Spatio-temporal Approach Research Articles

Spatial and spatio-temporal epidemiological approaches to inform COVID-19 surveillance and control: a systematic review of statistical and modelling methods in Africa

ObjectiveVarious studies have been published to better understand the underlying spatial and temporal dynamics of COVID-19. This review sought to identify different spatial and spatio-temporal modelling methods that have been...

A Neural Emulator for Uncertainty Estimation of Fire Propagation

Wildfire propagation is a highly stochastic process where small changes in environmental conditions (such as wind speed and direction) can lead to large changes in observed behaviour. A traditional approach to quantify uncertainty in fire-front progression is to generate probability maps via ensembles of simulations. However, use of ensembles is typically computationally expensive, which can limit the scope of uncertainty analysis. To address this, we explore the use of a spatio-temporal neural-based modelling approach to directly estimate the likelihood of fire propagation given uncertainty in input parameters. The uncertainty is represented by deliberately perturbing the input weather forecast during model training. The computational load is concentrated in the model training process, which allows larger probability spaces to be explored during deployment. Empirical evaluations indicate that the proposed model achieves comparable fire boundaries to those produced by the traditional SPARK simulation platform, with an overall Jaccard index (similarity score) of 67.4% on a set of 35 simulated fires. When compared to a related neural model (emulator) which was employed to generate probability maps via ensembles of emulated fires, the proposed approach produces competitive Jaccard similarity scores while being approximately an order of magnitude faster.

Spatiotemporal deep learning approach for estimating water content profiles in soil layers

Land subsidence associated with using natural groundwater resources for serving the growing population needs has been receiving extensive research attention in the literature over the past few decades. The water content fluctuation in the of subsurface soil layers significantly impacts the land subsidence. The key objective of this study is to predict changes in water content profiles in soil layers over a long period of time using a deep learning-based approach. A convolution neural network algorithm that is commonly used in Artificial Intelligence (AI) applications is modified in the present study for processing in-situ measurement water content profiles. The approach used in the proposed AI method has a distinct advantage for generating dynamic predictions based on the extracted spatiotemporal characteristics of the data. In addition, three different algorithms are compared with respect to time series prediction, including long-short-term memory (LSTM), multiple-layer perceptron (MLP) networks and autoregressive integrated moving average (ARIMA).

RESTEP Into the Future: Relational Spatio-Temporal Learning for Multi-Person Action Forecasting

Multi-person action forecasting is an emerging topic in the computer vision field, and it is a pivotal step toward video understanding at a semantic level. This task is difficult due to the complexity of spatial and temporal dependencies. Yet, the state- of-the-art literature does not seem to be adequately responsive to this challenge. Hence, how to better foresee the forthcoming actions per actor has to be further pursued. Toward this end, we put forth a novel RElational Spatio-TEmPoral learning approach (RESTEP) for multi-person action forecasting. Our RESTEP explores the key that inherently characterizes actions from a perspective of incorporating the spatial and temporal information in a single pass (spatio-temporal dependencies) by extending rela- tional reasoning. As a result, the RESTEP enables simultaneously predicting the actions of all actors in the scene. Our proposal significantly differs from mainstream works that heavily rely on independently processing the spatial and temporal dependencies. The proposed RESTEP first perceives a graph building upon the historical observations, then reasons the relational spatio- temporal context to extrapolate future actions. In order to augment the comprehension of individual actions that might vary over time, we further delve deeper into the essence behind this point the evolution of spatio-temporal dependencies via optimizing the corresponding mutual information. We assess the RESTEP method on the large-scale Atomic Visual Actions (AVA) dataset, Activities in Extended Videos (ActEV/VIRAT) dataset and Joint-annotated Human Motion Data Base (J-HMDB). The experimental outcomes reveal that RESTEP can introduce con- siderable improvements with respect to recent leading studies.

