Spatiotemporal Autocorrelation Research Articles

Estimation of hourly PM1 concentration in China and its application in population exposure analysis.

Particulate pollution is closely related to public health. PM1 (particles with an aerodynamic size not larger than 1μm) is much more harmful than particles with larger sizes because it goes deeper into the body and hence arouses social concern. However, the sparse and unevenly distributed ground-based observations limit the understanding of spatio-temporal distributions of PM1 in China. In this study, hourly PM1 concentrations in central and eastern China were retrieved based on a random forest model using hourly aerosol optical depth (AOD) from Himawari-8, meteorological and geographic information as inputs. Here the spatiotemporal autocorrelation of PM1 was also considered in the model. Experimental results indicate that although the performance of the proposed model shows diurnal, seasonal and spatial variations, it is relatively better than others, with a determination coefficient (R2) of 0.83 calculated based on the 10-fold cross validation method. Geographical map implies that PM1 pollution level in Beijing-Tianjin-Hebei is much higher than in other regions, with the mean value of ∼55μg/m3. Based on the exposure analysis, we found about 75% of the population lives in an environment with PM1 higher than 35μg/m3 in the whole study area. The retrieval dataset in this study is of great significance for further exploring the impact of PM1 on public health.

Multi-aspect renewable energy forecasting

The increasing presence of renewable energy plants has created new challenges such as grid integration, load balancing and energy trading, making it fundamental to provide effective prediction models. Recent approaches in the literature have shown that exploiting spatio-temporal autocorrelation in data coming from multiple plants can lead to better predictions. Although tensor models and techniques are suitable to deal with spatio-temporal data, they have received little attention in the energy domain. In this paper, we propose a new method based on the Tucker tensor decomposition, capable of extracting a new feature space for the learning task. For evaluation purposes, we have investigated the performance of predictive clustering trees with the new feature space, compared to the original feature space, in three renewable energy datasets. The results are favorable for the proposed method, also when compared with state-of-the-art algorithms.

Criteria for acceptable studies of animal personality and behavioural syndromes

Criteria for acceptable studies of animal personality and behavioural syndromes

Deep Multi-view Representation Learning for Video Anomaly Detection Using Spatiotemporal Autoencoders

Visual perception is a transformative technology that can recognize patterns from environments through visual inputs. Automatic surveillance of human activities has gained significant importance in both public and private spaces. It is often difficult to understand the complex dynamics of events in real-time scenarios due to camera movements, cluttered backgrounds, and occlusion. Existing anomaly detection systems are not efficient because of high intra-class variations and inter-class similarities existing among activities. Hence, there is a demand to explore different kinds of information extracted from surveillance videos to improve overall performance. This can be achieved by learning features from multiple forms (views) of the given raw input data. We propose two novel methods based on the multi-view representation learning framework. The first approach is a hybrid multi-view representation learning that combines deep features extracted from 3D spatiotemporal autoencoder (3D-STAE) and robust handcrafted features based on spatiotemporal autocorrelation of gradients. The second approach is a deep multi-view representation learning that combines deep features extracted from two-stream STAEs to detect anomalies. Results on three standard benchmark datasets, namely Avenue, Live Videos, and BEHAVE, show that the proposed multi-view representations modeled with one-class SVM perform significantly better than most of the recent state-of-the-art methods.

Tracking and Seismological Analysis of Multiple Coronal Loops in an Active Region

Abstract We present a new method to track the position and evolution of coronal loops designed for observations such as active regions in which multiple loops appear in close proximity or overlap with each other along the observational line of sight. The method is based on modeling a time–distance map containing one or more loops and fitting the modeled map to observational data, as opposed to the commonly used technique of analyzing each frame independently. This allows us to control the variability of the model, informed by our physical interpretation, and use the trends present to help constrain the model parameters. We apply our method to an observation of a bundle of coronal loops previously investigated using a spatiotemporal autocorrelation method and compare our results. A benefit of our method is that it provides the time series for the position of the loops that may be used for further analysis using established seismological techniques. We demonstrate this by modeling the oscillation of several loops in response to flaring energy releases that occur during the observation, and we find evidence of loop evolution consistent with the Kelvin–Helmholtz instability.

Is between-farm water-borne pathogen dissemination an important driver in the epidemiology of salmonid rickettsial septicaemia in Chile?

