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Identification Of Temporal And Spatial Patterns In Multivariate Air Pollution Data Sets Using Cluster Analysis Methods
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Book Synopsis Identification of Temporal and Spatial Patterns in Multivariate Air Pollution Data Sets Using Cluster Analysis Methods by : Elena Austin
Download or read book Identification of Temporal and Spatial Patterns in Multivariate Air Pollution Data Sets Using Cluster Analysis Methods written by Elena Austin and published by . This book was released on 2013 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Proceedings of the Conference on Environmental Modeling and Simulation, April 19-22, 1976, Cincinnati, Ohio by : Wayne R. Ott
Download or read book Proceedings of the Conference on Environmental Modeling and Simulation, April 19-22, 1976, Cincinnati, Ohio written by Wayne R. Ott and published by . This book was released on 1976 with total page 868 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book EPA-600/9 written by and published by . This book was released on 1976-07 with total page 874 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Identifying Clusters in Multivariate Temporal and Spatial Data with Application to Environmental Processes by : Karen E. Kazor
Download or read book Identifying Clusters in Multivariate Temporal and Spatial Data with Application to Environmental Processes written by Karen E. Kazor and published by . This book was released on 2016 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Air Quality Management in the United States by : National Research Council
Download or read book Air Quality Management in the United States written by National Research Council and published by National Academies Press. This book was released on 2004-08-30 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Managing the nation's air quality is a complex undertaking, involving tens of thousands of people in regulating thousands of pollution sources. The authors identify what has worked and what has not, and they offer wide-ranging recommendations for setting future priorities, making difficult choices, and increasing innovation. This new book explores how to better integrate scientific advances and new technologies into the air quality management system. The volume reviews the three-decade history of governmental efforts toward cleaner air, discussing how air quality standards are set and results measured, the design and implementation of control strategies, regulatory processes and procedures, special issues with mobile pollution sources, and more. The book looks at efforts to spur social and behavioral changes that affect air quality, the effectiveness of market-based instruments for air quality regulation, and many other aspects of the issue. Rich in technical detail, this book will be of interest to all those engaged in air quality management: scientists, engineers, industrial managers, law makers, regulators, health officials, clean-air advocates, and concerned citizens.
Book Synopsis Temporal, Spatial, and Spatio-Temporal Data Mining by : John F. Roddick
Download or read book Temporal, Spatial, and Spatio-Temporal Data Mining written by John F. Roddick and published by Springer. This book was released on 2003-06-29 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains updated versions of the ten papers presented at the First International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining (TSDM 2000) held in conjunction with the 4th European Conference on Prin- ples and Practice of Knowledge Discovery in Databases (PKDD 2000) in Lyons, France in September, 2000. The aim of the workshop was to bring together experts in the analysis of temporal and spatial data mining and knowledge discovery in temporal, spatial or spatio-temporal database systems as well as knowledge engineers and domain experts from allied disciplines. The workshop focused on research and practice of knowledge discovery from datasets containing explicit or implicit temporal, spatial or spatio-temporal information. The ten original papers in this volume represent those accepted by peer review following an international call for papers. All papers submitted were refereed by an international team of data mining researchers listed below. We would like to thank the team for their expert and useful help with this process. Following the workshop, authors were invited to amend their papers to enable the feedback received from the conference to be included in the ?nal papers appearing in this volume. A workshop report was compiled by Kathleen Hornsby which also discusses the panel session that was held.
