cross‐domain of geoscience and information science, which provides a novel perspective for solving geoscience problems. Most contemporary research is driven by data but neglect the potential value of knowledge. As a new scientific language in Geoscience, GeoKG is essential for understanding, representing, and mining geoscience knowledge, and can contribute to the integration of big earth data, geoscience knowledge, and geoscience models. However, research on GeoKG lack spatiotemporal perspectives in knowledge cognition, representation, acquisition and management. To this end, this article first outlines a cognitive mechanism from the human–machine double perspective and categorizes the characteristics and content of geoscience knowledge. To express evolution and complex natural rules, a knowledge representation framework is proposed through ‘state‐process’ and ‘condition‐result’ models. Aiming at multimodal data, a workflow is put forward to acquire knowledge from a small sample, a knowledge graph, a map, and a schematic diagram. Furthermore, we discuss the organization of GeoKG by improving existing data models, developing spatiotemporal correlation indexing and advancing knowledge graph completion. The concrete construction process of GeoKG is analyzed thoroughly in this study, which can support the synthetic analysis of big earth data, promote the development of knowledge engineering and provide a foundation for improving intelligent geoscience.

Prediction of long lead monthly three-dimensional ocean temperature using time series gridded Argo data and a deep learning method

• A deep learning model is proposed to predict long lead monthly 3D ocean temperature. • Nearly 17-year monthly time series gridded Argo data with 58 depth layers are used. • A case study is demonstrated in the South Pacific Ocean from multiple perspectives. • The model accurately predicts 1 year ahead ocean temperature from 0 to 1975dbar. • The model outperforms three baseline models on multiple metrics. Ocean temperature is a vital physical variable of the oceans. Accurately predicting the long lead dynamics of the three-dimensional ocean temperature (3D-OT) can help us identify in advance potential extreme events (e.g., droughts and floods) that may be caused by the changes of the 3D-OT, which however remains a challenge. To achieve this goal, a deep learning (DL) model was proposed to make predictions of the monthly 3D-OT for one year ahead using time series gridded Argo data. The DL model is comprised of a one-dimensional convolution (Conv1D) layer which is used for extracting latent features from the time series ocean temperature data, two long short-term memory (LSTM) layers which are used for capturing the long-term temporal dependencies hidden in the 3D-OT based on the features extracted by the Conv1D layer, and a fully-connected layer to output the predictions. The proposed DL model can well model the temporal dependencies and dynamic patterns of the ocean temperature at different spatial locations and in different depths by learning from simply the historical time series gridded Argo data. Experiments conducted in a sub-area of the South Pacific Ocean that predict the monthly 3D-OT with the lead time from 1 to 12 months show that the developed DL model surpasses the persistence model, the AdaBoost model, and the feedforward backpropagation neural network model (BPNN) when compared from multiple spatiotemporal perspectives using multiple statistics, indicating that the proposed DL model is a highly strong model for long lead monthly 3D-OT predictions.

Regeneration Innovation: The Adventure of Innovation in Time and Place Towards Sustainability

This paper explores how a sense of time and a sense of place shape the development of different innovation types to tackle sustainability challenges. Innovation which refers to new and potentially useful ideas, products, services, or processes is developed based on given cognitions (or cognitive frame). Such cognitive frames are influenced by time and place as fundamental cultural assumptions. Given that globalization and digitalization have fundamentally altered how organizations experience and value time and place, this paper proposes a typology of sustainability-oriented innovation and theorizes how two cognitive frames—a sense of time and a sense of place interact to shape the content and structure of multiple types of innovation for sustainability. In so doing, this study provides a spatiotemporal perspective to the sustainability and innovation literature by bringing together previously disconnected research streams on time and place. Moreover, this work also highlights the importance of understanding the origins, not just the outcomes, of innovation development.

Spatial and temporal distribution of the prevalence of unemployment and early retirement in people with multiple sclerosis: A systematic review with meta-analysis.

