Analysis Of Spatio-temporal Data Research Articles

Single feature spatio-temporal architecture for EEG Based cognitive load assessment.

The study of electroencephalography (EEG) data for cognitive load analysis plays an important role in identification of stress-inducing tasks. This can be useful in applications such as optimal work allocation, increasing efficiency in the workplace and ensuring safety in difficult work environments. In order for such systems to be realistically deployable, easy acquisition and processing of the data on a wearable device is imperative. Current techniques primarily perform offline processing to analyse a multi-channel EEG to make a post facto assessment. This work focusses on building a new deep learning architecture that performs a single feature based spatio-temporal analysis of EEG data. This is achieved by creating a brain topographic map based on a single feature followed by spatio-temporal analysis using the developed network architecture. Data from two cognitive load experiments on the Physionet EEGMAT dataset were used to validate the performance. The network achieves an accuracy of 98.3% which is better than similar state-of-the-art approaches. Moreover, the proposed approach facilitates analysis of the spatial propagation of a signal, which is not possible through conventional EEG signal representations.

Spatiotemporal variability analysis of air pollution data from IoT based participatory sensing

Spatiotemporal variability analysis of air pollution data from IoT based participatory sensing

Life expectancy and risk of death in 6791 communities in England from 2002 to 2019: high-resolution spatiotemporal analysis of civil registration data

SummaryBackgroundHigh-resolution data for how mortality and longevity have changed in England, UK are scarce. We aimed to estimate trends from 2002 to 2019 in life expectancy and probabilities of death at different ages for all 6791 middle-layer super output areas (MSOAs) in England.MethodsWe performed a high-resolution spatiotemporal analysis of civil registration data from the UK Small Area Health Statistics Unit research database using de-identified data for all deaths in England from 2002 to 2019, with information on age, sex, and MSOA of residence, and population counts by age, sex, and MSOA. We used a Bayesian hierarchical model to obtain estimates of age-specific death rates by sharing information across age groups, MSOAs, and years. We used life table methods to calculate life expectancy at birth and probabilities of death in different ages by sex and MSOA.FindingsIn 2002–06 and 2006–10, all but a few (0–1%) MSOAs had a life expectancy increase for female and male sexes. In 2010–14, female life expectancy decreased in 351 (5·2%) of 6791 MSOAs. By 2014–19, the number of MSOAs with declining life expectancy was 1270 (18·7%) for women and 784 (11·5%) for men. The life expectancy increase from 2002 to 2019 was smaller in MSOAs where life expectancy had been lower in 2002 (mostly northern urban MSOAs), and larger in MSOAs where life expectancy had been higher in 2002 (mostly MSOAs in and around London). As a result of these trends, the gap between the first and 99th percentiles of MSOA life expectancy for women increased from 10·7 years (95% credible interval 10·4–10·9) in 2002 to reach 14·2 years (13·9–14·5) in 2019, and for men increased from 11·5 years (11·3–11·7) in 2002 to 13·6 years (13·4–13·9) in 2019.InterpretationIn the decade before the COVID-19 pandemic, life expectancy declined in increasing numbers of communities in England. To ensure that this trend does not continue or worsen, there is a need for pro-equity economic and social policies, and greater investment in public health and health care throughout the entire country.FundingWellcome Trust, Imperial College London, Medical Research Council, Health Data Research UK, and National Institutes of Health Research.