Although the bacterium Piscirickettsia salmonis has been detected in many salmon-producing countries around the world, losses caused by salmonid rickettsial septicaemia (SRS) are mostly occurring in the Chilean aquaculture industry. Horizontal transmission of SRS between salmonid farms was suggested, based on the existence of spatiotemporal correlation in the level of disease between neighbouring sea farms. However, it remains unclear to which extent between-farm water-borne pathogen dissemination is important in the epidemiology of SRS in Chile. Such information is critical to assess the level of risk of transmission of SRS from one farm to another at different mortality incidence levels and to apply appropriate and cost-effective mitigation measures. In this study, we used weekly SRS mortality data from all salmonid farms in the Los Lagos region between January 2012 and September 2018 to model the spatiotemporal autocorrelation in the SRS-attributed mortality in the study area. A generalized additive regression modelling framework was adopted, using a linear functional component to model the influence of other farms on the target farm. Several nested statistical models were built to compare the significance of different covariates. Predicted values of SRS mortality on the target farm, conditional on different distance, time lag and mortality values from the source farms were estimated from the best model. The results showed that there was a statistically significant association between the weekly mortality incidence at source farms and the mortality incidence at target farms during the same week and during the previous weeks. This study did not provide evidence that the spatiotemporal correlation observed in SRS mortality may be due to water-borne pathogen dissemination between farms and alternative explanatory mechanisms should be investigated. It remains possible that the patterns of lagged correlation observed between source and target farm mortality may be due to a model artefact. In addition, there was no evidence of a threshold effect above which farms pose a substantially larger health risk to their neighbours. Stronger evidence for or against between-farm transmission of P. salmonis may be obtained by different methods.

Toward Measuring the Level of Spatiotemporal Clustering of Multi-Categorical Geographic Events

Human activity events are often recorded with their geographic locations and temporal stamps, which form spatial patterns of the events during individual time periods. Temporal attributes of these events help us understand the evolution of spatial processes over time. A challenge that researchers still face is that existing methods tend to treat all events as the same when evaluating the spatiotemporal pattern of events that have different properties. This article suggests a method for assessing the level of spatiotemporal clustering or spatiotemporal autocorrelation that may exist in a set of human activity events when they are associated with different categorical attributes. This method extends the Voronoi structure from 2D to 3D and integrates a sliding-window model as an approach to spatiotemporal tessellations of a space-time volume defined by a study area and time period. Furthermore, an index was developed to evaluate the partial spatiotemporal clustering level of one of the two event categories against the other category. The proposed method was applied to simulated data and a real-world dataset as a case study. Experimental results show that the method effectively measures the level of spatiotemporal clustering patterns among human activity events of multiple categories. The method can be applied to the analysis of large volumes of human activity events because of its computational efficiency.

Autocorrelation of gradients based violence detection in surveillance videos

Abstract Automated monitoring of videos is becoming mandatory due to its widespread applications over public and private domains. Especially, research over detecting anomalous human behavior in crowded scenes has created much attention among computer vision researchers. Understanding patterns in crowded scenes is always challenging due to the rapid movement of the crowd, occlusions and cluttered backgrounds. In this work, we explore spatio-temporal autocorrelation of gradient-based features to efficiently recognize violent activities in crowded scenes. A discriminative classifier is then used to recognize violent actions in videos. Experimental results have shown improved performance of the proposed approach when compared to existing state-of-art-approaches.

A systematic review of changing malaria disease burden in sub-Saharan Africa since 2000: comparing model predictions and empirical observations

BackgroundThe most widely used measures of declining burden of malaria across sub-Saharan Africa are predictions from geospatial models. These models apply spatiotemporal autocorrelations and covariates to parasite prevalence data and then use a function of parasite prevalence to predict clinical malaria incidence. We attempted to assess whether trends in malaria cases, based on local surveillance, were similar to those captured by Malaria Atlas Project (MAP) incidence surfaces.MethodsWe undertook a systematic review (PROSPERO International Prospective Register of Systematic Reviews; ID = CRD42019116834) to identify empirical data on clinical malaria in Africa since 2000, where reports covered at least 5 continuous years. The trends in empirical data were then compared with the trends of time-space matched clinical malaria incidence from MAP using the Spearman rank correlation. The correlations (rho) between changes in empirically observed and modelled estimates of clinical malaria were displayed by forest plots and examined by meta-regression.ResultsSixty-seven articles met our inclusion criteria representing 124 sites from 24 African countries. The single most important factor explaining the correlation between empirical observations and modelled predictions was the slope of empirically observed data over time (rho = − 0.989; 95% CI − 0.998, − 0.939; p < 0.001), i.e. steeper declines were associated with a stronger correlation between empirical observations and modelled predictions. Factors such as quality of study, reported measure of malaria and endemicity were only slightly predictive of such correlations.ConclusionsIn many locations, both local surveillance data and modelled estimates showed declines in malaria burden and hence similar trends. However, there was a weak association between individual surveillance datasets and the modelled predictions where stalling in progress or resurgence of malaria burden was empirically observed. Surveillance data were patchy, indicating a need for improved surveillance to strengthen both empiric reporting and modelled predictions.

