Spatial Database Transfer Standards 2: Characteristics for Assessing Standards and Full Descriptions of the National and International Standards in ... Data (International Cartographic Association)

Spatial Database Transfer Standards 2: Characteristics for Assessing Standards and Full Descriptions of the National and International Standards in ... Data (International Cartographic Association) Review

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Spatial Database Transfer Standards 2: Characteristics for Assessing Standards and Full Descriptions of the National and International Standards in ... Data (International Cartographic Association) Feature

This book represents five and a half years of work by the ICA Commission on Standards for the Transfer of Spatial Data during the 1991- 95 ICA cycle. The effort began with the Commission working to develop a set of scientific characteristics by which every kind of spatial data transfer standard could be understood and assessed. This implies that every facet of the transfer process must be understood so that the scientific characteristics could be most efficiently specified. The members of the Commission spent hours looking at their own standard and many others, to ascertain how to specify most effectively the characteristic or subcharacteristic in question. The result is a set of internationally agreed scientific characteristics with 13 broad primary level classes of characteristics, 85 secondary characteristics, and about 220 tertiary characteristics that recognizes almost every possible capability that a spatial data transfer standard might have.
It is recognized that no one standard possesses all of these characteristics, but contains a subset of these characteristics. However, these characteristics have been specified in such a way to facilitate understanding of individual standards, and use by interested parties of making comparisons for their own purposes. Although individual applications of a standard may be for different purposes, this set of characteristics provides a uniform measure by which the various standards may be assessed.
The book presents an Introduction and four general chapters that describe the spatial data transfer standards activities happening in Europe, North America, Asia/Pacific, and the ISO community. This provides the context so the reader can more easily understand the scientific and technical framework from which a particular standard has come. The third section is a complete listing of all of the three levels of characteristics and their meaning by the inclusion of a set of definitions for terms used in the book. The fourth section, and by far the largest, contains 22 chapters that assess each of the major national and international spatial data transfer standards in the world in terms of all three levels of characteristics. Each assessment has been done by a Commission member who has been an active participant in the development of the standard being assessed in the native language of that standard. A cross-table chart is also provided.

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Spatial Database Transfer Standards 2: Characteristics for Assessing Standards and Full Descriptions of the National and International Standards in ... on Standards for the Transfer of Spatial Data

Spatial Database Transfer Standards 2: Characteristics for Assessing Standards and Full Descriptions of the National and International Standards in ... on Standards for the Transfer of Spatial Data Review

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Spatial Database Transfer Standards 2: Characteristics for Assessing Standards and Full Descriptions of the National and International Standards in ... on Standards for the Transfer of Spatial Data Feature

This book represents five and a half years of work by the ICA Commission on Standards for the Transfer of Spatial Data during the 1991- 95 ICA cycle. The effort began with the Commission working to develop a set of scientific characteristics by which every kind of spatial data transfer standard could be understood and assessed. This implies that every facet of the transfer process must be understood so that the scientific characteristics could be most efficiently specified. The members of the Commission spent hours looking at their own standard and many others, to ascertain how to specify most effectively the characteristic or subcharacteristic in question. The result is a set of internationally agreed scientific characteristics with 13 broad primary level classes of characteristics, 85 secondary characteristics, and about 220 tertiary characteristics that recognizes almost every possible capability that a spatial data transfer standard might have. It is recognized that no one standard possesses all of these characteristics, but contains a subset of these characteristics. However, these characteristics have been specified in such a way to facilitate understanding of individual standards, and use by interested parties of making comparisons for their own purposes. Although individual applications of a standard may be for different purposes, this set of characteristics provides a uniform measure by which the various standards may be assessed. The book presents an Introduction and four general chapters that describe the spatial data transfer standards activities happening in Europe, North America, Asia/Pacific, and the ISO community. This provides the context so the reader can more easily understand the scientific and technical framework from which a particular standard has come. The third section is a complete listing of all of the three levels of characteristics and their meaning by the inclusion of a set of definitio

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Fundamentals of Data Warehouses

Fundamentals of Data Warehouses Review

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Fundamentals of Data Warehouses Feature

This book presents the first comparative review of the state of the art and the best current practices of data warehouses. It covers source and data integration, multidimensional aggregation, query optimization, metadata management, quality assessment, and design optimization. A conceptual framework is presented by which the architecture and quality of a data warehouse can be assessed and improved using enriched metadata management combined with advanced techniques from databases, business modeling, and artificial intelligence.

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User's manual for the National Water-Quality Assessment Program Invertebrate Data Analysis System (IDAS) software, version 3

User's manual for the National Water-Quality Assessment Program Invertebrate Data Analysis System (IDAS) software, version 3 Review

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User's manual for the National Water-Quality Assessment Program Invertebrate Data Analysis System (IDAS) software, version 3 Feature

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Gnuplot in Action: Understanding Data with Graphs

Gnuplot in Action: Understanding Data with Graphs Review

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Statistical data is only as valuable as your ability to analyze, interpret, and present it in a meaningful way. Gnuplot is the most widely used program to plot and visualize data for Unix/Linux systems and it is also popular for Windows and the Mac. It's open-source (as in free!), actively maintained, stable, and mature. It can deal with arbitrarily large data sets and is capable of producing high-quality, publication-ready graphics.

So far, the only comprehensive documentation available about gnuplot is the online reference documentation, which makes it both hard to get started and almost impossible to get a complete overview over all of its features. If you've never tried gnuplot "or have found it tough to get your arms around "read on.