Ensemble machine learning methods for spatio-temporal data analysis of plant and ratoon sugarcane

Recent technological enhancements in the field of information technology and statistical techniques allowed the sophisticated and reliable analysis based on machine learning methods. A number of machine learning data analytical tools may be exploited for the classification and regression problems. These tools and techniques can be effectively used for the highly data-intensive operations such as agricultural and meteorological applications, bioinformatics and stock market analysis based on the daily prices of the market. Machine learning ensemble methods such as Decision Tree (C5.0), Classification and Regression (CART), Gradient Boosting Machine (GBM) and Random Forest (RF) has been investigated in the proposed work. The proposed work demonstrates that temporal variations in the spectral data and computational efficiency of machine learning methods may be effectively used for the discrimination of types of sugarcane. The discrimination has been considered as a binary classification problem to segregate ratoon from plantation sugarcane. Variable importance selection based on Mean Decrease in Accuracy (MDA) and Mean Decrease in Gini (MDG) have been used to create the appropriate dataset for the classification. The performance of the binary classification model based on RF is the best in all the possible combination of input images. Feature selection based on MDA and MDG measures of RF is also important for the dimensionality reduction. It has been observed that RF model performed best with 97% accuracy, whereas the performance of GBM method is the lowest. Binary classification based on the remotely sensed data can be effectively handled using random forest method.

Novel Intelligent Spatiotemporal Grid Earthquake Early-Warning Model

The integration analysis of multi-type geospatial information poses challenges to existing spatiotemporal data organization models and analysis models based on deep learning. For earthquake early warning, this study proposes a novel intelligent spatiotemporal grid model based on GeoSOT (SGMG-EEW) for feature fusion of multi-type geospatial data. This model includes a seismic grid sample model (SGSM) and a spatiotemporal grid model based on a three-dimensional group convolution neural network (3DGCNN-SGM). The SGSM solves the problem concerning that the layers of different data types cannot form an ensemble with a consistent data structure and transforms the grid representation of data into grid samples for deep learning. The 3DGCNN-SGM is the first application of group convolution in the deep learning of multi-source geographic information data. It avoids direct superposition calculation of data between different layers, which may negatively affect the deep learning analysis model results. In this study, taking the atmospheric temperature anomaly and historical earthquake precursory data from Japan as an example, an earthquake early warning verification experiment was conducted based on the proposed SGMG-EEW. Five groups of control experiments were designed, namely with the use of atmospheric temperature anomaly data only, use of historical earthquake data only, a non-group convolution control group, a support vector machine control group, and a seismic statistical analysis control group. The results showed that the proposed SGSM is not only compatible with the expression of a single type of spatiotemporal data but can also support multiple types of spatiotemporal data, forming a deep-learning-oriented data structure. Compared with the traditional deep learning model, the proposed 3DGCNN-SGM is more suitable for the integration analysis of multiple types of spatiotemporal data.

Spatio-temporal analysis of rainfall data in Kuala Krai Kelantan

This paper interpreted spatio-temporally the relationship between event of weather activities and flooding in Kuala Krai Kelantan. During the Kelantan big yellow flood event in December 2014, Kuala Krai was one of the worst affected area with the rarity of the return period approximately 1 in 1000 years. Many say this is due to the unusual occurrence of extreme rainfall. Therefore, this research work on spatio-temporal analysis of the time series rainfall data in Kuala Krai Kelantan, to describe and determine the behaviour and pattern of the data. The data on the monthly amount of rainfall between (2013-2019), were collected from Department Irrigation and Drainage (DID). The main aim of this study is to statistically determine whether it is true the flooding in Kuala Krai Kelantan on 2014 is because of heavy rainfall. The collected rainfall data had been spatially analysed using ArcGIS to compare the distribution of rainfall from year 2013 to 2019. Then, the trend pattern of the data had been determined using Mann Kendall Test and Sen’s slope Test. From those analysis it can be concluded that the huge amount of rainfall intensity is the major factor that contribute the extreme flood event in Kuala Krai Kelantan on December 2014.