Book Synopsis Spatial Cluster Modelling by : Andrew B. Lawson
Download or read book Spatial Cluster Modelling written by Andrew B. Lawson and published by CRC Press. This book was released on 2002-05-16 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this b
Book Synopsis Patterns Identification and Data Mining in Weather and Climate by : Abdelwaheb Hannachi
Download or read book Patterns Identification and Data Mining in Weather and Climate written by Abdelwaheb Hannachi and published by Springer Nature. This book was released on 2021-05-06 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in computer power and observing systems has led to the generation and accumulation of large scale weather & climate data begging for exploration and analysis. Pattern Identification and Data Mining in Weather and Climate presents, from different perspectives, most available, novel and conventional, approaches used to analyze multivariate time series in climate science to identify patterns of variability, teleconnections, and reduce dimensionality. The book discusses different methods to identify patterns of spatiotemporal fields. The book also presents machine learning with a particular focus on the main methods used in climate science. Applications to atmospheric and oceanographic data are also presented and discussed in most chapters. To help guide students and beginners in the field of weather & climate data analysis, basic Matlab skeleton codes are given is some chapters, complemented with a list of software links toward the end of the text. A number of technical appendices are also provided, making the text particularly suitable for didactic purposes. The topic of EOFs and associated pattern identification in space-time data sets has gone through an extraordinary fast development, both in terms of new insights and the breadth of applications. We welcome this text by Abdel Hannachi who not only has a deep insight in the field but has himself made several contributions to new developments in the last 15 years. - Huug van den Dool, Climate Prediction Center, NCEP, College Park, MD, U.S.A. Now that weather and climate science is producing ever larger and richer data sets, the topic of pattern extraction and interpretation has become an essential part. This book provides an up to date overview of the latest techniques and developments in this area. - Maarten Ambaum, Department of Meteorology, University of Reading, U.K. This nicely and expertly written book covers a lot of ground, ranging from classical linear pattern identification techniques to more modern machine learning, illustrated with examples from weather & climate science. It will be very valuable both as a tutorial for graduate and postgraduate students and as a reference text for researchers and practitioners in the field. - Frank Kwasniok, College of Engineering, University of Exeter, U.K.
Book Synopsis Spatiotemporal Analysis of Air Pollution and Its Application in Public Health by : Lixin Li
Download or read book Spatiotemporal Analysis of Air Pollution and Its Application in Public Health written by Lixin Li and published by Elsevier. This book was released on 2019-11-13 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatiotemporal Analysis of Air Pollution and Its Application in Public Health reviews, in detail, the tools needed to understand the spatial temporal distribution and trends of air pollution in the atmosphere, including how this information can be tied into the diverse amount of public health data available using accurate GIS techniques. By utilizing GIS to monitor, analyze and visualize air pollution problems, it has proven to not only be the most powerful, accurate and flexible way to understand the atmosphere, but also a great way to understand the impact air pollution has in diverse populations. This book is essential reading for novices and experts in atmospheric science, geography and any allied fields investigating air pollution. Introduces readers to the benefits and uses of geo-spatiotemporal analyses of big data to reveal new and greater understanding of the intersection of air pollution and health Ties in machine learning to improve speed and efficacy of data models Includes developing visualizations, historical data, and real-time air pollution in large geographic areas
Book Synopsis Cluster Detection and Analysis with Geo-spatial Datasets Using a Hybrid Statistical and Neural Networks Hierarchical Approach by : Salar Mustafa Majeed
Download or read book Cluster Detection and Analysis with Geo-spatial Datasets Using a Hybrid Statistical and Neural Networks Hierarchical Approach written by Salar Mustafa Majeed and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial datasets contain information relating to the locations of incidents of phenomena for example, crime and disease. Areas that contain a higher than expected incidence of the phenomena, given background population and census datasets, are of particular interest. By analysing the locations of potential influence, it may be possible to establish where a cause and effect relationship is present in the observed process. Cluster detection techniques can be applied to such datasets in order to reveal information relating to the spatial distribution of the cases. Research in these areas has mainly concentrated on either computational or statistical aspects of cluster detection. Each clustering algorithm has its own strengths and weakness. Their main weaknesses causing their unreliability can be estimating the number of clusters, testing the number of components, selecting initial