Spatiotemporal Edges for Arbitrarily Moving Video Classification in Protected and Sensitive Scenes

Classification of arbitrary moving objects including vehicles and human beings in a real environment (such as protected and sensitive areas) is challenging due to arbitrary deformation and directions caused by shaky camera and wind. This work aims at adopting a spatio-temporal approach for classifying arbitrarily moving objects. The intuition to propose the approach is that the behavior of the arbitrary moving objects caused by wind and shaky camera are inconsistent and unstable while for static objects, the behavior is consistent and stable. The proposed method segments foreground objects from background using the frame difference between median frame and individual frame. This step outputs several different foreground information. The method finds static and dynamic edges by subtracting Canny of foreground information from the Canny edges of respective input frames. The ratio of the number of static and dynamic edges of each frame is considered as features. The features are normalized to avoid the problems of imbalanced feature size and irrelevant features. For classification, the work uses 10-fold cross-validation to choose the number of training and testing samples and the random forest classifier is used for the final classification of frames with static objects and arbitrary movement objects. For evaluating the proposed method, we construct our own dataset, which contains video of static and arbitrarily moving objects caused by shaky camera and wind. The results on the video dataset show that the proposed method achieves the state-of-the-art performance (76% classification rate) which is 14% better than the best existing method.

Current COVID-19 Epidemic Risks in Brazil

This research uses raw clinical observational data to propose a new spatio-temporal approach for the precise prediction of the likelihood of the COVID-19 epidemic occurring at any moment in any interest Brazil state. This article presents a new bio-system reliability method that is especially appropriate for multi-regional environmental and health systems and that has been watched for a long enough time to produce a reliable long-term forecast of the likelihood of a virus outbreak. All impacted Brazilian states' daily COVID-19 recorded patient counts were taken into consideration. This research sought to benchmark a cutting-edge technique that allows for the analysis of dynamically witnessed patient numbers while accounting for pertinent regional mapping. The suggested strategy might assist in keeping track of and forecasting potential pandemic breakouts in a wide range of multi-regional biological systems.

App-ExploraCítricos, a digital development for integrate pest surveillance in citrus crops

App-ExploraCitricos v2.0 is an application for assessing epidemiological variables with a spatio-temporal, multicrop, multivariety-species, multivariable, multi-pest, multi-user and multi-criteria approach for risk analysis in the citrus productive chain. This work aimed to develop an application for Android® mobile devices higher than 5.0, allowing in situ evaluations associated with epidemiological or phytosanitary processes through a flexible design customized to the user's criteria. The process is initiated by logging into the application with passwords, previously registered, for authentication. Before an assessment, users are required to register only once n-pests and/or n-diseases associated with the citrus crop(s)/species concerned, providing the common and scientific name, and organism type (fungus, virus, bacterium, etc.). Entries are stored locally available for future assessments. Later, set up assessment n-scales with n-classes to quantify damage/severity. Versatility allows entry of qualitative (e.g. healthy, sick, dead) or quantitative scales (e.g. 0, <25%, 25-50% and >50%). It also enables to assess presence/absence and quantify vector if required. By customizing the assessment, choose the pest(s) concerned and associated scale. At the moment of evaluation, a plantation is parameterized by 26 epidemiological-productive variables, e.g., agronomic condition, age, crop, variety/cultivar, irrigation type, nutrition, management, etc. Assessment is performed for n-plants defined by the user. Per plant, the selected pathogen-pest(s) and scale class is assessed. Optionally, up to three geo-referenced photographs of symptoms or other aspect of concern can be taken. At the end, assessment(s) are sent to a database web platform. Assessments per planting are exported in MS-Excel for sharing via email, bluetooth, social networks or other device tools.

