Products related to Analysis:
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Spatial Epidemiological Approaches in Disease Mapping and Analysis
Containing method descriptions and step-by-step procedures, the Spatial Epidemiological Approaches in Disease Mapping and Analysis equips readers with skills to prepare health-related data in the proper format, process these data using relevant functions and software, and display the results as mapped or statistical summaries.Describing the wide range of available methods and key GIS concepts for spatial epidemiology, this book illustrates the utilities of the software using real-world data.Additional topics include geographic data models, address matching, geostatistical analysis, universal kriging, point pattern analysis, kernel density, spatio-temporal display, and disease surveillance.
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Crime Mapping and Spatial Data Analysis using R
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.
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Spatial Analysis with R : Statistics, Visualization, and Computational Methods
In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R.The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis.Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes.New in the Second Edition: Includes new practical exercises and worked-out examples using R Presents a wide range of hands-on spatial analysis worktables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data miningThis second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.
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Spatial Analysis with R : Statistics, Visualization, and Computational Methods
In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R.The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis.Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes.New in the Second Edition: Includes new practical exercises and worked-out examples using R Presents a wide range of hands-on spatial analysis worktables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data miningThis second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.
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How do you conduct a spatial analysis?
To conduct a spatial analysis, you first need to gather relevant spatial data such as maps, satellite images, or GPS coordinates. Next, you use Geographic Information System (GIS) software to analyze and visualize the data. This can involve tasks such as overlaying different layers of data, performing spatial queries, and creating maps or models to identify patterns or relationships within the data. Finally, you interpret the results of the analysis to draw conclusions or make decisions based on the spatial information.
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What is the spatial analysis for Brazil?
Spatial analysis for Brazil involves examining the geographical patterns and relationships within the country. This includes analyzing the distribution of population, natural resources, infrastructure, and economic activities across different regions. It also involves studying the impact of spatial factors on social, environmental, and economic issues, such as urbanization, deforestation, and regional disparities. By using geographic information systems (GIS) and other spatial analysis tools, researchers and policymakers can better understand and address the spatial dynamics of Brazil's development and challenges.
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What is the geographical spatial analysis of Malaysia?
Malaysia is located in Southeast Asia and is divided into two main regions: Peninsular Malaysia and Malaysian Borneo. Peninsular Malaysia is located on the Malay Peninsula, bordered by Thailand to the north and Singapore to the south. Malaysian Borneo is situated on the island of Borneo, sharing borders with Indonesia and Brunei. The country has a diverse geography, including coastal plains, mountains, and tropical rainforests, making it rich in biodiversity. Additionally, Malaysia is known for its strategic location along major maritime routes, contributing to its importance in regional trade and commerce.
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What is the spatial analysis for South America?
Spatial analysis for South America involves examining the geographical patterns and relationships within the continent. This can include studying the distribution of natural resources, population density, land use, and environmental factors. Spatial analysis can also be used to analyze the impact of human activities on the landscape, such as deforestation, urbanization, and infrastructure development. By using geographic information systems (GIS) and other spatial analysis tools, researchers can gain valuable insights into the spatial dynamics of South America and make informed decisions for sustainable development and resource management.
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An Introduction to R for Spatial Analysis and Mapping
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.
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An Introduction to R for Spatial Analysis and Mapping
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.
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An Introduction to R for Spatial Analysis and Mapping
This is a new edition of the accessible and student-friendly 'how to' for anyone using R for the first time, for use in spatial statistical analysis, geocomputation and digital mapping.The authors, once again, take readers from ‘zero to hero’, updating the now standard text to further enable practical R applications in GIS, spatial analyses, spatial statistics, web-scraping and more. Revised and updated, each chapter includes: example data and commands to explore hands-on;scripts and coding to exemplify specific functionality;self-contained exercises for students to work through;embedded code within the descriptive text. The new edition includes detailed discussion of new and emerging packages within R like sf, ggplot, tmap, making it the go to introduction for all researchers collecting and using data with location attached.This is the introduction to the use of R for spatial statistical analysis, geocomputation, and GIS for all researchers - regardless of discipline - collecting and using data with location attached.
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Spatial Analysis
SPATIAL ANALYSIS Explore the foundations and latest developments in spatial statistical analysis In Spatial Analysis, two distinguished authors deliver a practical and insightful exploration of the statistical investigation of the interdependence of random variables as a function of their spatial proximity.The book expertly blends theory and application, offering numerous worked examples and exercises at the end of each chapter.Increasingly relevant to fields as diverse as epidemiology, geography, geology, image analysis, and machine learning, spatial statistics is becoming more important to a wide range of specialists and professionals.The book includes: Thorough introduction to stationary random fields, intrinsic and generalized random fields, and stochastic modelsComprehensive exploration of the estimation of spatial structurePractical discussion of kriging and the spatial linear model Spatial Analysis is an invaluable resource for advanced undergraduate and postgraduate students in statistics, data science, digital imaging, geostatistics, and agriculture.It’s also an accessible reference for professionals who are required to use spatial models in their work.
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What is spatial visualization ability?
Spatial visualization ability refers to the capacity to mentally manipulate and comprehend spatial relationships between objects. Individuals with strong spatial visualization skills can easily visualize and understand how objects relate to each other in space, such as rotating or manipulating shapes in their mind. This ability is crucial in various fields such as engineering, architecture, and mathematics, as it allows individuals to solve complex problems and understand spatial concepts more effectively. Improving spatial visualization ability can enhance problem-solving skills and overall cognitive performance.
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What is the main question for the spatial analysis?
The main question for the spatial analysis is: "What is the relationship between the geographic location of a phenomenon and its characteristics or behavior?" This analysis seeks to understand how the spatial distribution of data points may influence patterns, trends, or relationships within a specific area. By examining the spatial relationships between different variables, researchers can uncover insights into the underlying processes driving the observed patterns.
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Is spatial visualization important for engineers?
Yes, spatial visualization is important for engineers as it allows them to mentally manipulate and understand complex 3D objects and structures. Engineers often need to design and analyze various components and systems, and spatial visualization skills help them to conceptualize and communicate their ideas effectively. Whether it's designing a new product, creating blueprints for a building, or solving complex problems, spatial visualization is a crucial skill that allows engineers to think critically and innovate in their field.
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How is gene mapping done through a 3-point analysis?
Gene mapping through a 3-point analysis involves examining the recombination frequencies between three genetic markers located on the same chromosome. By analyzing the frequency of recombination events between these markers, the relative distances between them can be estimated. This information helps in constructing a genetic map of the chromosome, showing the order and relative positions of the markers. The closer two markers are to each other, the lower the frequency of recombination events between them, indicating they are more tightly linked.
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