BackgroundWe aimed to summarise the prevalence of unemployment and early retirement among people with MS and analyze data according to a spatio-temporal perspective.MethodsWe undertook a systematic search of PubMed/MEDLINE, Scopus, SciVerse ScienceDirect, and Web of Science. We included any peer-reviewed original article reporting the prevalence of unemployment and early retirement in the working-age population with MS. We excluded articles off-topic, with other study designs, whose study sample were unlikely to be representative of the MS population and in case of unavailability of the full text or essential information. A random-effects meta-analysis was used to measure overall prevalence estimates of unemployment and early retirement. We used meta-regression and subgroup analysis to evaluate potential moderators of prevalence estimates and the leave-one-out method for sensitivity analyses.ResultsOur research identified 153 studies across 29 countries encompassing 188436 subjects with MS. The pooled overall effect size for unemployment and early retirement was 35.6% (95% CI 32.8–38.4; I2 = 99.31) and 17.2% (95% CI 14.6–20.2; I2 = 99.13), respectively. The prevalence of unemployment varied according to the year of publication (p < 0.001) and there was a statistically significant decrease in the prevalence of unemployment over time (p = 0.042). Regarding early retirement, only seven (31.8%) estimates obtained from studies that were published before 2010 were below the overall effect size in comparison to 27 (60.0%) estimates extracted from data published between 2010 and 2021 (p = 0.039). There was a significant difference in prevalence according to countries (p < 0.001). Psychiatric illness was an important clinical feature responsible for patients leaving the workforce in regions with a high MS prevalence.ConclusionsUnemployment and early retirement due to MS remain highly prevalent, despite a slight decline in the last decade. The prevalence of unemployment and early retirement varies globally.

The effect of COVID-19 on cross-border mobilities of people and functional border regions: the Nordic case study from Twitter data

ABSTRACT Mobility is a global megatrend in our contemporary world as people are constantly crossing nation-state borders for migration, tourism, work and due to mobile transnational lives. Cross-border practices contribute to (re)produce functional border regions between different countries. The current amount of information on the geographies of cross-border mobilities of people, the creation of functional cross-border regions and how these regions change over time is inadequate. As transnational phenomena are fragile to global disruptions and prone to ‘rebordering’ in times of emergency, we lack knowledge of how the COVID-19 crisis affected border practices and functioning regions. We consider mobility to be a tool for understanding society and used big data to examine cross-border mobilities between the five Nordic countries: when and where borders were crossed, how mobilities captured functional border regions, and evaluated the influence of COVID-19 on mobilities and functional border regions from a spatio-temporal perspective. The feasibility of the proposed methodology for monitoring cross-border mobilities and border regions to improve planning and development of border regions and decision-making in future crises is discussed. We studied a 4-year Twitter dataset, including the first year of the pandemic until February 2021. We found that overall cross-border mobility decreased by 68% due to the pandemic, yet with geographical and temporal variations. We showed how the influence of the pandemic on the spatial extent of functional border regions varies for a range of reasons for cross-border mobility. We discuss the feasibility of the proposed approach for monitoring cross-border interactions as the proof-of-concept for capturing functional border regions to improve planning and development of border regions and decision-making in future crises. Finally, we highlight future avenues in enhancing our proposed methodology to improve information on cross-border mobilities derived from social media data such as Twitter.

Depth-based geolocation processing of multi-year striped marlin archival tag data reveals residency in the Eastern Pacific Ocean

BackgroundThe first successful application of implanted archival tags on striped marlin showed great potential in obtaining long-term tracks for an improved understanding of movement ecology, which is important for informing fisheries management decisions. Ten tags were physically recovered from fish tagged in the Eastern Pacific between 2008 and 2016, but unfortunately due to the failure of the external sensor stalk which houses the light sensor, full records of daily fish position could not be attained.ResultsDepth-based geolocation, which exploits the diel swimming patterns of striped marlin, was applied to derive position estimates for archival tagged fish up to 7.7 years. Reconstructed tracks revealed tagged striped marlin remained in the Eastern Pacific throughout the tracked duration. Trans-equatorial movements were also documented for the first time for striped marlin in this region, as were extended occupancy of > 1 year in pelagic waters.ConclusionsStriped marlin connected both coastal and offshore habitats with seasonal runs, likely in fulfillment of their life history requirements from foraging to reproduction. Circadian rhythms in billfish and other pelagic fishes are well-established, and could provide a viable, alternative means to position an individual in a low or no light environment, and situations with sub-optimal or limited bio-logging capabilities. Depth-based geolocation, however rudimentary, has revealed variability in striped marlin horizontal movements over the multi-year observation period, and offered a unique spatiotemporal perspective that was unavailable to scientists and fisheries managers until now.