A Spatiotemporal Approach in Detecting and Analyzing Hydro-climatic Change in Northwest Algeria

Understanding climatic behavior, particularly that of semi-arid regions, is essential in order to optimize water resources management and provide protection from climatic risks. Water resources have great socio-economic and environmental importance. This paper focuses on the statistical analysis of the rainfall regime of northwest Algeria and estimates its spatial distribution and temporal variation. To this end, time series and principal component analysis were performed on rainfall series recorded from 1913 to 2009, representing the annual precipitation from thirty meteorological stations to discover patterns and trends in the studied region. Furthermore, the applied spectral analysis of the time series reveals the existence of a period of approximately 97 years at all stations. ArcGIS along with statistical and analytical tools like SPSS and XLSTAT were utilized in this study of the climatic behavior in northwest Algeria.

Diversity patterns of Mexican land and freshwater snails: a spatiotemporal approach

This study used a biogeographic approach to identify panbiogeographic nodes and evaluate the spatial diversity patterns of the Mexican continental gastropods. A total of 86 individual nodes and one generalized node were identified for the continental gastropods. Most grid cells showed low diversity values, except for 3 cells with diversity values greater than 10%, which can be considered hotspots. On the other hand, despite the different environments, land and freshwater snails showed similar historical and spatial distribution patterns.

Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives.

With the rapid development of the internet, the application of internet search data has been seen as a novel data source to offer timely infectious disease surveillance intelligence. Moreover, the advancements in internet search data, which include rich information at both space and time scales, enable investigators to sufficiently consider the spatiotemporal uncertainty, which can benefit researchers to better monitor infectious diseases and epidemics. In the present study, we present the necessary groundwork and critical appraisal of the use of internet search data and spatiotemporal analysis approaches in infectious disease surveillance by updating the current stage of knowledge on them. The study also provides future directions for researchers to investigate the combination of internet search data with the spatiotemporal analysis in infectious disease surveillance. Internet search data demonstrate a promising potential to offer timely epidemic intelligence, which can be seen as the prerequisite for improving infectious disease surveillance.

Dark cities or cities of light? – sunlight amenity preservation at whole-city scale using a spatio-temporal decision support approach

Public open spaces are fundamentally important for the health and well-being of citizens in densely populated cities. If not carefully planned, high-density urban development can overshadow adjacent open spaces, resulting in poor quality, dark and oppressive winter conditions. Current planning control approaches for protecting light amenity in cities are often limited to simple overshadowing impact diagrams (e.g. shadows cast on the equinox at 9 a.m., 12 p.m. and 3 p.m.). In cities transitioning from low to higher density, comprising more complex urban forms and more extreme seasonal light amenity dynamics, these static approaches are insufficient. This paper outlines the development of a spatio-temporal design decision support system for analysing and protecting the light amenity of public open spaces, applied to a capital city in Australia. The system described has two parts: Firstly, to assess the overshadowing of existing public open spaces and identify those in need of protection (Part A), and secondly to generate planning restrictions to protect designated open spaces from future deprivation of light (Part B). For Part A, we use a graphics processing unit accelerated aggregate-shadow (15 minute increments) calculation applied to a detailed city-wide 3D model generated from billions of points of aerial survey (LiDAR) data. For Part B, we use a reversed solar ray casting approach we call the ‘Subtracto-Sun’ which allows a user to specify a time range (e.g. 9 a.m.–4 p.m.) for multiple days of the year and subsequent generation of 3D maximum building height development envelopes. The output of this system was used by the local government to inform a proposed planning policy amendment for the City of Melbourne. The findings illustrate the potential for urban professionals to use the system to rapidly assess shadow impacts for existing and proposed, contextually accurate, large, complex urban environments with high levels of geometric and temporal details. The presented results are significant in that we develop and apply our spatio-temporal decision-support approach to a local government area, successfully informing planning height restriction decisions to protect daylight amenity of public open spaces in need of protection. Our method for setting development height restrictions allows for higher density to be achieved, while not increasing the overshadowing of critical open space infrastructure during designated times.

Detection of SARS-CoV-2 infection clusters: The useful combination of spatiotemporal clustering and genomic analyses.