Characterizing Construction Demand Surge Using Spatial Panel Data Models

The socioeconomic losses in the aftermath of large-scale natural disasters have increased dramatically over the last few decades. The postdisaster survival of cities and communities depend on their capabilities to reconstruct and repair damage to buildings and other infrastructure systems following large-scale natural disasters. The significant postdisaster increases in the repair costs, also referred to as demand surge, slows down the repair process and lengthens the recovery time for many touched by large-scale natural disasters. The objective of this research was to explore spatiotemporal relationships in modeling construction demand surge using spatial panel data models. We obtained the data of predisaster construction economic indicators from the Bureau of Labor Statistics (BLS). These indicators had been shown to affect the postdisaster demand surge through cross-sectional studies. The results of this study showed that a positive spatial autocorrelation among the values of the dependent variable exists. In other words, an increase (decrease) in the construction labor wage in a county will lead to an increase (decrease) in the construction labor wage in neighboring counties. The results of this study also showed that spatiotemporal autocorrelations exist among the values of the dependent variable (labor wage change) and the values of error terms across the neighboring counties (counties within 100 km distance from each other). It is expected that the results of this study and the methodology used to create spatial paned data model (SPDM) demand surge models will help demand surge modelers create more accurate models.

Estimating hourly and continuous ground-level PM2.5 concentrations using an ensemble learning algorithm: The ST-stacking model

Estimation of hourly and continuous ground-level fine particulate matter (PM2.5) concentrations is essential for PM2.5 pollution sources identifications, targeted policy development and population exposure research. However, current PM2.5 estimation studies rely heavily on satellite-based aerosol optical depth (AOD) data, and the limited transit times of polar-orbiting satellites such as Terra and Aqua, nighttime gaps in data from geostationary satellites such as Himawari-8, and cloud contamination reported for both types of satellites challenge the estimation of spatiotemporally continuous PM2.5 concentrations. In this study, spatiotemporal PM2.5 characteristic was constructed by the spatiotemporal fusion method. Specifically, multi-source data, including spatiotemporal, periodic, meteorological, vegetation, anthropogenic and topological characteristics, were incorporated into an ensemble learning method that combined extreme gradient boosting (XGBoost), k-nearest neighbour (KNN) and back-propagation neural network (BPNN) algorithms in level 1 and used linear regression (LR) for integration in level 2. The optimized stacking strategy that considered PM2.5 spatiotemporal autocorrelation was called the ST-stacking model. The model was trained, validated and tested with data acquired for China in 2017. The ST-stacking model outperformed XGBoost, KNN and BPNN models by 9.27% on average, with an R2 = 0.9191. Using the model, the 24-h and continuous ground-level PM2.5 concentrations in mainland China on 11 May 2017 were mapped, and parts of Beijing and Chengdu were selected for more detailed analysis. The PM2.5 concentrations in Taklimakan Desert, North China Plain, Sichuan Basin and Yangtze Plain were much higher than those in other locations on this day, which was generally consistent with the long-term patterns reported in previous studies.

Incorporating Smart Card Data in Spatio-Temporal Analysis of Metro Travel Distances

The primary objective of this study is to explore spatio-temporal effects of the built environment on station-based travel distances through large-scale data processing. Previous studies mainly used global models in the causal analysis, but spatial and temporal autocorrelation and heterogeneity issues among research zones have not been sufficiently addressed. A framework integrating geographically and temporally weighted regression (GTWR) and the Shannon entropy index (SEI) was thus proposed to investigate the spatio-temporal relationship between travel behaviors and built environment. An empirical study was conducted in Nanjing, China, by incorporating smart card data with metro route data and built environment data. Comparative results show GTWR had a better performance of goodness-of-fit and achieved more accurate predictions, compared to traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR). The spatio-temporal relationship between travel distances and built environment was further analyzed by visualizing the average variation of local coefficients distributions. Effects of built environment variables on metro travel distances were heterogeneous over space and time. Non-commuting activity and exurban area generally had more influences on the heterogeneity of travel distances. The proposed framework can address the issue of spatio-temporal autocorrelation and enhance our understanding of impacts of built environment on travel behaviors, which provides useful guidance for transit agencies and planning departments to implement targeted investment policies and enhance public transit services.

