Resultado de la búsqueda
12 búsqueda de la palabra clave 'Monitoring/Analysis'
Refinar búsqueda Generar rss de la búsqueda
Link de la búsquedageoENV VII – Geostatistics for Environmental Applications / SpringerLink (Online service) ; P.M. Atkinson ; C. D. Lloyd (2010)
Título : geoENV VII – Geostatistics for Environmental Applications Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; P.M. Atkinson ; C. D. Lloyd Editorial: Dordrecht : Springer Netherlands Fecha de publicación: 2010 Otro editor: Imprint: Springer Colección: Quantitative Geology and Geostatistics, ISSN 0924-1973 num. 16 Número de páginas: XVI, 420 p. 185 illus., 35 illus. in color Il.: online resource ISBN/ISSN/DL: 978-90-481-2322-3 Idioma : Inglés (eng) Palabras clave: Earth sciences Statistics Environmental Sciences Sciences, general for Engineering, Physics, Computer Science, Chemistry and Monitoring/Analysis Math. Appl. in Science Clasificación: 51 Matemáticas Resumen: This volume brings together selected contributions from geoENV 2008, the 7th International Conference on Geostatistics for Environmental Applications, held in Southampton, UK, in September 2008. This book presents the state-of-the-art in geostatistics for the environmental sciences. It includes a wide range of methodological advances and applications. It offers insight and guidance for researchers, professionals, graduate students and others seeking information on the latest perspectives in the field. The rich body of applications will enable those new to geostatistics to assess the utility of the methods for their own applications. The book includes 35 chapters on topics as diverse as methodological developments, applications in the soil sciences, climatology, pollution, health, wildlife mapping, fisheries and remote sensing, amongst other areas. With its focus on environmental applications of geostatistics, rather than the more traditional geostatistical remit of mining and petroleum exploration, this book is part of a series that presents an invaluable resource. This book will be a first port of call for those who wish to apply geostatistical methods in the environmental sciences. Audience: Researchers, scientists, professionals, institutes, libraries, graduate students of geosciences, geostatistics, spatial statistics, environmental science and engineering, ecology, oceanography, climatology, hydrology, soil and forestry science Nota de contenido: Geostatistical Modelling of Wildlife Populations: A Non-stationary Hierarchical Model for Count Data -- Incorporating Survey Data to Improve Space–Time Geostatistical Analysis of King Prawn Catch Rate -- Multivariate Interpolation of Monthly Precipitation Amount in the United Kingdom -- Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms -- On Geostatistical Analysis of Rainfall Using Data from Boundary Sites -- Geostatistics Applied to the City of Porto Urban Climatology -- Integrating Meteorological Dynamic Data and Historical Data into a Stochastic Model for Predicting Forest Fires Risk Maps -- Using Geostatistical Methods in the Analysis of Public Health Data: The Final Frontier? -- Second-Order Analysis of the Spatio-temporal Distribution of Human Campylobacteriosis in Preston, Lancashire -- Application of Geostatistics in Cancer Studies -- Blocking Markov Chain Monte Carlo Schemes for Inverse Stochastic Hydrogeological Modeling -- Simulation of Fine-Scale Heterogeneity of Meandering River Aquifer Analogues: Comparing Different Approaches -- Application of Multiple-Point Geostatistics on Modelling Groundwater Flow and Transport in a Cross-Bedded Aquifer -- Assessment of the Impact of Pollution by Arsenic in the Vicinity of Panasqueira Mine (Portugal) -- Simulation of Continuous Variables at Meander Structures: Application to Contaminated Sediments of a Lagoon -- Joint Space–Time Geostatistical Model for Air Quality Surveillance/Monitoring System -- Geostatistical Methods for Polluted Sites Characterization -- Geostatistical Mapping of Outfall Plume Dispersion Data Gathered with an Autonomous Underwater Vehicle -- Change of the A Priori Stochastic Structure in the Conditional Simulation of Transmissivity Fields -- Geostatistical Interpolation of Soil Properties in Boom Clay in Flanders -- An Examination of Transformation Techniques to Investigate and Interpret Multivariate Geochemical Data Analysis: Tellus Case Study -- Shelling in the First World War Increased the Soil Heavy Metal Concentration -- A Geostatistical Analysis of Rubber Tree Growth Characteristics and Soil Physical Attributes -- Investigating the Potential of Area-to-Area and Area-to-Point Kriging for Defining Management Zones for Precision Farming of Cranberries -- Estimating the Local Small Support Semivariogram for Use in Super-Resolution Mapping -- Modeling Spatial Uncertainty for Locally Uncertain Data -- Spatial Interpolation Using Copula-Based Geostatistical Models -- Exchanging Uncertainty: Interoperable Geostatistics? -- Hierarchical Bayesian Model for Gaussian, Poisson and Ordinal Random Fields -- Detection of Optimal Models in Parameter Space with Support Vector Machines -- Robust Automatic Mapping Algorithms in a Network Monitoring Scenario -- Parallel Geostatistics for Sparse and Dense Datasets -- Multiple Point Geostatistical Simulation with Simulated Annealing: Implementation Using Speculative Parallel Computing -- Application of Copulas in Geostatistics -- Integrating Prior Knowledge and Locally Varying Parameters with Moving-GeoStatistics: Methodology and Application to Bathymetric Mapping En línea: http://dx.doi.org/10.1007/978-90-481-2322-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33809 geoENV VII – Geostatistics for Environmental Applications [documento electrónico] / SpringerLink (Online service) ; P.M. Atkinson ; C. D. Lloyd . - Dordrecht : Springer Netherlands : Imprint: Springer, 2010 . - XVI, 420 p. 185 illus., 35 illus. in color : online resource. - (Quantitative Geology and Geostatistics, ISSN 0924-1973; 16) .
ISBN : 978-90-481-2322-3
Idioma : Inglés (eng)
Palabras clave: Earth sciences Statistics Environmental Sciences Sciences, general for Engineering, Physics, Computer Science, Chemistry and Monitoring/Analysis Math. Appl. in Science Clasificación: 51 Matemáticas Resumen: This volume brings together selected contributions from geoENV 2008, the 7th International Conference on Geostatistics for Environmental Applications, held in Southampton, UK, in September 2008. This book presents the state-of-the-art in geostatistics for the environmental sciences. It includes a wide range of methodological advances and applications. It offers insight and guidance for researchers, professionals, graduate students and others seeking information on the latest perspectives in the field. The rich body of applications will enable those new to geostatistics to assess the utility of the methods for their own applications. The book includes 35 chapters on topics as diverse as methodological developments, applications in the soil sciences, climatology, pollution, health, wildlife mapping, fisheries and remote sensing, amongst other areas. With its focus on environmental applications of geostatistics, rather than the more traditional geostatistical remit of mining and petroleum exploration, this book is part of a series that presents an invaluable resource. This book will be a first port of call for those who wish to apply geostatistical methods in the environmental sciences. Audience: Researchers, scientists, professionals, institutes, libraries, graduate students of geosciences, geostatistics, spatial statistics, environmental science and engineering, ecology, oceanography, climatology, hydrology, soil and forestry science Nota de contenido: Geostatistical Modelling of Wildlife Populations: A Non-stationary Hierarchical Model for Count Data -- Incorporating Survey Data to Improve Space–Time Geostatistical Analysis of King Prawn Catch Rate -- Multivariate Interpolation of Monthly Precipitation Amount in the United Kingdom -- Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms -- On Geostatistical Analysis of Rainfall Using Data from Boundary Sites -- Geostatistics Applied to the City of Porto Urban Climatology -- Integrating Meteorological Dynamic Data and Historical Data into a Stochastic Model for Predicting Forest Fires Risk Maps -- Using Geostatistical Methods in the Analysis of Public Health Data: The Final Frontier? -- Second-Order Analysis of the Spatio-temporal Distribution of Human Campylobacteriosis in Preston, Lancashire -- Application of Geostatistics in Cancer Studies -- Blocking Markov Chain Monte Carlo Schemes for Inverse Stochastic Hydrogeological Modeling -- Simulation of Fine-Scale Heterogeneity of Meandering River Aquifer Analogues: Comparing Different Approaches -- Application of Multiple-Point Geostatistics on Modelling Groundwater Flow and Transport in a Cross-Bedded Aquifer -- Assessment of the Impact of Pollution by Arsenic in the Vicinity of Panasqueira Mine (Portugal) -- Simulation of Continuous Variables at Meander Structures: Application to Contaminated Sediments of a Lagoon -- Joint Space–Time Geostatistical Model for Air Quality Surveillance/Monitoring System -- Geostatistical Methods for Polluted Sites Characterization -- Geostatistical Mapping of Outfall Plume Dispersion Data Gathered with an Autonomous Underwater Vehicle -- Change of the A Priori Stochastic Structure in the Conditional Simulation of Transmissivity Fields -- Geostatistical Interpolation of Soil Properties in Boom Clay in Flanders -- An Examination of Transformation Techniques to Investigate and Interpret Multivariate Geochemical Data Analysis: Tellus Case