seeds (centroids), running time and memory requirements. Consequently, a new cluster detection methodology has been developed in this thesis based on knowledge drawn from both statistical and computing domains. This methodology is based on a hybrid of statistical methods using properties of probability rather than distance to associate data with clusters. No previous knowledge of the dataset is required and the number of clusters is not predetermined. It performs efficiently in terms of memory requirements, running time and cluster quality. The algorithm for determining both the centre of clusters and the existence of the clusters themselves was applied and tested on simulated and real datasets. The results which were obtained from identification of hotspots were compared with results of other available algorithms such as CLAP (Cluster Location Analysis Procedure), Satscan and GAM (Geographical Analysis Machine). The outputs are very similar. XVI GIS presented in this thesis encompasses the SCS algorithm, statistics and neural networks for developing a hybrid predictive crime model, mapping, visualizing crime data and the corresponding population in the study region, visualizing the location of obtained clusters and burglary incidence concentration 'hotspots' which was specified by clustering algorithm SCS. Naturally the quality of results is subject to the accuracy of the used data. GIS is used in this thesis for developing a methodology for modelling data containing multiple functions. The census data used throughout this construction provided a useful source of geo-demographic information. The obtained datasets were used for predictive crime modelling. This thesis has benefited from several existing methodologies to develop a hybrid modelling approach. The methodology was applied to real data on burglary incidence distribution in the study region. Relevant principles of statistics, Geographical Information System, Neural Networks and SCS algorithm were utilized for the analysis of observed data. Regression analysis was used for building a predictive crime model and combined with Neural Networks with the aim of developing a new hierarchical neural Network approaches to generate a more reliable prediction. The promising results were compared with the non-hierarchical neural Network back-propagation network and multiple regression analysis. The average percentage accuracy achieved by the new methodology at testing stage increase 13% compared with the non-hierarchical BP performance. In general the analysis reveals a number of predictors that increase the risk of burglary in the study region. Specifically living in a household in which there is 'one person', 'lone parent', household where occupations are in elementary or intermediate and unemployed. For the influence of Household space, the results indicate that the risk of burglary rate increases within the household living in shared houses.
Book Synopsis Spatio-Temporal Modeling and Forecasting of Air Quality Data by : Tsz-Leung Yan
Download or read book Spatio-Temporal Modeling and Forecasting of Air Quality Data written by Tsz-Leung Yan and published by Open Dissertation Press. This book was released on 2017-01-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Spatio-temporal Modeling and Forecasting of Air Quality Data" by Tsz-leung, Yan, 甄子良, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Respirable Suspended Particulate (RSP) time series data sampled in an air quality monitoring network are found strongly correlated and they are varying in highly similar patterns. This study provides a methodology for spatio-temporal modeling and forecasting of multiple RSP time series, in which the dynamic spatial correlations amongst the series can be effectively utilized. The efficacy of the Spatio-Temporal Dynamic Harmonic Regression (STDHR) model is demonstrated. Based on the decomposition of the observed time series into the trend and periodic components, the model is capable of making forecast of RSP data series that exhibit variation patterns during air pollution episodes and typhoons with dynamic weather conditions. It is also capable to produce spatial predictions of RSP time series up to three unobserved sites. The Noise-variance-ratio (NVR) form of the multivariate recursive algorithm ((M2) algorithm) that derived by the author can greatly facilitate its practical application in both multivariate and univariate time series analysis. The (M2) algorithm allows the spatial correlations to be specified at parametric levels. The state-space (SS) model formulation can flexibly accommodate the existing inter or intra (auto) correlations amongst the parameters of the data series. Applications of the variance intervention (VI) are exploited and illustrated with a real life case study which involves forecasting of RSP data series during an air pollution episode. This illustrates that time series with abrupt changes can be predicted by automatic implementation of the VI approach. The present study also extended the anisotropic Matern model to estimate the dynamic spatial correlation structure of the air quality data by using mean wind speed and prevailing wind direction in defining the spatial anisotropy. The Anisotropic Matern model by Mean Wind Speed and Prevailing Wind Direction (AMMP) model that devised by the author can avoid huge computational burden in estimating variogram at every variation of the underlying spatial structure. Finally, the findings of this dissertation have laid the foundation for further research on multiple time series analysis and estimation of dynamic spatial structure. DOI: 10.5353/th_b5194796 Subjects: Air - Pollution - Mathematical models
