Geographic Data Science With R

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Geographic Data Science With R

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Author : Michael C. Wimberly
language : en
Publisher: CRC Press
Release Date : 2023-05-08

Geographic Data Science With R written by Michael C. Wimberly and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-08 with Business & Economics categories.

The burgeoning field of data science has provided a wealth of techniques for analysing large and complex geospatial datasets, including descriptive, explanatory, and predictive analytics. However, applying these methods is just one part of the overall process of geographic data science. Other critical steps include screening for suspect data values, handling missing data, harmonizing data from multiple sources, summarizing the data, and visualizing data and analysis results. Although there are many books available on statistical and machine learning methods, few encompass the broader topic of scientific workflows for geospatial data processing and analysis. The purpose of Geographic Data Science with R is to fill this gap by providing a series of tutorials aimed at teaching good practices for using geospatial data to address problems in environmental geography. It is based on the R language and environment, which currently provides the best option for working with diverse spatial and non-spatial data in a single platform. Fundamental techniques for processing and visualizing tabular, vector, and raster data are introduced through a series of practical examples followed by case studies that combine multiple types of data to address more complex problems. The book will have a broad audience. Both students and professionals can use it as a workbook to learn high-level techniques for geospatial data processing and analysis with R. It is also suitable as a textbook. Although not intended to provide a comprehensive introduction to R, it is designed to be accessible to readers who have at least some knowledge of coding but little to no experience with R. Key Features: Focus on developing practical workflows for processing and integrating multiple sources of geospatial data in R Example-based approach that teaches R programming and data science concepts through real-world applications related to climate, land cover and land use, and natural hazards. Consistent use of tidyverse packages for tabular data manipulation and visualization. Strong focus on analysing continuous and categorical raster datasets using the new terra package Organized so that each chapter builds on the topics and techniques covered in the preceding chapters Can be used for self-study or as the textbook for a geospatial science course.

Spatial Data Science

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Author : Edzer Pebesma
language : en
Publisher: CRC Press
Release Date : 2023-05-10

Spatial Data Science written by Edzer Pebesma and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-10 with Mathematics categories.

Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries. In the second part of the book, these concepts are illustrated with data science examples using the R language. In the third part, statistical modelling approaches are demonstrated using real world data examples. After reading this book, the reader will be well equipped to avoid a number of major spatial data analysis errors. The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes – array data with spatial and temporal dimensions. It also shows how geometrical operations change when going from a flat space to the surface of a sphere, which is what sf and stars use when coordinates are not projected (degrees longitude/latitude). Separate chapters detail a variety of plotting approaches for spatial maps using R, and different ways of handling very large vector or raster (imagery) datasets, locally, in databases, or in the cloud. The data used and all code examples are freely available online from https://r-spatial.org/book/. The solutions to the exercises can be found here: https://edzer.github.io/sdsr_exercises/.

Geographical Data Science And Spatial Data Analysis

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Author : Lex Comber
language : en
Publisher: SAGE
Release Date : 2020-12-02

Geographical Data Science And Spatial Data Analysis written by Lex Comber and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-02 with Science categories.

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.

Geographic Data Analysis Using R

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Author : Xindong He
language : en
Publisher: Springer Nature
Release Date : 2024-08-02

Geographic Data Analysis Using R written by Xindong He and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-02 with Science categories.

This book is structured to encompass both the foundational and specialized aspects of quantitative analysis in geography. The basic content covers descriptive statistical analysis and correlation analysis of geographical data, while the professional content delves into more advanced topics like linear regression analysis, geographically weighted regression analysis, time series analysis, cluster analysis, principal component analysis, Markov chain analysis, and geographical network analysis. The methodologies span from widely utilized techniques to more recent developments, and the data primarily originates from reputable sources in China. The example code provided in the book can be executed using R packages available on the CRAN website. This book is an invaluable resource for undergraduate and graduate students, as well as researchers interested in learning and applying R for processing, visualizing, and analyzing geographic data. It serves as an introductory course in quantitative methods in geography for students in geography departments. Additionally, it is an ideal supplementary text for applied methods courses across various disciplines that involve geographic data, such as human and physical geography, geographic information science, ecology, public health, crime, and economics.

Introduction To Nfl Analytics With R

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Author : Bradley J. Congelio
language : en
Publisher: CRC Press
Release Date : 2023-12-19

Introduction To Nfl Analytics With R written by Bradley J. Congelio and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-19 with Sports & Recreation categories.

It has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plans, and commentators, throughout broadcasts, regularly use terms such as air yards, CPOE, and EPA on a casual basis. This rapid growth, combined with an increasing accessibility to NFL data, has helped create a burgeoning amateur analytics movement, highlighted by the NFL’s annual Big Data Bowl. Because learning a coding language can be a difficult enough endeavor, Introduction to NFL Analytics with R is purposefully written in a more informal format than readers of similar books may be accustomed to, opting to provide step-by-step instructions in a structured, jargon-free manner. Key Coverage: Installing R, RStudio, and necessary packages Working and becoming fluent in the tidyverse Finding meaning in NFL data with examples from all the functions in the nflverse family of packages Using NFL data to create eye-catching data visualizations Building statistical models starting with simple regressions and progressing to advanced machine learning models using tidymodels and eXtreme Gradient Boosting The book is written for novices of R programming all the way to more experienced coders, as well as audiences with differing expected outcomes. Professors can use Introduction to NFL Analytics with R to provide data science lessons through the lens of the NFL, while students can use it as an educational tool to create robust visualizations and machine learning models for assignments. Journalists, bloggers, and arm-chair quarterbacks alike will find the book helpful to underpin their arguments by providing hard data and visualizations to back up their claims.