Energy and spin diffusion in the one-dimensional classical Heisenberg spin chain at finite and infinite temperatures.

The energy and spin diffusion behaviors in the one-dimensional classical Heisenberg spin chain have been systematically investigated using the equilibrium diffusion method. The spatiotemporal autocorrelation functions for energy and spin are calculated at finite and infinite temperatures. As conserved quantities, the spreading of excess energy and spin can be used to determine their actual diffusion behaviors. At low temperatures, the energy diffusion shows almost ballistic behavior, and spin shows superdiffusion behavior for finite chain size. For energy diffusion, normal diffusion behavior can be obtained when the temperature is higher than 0.75. For spin diffusion, normal diffusion behavior is observed at infinite temperature.

A Spatio-Temporal Bayesian Model for Estimating the Effects of Land Use Change on Urban Heat Island

The urban heat island (UHI) phenomenon has been identified and studied for over two centuries. As one of the most important factors, land use, in terms of both composition and configuration, strongly influences the UHI. As a result of the availability of detailed data, the modeling of the residual spatio-temporal autocorrelation of UHI, which remains after the land use effects have been removed, becomes possible. In this study, this key statistical problem is tackled by a spatio-temporal Bayesian hierarchical model (BHM). As one of the hottest areas in China, southwest China is chosen as our study area. Results from this study show that the difference of UHI levels between different cities in southwest China becomes large from 2000 to 2015. The variation of the UHI level is dominantly driven by temporal autocorrelation, rather than spatial autocorrelation. Compared with the composition of land use, the configuration has relatively minor influence upon UHI, due to the terrain in the study area. Furthermore, among all land use types, the water body is the most important UHI mitigation factor at the regional scale.

Spatiotemporal clusters of HIV/AIDS infections caused by drug use and heterosexual contact in Ruili city, China 1989\u20132016

BackgroundRuili is a border city in southwest China along the heroin trafficking route. In recent decades, the city has witnessed increased in HIV transmission. The current study aims to explore the spatiotemporal trends in HIV prevalence identify and map the spatial variation and clustering of factors associated with HIV transmission through drug use and heterosexual contact transmissions at the village level from 1989 through 2016.MethodsGeographic information system-based spatiotemporal analyses, including global and local spatial autocorrelation analyses and space-time scanning statistics, were applied to detect the location and extent of HIV/AIDS high-risk areas.ResultsDrug use and heterosexual contact were identified as the major transmission routes causing infection in Ruili. Results of global spatial analysis showed significant clustering throughout the city caused by transmission via drug use in the early phase of the epidemic and transmission via heterosexual contact in the late phase of the epidemic during the study period. Hotspots of transmission from drug use were randomly distributed throughout the city. However, the hotspots of transmission by heterosexual contact were located in the central area only around the Jiegao China-Myanmar land port. Space-time scanning showed that transmission from drug use clustered in the southwest area between 1989 and 1990, while transmission by heterosexual contact clustered in the central area between 2004 and 2014.ConclusionsHeterosexual contact has become the dominant mode of transmission. Interventions should focus on highly clustered area where is around the Jiegao land port.

Spatiotemporal Dengue Disease Clustering by Means Local Spatiotemporal Moran’s Index

Spatiotemporal analysis has been used widely to explain some geographic phenomenon, especially in an epidemiology study. Spatial and temporal autocorrelation coefficients are usually used to assess the spatial and temporal dependencies in set geographic events. However, those statistics are usually computed separately and may lead to the misleading conclusion. Analysing spatiotemporal autocorrelation would be useful to understand the geographical evolution simultaneously. Spatiotemporal autocorrelation can be used to identify the spatiotemporal clustering and outlier via local spatiotemporal autocorrelation. This paper develops a method to estimate and test the local spatiotemporal autocorrelation based on the local spatial Moran’s Index. Randomization permutation test is used to obtain the p-value which is used to construct the disease clustering. The method was applied to identify the spatiotemporal clustering and outlier detection for dengue disease data in Bandung city. Based on image analysis, this method presents the better result compare than the local spatial Moran’s Index which is done for every time separately.