The need for effective public health surveillance systems to track virus spread for targeted interventions was highlighted during the COVID-19 pandemic. It spurred an interest in the use of spatiotemporal clustering and genomic analyses to identify high-risk areas and track the spread of the SARS-CoV-2 virus. However, these two approaches are rarely combined in surveillance systems to complement each one's limitations; spatiotemporal clustering approaches usually consider only one source of virus transmission (i.e., the residential setting) to detect case clusters, while genomic studies require significant resources and processing time that can delay decision-making. Here, we clarify the differences and possible synergies of these two approaches in the context of infectious disease surveillance systems by investigating to what extent geographically-defined clusters are confirmed as transmission clusters based on genome sequences, and how genomic-based analyses can improve the epidemiological investigations associated with spatiotemporal cluster detection. For this purpose, we sequenced the SARS-CoV-2 genomes of 172 cases that were part of a collection of spatiotemporal clusters found in a Swiss state (Vaud) during the first epidemic wave. We subsequently examined intra-cluster genetic similarities and spatiotemporal distributions across virus genotypes. Our results suggest that the congruence between the two approaches might depend on geographic features of the area (rural/urban) and epidemic context (e.g., lockdown). We also identified two potential superspreading events that started from cases in the main urban area of the state, leading to smaller spreading events in neighboring regions, as well as a large spreading in a geographically-isolated area. These superspreading events were characterized by specific mutations assumed to originate from Mulhouse and Milan, respectively. Our analyses propose synergistic benefits of using two complementary approaches in public health surveillance, saving resources and improving surveillance efficiency.

The spatial and temporal reconstruction of a medieval moat ecosystem

Moats and other historical water features had great importance for past societies. The functioning of these ecosystems can now only be retrieved through palaeoecological studies. Here we aimed to reconstruct the history of a stronghold’s moat during its period of operation. Our spatio-temporal approach allowed mapping of the habitat changes within a medieval moat for the first time. Using data from four cores of organic deposits taken within the moat system, we describe ecological states of the moat based on subfossil Chironomidae and Ceratopogonidae assemblages. We found that over half (57%) of the identified dipteran taxa were indicative of one of the following ecological states: limnetic conditions with or without periodic water inflow, or marshy conditions. Samples representing conditions unfavourable for aquatic insects were grouped in a separate cluster. Analyses revealed that the spatio-temporal distribution of midge assemblages depended mostly on depth differences and freshwater supply from an artificial channel. Paludification and terrestrialization did not happen simultaneously across the moat system, being greatly influenced by human activity. The results presented here demonstrate the importance of a multi-aspect approach in environmental archaeology, focusing not only on the human environment, but also on the complex ecology of the past ecosystems.

Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification

High-spatial-resolution (HSR) images and high-temporal-resolution (HTR) images have their unique advantages and can be replenished by each other effectively. For land cover classification, a series of spatiotemporal fusion algorithms were developed to acquire a high-resolution land cover map. The fusion processes focused on the single level, especially the pixel level, could ignore the different phenology changes and land cover changes. Based on Bayesian decision theory, this paper proposes a novel decision-level fusion for multisensor data to classify the land cover. The proposed Bayesian fusion (PBF) combines the classification accuracy of results and the class allocation uncertainty of classifiers in the estimation of conditional probability, which consider the detailed spectral information as well as the various phenology information. To deal with the scale inconsistency problem at the decision level, an object layer and an area factor are employed for unifying the spatial resolution of distinct images, which would be applied for evaluating the classification uncertainty related to the conditional probability inference. The approach was verified on two cases to obtain the HSR land cover maps, in comparison with the implementation of two single-source classification methods and the benchmark fusion methods. Analyses and comparisons of the different classification results showed that PBF outperformed the best performance. The overall accuracy of PBF for two cases rose by an average of 27.8% compared with two single-source classifications, and an average of 13.6% compared with two fusion classifications. This analysis indicated the validity of the proposed method for a large area of complex surfaces, demonstrating the high potential for land cover classification.

