Spatial Analytics With Arcgis
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| Author | : Eric Pimpler |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 285 |
| Release | : 2017-04-26 |
| Genre | : Computers |
| ISBN | : 1787124622 |
Pattern Analysis and cluster mapping made easy About This Book Analyze patterns, clusters, and spatial relationships using ArcGIS tools Get up to speed in R programming to create custom tools for analysis Sift through tons of crime and real estate data and analyze it using the tools built in the book Who This Book Is For This book is for ArcGIS developers who want to perform complex geographic analysis through the use of spatial statistics tools including ArcGIS and R. No knowledge of R is assumed. What You Will Learn Get to know how to measure geographic distributions Perform clustering analysis including hot spot and outlier analysis Conduct data conversion tasks using the Utilities toolset Understand how to use the tools provided by the Mapping Clusters toolset in the Spatial Statistics Toolbox Get to grips with the basics of R for performing spatial statistical programming Create custom ArcGIS tools with R and ArcGIS Bridge Understand the application of Spatial Statistics tools and the R programming language through case studies In Detail Spatial statistics has the potential to provide insight that is not otherwise available through traditional GIS tools. This book is designed to introduce you to the use of spatial statistics so you can solve complex geographic analysis. The book begins by introducing you to the many spatial statistics tools available in ArcGIS. You will learn how to analyze patterns, map clusters, and model spatial relationships with these tools. Further on, you will explore how to extend the spatial statistics tools currently available in ArcGIS, and use the R programming language to create custom tools in ArcGIS through the ArcGIS Bridge using real-world examples. At the end of the book, you will be presented with two exciting case studies where you will be able to practically apply all your learning to analyze and gain insights into real estate data. Style and approach Filled with live examples that you can code along with, this book will show you different methods and techniques to effectively analyze spatial data with ArcGIS and the R language. The exciting case studies at the end will help you immediately put your learning to practice.
| Author | : Michael John De Smith |
| Publisher | : Troubador Publishing Ltd |
| Total Pages | : 417 |
| Release | : 2007 |
| Genre | : Mathematics |
| ISBN | : 1905886608 |
Addresses a range of analytical techniques that are provided within modern Geographic Information Systems and related geospatial software products. This guide covers: the principal concepts of geospatial analysis; core components of geospatial analysis; and, surface analysis, including surface form analysis, gridding and interpolation methods.
| Author | : Chris Brunsdon |
| Publisher | : SAGE |
| Total Pages | : 386 |
| Release | : 2014-04-30 |
| Genre | : Social Science |
| ISBN | : 1473911192 |
"In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using ′out of the box′ software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research. Providing practical tips as well as fully working code, this is a practical ′how to′ guide ideal for undergraduates as well as those using R for the first time. It will be required reading on my own courses." - Richard Harris, Professor of Quantitative Social Science, University of Bristol R is a powerful open source computing tool that supports geographical analysis and mapping for the many geography and ‘non-geography’ students and researchers interested in spatial analysis and mapping. This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for researchers collecting and using data with location attached, largely through increased GPS functionality. Brunsdon and Comber take readers from ‘zero to hero’ in spatial analysis and mapping through functions they have developed and compiled into R packages. This enables practical R applications in GIS, spatial analyses, spatial statistics, mapping, and web-scraping. Each chapter includes: Example data and commands for exploring it Scripts and coding to exemplify specific functionality Advice for developing greater understanding - through functions such as locator(), View(), and alternative coding to achieve the same ends Self-contained exercises for students to work through Embedded code within the descriptive text. This is a definitive ′how to′ that takes students - of any discipline - from coding to actual applications and uses of R.
| Author | : Christian Harder |
| Publisher | : ESRI Press |
| Total Pages | : 172 |
| Release | : 2017 |
| Genre | : Computers |
| ISBN | : 9781589484870 |
This is a hands-on book about ArcGIS that you work with as much as read. By the end, using Learn ArcGIS lessons, you'll be able to say you made a story map, conducted geographic analysis, edited geographic data, worked in a 3D web scene, built a 3D model of Venice, and more.