Study -- Shelling in the First World War Increased the Soil Heavy Metal Concentration -- A Geostatistical Analysis of Rubber Tree Growth Characteristics and Soil Physical Attributes -- Investigating the Potential of Area-to-Area and Area-to-Point Kriging for Defining Management Zones for Precision Farming of Cranberries -- Estimating the Local Small Support Semivariogram for Use in Super-Resolution Mapping -- Modeling Spatial Uncertainty for Locally Uncertain Data -- Spatial Interpolation Using Copula-Based Geostatistical Models -- Exchanging Uncertainty: Interoperable Geostatistics? -- Hierarchical Bayesian Model for Gaussian, Poisson and Ordinal Random Fields -- Detection of Optimal Models in Parameter Space with Support Vector Machines -- Robust Automatic Mapping Algorithms in a Network Monitoring Scenario -- Parallel Geostatistics for Sparse and Dense Datasets -- Multiple Point Geostatistical Simulation with Simulated Annealing: Implementation Using Speculative Parallel Computing -- Application of Copulas in Geostatistics -- Integrating Prior Knowledge and Locally Varying Parameters with Moving-GeoStatistics: Methodology and Application to Bathymetric Mapping En línea: http://dx.doi.org/10.1007/978-90-481-2322-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33809 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar
Título : Statistical Analysis of Environmental Space-Time Processes Tipo de documento: documento electrónico Autores: Nhu D. Le ; SpringerLink (Online service) ; James V. Zidek Editorial: New York, NY : Springer New York Fecha de publicación: 2006 Colección: Springer Series in Statistics, ISSN 0172-7397 Número de páginas: XVI, 342 p Il.: online resource ISBN/ISSN/DL: 978-0-387-35429-3 Idioma : Inglés (eng) Palabras clave: Environment Earth sciences Epidemiology Statistics Ecotoxicology Environmental Monitoring/Analysis Sciences, general Statistical Theory and Methods Clasificación: 51 Matemáticas Resumen: This book provides a broad introduction to the fascinating subject of environmental space-time processes; addressing the role of uncertainty. Within that context, it covers a spectrum of technical matters from measurement to environmental epidemiology to risk assessment. It showcases non-stationary vector-valued processes, while treating stationarity as a special case. The contents reflect the authors’ cumulative knowledge gained over many years of consulting and research collaboration. In particular, with members of their research group, they developed within a hierarchical Bayesian framework, the new statistical approaches presented in the book for analyzing, modeling, and monitoring environmental spatio-temporal processes. Furthermore they indicate new directions for development. This book contains technical and non-technical material and it is written for statistical scientists as well as consultants, subject area researchers and students in related fields. Novel chapters present the authors’ hierarchical Bayesian approaches to spatially interpolating environmental processes designing networks to monitor environmental processes multivariate extreme value theory incorporating risk assessment. In addition, they present a comprehensive and critical survey of other approaches, highlighting deficiencies that their method seeks to overcome. Special sections marked by an asterisk provide rigorous development for readers with a strong technical background. Alternatively readers can go straight to the tutorials supplied in chapter 14 and learn how to apply the free, downloadable modeling and design software that the authors and their research partners have developed. Nhu Le is a Senior Scientist in Cancer Control Research and a former Director of the Occupational Oncology Research Program at the British Columbia Cancer Agency (BCCA). An Adjunct Professor of Statistics at the University of British Columbia since 1992, he also teaches graduate courses and supervises graduate students. He is heavily involved in epidemiological research and the impact environmental and occupational factors have on cancer development. He has published over 100 peer-reviewed research articles in statistical- and subject-area journals. He received his Ph.D. in statistics from the University of Washington in Seattle. Jim Zidek is a Professor Emeritus and Founding Head of the Department of Statistics at the University of British Columbia. He has served on a number of scientific advisory committees, most notably on the United States’ EPA’s Clean Air Scientific Advisory Committees Ozone Panel. His scientific interests lie equally in environmetrics (the subject of this book) and in the theory of decision analysis (particularly, the compilation of expert opinion). His work has been published extensively and he has been invited to give numerous presentations. He received his