Using data mining technology to analyse the spatiotemporal public opinion of COVID-19 vaccine on social media

PurposeThe deployment of vaccines is the primary task in curbing the COVID-19 pandemic. The purpose of this paper is to understand the public’s opinions on vaccines and then design effective interventions to promote vaccination coverage.Design/methodology/approachThis paper proposes a research framework based on the spatiotemporal perspective to analyse the public opinion evolution towards COVID-19 vaccine in China. The framework first obtains data through crawler tools. Then, with the help of data mining technologies, such as emotion computing and topic extraction, the evolution characteristics of discussion volume, emotions and topics are explored from spatiotemporal perspectives.FindingsIn the temporal perspective, the public emotion declines in the later stage, but overall emotion performance is positive and stabilizing. This decline in emotion is mainly associated with ambiguous information about the COVID-19 vaccine. The research progress of vaccines and the schedule of vaccination have driven the evolution of public discussion topics. In the spatial perspective, the public emotion tends to be positive in 31 regions, whereas local emotion increases and decreases in different stages. The dissemination of distinctive information and the local epidemic prevention and control status may be potential drivers of topic evolution in local regions.Originality/valueThe analysis results of media information can assist decision-makers to accurately grasp the subjective thoughts and emotional expressions of the public in terms of spatiotemporal perspective and provide decision support for macro-control response strategies and risk communication.

A chaotic lattice field theory in one dimension* *Dedicated to Fritz Haake 1941–2019 [

Motivated by Gutzwiller’s semiclassical quantization, in which unstable periodic orbits of low-dimensional deterministic dynamics serve as a WKB ‘skeleton’ for chaotic quantum mechanics, we construct the corresponding deterministic skeleton for infinite-dimensional lattice-discretized scalar field theories. In the field-theoretical formulation, there is no evolution in time, and there is no ‘Lyapunov horizon’; there is only an enumeration of lattice states that contribute to the theory’s partition sum, each a global spatiotemporal solution of system’s deterministic Euler–Lagrange equations. The reformulation aligns ‘chaos theory’ with the standard solid state, field theory, and statistical mechanics. In a spatiotemporal, crystallographer formulation, the time-periodic orbits of dynamical systems theory are replaced by periodic d-dimensional Bravais cell tilings of spacetime, each weighted by the inverse of its instability, its Hill determinant. Hyperbolic shadowing of large cells by smaller ones ensures that the predictions of the theory are dominated by the smallest Bravais cells. The form of the partition function of a given field theory is determined by the group of its spatiotemporal symmetries, that is, by the space group of its lattice discretization, best studied on its reciprocal lattice. Already one-dimensional lattice discretization is of sufficient interest to be the focus of this paper. In particular, from a spatiotemporal field theory perspective, ‘time’-reversal is a purely crystallographic notion, a reflection point group, leading to a novel, symmetry quotienting perspective of time-reversible theories and associated topological zeta functions.

A Spatiotemporal Perspective on the 31 May 2013 Tornado Evacuation in the Oklahoma City Metropolitan Area

Abstract On 31 May 2013, an extremely large and violent tornado hit near the town of El Reno, Oklahoma, a small town in the Oklahoma City metropolitan area. The size and intensity of this tornado, coupled with the fact that it was heading toward Oklahoma City, prompted local broadcasters to warn residents to evacuate their homes and head south if they could not shelter belowground. This warning led to a large-scale evacuation of the metropolitan area and massive traffic jams on the interstates and major highways that could have caused casualties in the hundreds if the tornado had not dissipated before reaching Oklahoma City. The focus of this study was to understand the magnitude of the 31 May 2013 evacuation through the evaluation of traffic volume data and to determine how frequently such evacuations occur in Oklahoma City and other metropolitan areas. We found that of the six metropolitan areas tested, only Oklahoma City had mass anomalous traffic reversal (ATR) days (days with a mass evacuation signal) with 31 May 2013 having the largest mass ATR day by far. Despite the rarity of mass ATR days, the potential consequences of a large, violent tornado hitting gridlocked traffic is significant, and we recommend that communicators encourage more local sheltering options. Significance Statement On the evening of 31 May 2013, a large-scale evacuation of the Oklahoma City metropolitan area occurred as a result of a very large and dangerous tornado that had formed near the town of El Reno and was moving east toward Oklahoma City. If the tornado had not dissipated before it reached the city it could have caused hundreds of casualties as it passed over gridlocked roads. We sought to understand the frequency of such mass evacuations and found that no other event in six metropolitan areas studied during 2011–18 could compare. While such evacuations fortunately appear rare, more work should be done to understand why they happen when they do and to connect individuals with better local sheltering options.

Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership

Identifying the determinants of metro ridership is essential for metro planning and passenger flow management. However, few studies to date have empirically examined how accessibility affects metro ridership and even fewer have emphasized the non-linear impacts from a spatiotemporal perspective. This study demarcates station areas via the network-distance method and precisely quantifies the accessibility of metro stations both internally and externally. This is combined with a gradient boosting regression trees (GBRT) model and a Shapley additive explanations (SHAP) model to understand the non-linear impacts of accessibility on metro ridership from a spatiotemporal perspective. The results show that accessibility indicators collectively contribute more than 60% of the predictive power for metro ridership at different times and the external accessibility has a greater impact on metro ridership than internal accessibility. Some indicators, such as the shortest path and population density show threshold effects on metro ridership. More importantly, the results demonstrate significant spatial heterogeneity in the effects of accessibility indicators on metro ridership and geographic trends generally from urban to suburban areas. The findings are expected to help planning departments and transit agencies improve the coordinated development of metro systems.

Toward healthy and liveable cities: a new framework linking public health to urbanization

Urbanization and public health are becoming intertwined together with global natural and social changes. As recommended by the Tsinghua-Lancet Commission on Health Cities in China, cities hold the key to a healthy China. However, China is facing enormous health risks and challenges stemming from rapid and unhealthy urbanization patterns, which are still dominated by centralized planning for large-scale spatial extension and industrial production facilities. We propose a new conceptual framework to explain the evolving relationship between urbanization and public health, from a spatiotemporal perspective. Healthy China should be given a strong focus on healthy urbanization, wellbeing, and sustainable development. To achieve this objective, we explore the potentials of the concept and approaches of liveable cities to complement the deficiencies of the current centralized planning approach of urbanization. Furthermore, to make the concept of Healthy China operational for policy and urban planning, we propose a set of indicators that can be applied together with the spatiotemporal framework to assess the health and livability of cities.

Understanding the impact of the built environment on ride-hailing from a spatio-temporal perspective: A fine-scale empirical study from China

The explosive growth of ride-hailing as a new mode of transportation worldwide has attracted great attention from both academic and planning communities. Ride-hailing services have induced profound changes in residential travel patterns. However, the mechanisms and spatio-temporal roles of the influence the built environment imposes on ride-hailing services remain unclear. Based on ride-hailing GPS data and Web map big data, in this study, spatio-temporal entropy indicators of ride-hailing travel behavior and fine-scaled built environment indicators are constructed. Based on these, global and local regression models are developed to explore the spatio-temporal impact mechanisms of various built environments and socio-economic factors on ride-hailing travel in Chengdu, China. A strong correlation is found between various types of built environment and the spatio-temporal entropy of ride-hailing travel. Specifically, whether working on weekdays or weekends, building density and education facilities impose a significant inhibitory effect on ride-hailing spatio-temporal entropy, while mixed land use, house prices, catering, corporate businesses, lifestyle facilities, tourist attractions, and distance to transit significantly increase ride-hailing spatio-temporal entropy. In addition, the uncertain geographic context and the differences in residents' activities on different days of the week cause significant spatio-temporal heterogeneity in the impact the built environment has on ride-hailing spatio-temporal entropy. Noteworthy, mixed land use exerts the strongest promoting effect on ride-hailing and a positive effect on ride-hailing pick-ups and drop-offs in the whole region. The intensity of this effect tends to decay gradually from the urban center to the periphery. Furthermore, ride-hailing can both substitute for and complement public transport. More interestingly, the study also found an “olive-shaped” social structure between the socio-economic level and the demand for ride-hailing. This study thus not only enriches the theory of spatial behavior within the context of the new era, the results also help planners to reduce car travel dependence by optimizing the built environment. This holds important practical significance for sustainable urban development.

