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Author: Robin Lovelace Publisher: CRC Press ISBN: 1351396900 Category : Mathematics Languages : en Pages : 354
Book Description
Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/.
Author: Robin Lovelace Publisher: CRC Press ISBN: 1351396900 Category : Mathematics Languages : en Pages : 354
Book Description
Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/.
Author: Paula Moraga Publisher: CRC Press ISBN: 1000732150 Category : Medical Languages : en Pages : 216
Book Description
Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulate and transform point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches, Create interactive and static visualizations such as disease maps and time plots, Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policy makers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners.
Author: Jim Thatcher Publisher: U of Nebraska Press ISBN: 0803278829 Category : Social Science Languages : en Pages : 322
Book Description
Intro -- Title Page -- Copyright Page -- Contents -- List of Illustrations -- List of Tables -- Introduction -- Part 1 -- 1. Toward Critical Data Studies -- 2. Big Data ... Why (Oh Why?) This Computational Social Science? -- Part 2 -- 3. Smaller and Slower Data in an Era of Big Data -- 4. Reflexivity, Positionality, and Rigor in the Context of Big Data Research -- Part 3 -- 5. A Hybrid Approach to Geotweets -- 6. Geosocial Footprints and Geoprivacy Concerns -- 7. Foursquare in the City of Fountains -- Part 4 -- 8. Big City, Big Data -- 9. Framing Digital Exclusion in Technologically Mediated Urban Spaces -- Part 5 -- 10. Bringing the Big Data of Climate Change Down to Human Scale -- 11. Synergizing Geoweb and Digital Humanitarian Research -- Part 6 -- 12. Rethinking the Geoweb and Big Data -- Bibliography -- List of Contributors -- Index -- About Jim Thatcher -- About Josef Eckert -- About Andrew Shears
Author: Nigel Walford Publisher: John Wiley & Sons ISBN: 9780471970859 Category : Science Languages : en Pages : 294
Book Description
Provides a comprehensive overview of the characteristics relatingto remote sensing, cartography, censuses/surveys andadministrative/statutory. Presents up-to-date information in thisremarkable growth area, with clear explanations of the problems andpitfalls as well as the potentiality of analysing geographicaldata. An ideal book for both those already confident with GIS technology,and the novice user. * Covers spatial and thematic worldwide geographical data * Exemplifies the problems and pitfalls of using geographical data * Provides introductory details of GIS without the need for aseparate text * Refers to the latest internet sources * Covers geographical data relating to the environmental and humandomains * Focuses on digital geographical data
Author: Yong-Qi Chen Publisher: Springer Science & Business Media ISBN: 3709161835 Category : Science Languages : en Pages : 257
Book Description
This book presents the theory and methodology of geographical data acquisition, providing comprehensive coverage ranging from the definition of geo-referencing systems and transformation between these systems to the acquisition of geographical data using different methods. The material provides readers with a good understanding of the nature of spatial data, the accuracy of spatial data, and the theory behind various data acquisition methodologies.
Author: Martin Werner Publisher: Springer Nature ISBN: 3030554627 Category : Computers Languages : en Pages : 641
Book Description
This handbook covers a wide range of topics related to the collection, processing, analysis, and use of geospatial data in their various forms. This handbook provides an overview of how spatial computing technologies for big data can be organized and implemented to solve real-world problems. Diverse subdomains ranging from indoor mapping and navigation over trajectory computing to earth observation from space, are also present in this handbook. It combines fundamental contributions focusing on spatio-textual analysis, uncertain databases, and spatial statistics with application examples such as road network detection or colocation detection using GPUs. In summary, this handbook gives an essential introduction and overview of the rich field of spatial information science and big geospatial data. It introduces three different perspectives, which together define the field of big geospatial data: a societal, governmental, and governance perspective. It discusses questions of how the acquisition, distribution and exploitation of big geospatial data must be organized both on the scale of companies and countries. A second perspective is a theory-oriented set of contributions on arbitrary spatial data with contributions introducing into the exciting field of spatial statistics or into uncertain databases. A third perspective is taking a very practical perspective to big geospatial data, ranging from chapters that describe how big geospatial data infrastructures can be implemented and how specific applications can be implemented on top of big geospatial data. This would include for example, research in historic map data, road network extraction, damage estimation from remote sensing imagery, or the analysis of spatio-textual collections and social media. This multi-disciplinary approach makes the book unique. This handbook can be used as a reference for undergraduate students, graduate students and researchers focused on big geospatial data. Professionals can use this book, as well as practitioners facing big collections of geospatial data.