Ph.D. from Stanford University and his honors include Fellowships in the Royal Society of Canada, the American Statistical Association (ASA), and the Institute of Mathematical Statistics. He has earned the Distinguished Achievement Medal in Environmental Statistics of the ASA and the Gold Medal of the Statistical Society of Canada Nota de contenido: Environmental Processes -- First Encounters -- Case Study -- Uncertainty -- Measurement -- Modeling -- Space-Time Modeling -- Covariances -- Spatial Prediction: Classical Approaches -- Bayesian Kriging -- Hierarchical Bayesian Kriging -- Design and Risk Assessment -- Multivariate Modeling*** -- Environmental Network Design -- Extremes -- Implementation -- Risk Assessment -- A Tutorial in R -- Appendices -- References En línea: http://dx.doi.org/10.1007/0-387-35429-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34817 Statistical Analysis of Environmental Space-Time Processes [documento electrónico] / Nhu D. Le ; SpringerLink (Online service) ; James V. Zidek . - New York, NY : Springer New York, 2006 . - XVI, 342 p : online resource. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-35429-3
Idioma : Inglés (eng)
Palabras clave: Environment Earth sciences Epidemiology Statistics Ecotoxicology Environmental Monitoring/Analysis Sciences, general Statistical Theory and Methods Clasificación: 51 Matemáticas Resumen: This book provides a broad introduction to the fascinating subject of environmental space-time processes; addressing the role of uncertainty. Within that context, it covers a spectrum of technical matters from measurement to environmental epidemiology to risk assessment. It showcases non-stationary vector-valued processes, while treating stationarity as a special case. The contents reflect the authors’ cumulative knowledge gained over many years of consulting and research collaboration. In particular, with members of their research group, they developed within a hierarchical Bayesian framework, the new statistical approaches presented in the book for analyzing, modeling, and monitoring environmental spatio-temporal processes. Furthermore they indicate new directions for development. This book contains technical and non-technical material and it is written for statistical scientists as well as consultants, subject area researchers and students in related fields. Novel chapters present the authors’ hierarchical Bayesian approaches to spatially interpolating environmental processes designing networks to monitor environmental processes multivariate extreme value theory incorporating risk assessment. In addition, they present a comprehensive and critical survey of other approaches, highlighting deficiencies that their method seeks to overcome. Special sections marked by an asterisk provide rigorous development for readers with a strong technical background. Alternatively readers can go straight to the tutorials supplied in chapter 14 and learn how to apply the free, downloadable modeling and design software that the authors and their research partners have developed. Nhu Le is a Senior Scientist in Cancer Control Research and a former Director of the Occupational Oncology Research Program at the British Columbia Cancer Agency (BCCA). An Adjunct Professor of Statistics at the University of British Columbia since 1992, he also teaches graduate courses and supervises graduate students. He is heavily involved in epidemiological research and the impact environmental and occupational factors have on cancer development. He has published over 100 peer-reviewed research articles in statistical- and subject-area journals. He received his Ph.D. in statistics from the University of Washington in Seattle. Jim Zidek is a Professor Emeritus and Founding Head of the Department of Statistics at the University of British Columbia. He has served on a number of scientific advisory committees, most notably on the United States’ EPA’s Clean Air Scientific Advisory Committees Ozone Panel. His scientific interests lie equally in environmetrics (the subject of this book) and in the theory of decision analysis (particularly, the compilation of expert opinion). His work has been published extensively and he has been invited to give numerous presentations. He received his Ph.D. from Stanford University and his honors include Fellowships in the Royal Society of Canada, the American Statistical Association (ASA), and the Institute of Mathematical Statistics. He has earned the Distinguished Achievement Medal in Environmental Statistics of the ASA and the Gold Medal of the Statistical Society of Canada Nota de contenido: Environmental Processes -- First Encounters -- Case Study -- Uncertainty -- Measurement -- Modeling -- Space-Time Modeling -- Covariances -- Spatial Prediction: Classical Approaches -- Bayesian Kriging -- Hierarchical Bayesian Kriging -- Design and Risk Assessment -- Multivariate Modeling*** -- Environmental Network Design -- Extremes -- Implementation -- Risk Assessment -- A Tutorial in R -- Appendices -- References En línea: http://dx.doi.org/10.1007/0-387-35429-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34817 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar
Título : Applied Spatial Data Analysis with R Tipo de documento: documento electrónico Autores: Roger S. Bivand ; SpringerLink (Online service) ; Edzer J. Pebesma ; Virgilio Gómez Rubio Editorial: New York, NY : Springer New York Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: Use R!, ISSN 2197-5736 num. 10 Número de páginas: XVIII, 405 p. 121 illus., 89 illus. in color Il.: online resource ISBN/ISSN/DL: 978-1-4614-7618-4 Idioma : Inglés (eng) Palabras clave: Statistics Geography for Life Sciences, Medicine, Health Sciences Engineering, Physics, Computer Science, Chemistry and Earth Statistics, general Environmental Monitoring/Analysis Geography, Clasificación: 51 Matemáticas Resumen: Applied Spatial Data Analysis with R, Second Edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003 Nota de contenido: Preface 2nd edition -- Preface 1st edition -- Hello World: Introducing Spatial Data -- Classes for Spatial Data in R -- Visualising Spatial Data -- Spatial Data Import and Export -- Further Methods for Handling Spatial Data -- Classes for spatio-temporal Data -- Spatial Point Pattern Analysis -- Interpolation and Geostatistics -- Modelling Areal Data -- Disease Mapping En línea: http://dx.doi.org/10.1007/978-1-4614-7618-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32360 Applied Spatial Data Analysis with R [documento electrónico] / Roger S. Bivand ; SpringerLink (Online service) ; Edzer J. Pebesma ; Virgilio Gómez Rubio . - New York, NY : Springer New York : Imprint: Springer, 2013 . - XVIII, 405 p. 121 illus., 89 illus. in color : online resource. - (Use R!, ISSN 2197-5736; 10) .
ISBN : 978-1-4614-7618-4
Idioma : Inglés (eng)
Palabras clave: Statistics Geography for Life Sciences, Medicine, Health Sciences Engineering, Physics, Computer Science, Chemistry and Earth Statistics, general Environmental Monitoring/Analysis Geography, Clasificación: 51 Matemáticas Resumen: Applied Spatial Data Analysis with R, Second Edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003 Nota de contenido: Preface 2nd edition -- Preface 1st edition -- Hello World: Introducing Spatial Data -- Classes for Spatial Data in R -- Visualising Spatial Data -- Spatial Data Import and Export -- Further Methods for Handling Spatial Data -- Classes for spatio-temporal Data -- Spatial Point Pattern Analysis -- Interpolation and Geostatistics -- Modelling Areal Data -- Disease Mapping En línea: http://dx.doi.org/10.1007/978-1-4614-7618-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32360 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar
Título : Applied Spatial Data Analysis with R Tipo de documento: documento electrónico Autores: Roger S. Bivand ; SpringerLink (Online service) ; Edzer J. Pebesma ; Virgilio Gómez Rubio Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Use R!, ISSN 2197-5736 Número de páginas: XIV, 376 p Il.: online resource ISBN/ISSN/DL: 978-0-387-78171-6 Idioma : Inglés (eng) Palabras clave: Medicine Epidemiology Geography Ecology Econometrics Regional economics Spatial & Public Health Regional/Spatial Science Environmental Monitoring/Analysis Geography, general Clasificación: 51 Matemáticas Resumen: Applied Spatial Data Analysis with R is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where coloured figures, complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003. Roger Bivand is Professor of Geography in the Department of Economics at Norges Handelshøyskole, Bergen, Norway. Edzer Pebesma is Professor of Geoinformatics at Westfälische Wilhelms-Universität, Münster, Germany. Virgilio Gómez-Rubio is Research Associate in the Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom Nota de contenido: Handling Spatial Data in R -- Hello World: Introducing Spatial Data -- Classes for Spatial Data in R -- Visualising Spatial Data -- Spatial Data Import and Export -- Further Methods for Handling Spatial Data -- Customising Spatial Data Classes and Methods -- Analysing Spatial Data -- Spatial Point Pattern Analysis -- Interpolation and Geostatistics -- Areal Data and Spatial Autocorrelation -- Modelling Areal Data -- Disease Mapping En línea: http://dx.doi.org/10.1007/978-0-387-78171-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34246 Applied Spatial Data Analysis with R [documento electrónico] / Roger S. Bivand ; SpringerLink (Online service) ; Edzer J. Pebesma ; Virgilio Gómez Rubio . - New York, NY : Springer New York, 2008 . - XIV, 376 p : online resource. - (Use R!, ISSN 2197-5736) .
