4 edition of Spatiotemporal data analysis found in the catalog.
Includes bibliographical references and index.
|LC Classifications||QA278.2 .E84 2011|
|The Physical Object|
|LC Control Number||2011032275|
Gidon Eshel begins with a concise yet detailed primer on linear algebra, providing readers with the mathematical foundations needed for data analysis. He then fully explains the theory and methods for analyzing spatiotemporal data, guiding readers Brand: Princeton University Press. High daily temperatures in the Mediterranean and Europe have been documented in observation and modeling studies. Long-term temperature data, from to , from a suburban station and an urban station in Nicosia, Cyprus have been analyzed, and the diurnal temperature range (DTR) trend was investigated. The seasonal Mann–Kendall test revealed a decreasing DTR trend of − °C/decade.
Spatio-temporal Analysis of Extreme Hydrological Events offers an extensive view of the experiences and applications of the latest developments and methodologies for analyzing and understanding extreme environmental and hydrological events. The book addresses the topic using spatio-temporal methods, such as space-time geostatistics, machine learning, statistical theory, hydrological modelling. This is a book about multidimensional data analysis. Although the word “spatiotemporal” in the title makes the book sound more specialized, many of the techniques described are broadly relevant to all kinds of data analysis. The author mainly focuses on data that possess a well defined covariance matrix and have at least one dimension in.
"It is a wonderful place to begin studying spatio-temporal processes." (Mathematical Reviews Clippings, 1 January ) "Overall, I believe this academic monograph would be an excellent reference book for researchers and graduate students who are interested in a systematic and indepth understanding of statistical approaches to spatio-temporal data analysis and modeling."Reviews: The analysis of big data involves determined attempts on previous data. As new era of data has spatiotemporal facts that involve the time and space factors, which make them distinct from.
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Spatiotemporal Data Analysis 1st Edition by Gidon Eshel (Author) › Visit Amazon's Gidon Eshel Page. Find all the books, read about the author, and more. See search results for this author. Are you an author. Learn about Author Central. Gidon Eshel (Author) ISBN Cited by: 4. "Spatiotemporal Data Analysis is accessible and applicable without sacrificing rigor.
The key is a steady stream of well-chosen examples and, most unusual in any textbook, a distinctive narrative voice that guides readers through the material, explaining the details while making sure the. He then fully explains the theory and methods for analyzing spatiotemporal data, guiding readers from the basics to the most advanced applications.
This self-contained, practical guide to the analysis of multidimensional data sets features a wealth of real-world examples as well as sample homework exercises and suggested exams.
Spatiotemporal data mining has become increasingly important and has far-reaching implications, given the popularity of mobile phones, GPS devices, Internet-based map services, weather services, and digital Earth, as well as satellite, RFID, sensor, wireless, and video technologies.
Among many kinds of spatiotemporal data, moving-object data (i. Spatiotemporal Data Analysis Written for undergraduate and graduate students, Spatiotemporal Data Analysis provides comprehensive coverage to introduce readers to the statistical and algebraic methods used to analyze spatiotemporal data in a range of fields; including climate science, geophysics, ecology, astrophysics, and medicine.
Spatiotemporal Data Analysis. PRINCETON UNIVERSITY PRESS ,95 € / $ / £* Add to Cart. eBook (PDF) Free shipping for non-business customers when ordering books at De Gruyter Online. Please find details to our shipping fees here.
RRP: Recommended Retail Price. Methods of Data Analysis. Six. The Gray World of Practical. The book will be especially useful for scientists and researchers seeking a hands-on approach to statistical modeling and analysis for spatio-temporal data.
The text is organized beautifully and offers a pleasing blend of technical material and computer programs for implementing a variety of spatio-temporal models.
A simplified way to aggregate global data is used for the spatiotemporal analysis of precipitation and temperature. To carry out this analysis, the Spatio-TempoRal distribution and Interannual VarIability of projections (STRIVIng) toolbox is proposed for statistical exploratory analysis of climate projections.
A spatiotemporal database is a database that manages both space and time information. Common examples include: Tracking of moving objects, which typically can occupy only a single position at a given time. A database of wireless communication networks, which may exist only for a short timespan within a geographic region.
A network-based model for spatiotemporal data analysis. network-science pattern-recognition complex-networks graph-mining spatio-temporal-analysis chronnet Updated ; R; dbvis-ukon / movekit Star 1 Code Issues Pull requests Simple and effective tools for the analysis of movement data.
Handling and Analyzing Spatial, Spatiotemporal and. The book will be especially useful for scientists and researchers seeking a hands-on approach to statistical modeling and analysis for spatio-temporal data.
The text is organized beautifully and offers a pleasing blend of technical material and computer programs for implementing a variety of spatio-temporal models. Empirical Orthogonal Functions Introduction Data Matrix Structure Convention Reshaping Multidimensional Data Sets for EOF Analysis Forming Anomalies and Removing Time Mean Missing Values, Take 1 Choosing and Interpreting the Covariability Matrix Calculating the EOFs Missing.
“It is a wonderful place to begin studying spatio-temporal processes.” (Mathematical Reviews Clippings, 1 January )“Overall, I believe this academic monograph would be an excellent reference book for researchers and graduate students who are interested in a systematic and indepth understanding of statistical approaches to spatio-temporal data analysis and modeling.” (Journal of the.
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. *The 50% discount is offered for all e-books and e-journals purchased on IGI Global’s Online Bookstore.
E-books and e-journals are hosted on IGI Global’s InfoSci® platform and available for PDF and/or ePUB download on a perpetual or subscription basis. This discount cannot be combined with any other discount or promotional offer. Analyzing spatiotemporal data is useful for deriving statistics from the data or visualizing changes in the data over time.
ENVI allows you to build a series of images (called a raster series) for spatiotemporal analysis, then view the images incrementally. Spatiotemporal analysis involves the following steps: Build a Series; Regrid a Series.
Based on spatiotemporal data from the German Bundesliga, the six individual studies in this book present innovative mathematical approaches for game analysis and player assessment. The findings can support coaches and analysts to improve performance of their athletes and inspire other researchers to advance the research field of sports analytics.
Spatiotemporal analysis of NHS Direct syndromic data should supplement rather than replace the current analyses of regional call data and other components of the UK influenza surveillance programme. It may help to identify sub-regional variation in influenza rates that may not manifest themselves within data from sentinel networks of GPs.
The ﬁrst work which looked at using spatiotemporal data for team behavior analysis was conducted over 10 years ago by Intille and Bobick , .
In this seminal work, the authors used a probabilistic model to recognize a single football play from hand annotated player trajectories.
Since then, multiple. Based on spatiotemporal data from the German Bundesliga, the six individual studies in this book present innovative mathematical approaches for game analysis and player assessment. The findings can support coaches and analysts to improve performance of their athletes and inspire other researchers to advance the research field of sports analytics.2/5(1).Spatiotemporal definition is - having both spatial and temporal qualities.Spatio-Temporal Data Analysis In recent years, rapid advances in location-acquisition technologies have led to large amounts of time-stamped location data.
Positioning technologies like Global Positioning System (GPS)-based, communication network-based (e.g. 4G or Wi-Fi), and proximity-based (e.g. Radio Frequency Identification) systems enable.