Spatiotemporal model based on transformer for bias correction and temporal downscaling of forecasts

Numerical weather prediction (NWP) provides the future state of the atmosphere and is a major tool for weather forecasting. However, NWP has inevitable errors and requires bias correction to obtain more accurate forecasts. NWP is based on discrete numerical calculations, which inevitably result in a loss in resolution, and downscaling provides important support for obtaining detailed weather forecasts. In this paper, based on the spatio-temporal modeling approach, the Spatio-Temporal Transformer U-Net (ST-UNet) is constructed based on the U-net framework using the swin transformer and convolution to perform bias correction and temporal downscaling. The encoder part extracts features from the multi-time forecasts, and the decoder part uses the features from the encoder part and the constructed query vector for feature reconstruction. Besides, the query builder block generates different query vectors to accomplish different tasks. Multi-time bias correction was conducted for the 2-m temperature and the 10-m wind component. The results showed that the deep learning model significantly outperformed the anomaly numerical correction with observations, and ST-UNet also outperformed the U-Net model for single-time bias correction and the 3-dimensional U-Net (3D-UNet) model for multi-time bias correction. Forecasts from ST-UNet obtained the smallest root mean square error and the largest accuracy and correlation coefficient on both the 2-m temperature and 10-m wind component experiments. Meanwhile, temporal downscaling was performed to obtain hourly forecasts based on ST-UNet, which increased the temporal resolution and reduced the root mean square error by 0.78 compared to the original forecasts. Therefore, our proposed model can be applied to both bias correction and temporal downscaling tasks and achieve good accuracy.

Bayesian spatio-temporal modelling of child anemia in Ethiopia using conditional autoregressive model

Anemia is a common health problem for women and under five children in low income countries. According to the WHO, anemia is considered a serious public health problem when the prevalence is greater than 40%. The prevalence of anemia among children under five in Ethiopia changes over time, and is spatially correlated because it is influenced by environmental, socio-economic and other related factors. However, to our knowledge, there is no small area level estimates of anemia among children under five in Ethiopia. Therefore, this study aimed to assess zonal level estimates of anemia using a Bayesian spatio-temporal conditional autoregressive modeling approach. The data for the study was extracted from the Ethiopian Demographic and Health Surveys (EDHS) from 2005 to 2016. A sample of 18,939 children aged 6–59 months were considered for this study. A Bayesian spatio-temporal conditional autoregressive model was implemented to identify the risk of child anemia. Smoothed relative risks along with the 95% credible interval were reported. The queen’s adjacency matrix method was used in spatial smoothing and in estimating the relative risk. The prevalence of anemia among children aged 6–59 months in Ethiopia was 54% in 2005, 44% in 2011 and 57% in 2016. This study showed that low maternal education, low socio-economic status of women, and maternal anemia at zone level were strongly associated with child anemia in Ethiopia. Therefore, enhancing education for women, improving women's socioeconomic status, and mitigating maternal anemia are crucial to reduce the prevalence of childhood anemia in Ethiopia.

Detection of potential gas accumulations in 2D seismic images using spatio-temporal, PSO, and convolutional LSTM approaches

Seismic reflection is one of the most widely used geophysical methods in the oil and gas (O&G) industry for hydrocarbon prospecting. In particular, for some Brazilian onshore fields, this method has been used to estimate the location and volume of gas accumulations. However, the analysis and interpretation of seismic data are time-consuming due to the large amount of information and noisy nature of the acquisitions. To help geoscientists with these tasks, computational tools based on machine learning have been proposed considering direct hydrocarbon indicators. In this study, we present a methodology for detecting gas accumulation based on the convolutional long short-term memory model and particle swarm optimization scheme. In the best scenario, the proposed method achieved an F1-score of 84.22%, sensitivity of 98.06%, specificity of 99.44%, and accuracy of 99.42%. We present tests performed on the Parnaíba Basin, indicating that the proposed method is promising for gas exploration.