| Author | : Manfred M. Fischer |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 801 |
| Release | : 2009-12-24 |
| Genre | : Business & Economics |
| ISBN | : 3642036473 |
The Handbook is written for academics, researchers, practitioners and advanced graduate students. It has been designed to be read by those new or starting out in the field of spatial analysis as well as by those who are already familiar with the field. The chapters have been written in such a way that readers who are new to the field will gain important overview and insight. At the same time, those readers who are already practitioners in the field will gain through the advanced and/or updated tools and new materials and state-of-the-art developments included. This volume provides an accounting of the diversity of current and emergent approaches, not available elsewhere despite the many excellent journals and te- books that exist. Most of the chapters are original, some few are reprints from the Journal of Geographical Systems, Geographical Analysis, The Review of Regional Studies and Letters of Spatial and Resource Sciences. We let our contributors - velop, from their particular perspective and insights, their own strategies for m- ping the part of terrain for which they were responsible. As the chapters were submitted, we became the first consumers of the project we had initiated. We gained from depth, breadth and distinctiveness of our contributors’ insights and, in particular, the presence of links between them.
| Author | : Roger S. Bivand |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 414 |
| Release | : 2013-06-21 |
| Genre | : Medical |
| ISBN | : 1461476186 |
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.
| Author | : Jill McCoy |
| Publisher | : ESRI Press |
| Total Pages | : 0 |
| Release | : 2004 |
| Genre | : ArcGIS |
| ISBN | : 9781589481053 |
This book is an excellent reference for users of ESRI ArcGIS Spatial Analyst, one of the extensions to the ArcGIS Desktop products ArcInfo, ArcEditor, and ArcView. ArcGIS Spatial Analyst lets ArcGIS Desktop users create, query, and analyze cell-based raster maps; derive new information from existing data; query information across multiple data layers; and fully integrate cell-based raster data with traditional vector data sources. ArcGIS Spatial Analyst helps you answer questions such as How steep is it in a certain location? or What is the least-cost path from point A to point B? Begin with the quick-start tutorial for an overview of performing spatial analysis using the functions of ArcGIS Spatial Analyst. If you prefer, jump right in and experiment on your own. The book also includes concise, step-by-step, fully illustrated examples.
| Author | : Robin Lovelace |
| Publisher | : CRC Press |
| Total Pages | : 354 |
| Release | : 2019-03-22 |
| Genre | : Mathematics |
| ISBN | : 1351396900 |
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 | : George Grekousis |
| Publisher | : Cambridge University Press |
| Total Pages | : 535 |
| Release | : 2020-06-11 |
| Genre | : Reference |
| ISBN | : 1108498981 |
An introductory overview of spatial analysis and statistics through GIS, including worked examples and critical analysis of results.
| Author | : Marco Painho |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 427 |
| Release | : 2010-07-20 |
| Genre | : Science |
| ISBN | : 3642123260 |
For the fourth consecutive year, the Association of Geographic Infor- tion Laboratories for Europe (AGILE) promoted the edition of a book with the collection of the scientific papers that were submitted as full-papers to the AGILE annual international conference. Those papers went through a th competitive review process. The 13 AGILE conference call for fu- papers of original and unpublished fundamental scientific research resulted in 54 submissions, of which 21 were accepted for publication in this - lume (acceptance rate of 39%). Published in the Springer Lecture Notes in Geoinformation and Car- th graphy, this book is associated to the 13 AGILE Conference on G- graphic Information Science, held in 2010 in Guimarães, Portugal, under the title “Geospatial Thinking”. The efficient use of geospatial information and related technologies assumes the knowledge of concepts that are fundamental components of Geospatial Thinking, which is built on reasoning processes, spatial conc- tualizations, and representation methods. Geospatial Thinking is associated with a set of cognitive skills consisting of several forms of knowledge and cognitive operators used to transform, combine or, in any other way, act on that same knowledge. The scientific papers published in this volume cover an important set of topics within Geoinformation Science, including: Representation and Visualisation of Geographic Phenomena; Spatiotemporal Data Analysis; Geo-Collaboration, Participation, and Decision Support; Semantics of Geoinformation and Knowledge Discovery; Spatiotemporal Modelling and Reasoning; and Web Services, Geospatial Systems and Real-time Appli- tions.