Gnuplot in Action is the first comprehensive introduction to gnuplot "from the basics to the power features and beyond. Besides providing a tutorial on gnuplot itself, it demonstrates how to apply and use gnuplot to extract intelligence from data. Particular attention is paid to tricky or poorly-explained areas. You will learn how to apply gnuplot to actual data analysis problems. This book looks at different types of graphs that can be generated with gnuplot and will discuss when and how to use them to extract actual information from data.

One of gnuplot's main advantages is that it requires no programming skills nor knowledge of advanced mathematical or statistical concepts. Gnuplot in Action assumes you have no previous knowledge of either gnuplot or statistics and data analysis. The books starts out with basic gnuplot concepts, then describes in depth how to get a graph ready for final presentation and to make it look "just right" by including arrows, labels, and other decorations.

Next the book looks at advanced concepts, such as multi-dimensional graphs and false-color plots "powerful features for special purposes. The author also describes advanced applications of gnuplot, such as how to script gnuplot so that it can run unattended as a batch job, and how to call gnuplot from within a CGI script to generate graphics for dynamic websites on demand.

Gnuplot in Action makes gnuplot easy for anyone who needs to do data analysis, but doesn't have an education in analytical tools and methods. It's perfect for DBAs, programmers, and performance engineers; business analysts and MBAs; and Six-Sigma Black Belts and process engineers.

What's Inside:

* Creating graphs with gnuplot * Data transformations and filters * Preparing/polishing graphs for final presentation * Publishing graphs in print or on the Web * Using gnuplot's power features * Gnuplot scripting and programming * Types of graphs and when to use them * Techniques of graphical analysis * How to build, install, and develop for gnuplot * Command and Option reference organized by concept

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Knowledge Discovery and Data Mining: Challenges and Realities

Knowledge Discovery and Data Mining: Challenges and Realities Review

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Knowledge Discovery and Data Mining: Challenges and Realities Feature

Knowledge discovery and data mining (KDD) is dedicated to exploring meaningful information from a large volume of data. Knowledge Discovery and Data Mining: Challenges and Realities is the most comprehensive reference publication for researchers and real-world data mining practitioners to advance knowledge discovery from low-quality data. This Premier Reference Source presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying, low-quality data. International experts in the field of data mining have contributed all-inclusive chapters focusing on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing.

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Modeling with Data: Tools and Techniques for Scientific Computing

Modeling with Data: Tools and Techniques for Scientific Computing Review

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Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.

Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date.

Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

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Nondetects and Data Analysis: Statistics for Censored Environmental Data (Statistics in Practice)

Nondetects and Data Analysis: Statistics for Censored Environmental Data (Statistics in Practice) Review

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STATISTICS IN PRACTICE

Statistical methods for interpreting and analyzing censored environmental data

Nondetects And Data Analysis: Statistics for Censored Environmental Data provides solutions for environmental scientists and professionals who need to interpret and analyze data that fall below the laboratory detection limit. Adapting survival analysis methods that have been successfully used in medical and industrial research, the author demonstrates, for the first time, their practical applications for studies of trace chemicals in air, water, soils, and biota. Readers quickly become proficient in these methods through the use of real-world examples that are solved using MINITAB® Release 14, a popular statistical software package, as well as other commonly used software packages.

Everything needed to master these innovative statistical methods is provided, including:

• Accompanying Web site featuring answers to book exercises and datasets, as well as MINITAB® macros to perform methods, which are not available in the commercial version
• Methods for data with multiple detection limits
• Solutions for research studies in which all data are below detection limits
• Techniques for constructing confidence, prediction, and tolerance intervals for data with nond-tects
• Methods for data with multiple detection limits

Chapters are organized by objective, such as computing intervals, comparing groups, and correlations, which enables readers to more easily apply the text to their particular research and goals. Extensive references to the literature for more in-depth research are provided; however, the text itself avoids complex math and calculus making it accessible to anyone in the environmental sciences. Environmental scientists and professionals will find the hands-on guidance and practical examples invaluable.

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Exploratory Data Mining and Data Cleaning

Exploratory Data Mining and Data Cleaning Review

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Exploratory Data Mining and Data Cleaning Feature

• Written for practitioners of data mining, data cleaning and database management.
• Presents a technical treatment of data quality including process, metrics, tools and algorithms.
• Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge.
• Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches.
• Uses case studies to illustrate applications in real life scenarios.
• Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques.

Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.

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A recursive estimation approach to the spatio-temporal analysis and modelling of air quality data [An article from: Environmental Modelling and Software]

A recursive estimation approach to the spatio-temporal analysis and modelling of air quality data [An article from: Environmental Modelling and Software] Review

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This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
This paper presents the methodology for the spatial and temporal interpolation of air quality data. As a practical example, the methodology is applied to the daily nitric oxide NO concentrations measured at 23 stations around Paris. Analysis of the temporal and spatial variability of observations of NO in the Paris area is divided into: (i) time series analysis of AirParif data; and (ii) development of combined spatial and temporal analysis techniques using NO observations from 19 stations. The first part of the paper shows how advanced methods of nonstationary time series analysis can be used to interpolate the data sets of NO concentrations over periods where measurements are missing and to decompose the time series into trend and harmonic components. The results of this analysis applied to 19 stations around Paris are then used in further spatio-temporal analysis of the data. This consists of two steps: (i) preliminary analysis of spatial relations within the data sets; and (ii) the development of a spatio-temporal model for log-transformed NO measurements. The results of the analysis indicate that the simple spatio-temporal model consisting of trend and noise efficiently represents the spatio-temporal variations in the data and it can be applied to predict air pollution variations in time and space at un-sampled locations.

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