ISBN : 978-0-387-78171-6
Idioma : Inglés (eng)
Palabras clave: Medicine Epidemiology Geography Ecology Econometrics Regional economics Spatial & Public Health Regional/Spatial Science Environmental Monitoring/Analysis Geography, general Clasificación: 51 Matemáticas Resumen: Applied Spatial Data Analysis with R is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where coloured figures, complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003. Roger Bivand is Professor of Geography in the Department of Economics at Norges Handelshøyskole, Bergen, Norway. Edzer Pebesma is Professor of Geoinformatics at Westfälische Wilhelms-Universität, Münster, Germany. Virgilio Gómez-Rubio is Research Associate in the Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom Nota de contenido: Handling Spatial Data in R -- Hello World: Introducing Spatial Data -- Classes for Spatial Data in R -- Visualising Spatial Data -- Spatial Data Import and Export -- Further Methods for Handling Spatial Data -- Customising Spatial Data Classes and Methods -- Analysing Spatial Data -- Spatial Point Pattern Analysis -- Interpolation and Geostatistics -- Areal Data and Spatial Autocorrelation -- Modelling Areal Data -- Disease Mapping En línea: http://dx.doi.org/10.1007/978-0-387-78171-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34246 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar
Título : Advances in Modeling Agricultural Systems Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Panos M. Pardalos ; Petraq J. Papajorgji Editorial: Boston, MA : Springer US Fecha de publicación: 2009 Colección: Springer Optimization and Its Applications, ISSN 1931-6828 num. 25 Número de páginas: X, 522 p. 172 illus Il.: online resource ISBN/ISSN/DL: 978-0-387-75181-8 Idioma : Inglés (eng) Palabras clave: Mathematics Information technology Business Data processing Software engineering Computer simulation Agriculture mathematics Computational Science and Engineering Simulation Modeling Environmental Monitoring/Analysis IT in Clasificación: 51 Matemáticas Resumen: This book presents an up-to-date review of advances in the mathematical modeling of agricultural systems. It covers a broad spectrum of problems and applications based on internet and communications technology, as well as methodological approaches based on the integration of different simulation and data management tools. Using real-world cases, each chapter presents a detailed solution of a problem in a particular field. This book demonstrates that regardless of the nature of the problem and the application domain, modeling is a central and important activity in the process of developing agricultural systems. Researchers and graduate students in the fields of agriculture and environmental studies will benefit from this book. It will also serve as an excellent reference for managers, team leaders, developers and modelers of agricultural and environmental systems and researchers in the applied computation field Nota de contenido: The Model Driven ArchitectureModel Driven Architecture MDA Approach: A Framework for Developing Complex Agricultural Systems -- A New Methodology to Automate the Transformation of GISGIS Models in an Iterative iterative Development Process -- Application of a Model Transformation Transformation Paradigm in Agriculture: A Simple Environmental environmental System Case Study -- Constraints Modeling in Agricultural Databases -- Design of a Model model -Driven Web Decision Support System decision support system in Agriculture agriculture : From Scientific Models to the Final Software -- How2QnD: Design and Construction of a Game-Style, Environmental Simulation Engine and Interface Using UML, XML, and Java -- The Use of UML as a Tool for the Formalisation of Standards and the Design of Ontologies in Agriculture -- Modeling External Information Needs of Food Business Networks -- Enterprise Business Modelling Languages Applied to Farm Enterprise: A Case Study for IDEF0, GRAI Grid, and AMS Languages -- A UML-Based Plug&Play Version of RothC -- Ontology-Based Simulation Applied to Soil, Water, and Nutrient Management -- Precision Farming, Myth or Reality: Selected Case Studies from Mississippi Cotton Fields -- Rural Development Through Input–Output Modeling -- Modeling in Nutrient Sensing for Agricultural and Environmental Applications -- Estimation of Land Surface Parameters Through Modeling Inversion of Earth Observation Optical Data -- A Stochastic Dynamic Programming Model for Valuing a Eucalyptus Investment -- Modelling Water Flow water flow and Solute Transport in Heterogeneous Unsaturated Porous Media -- Genome Analysis of Species of Agricultural Interest -- Modeling and Solving Real-Life Global Optimization Problems with Meta-heuristic Methods -- Modeling and Device Development for Chlorophyll Estimation in Vegetation -- Clustering and Classification Algorithms in Food and Agricultural Applications: A Survey -- Mathematical Modelling of Modified Atmosphere Package: An Engineering Approach to Design Packaging Systems for Fresh-Cut Produce En línea: http://dx.doi.org/10.1007/978-0-387-75181-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33858 Advances in Modeling Agricultural Systems [documento electrónico] / SpringerLink (Online service) ; Panos M. Pardalos ; Petraq J. Papajorgji . - Boston, MA : Springer US, 2009 . - X, 522 p. 172 illus : online resource. - (Springer Optimization and Its Applications, ISSN 1931-6828; 25) .