Understanding spatial propagation using metric geometry with application to the spread of COVID-19 in the United States

This paper introduces a novel approach to spatio-temporal data analysis using metric geometry to study the propagation of COVID-19 across the United States. Using a geodesic Wasserstein metric, we analyse discrepancies between the density functions of new case counts on any given day, incorporating the geographic spread of cases. First, we apply this to identify the periods during which the changes in the geographic distribution of COVID-19 were most profound. The greatest shift occurred between May and June of 2020, when COVID-19 shifted from mostly dominating the Northeastern states to a wider distribution across the country. We support our findings with a new measure of the extent of geodesic variance of a distribution, demonstrating that the geographic imprint of COVID-19 was most concentrated in May 2020. Next, we investigate whether the epidemic exhibited meaningful patterns of spatial reversion, where similar geographic distributions return later. We identify broad similarity between the spread of COVID-19 across the US between the second and third waves, and to a lesser extent, the reemergence of the first wave's Northeastern dominance closer to the present day. This methodology could provide new insights for analysts to monitor the dynamical spread of epidemics and enable regional policymakers to protect their localities. More broadly, the framework we introduce could be applied to a variety of problems evolving over space and time.

Spatiotemporal representation learning for rescue route selection: An optimized regularization based method

Abstract Emergency medical services (EMS) are emergency services that provide urgent pre-hospital treatment for serious illness and injuries. However, in most countries, EMS is faced with the problem of untimely emergency response. In this paper, we develop an Optimized Regularization based framework (OpRe-RRS) by optimizing the Rescue Route Selection problem to increase the rescue speed. Specifically, through the analysis of spatio-temporal data, we predict the ranking of road priority and select the rescue route for ambulances to lift speed. Along this line, we match the GPS data of ambulances to the correct road section through a map matching algorithm. Then, we extract different features from three perspectives: (i) basic features, (ii) POI features and (iii) traffic features. To effectively exploit the roads similarity, we develop a loss function with regularization to solve this prediction problem. Finally, experiments on real-world data demonstrate that our method can effectively reduce rescue time.

Identifying spatiotemporal transit deserts in Seoul, South Korea

Transit deserts can result from the inequitable distribution of resources and services, and people living in transit deserts have limited access to transportation system. The aim of this study was to perform spatiotemporal data analysis to identify transit desert areas in Seoul in three steps. First, the transit gap between peak and off-peak hours is evaluated and various spatial changes in each temporal transit desert area are identified. Second, a spatial analysis is conducted to identify transit desert and transit oasis areas that emerge during peak hours. Lastly, an independent T-test is conducted to identify how the socio-economic characteristics of transit deserts and transit oases are statistically different. Our analysis show that, transit deserts changed across space and over the time in Seoul. Furthermore, transit deserts in general appear to be associated with socio-economically vulnerable residential areas.

Spatio-temporal analysis of yield and weather data for defining site-specific crop management zones

Spatio-temporal analysis of yield and weather data for defining site-specific crop management zones

MDST-DBSCAN: A Density-Based Clustering Method for Multidimensional Spatiotemporal Data

The increasing use of mobile devices and the growing popularity of location-based ser-vices have generated massive spatiotemporal data over the last several years. While it provides new opportunities to enhance our understanding of various urban dynamics, it poses challenges at the same time due to the complex structure and large-volume characteristic of the spatiotemporal data. To facilitate the process and analysis of such spatiotemporal data, various data mining and clustering methods have been proposed, but there still needs to develop a more flexible and computationally efficient method. The purpose of this paper is to present a clustering method that can work with large-scale, multidimensional spatiotemporal data in a reliable and efficient manner. The proposed method, called MDST-DBSCAN, is applied to idealized patterns and a real data set, and the results from both examples demonstrate that it can identify clusters accurately within a reasonable amount of time. MDST-DBSCAN performs well on both spatial and spatiotemporal data, and it can be particularly useful for exploring massive spatiotemporal data, such as detailed real estate transactions data in Seoul, Korea.