Book Synopsis Time Series Clustering and Classification by : Elizabeth Ann Maharaj
Download or read book Time Series Clustering and Classification written by Elizabeth Ann Maharaj and published by CRC Press. This book was released on 2019-03-19 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including unsupervised and supervised approaches Includes a range of real examples from medicine, finance, environmental science, and more R and MATLAB code, and relevant data sets are available on a supplementary website
Book Synopsis Determining Spatial Patterns in Delhi's Ambient Air Quality Data Using Cluster Analysis by : Sumeet Saksena
Download or read book Determining Spatial Patterns in Delhi's Ambient Air Quality Data Using Cluster Analysis written by Sumeet Saksena and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Spatiotemporal Data Analytics and Modeling by : John A
Download or read book Spatiotemporal Data Analytics and Modeling written by John A and published by Springer Nature. This book was released on with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Joint Conference on Applications of Air Pollution Meteorology by :
Download or read book Joint Conference on Applications of Air Pollution Meteorology written by and published by . This book was released on 1988 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Regularized and Multi-model Methods for Detecting Spatial and Spatio-temporal Clusters with Applications in Epidemiology by : Maria Kamenetsky
Download or read book Regularized and Multi-model Methods for Detecting Spatial and Spatio-temporal Clusters with Applications in Epidemiology written by Maria Kamenetsky and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many diseases, there are geographic patterns known as spatial clusters that can indicate areas of elevated or reduced disease risk. Such areas may be indicative of an outbreak or harmful environmental exposures and identifying these clusters can help guide public health interventions. The detection of clusters has typically been approached as a large multiple testing problem, using a spatial or spatio-temporal scan statistic. We recast the spatial and spatio-temporal cluster detection problem in a high-dimensional data analytical framework with Poisson or quasi-Poisson regression with the Lasso penalty. We next extend this to case-control data using a two-step procedure to identify multiple overlapping clusters and illustrate the approach with breast cancer data from the Wisconsin Women's Health Study. We use an information-theoretic approach to select the number of clusters in each neighborhood. We include the identified clusters into a participant-level logistic regression model, allowing us to adjust for known covariates. Lastly, while standard methods are limited to identifying a single correct model, we develop an approach that stacks all single cluster models into an ensemble of models using likelihood-based weights. We calculate confidence bounds for cells inside the cluster using model-averaged tail area intervals, which we compare to several other methods using coverage and confidence bound widths. These approaches not only efficiently identify multiple overlapping clusters, but they also enable us to discern gradients of spatial risk. Our approaches detect both spatial and spatio-temporal overlapping clusters and are flexible in their application to other epidemiologic study designs.
Book Synopsis Visual Analytics for Spatiotemporal Cluster Analysis by : Yifan Zhang
Download or read book Visual Analytics for Spatiotemporal Cluster Analysis written by Yifan Zhang and published by . This book was released on 2016 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditionally, visualization is one of the most important and commonly used methods of generating insight into large scale data. Particularly for spatiotemporal data, the translation of such data into a visual form allows users to quickly see patterns, explore summaries and relate domain knowledge about underlying geographical phenomena that would not be apparent in tabular form. However, several critical challenges arise when visualizing and exploring these large spatiotemporal datasets. While, the underlying geographical component of the data lends itself well to univariate visualization in the form of traditional cartographic representations (e.g., choropleth, isopleth, dasymetric maps), as the data becomes multivariate, cartographic representations become more complex. To simplify the visual representations, analytical methods such as clustering and feature extraction are often applied as part of the classification phase. The automatic classification can then be rendered onto a map; however, one common issue in data classification is that items near a classification boundary are often mislabeled. This thesis explores methods to augment the automated spatial classification by utilizing interactive machine learning as part of the cluster creation step. First, this thesis explores the design space for spatiotemporal analysis through the development of a comprehensive data wrangling and exploratory data analysis platform. Second, this system is augmented with a novel method for evaluating the visual impact of edge cases for multivariate geographic projections. Finally, system features and functionality are demonstrated through a series of case studies, with key features including similarity analysis, multivariate clustering, and novel visual support for cluster comparison.