Spatio-temporal regression kriging for modelling urban NO2 concentrations

ABSTRACT Recently developed urban air quality sensor networks are used to monitor air pollutant concentrations at a fine spatial and temporal resolution. The measurements are however limited to point support. To obtain areal coverage in space and time, interpolation is required. A spatio-temporal regression kriging approach was applied to predict nitrogen dioxide (NO2) concentrations at unobserved space-time locations in the city of Eindhoven, the Netherlands. Prediction maps were created at 25 m spatial resolution and hourly temporal resolution. In regression kriging, the trend is separately modelled from autocorrelation in the residuals. The trend part of the model, consisting of a set of spatial and temporal covariates, was able to explain 49.2% of the spatio-temporal variability in NO2 concentrations in Eindhoven in November 2016. Spatio-temporal autocorrelation in the residuals was modelled by fitting a sum-metric spatio-temporal variogram model, adding smoothness to the prediction maps. The accuracy of the predictions was assessed using leave-one-out cross-validation, resulting in a Root Mean Square Error of 9.91 μg m−3, a Mean Error of −0.03 μg m−3 and a Mean Absolute Error of 7.29 μg m−3. The method allows for easy prediction and visualization of air pollutant concentrations and can be extended to a near real-time procedure.

A New Analysis Procedure for Detecting Periodicities within Complex Solar Coronal Arcades

Abstract We study intensity variations, as measured by the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory, in a solar coronal arcade using a newly developed analysis procedure that employs spatio-temporal autocorrelations. We test our new procedure by studying large-amplitude oscillations excited by nearby flaring activity within a complex arcade and detect a dominant periodicity of 12.31 minutes. We compute this period in two ways: from the traditional time–distance fitting method and using our new autocorrelation procedure. The two analyses yield consistent results. The autocorrelation procedure is then implemented on time series for which the traditional method would fail due to the complexity of overlapping loops and a poor contrast between the loops and the background. Using this new procedure, we discover the presence of small-amplitude oscillations within the same arcade with 9.13 and 9.81 minute periods prior and subsequent to the large-amplitude oscillations, respectively. Consequently, we identify these as “decayless” oscillations that have only been previously observed in nonflaring loop systems.

Measuring spatio-temporal autocorrelation in time series data of collective human mobility

ABSTRACTMassive spatio-temporal big data about human mobility have become increasingly available. Revealing underlying dynamic patterns from these data is essential for understanding people’s behavior and urban deployment. Spatio-temporal autocorrelation analysis is an exploratory approach to recognizing data distribution in space and time. The most widely used spatial autocorrelation measurements, such as Moran’s I and local indicators of spatial association (LISA), only apply to static data, so are powerless to spatio-temporal big data about human mobility. Thus, we proposed a new method by extending Moran’s I to measure the spatial autocorrelation of time series data. Then the method was applied to taxi ride data in Beijing, China to reveal the spatial pattern of collective human mobility. The result shows that there is strong positive spatio-temporal autocorrelation within the 5th Ring Road, weak negative spatio-temporal autocorrelation nearby the Sixth Ring Road, and almost no spatio-temporal autocorrelation between the roads. Local spatial patterns of taxi travel were also recognized. This method is useful for discovering underlying patterns from spatio-temporal big data to understand human mobility.

A Regional Approach to Assessing and Visualizing Spatiotemporal Clustering of Crime Events

Examining the levels of both the spatial and the temporal clustering of crime events raises the analysis of events as spatial patterns at different time periods to understanding them as spatial processes. There have been, however, only limited efforts in doing so, possibly due to a lack of effective methods for estimating, identifying the difference in the levels of spatial clustering patterns, and visualizing the spatiotemporal trends among the events. We first describe a method for examining the periodicity of space–time crime events. Two indexes are extended from the Moran’s index for spatial autocorrelation and local indicator of spatial association (LISA) to ones for including spatiotemporal event-region data. We also suggest a three-dimensional approach to visualizing spatiotemporal associations among event regions that enables a better understanding of the spatial processes behind the evolution of event regions. An implementation of the suggested methods that use data of three types of crime events in Akron, Ohio, is reported. The suggested approach is capable of summarizing spatiotemporal autocorrelation as well as identifying spatiotemporal clusters of crime events. Compared to layered LISA, this approach works well, and it allows three-dimensional visualizations of spatiotemporal clusters of events that can help to develop effective policing strategies for combating crimes.