Research on The Effectiveness of Trajectory Similarity Measurement Algorithm

Under the background of big data era, massive trajectory data is constantly generated, which contains great value. The analysis and mining of spatiotemporal trajectory data is a research hotspot of spatial data, including trajectory retrieval, trajectory classification etc. In the process of analysis and mining, the similarity measurement between different trajectories is a key problem. Considering the amount of data, computational complexity, noise and other factors, different metrics need to be selected in different situations. In order to understand the performance of different similarity measurement algorithms, this paper selects the Longest Common Sub-Sequence Algorithm (LCSS), Fréchet Distance Algorithm and One Way Distance Algorithm (OWD) among the three kinds of trajectory similarity measurement algorithms and they were used for comparison. Experimental results show that LCSS algorithm can effectively resist the interference of noise points, Fréchet Distance Algorithm has strong robustness, and OWD Algorithm has low time complexity and short execution time.

Linear-Cost Covariance Functions for Gaussian Random Fields

Gaussian random fields (GRF) are a fundamental stochastic model for spatiotemporal data analysis. An essential ingredient of GRF is the covariance function that characterizes the joint Gaussian distribution of the field. Commonly used covariance functions give rise to fully dense and unstructured covariance matrices, for which required calculations are notoriously expensive to carry out for large data. In this work, we propose a construction of covariance functions that result in matrices with a hierarchical structure. Empowered by matrix algorithms that scale linearly with the matrix dimension, the hierarchical structure is proved to be efficient for a variety of random field computations, including sampling, kriging, and likelihood evaluation. Specifically, with n scattered sites, sampling and likelihood evaluation has an O(n) cost and kriging has an cost after preprocessing, particularly favorable for the kriging of an extremely large number of sites (e.g., predicting on more sites than observed). We demonstrate comprehensive numerical experiments to show the use of the constructed covariance functions and their appealing computation time. Numerical examples on a laptop include simulated data of size up to one million, as well as a climate data product with over two million observations.

Inductive Graph Neural Networks for Spatiotemporal Kriging

Time series forecasting and spatiotemporal kriging are the two most important tasks in spatiotemporal data analysis. Recent research on graph neural networks has made substantial progress in time series forecasting, while little attention has been paid to the kriging problem---recovering signals for unsampled locations/sensors. Most existing scalable kriging methods (e.g., matrix/tensor completion) are transductive, and thus full retraining is required when we have a new sensor to interpolate. In this paper, we develop an Inductive Graph Neural Network Kriging (IGNNK) model to recover data for unsampled sensors on a network/graph structure. To generalize the effect of distance and reachability, we generate random subgraphs as samples and the corresponding adjacency matrix for each sample. By reconstructing all signals on each sample subgraph, IGNNK can effectively learn the spatial message passing mechanism. Empirical results on several real-world spatiotemporal datasets demonstrate the effectiveness of our model. In addition, we also find that the learned model can be successfully transferred to the same type of kriging tasks on an unseen dataset. Our results show that: 1) GNN is an efficient and effective tool for spatial kriging; 2) inductive GNNs can be trained using dynamic adjacency matrices; 3) a trained model can be transferred to new graph structures and 4) IGNNK can be used to generate virtual sensors.

METHOD OF MULTI-MODAL VIDEO ANALYSIS OF HAND MOVEMENTS FOR AUTOMATIC RECOGNITION OF ISOLATED SIGNS OF RUSSIAN SIGN LANGUAGE

Abstract. This paper presents a new method for collecting multimodal sign language (SL) databases, which is distinguished by the use of multimodal video data. The paper also proposes a new method of multimodal sign recognition, which is distinguished by the analysis of spatio-temporal visual features of SL units (i.e. lexemes). Generally, gesture recognition is a processing of a video sequence, which helps to extract information on movements of any articulator (a part of the human body) in time and space. With this approach, the recognition accuracy of isolated signs was 88.92%. The proposed method, due to the extraction and analysis of spatio-temporal data, makes it possible to identify more informative features of signs, which leads to an increase in the accuracy of SL recognition.

