Spatiotemporal Outlier Detection Research Articles

Long term exposure to ambient air particulate matter and mortality effects in Megacity of Tehran, Iran: 2012–2017

Ambient particulate matter (PM) is the major environmental concern in Tehran, Iran. Exposure to such particles is a serious global health concern and substantial evidence indicates that exposure to particulate matter contributes to cardiovascular and respiratory disease. Here, we provide a detailed health impact of ambient air pollution in Tehran by linking PM pollution with mortality caused by cardiovascular and respiratory disease. Air quality data of PM obtained from Tehran Air Quality Monitoring Stations (TAQMSs) during 2012–2017. Data mining methods conducted by Spatio-temporal outlier detection and “Air Pollution and Health: a European Approach (APHEA) project” was applied to impute missing values. Time-series data were analyzed to estimate the associations between PM pollution and cause-specific mortality at different lags in Tehran. Based on descriptive analysis, PM2.5 is expected to be a major influencing factor in the increased cardiovascular-related death (CVD) and respiratory dysfunction related death (RD) deaths. The cross-correlation analysis showed the same source for PM2.5 and PM10 in the ambient air of Tehran. Also, the results indicated delays of about 7–15 days from the increased concentrations of PMs and the observed rising in CVD and RD death. Trend analysis showed a more decreasing trend for PM2.5 pollutions than PM10 pollution and a slightly increasing trend for cause-specific deaths during 2012–2017. Long term exposure to high PM levels increases the CVD and RD deaths and control strategies along with control regulations should be considered to control PM.

A spatiotemporal outlier detection method based on partial least squares discriminant analysis and area Delaunay triangulation for image-based process monitoring

ABSTRACTOver the past two decades, statistical process control has evolved from monitoring individual data points to linear profiles to image data. Image sensors are now being deployed in complex systems at increasing rates due to the rich information they can provide. As a result, image data play an important role in process monitoring in different application domains ranging from manufacturing to service systems. Many of the existing process monitoring methods fail to take full advantage of the image data due to the data's complex nature in both the spatial and temporal domains. This article proposes a spatiotemporal outlier detection method based on the partial least squares discriminant analysis and a control statistic based on the area Delaunay triangulation of the squared prediction errors to improve the performance of an image-based monitoring scheme. First, the discriminant analysis of the partial least squares is used to efficiently extract the most important features from the high-dimensional image data to identify the benchmark images of the products and obtain the pixel value errors. Next, the squared errors resulting from the previous step are connected using a Delaunay triangulation to form a surface, the area of which is used as the control statistic for the purpose of outlier detection. A real case study at a paper product manufacturing company is used to compare the performance of the proposed method in detecting different types of outliers with some of the existing methods and demonstrate the merit of the proposed method.

Spatio-temporal outlier detection algorithms based on computing behavioral outlierness factor

A major task in spatio-temporal outlier detection is to identify objects that exhibit abnormal behavior either spatially, and/or temporally. There have only been a few algorithms proposed for detecting spatial and/or temporal outliers. One example is the Local Density-Based Spatial Clustering of Applications with Noise (LDBSCAN). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is mainly for clustering; it just tells us whether an object belongs to a cluster or it is an outlier. A measure known as Local Outlier Factor (LOF) gives a quantitative measure of outlierness to each object, where a high LOF score means it is potentially an outlier. LDBSCAN algorithm, which combines the above notions, considers only the spatial context. Furthermore, the notion of a cluster is defeated (i.e. LDBSCAN may report clusters having less than the minimum required points in a cluster), and some of the outliers may not be detected because of the limitation of the existing conditions in the LDBSCAN algorithm. In this paper, we propose two algorithms, namely Spatio-Temporal Behavioral Density-based Clustering of Applications with Noise (ST-BDBCAN) and Approx-ST-BDBCAN. ST-BDBCAN algorithm adopts the proposed, new concept, called Spatio-Temporal Behavioral Outlier Factor (ST-BOF), which is a spatio-temporal extension to LOF. It also uses both spatial and temporal attributes simultaneously to define the context. By doing so, the relative importance of spatial continuity or temporal continuity appropriate to the application at hand can be established. The Approx-ST-BDBCAN algorithm achieves improved scalability, with minimal loss of detection accuracy by partitioning data points for parallel processing. Experimental results on synthetic, and buoy datasets suggest that our proposed algorithms are accurate and computationally efficient. Additionally, new Outlier Association with Hurricane Intensity Index (OAHII) measures are introduced for quantitative evaluation of the results from buoy dataset.

Spatiotemporal Outlier Detection: Did Buoys Tell Where the Hurricanes Were?

ABSTRACTData points that exhibit abnormal behavior, either spatially, temporally, or both, are considered spatiotemporal outliers. Spatiotemporal outlier detection is important for the discovery of exceptional events due to the rapidly increasing amount of spatiotemporal data available, and the need to understand such data. A tropical cyclone system or a hurricane can be considered an abnormal activity of the atmosphere system. Discovery of such an abnormality usually leverages data from a satellite or radar. Not many people have thought about using a weather buoy, a floating device that provides meteorological and environmental information in real time for open ocean and coastal zones. The aim of this research is to see if a spatiotemporal outlier approach can help to discover the evolution and movement of the hurricane system from weather buoy observations. This article leverages an algorithm, spatiotemporal local density-based clustering of applications with noise (ST-LDBCAN), which has been developed and used by the authors to detect outliers in various scenarios. The ST-LDBCAN has a novel way of defining spatiotemporal context and can handle multivariate data, which is its advantage over existing algorithms. The results show a good correlation between detected spatiotemporal outliers and the paths and evolution of Hurricanes Katrina and Gustav.

A fuzzy index for detecting spatiotemporal outliers

The detection of spatial outliers helps extract important and valuable information from large spatial datasets. Most of the existing work in outlier detection views the condition of being an outlier as a binary property. However, for many scenarios, it is more meaningful to assign a degree of being an outlier to each object. The temporal dimension should also be taken into consideration. In this paper, we formally introduce a new notion of spatial outliers. We discuss the spatiotemporal outlier detection problem, and we design a methodology to discover these outliers effectively. We introduce a new index called the fuzzy outlier index, FoI, which expresses the degree to which a spatial object belongs to a spatiotemporal neighbourhood. The proposed outlier detection method can be applied to phenomena evolving over time, such as moving objects, pedestrian modelling or credit card fraud.