ISBN : 978-0-387-75181-8
Idioma : Inglés (eng)
Palabras clave: Mathematics Information technology Business Data processing Software engineering Computer simulation Agriculture mathematics Computational Science and Engineering Simulation Modeling Environmental Monitoring/Analysis IT in Clasificación: 51 Matemáticas Resumen: This book presents an up-to-date review of advances in the mathematical modeling of agricultural systems. It covers a broad spectrum of problems and applications based on internet and communications technology, as well as methodological approaches based on the integration of different simulation and data management tools. Using real-world cases, each chapter presents a detailed solution of a problem in a particular field. This book demonstrates that regardless of the nature of the problem and the application domain, modeling is a central and important activity in the process of developing agricultural systems. Researchers and graduate students in the fields of agriculture and environmental studies will benefit from this book. It will also serve as an excellent reference for managers, team leaders, developers and modelers of agricultural and environmental systems and researchers in the applied computation field Nota de contenido: The Model Driven ArchitectureModel Driven Architecture MDA Approach: A Framework for Developing Complex Agricultural Systems -- A New Methodology to Automate the Transformation of GISGIS Models in an Iterative iterative Development Process -- Application of a Model Transformation Transformation Paradigm in Agriculture: A Simple Environmental environmental System Case Study -- Constraints Modeling in Agricultural Databases -- Design of a Model model -Driven Web Decision Support System decision support system in Agriculture agriculture : From Scientific Models to the Final Software -- How2QnD: Design and Construction of a Game-Style, Environmental Simulation Engine and Interface Using UML, XML, and Java -- The Use of UML as a Tool for the Formalisation of Standards and the Design of Ontologies in Agriculture -- Modeling External Information Needs of Food Business Networks -- Enterprise Business Modelling Languages Applied to Farm Enterprise: A Case Study for IDEF0, GRAI Grid, and AMS Languages -- A UML-Based Plug&Play Version of RothC -- Ontology-Based Simulation Applied to Soil, Water, and Nutrient Management -- Precision Farming, Myth or Reality: Selected Case Studies from Mississippi Cotton Fields -- Rural Development Through Input–Output Modeling -- Modeling in Nutrient Sensing for Agricultural and Environmental Applications -- Estimation of Land Surface Parameters Through Modeling Inversion of Earth Observation Optical Data -- A Stochastic Dynamic Programming Model for Valuing a Eucalyptus Investment -- Modelling Water Flow water flow and Solute Transport in Heterogeneous Unsaturated Porous Media -- Genome Analysis of Species of Agricultural Interest -- Modeling and Solving Real-Life Global Optimization Problems with Meta-heuristic Methods -- Modeling and Device Development for Chlorophyll Estimation in Vegetation -- Clustering and Classification Algorithms in Food and Agricultural Applications: A Survey -- Mathematical Modelling of Modified Atmosphere Package: An Engineering Approach to Design Packaging Systems for Fresh-Cut Produce En línea: http://dx.doi.org/10.1007/978-0-387-75181-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33858 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar
Permalink
PermalinkPermalinkPermalink
Permalink