Testing the first-order separability hypothesis for spatio-temporal point patterns

First-order separability of a spatio-temporal point process plays a fundamental role in the analysis of spatio-temporal point pattern data. While it is often a convenient assumption that simplifies the analysis greatly, existing non-separable structures should be accounted for in the model construction. Three different tests are proposed to investigate this hypothesis as a step of preliminary data analysis. The first two tests are exact or asymptotically exact for Poisson processes. The first test based on permutations and global envelopes allows one to detect at which spatial and temporal locations or lags the data deviate from the null hypothesis. The second test is a simple and computationally cheap χ2-test. The third test is based on stochastic reconstruction method and can be generally applied for non-Poisson processes. The performance of the first two tests is studied in a simulation study for Poisson and non-Poisson models. The third test is applied to the real data of the UK 2001 epidemic foot and mouth disease.1

Bootstrap joint prediction regions for sequences of missing values in spatio-temporal datasets

Missing data reconstruction is a critical step in the analysis and mining of spatio-temporal data. However, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. To take into account the uncertainty in the point forecast, some prediction intervals may be of interest. In particular, for (possibly long) missing sequences of consecutive time points, joint prediction regions are desirable. In this paper we propose a bootstrap resampling scheme to construct joint prediction regions that approximately contain missing paths of a time components in a spatio-temporal framework, with global probability 1-alpha . In many applications, considering the coverage of the whole missing sample-path might appear too restrictive. To perceive more informative inference, we also derive smaller joint prediction regions that only contain all elements of missing paths up to a small number k of them with probability 1-alpha . A simulation experiment is performed to validate the empirical performance of the proposed joint bootstrap prediction and to compare it with some alternative procedures based on a simple nominal coverage correction, loosely inspired by the Bonferroni approach, which are expected to work well standard scenarios.

Classification of Gaussian spatio-temporal data with stationary separable covariances

The novel approach to classification of spatio-temporal data based on Bayes discriminant functions is developed. We focus on the problem of supervised classifying of the spatiotemporal Gaussian random field (GRF) observation into one of two classes specified by different drift parameters, separable nonlinear covariance functions and nonstationary label field. The performance of proposed classification rule is validated by the values of local Bayes and empirical error rates realized by leave one out procedure. A simulation study for spatial covariance functions belonging to powered-exponential family and temporal covariance functions of AR(1) models is carried out. The influence of the values of spatial and temporal covariance parameters to error rates for several label field models are studied. The results showed that the proposed classification methodology can be applied successfully in practice with small error rates and can be a useful tool for discriminant analysis of spatio-temporal data.

Estimation of spatio-temporal extreme distribution using a quantile factor model

This paper describes the estimation of the extreme spatio-temporal sea surface temperature data based on the quantile factor model implemented by the SNU multiscale team. The proposed method was developed for the EVA2019 Data Challenge. Various attempts have been conducted to use factor models in spatio-temporal data analysis to find hidden factors in high-dimensional data. Factor models represent high-dimensional data as a linear combination of several factors, and hence, can describe spatially and temporally correlated data in a simple form. Meanwhile, unlike ordinary factor models, there are asymmetric norm-based factor models, such as quantile factor models or expectile dynamic semiparametric factor models, that can help understand the quantitative behavior of data beyond their mean structure. For this purpose, we apply a quantile factor model to the data to obtain significant factors explaining the quantile response of the temperatures and find quantile estimates. We develop a new method for inference of quantiles of extremal levels by extrapolating quantile estimates from the factor model with extreme value theory. The proposed method provides better performance than the benchmark, gives some interpretable insights, and shows the potential to expand the factor model with various data.

A Literature Review of Spatio-temporal Data Analysis

In the era of massive data, people are paying more and more attention to the spatio-temporal attributes of data. Many studies have begun to explore the methods of spatio-temporal analysis and apply spatio-temporal data analysis techniques to various fields. This article surveys and summarizes many previous research results. The current situation of spatio-temporal analysis is analyzed and prospected from three aspects: spatio-temporal analysis method, spatio-temporal data model and platform construction and spatio-temporal data analysis application scenarios.