BIM-based analysis of construction safety tracking using behavior-based safety in Bangladeshi construction industry

PurposeIn comparison to other industries, the construction industry is one of the most dangerous industries. Behavior-based safety (BBS) is a common and useful technique for risk indicator processing. Almost all studies are based on the BBS checklist, but very few of them focus on the increasing dangers faced by construction workers and the important factors that lead to accidents. This research represents a risk spatiotemporal analysis and visual tracking approach based on BBS and Building Information Modeling (BIM).Design/methodology/approachAfter the literature review, a BBS checklist was developed. Then a survey was conducted based on the BBS checklist and the temporal evolution of risks has been completed. After that, managing the risk with the automatic rule checking (ARC) system using BIM was conducted simultaneously to develop a framework by conducting a case study.FindingsBased on the grey clustering analysis, this work provides a temporal evolution analysis approach for dynamic analyzing BBS risk. According to the grey relational analysis (GRA) data, the main key factor of risk was the missing guardrail/handrail system. After that, a case study was performed and the system automatically warn in the preconstruction phase that the barrier is missing as the system benefits.Originality/valueA systematic framework has been provided for risk analysis through which high health and safety performance outcomes can be achieved on construction projects. This study will assist design engineers in addressing the potential danger to employees during the preconstruction stage and monitoring dynamic changes in risk on any construction site.

Spatiotemporal Accessibility of COVID-19 Healthcare Facilities in Jakarta, Indonesia

During the first year of the COVID-19 pandemic in Jakarta, Indonesia, the government designated some hospitals as specific COVID-19 healthcare centers to meet demand and ensure accessibility. However, the policy demand evaluation was based on a purely spatial approach. Studies on accessibility to healthcare are widely available, but those that consider temporal as well as spatial dynamics are lacking. This study aims to analyze the spatiotemporal dynamics of healthcare accessibility against COVID-19 cases within the first year of the COVID-19 pandemic, and the overall pattern of spatiotemporal accessibility. A two-step floating catchment area (2SFCA) was used to analyze the accessibility of COVID-19 healthcare against the monthly data of the COVID-19 infected population, as the demand. Such a spatiotemporal approach to 2SFCA has never been used in previous studies. Furthermore, rather than the traditional buffer commonly used to define catchments, the 2SFCA in this study was improved with automated delineation based on the road network using ArcGIS Service Areas Analysis tools. The accessibility tends to follow the distance decay principle, which is relatively high in the city’s center and low in the outskirts. This contrasts with the city’s population distribution, which is higher on the outskirts and lower in the center. This research is a step toward optimizing the spatial distribution of hospital locations to correspond with the severity of the pandemic condition. One method to stop the transmission of disease during a pandemic that requires localizing the infected patient is to designate specific healthcare facilities to manage the sick individuals. ‘What-if’ scenarios may be used to experiment with the locations of these healthcare facilities, which are then assessed using the methodology described in this work to obtain the distribution that is most optimal.

A Spatio-Temporal Track Association Algorithm Based on Marine Vessel Automatic Identification System Data

Tracking multiple moving objects in real-time in a dynamic threat environment is an important element in national security and surveillance system. It helps pinpoint and distinguish potential candidates posing threats from other normal objects and monitor the anomalous trajectories until intervention. To locate the anomalous pattern of movements, one needs to have an accurate data association algorithm that can associate the sequential observations of locations and motion with the underlying moving objects, and therefore, build the trajectories of the objects as the objects are moving. In this work, we develop a spatio-temporal approach for tracking maritime vessels as the vessel’s location and motion observations are collected by an Automatic Identification System. The proposed approach is developed as an effort to address a data association challenge in which the number of vessels as well as the vessel identification are purposely withheld and time gaps are created in the datasets to mimic the real-life operational complexities under a threat environment. Three training datasets and five test sets are provided in the challenge and a set of quantitative performance metrics is devised by the data challenge organizer for evaluating and comparing resulting methods developed by participants. When our proposed track association algorithm is applied to the five test sets, the algorithm scores a very competitive performance.