Environmental and air quality based impacts of COVID-19 on some countries around the globe: a spatiotemporal perspective

Coronavirus disease (COVID-19) is a novel pandemic disease and was first identified in Wuhan, China, at the end of 2019. It has affected the whole world in a short period of time and declared as a sixth global health emergency as it disturbed routine of human being and all life activities. This study presents a review focusing on ongoing pandemic as a disaster that provided a way of opportunity for change towards potentially positive and negative impacts on the environment. The observed positive aspect for environment in the context of short time impacts are improvement in air quality due to reduction in greenhouse gas emission, noise, nitrogen dioxide, and particulate matter < = 2.5 μm experienced in countries including India, Pakistan, China, USA, Italy, France, Spain, and other European countries. The observed negative impacts include organic waste generation and its mismanagement due to lockdown, non-functioning of waste recycling plants, municipal activities, and medical waste of materials used for personnel care protection. The study is explored through image data available from European Space Agency and National Aeronautical Space Agency to assess positive and negative impacts on environment. With available environmental observations, this review aims to assess impacts of current pandemic on environment focusing on some major Asian, European countries and USA. The present approach enabled determining changes with positive and negative effects on the environment. The observed assessments provide spatiotemporal based support for theoretical perspective that pandemic disasters can provide a way of opportunity for positive change in environment with slight negative environmental change.

The Spatiotemporal Characteristics and Interactions between Urban Expansion and Tidal Flat Dynamics: A Case Study of Three Highly Urbanized Coastal Counties in the Southeastern United States

Tidal flats are widely recognized as sentinels of coastal environment change, and are also the guardians for beachfront communities. As a result of urban expansion, tidal flats have increasingly received environmental pressures and the surrounding ecosystem has been functionally downgraded. However, the existing studies could not provide an effective method to identify and quantify the interactions between urban areas and tidal flats, which is essential work particularly for the coastal preservations in the United States. Aiming at this environmental crisis, we proposed an approach which quantifies the change patterns from a spatiotemporal perspective. To justify the rationality and feasibility of this approach, this study selected three highly urbanized coastal counties in the southeastern United States as the study area. We analyzed the annual dynamics during 1985~2015, and the generated spatiotemporal regularities were used to identify and quantify the correlations between urban expansion and tidal flat dynamics. This study not only justified that the coastal urban expansion could considerably damage the environment of tidal flats, but also verified an effective approach to investigate the correlations between urban expansion and tidal flat loss on a large spatiotemporal scale.

A Spatiotemporal Graph Neural Network for session-based recommendation

Session-based recommendation is a challenging task dedicating to predict the user’s next item of interest based on a given sequence of anonymous behaviors. Most existing methods aim to model items’ temporal and spatial relationships, including recurrent neural network-based and graph neural network-based approaches. However, they have stagnated, expressed inadequacy, and rarely considered user behavior patterns hidden behind items that were common in the click process. In this paper, we propose a Spatiotemporal Graph Neural Network (SGNN) model for the session-based recommendation, which can effectively simulate the users’ behavior patterns from a spatiotemporal perspective to determine the next click. The proposed SGNN model mainly consists of two modules: a Spatiotemporal Session Graph (SSG) module and a Preference-aware AttentioN (PAN) module. In the SSG module, to simulate the possible behavior patterns of users, we model all session sequences as directed and weighted sequences. Then, these session sequences are integrated into a spatial graph, followed by an auto-regressive moving average convolution filter, thus availably assessing the time-decay of user preference. In the PAN module, considering that each item is not equally important to the user’s next click on the item, first of all, we divide the session into global behavioral interest and current interest, where the global behavioral interests fully capture the overall preferences in the session. Furthermore, we use the last clicked item in the session as the current interest to enhance the prediction of the next click behavior. Extensive experiments on two public datasets show that the proposed SGNN model consistently outperforms the comparative methods interns of accuracy and performance.

Spatiotemporal assessment of potential drivers of salt marsh dieback in the North Inlet-Winyah Bay estuary, South Carolina (1990–2019)