Author: Lex Comber Publisher: SAGE ISBN: 1526485435 Category : Science Languages : en Pages : 460
Book Description
We are in an age of big data where all of our everyday interactions and transactions generate data. Much of this data is spatial – it is collected some-where – and identifying analytical insight from trends and patterns in these increasing rich digital footprints presents a number of challenges. Whilst other books describe different flavours of Data Analytics in R and other programming languages, there are none that consider Spatial Data (i.e. the location attached to data), or that consider issues of inference, linking Big Data, Geography, GIS, Mapping and Spatial Analytics. This is a ‘learning by doing’ textbook, building on the previous book by the same authors, An Introduction to R for Spatial Analysis and Mapping. It details the theoretical issues in analyses of Big Spatial Data and developing practical skills in the reader for addressing these with confidence.
Author: Abdishakur Hassan Publisher: Packt Publishing Ltd ISBN: 1789809339 Category : Computers Languages : en Pages : 165
Book Description
Discover the power of location data to build effective, intelligent data models with Geospatial ecosystems Key FeaturesManipulate location-based data and create intelligent geospatial data modelsBuild effective location recommendation systems used by popular companies such as UberA hands-on guide to help you consume spatial data and parallelize GIS operations effectivelyBook Description Data scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models. This book demonstrates effective techniques for using the power of data science and geospatial intelligence to build effective, intelligent data models that make use of location-based data to give useful predictions and analyses. This book begins with a quick overview of the fundamentals of location-based data and how techniques such as Exploratory Data Analysis can be applied to it. We then delve into spatial operations such as computing distances, areas, extents, centroids, buffer polygons, intersecting geometries, geocoding, and more, which adds additional context to location data. Moving ahead, you will learn how to quickly build and deploy a geo-fencing system using Python. Lastly, you will learn how to leverage geospatial analysis techniques in popular recommendation systems such as collaborative filtering and location-based recommendations, and more. By the end of the book, you will be a rockstar when it comes to performing geospatial analysis with ease. What you will learnLearn how companies now use location dataSet up your Python environment and install Python geospatial packagesVisualize spatial data as graphsExtract geometry from spatial dataPerform spatial regression from scratchBuild web applications which dynamically references geospatial dataWho this book is for Data Scientists who would like to leverage location-based data and want to use location-based intelligence in their data models will find this book useful. This book is also for GIS developers who wish to incorporate data analysis in their projects. Knowledge of Python programming and some basic understanding of data analysis are all you need to get the most out of this book.
Author: David Abernathy Publisher: SAGE ISBN: 1473965780 Category : Social Science Languages : en Pages : 437
Book Description
"Abernathy provides a truly accessible and interdisciplinary introduction to geodata and geolocation covering both the conceptual and the practical. It is a must read for students or researchers looking to make the most of the spatial elements of their data" - Luke Sloan, Senior Lecturer in Quantitative Methods, Cardiff University Using Geodata and Geolocation in the Social Sciences: Mapping our Connected World provides an engaging and accessible introduction to the Geoweb with clear, step-by-step guides for: Capturing Geodata from sources including GPS, sensor networks and Twitter Visualizing Geodata using programmes including QGIS, GRASS and R Featuring colour images, practical exercises walking you through using data sources, and a companion website packed with resources, this book is the perfect guide for students and teachers looking to incorporate location-based data into their social science research.
Author: Robin Lovelace
Author: Robin Lovelace
Author: Paula Moraga
Author: Jim Thatcher
Author: Nigel Walford
Author: Yong-Qi Chen
Author: Martin Werner
Author: Lex Comber
Author: Abdishakur Hassan
Author: Katleen Janssen
Author: David Abernathy