Crime Mapping And Spatial Data Analysis Using R

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Author : Juan Medina Ariza
language : en
Publisher: CRC Press
Release Date : 2023-04-27

Crime Mapping And Spatial Data Analysis Using R written by Juan Medina Ariza and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-27 with Mathematics categories.

Crime mapping and analysis sit at the intersection of geocomputation, data visualisation and cartography, spatial statistics, environmental criminology, and crime analysis. This book brings together relevant knowledge from these fields into a practical, hands-on guide, providing a useful introduction and reference material for topics in crime mapping, the geography of crime, environmental criminology, and crime analysis. It can be used by students, practitioners, and academics alike, whether to develop a university course, to support further training and development, or to hone skills in self-teaching R and crime mapping and spatial data analysis. It is not an advanced statistics textbook, but rather an applied guide and later useful reference books, intended to be read and for readers to practice the learnings from each chapter in sequence. In the first part of this volume we introduce key concepts for geographic analysis and representation and provide the reader with the foundations needed to visualise spatial crime data. We then introduce a series of tools to study spatial homogeneity and dependence. A key focus in this section is how to visualise and detect local clusters of crime and repeat victimisation. The final chapters introduce the use of basic spatial models, which account for the distribution of crime across space. In terms of spatial data analysis the focus of the book is on spatial point pattern analysis and lattice or area data analysis.

Geographic Data Science With Python

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Author : Sergio Rey
language : en
Publisher: CRC Press
Release Date : 2023-06-14

Geographic Data Science With Python written by Sergio Rey and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-14 with Science categories.

This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data. Key Features: ● Showcases the excellent data science environment in Python. ● Provides examples for readers to replicate, adapt, extend, and improve. ● Covers the crucial knowledge needed by geographic data scientists. It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts. Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.

Geographic Information Systems Data Science Approach

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Author : Rifaat Abdalla
language : en
Publisher: BoD – Books on Demand
Release Date : 2024-03-13

Geographic Information Systems Data Science Approach written by Rifaat Abdalla and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-13 with Science categories.

Dive into the dynamic world of Geographic Information Systems (GIS) and data science with our comprehensive book in which innovation and insights converge. This book presents a pioneering exploration at the intersection of GIS and data science, providing a comprehensive view of their symbiotic relationship and transformative potential. It encapsulates advanced methodologies, real-world applications, and interdisciplinary approaches that redefine how we perceive and utilize spatial data. Offering a gateway to cutting-edge research and practical insights, this book serves as a crucial resource for scholars, practitioners, and enthusiasts alike. It addresses pressing challenges across diverse domains, from environmental studies to public health and predictive analytics, demonstrating the paramount significance of integrating GIS with data science methodologies. It is an essential compass guiding readers toward a deeper understanding and application of these dynamic fields in today's data-driven world.

An Introduction To R For Spatial Analysis And Mapping

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Author : Chris Brunsdon
language : en
Publisher: SAGE Publications Limited
Release Date : 2025-04-18

An Introduction To R For Spatial Analysis And Mapping written by Chris Brunsdon and has been published by SAGE Publications Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-18 with Social Science categories.

The ever-expanding availability of spatial data continues to revolutionise research. This book is your go-to guide to getting the most out of handling, mapping and analysing location-based data. Without assuming prior knowledge of GIS, geocomputation or R, this book helps you understand spatial analysis and mapping and develop your programming skills, from learning about scripting and writing functions to point pattern analysis and spatial attribute analysis. The book: Illustrates approaches to analysis on a range of datasets that are new to this edition. Enables you to put your skills into practice with embedded exercises and over 30 self-test questions. Showcases the possibilities of using spatial analysis to explore spatial inequalities. Whether you’re an R novice or experienced user, this book equips upper undergraduates, postgraduates and researchers with the tools needed for spatial data handling and rich analysis.

An Introduction To Spatial Data Science With Geoda

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Author : Luc Anselin
language : en
Publisher: CRC Press
Release Date : 2024-04-26

An Introduction To Spatial Data Science With Geoda written by Luc Anselin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-26 with Science categories.

This book is the first in a two-volume series that introduces the field of spatial data science. It offers an accessible overview of the methodology of exploratory spatial data analysis. It also constitutes the definitive user’s guide for the widely adopted GeoDa open-source software for spatial analysis. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques, using dynamic graphics for thematic mapping, statistical graphing, and, most centrally, the analysis of spatial autocorrelation. Key to this analysis is the concept of local indicators of spatial association, pioneered by the author and recently extended to the analysis of multivariate data. The focus of the book is on intuitive methods to discover interesting patterns in spatial data. It offers a progression from basic data manipulation through description and exploration to the identification of clusters and outliers by means of local spatial autocorrelation analysis. A distinctive approach is to spatialize intrinsically non-spatial methods by means of linking and brushing with a range of map representations, including several that are unique to the GeoDa software. The book also represents the most in-depth treatment of local spatial autocorrelation and its visualization and interpretation by means of GeoDa. The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns. Some basic familiarity with statistical concepts is assumed, but no previous knowledge of GIS or mapping is required. Key Features: • Includes spatial perspectives on cluster analysis • Focuses on exploring spatial data • Supplemented by extensive support with sample data sets and examples on the GeoDaCenter website This book is both useful as a reference for the software and as a text for students and researchers of spatial data science. Luc Anselin is the Founding Director of the Center for Spatial Data Science at the University of Chicago, where he is also the Stein-Freiler Distinguished Service Professor of Sociology and the College, as well as a member of the Committee on Data Science. He is the creator of the GeoDa software and an active contributor to the PySAL Python open-source software library for spatial analysis. He has written widely on topics dealing with the methodology of spatial data analysis, including his classic 1988 text on Spatial Econometrics. His work has been recognized by many awards, such as his election to the U.S. National Academy of Science and the American Academy of Arts and Science.