Previous studies in the Gulf of Mexico and Atlantic states have suggested that a suite of possibly abiotic and biotic attributes is responsible for salt marsh dieback, e.g., drought, soil waterlogging, soil chemistry, top-down consumers control, etc. However, there are no conclusive answers in current literature explaining what led to marsh dieback in past decades, especially from the spatiotemporal perspective. Exploring all Landsat-retrieved marsh dieback events in 1990–2019, this research investigates the spatiotemporal relationships between the dieback series and the associated environmental variables in an intertidal marsh in South Carolina (SC). Based on our previous study, a series of marsh dieback events in the past 30 years were identified and dieback pixels in the estuary were extracted. Among these were the most severe marsh dieback events (1991, 1999, 2000, 2002, 2004, and 2013). Daily Evaporative Demand Drought Index (EDDI), daily precipitation data from Parameter Elevation Regressions on Independent Slopes Model (PRISM), and station-based water quality observations (dissolved oxygen, specific conductivity, salinity, turbidity, pH, and temperature) in the estuary were retrieved. Integrated with the proof-by-exhaustion method, statistical analysis showed marsh dieback were highly related to moisture imbalance in a period of 90 days before the dieback events. Respectively, pH for Clambank and Debidue Creek, salinity and turbidity for Thousand Acre were found to be the key water quality variables influencing marsh dieback besides drought. This study cogitates the environmental influence on coastal marsh dieback from a spatiotemporal perspective using a long-term satellite time series analysis. The findings could provide insights into marsh ecological resilience and facilitate coastal ecosystem management.

The impact of anti-COVID-19 nonpharmaceutical interventions on hand, foot, and mouth disease-A spatiotemporal perspective in Xi'an, northwestern China.

Growing evidence has shown that anti‐COVID‐19 nonpharmaceutical interventions (NPIs) can support prevention and control of various infectious diseases, including intestinal diseases. However, most studies focused on the short‐term mitigating impact and neglected the dynamic impact over time. This study is aimed to investigate the dynamic impact of anti‐COVID‐19 NPIs on hand, foot, and mouth disease (HFMD) over time in Xi'an City, northwestern China. Based on the surveillance data of HFMD, meteorological and web search data, Bayesian Structural Time Series model and interrupted time series analysis were performed to quantitatively measure the impact of NPIs in sequent phases with different intensities and to predict the counterfactual number of HFMD cases. From 2013 to 2021, a total number of 172,898 HFMD cases were reported in Xi'an. In 2020, there appeared a significant decrease in HFMD incidence (−94.52%, 95% CI: −97.54% to −81.95%) in the first half of the year and the peak period shifted from June to October by a small margin of 6.74% compared to the previous years of 2013 to 2019. In 2021, the seasonality of HFMD incidence gradually returned to the bimodal temporal variation pattern with a significant average decline of 61.09%. In particular, the impact of NPIs on HFMD was more evident among young children (0–3 years), and the HFMD incidence reported in industrial areas had an unexpected increase of 51.71% in 2020 autumn and winter. Results suggested that both direct and indirect NPIs should be implemented as effective public health measures to reduce infectious disease and improve surveillance strategies, and HFMD incidence in Xi'an experienced a significant rebound to the previous seasonality after a prominent decline influenced by the anti‐COVID‐19 NPIs.

Susceptibility Prediction of Post-Fire Debris Flows in Xichang, China, Using a Logistic Regression Model from a Spatiotemporal Perspective

The post-fire debris flow (PFDF) is a commonly destructive hazard that may persist for several years following the wildfires. Susceptibility mapping is an effective method for mitigating hazard risk. Yet, the majority of susceptibility prediction models only focus on spatial probability in the specific period while ignoring the change associated with time. This study improves the predictive model by introducing the temporal factor. The area burned by the 30 March 2020 fire in Xichang City, China is selected as an illustrative example, and the susceptibility of the PFDF was predicted for different periods of seven months after the wildfires. 2214 hydrological response events, including 181 debris flow events and 2033 flood events from the 82 watersheds are adopted to construct the sample dataset. Seven conditioning factors consist of temporal factors and spatial factors are extracted by the remote sensing interpretation, field investigations, and in situ tests, after correlation and importance analysis. The logistic regression (LR) is adopted to establish prediction models through 10 cross-validations. The results show that the susceptibility to PFDF has significantly reduced over time. After two months of wildfire, the proportions of very low, low, moderate, high, and very high susceptibility are 1.2%, 3.7%, 24.4%, 23.2%, and 47.6%, respectively. After seven months of wildfire, the proportions of high and very high susceptibility decreased to 0, while the proportions of very low to medium susceptibility increased to 35.4%, 35.6%, and 28.1%, respectively. The reason is that the drone seeding of grass seeds and artificial planting of trees accelerated the natural recovery of vegetation and soil after the fire. This study can give insight into the evolution mechanism of PFDF over time and reflect the important influence of human activity after the wildfire.