Geocomputation with Python by
"Geocomputation with Python (geocompy) is motivated by the need for an introductory, yet rigorous and up-to-date, resource for working with geographic data with the most popular programming language in the world. A unique selling point of the book is its cohesive and joined-up coverage of both vector and raster geographic data models and consistent learning curve. This book is an excellent place to start for those starting our working on geographic data with Python, making it ideal for students and practitioners beginning their journey with Python. There are many resources on Python on 'GeoPython' but none that fill this need for an introductory resource that provides strong foundations for future work. This book fills an 'ecological niche' in the wider free and open-source software for geospatial ecosystem. Another unique feature is that this book is part of a wider community. Geocomputation with Python is a sister project of Geocomputation with R (Lovelace, Nowosad, and Muenchow 2019), a book on geographic data analysis, visualization, and modeling using the R programming language that has 60+ contributors and an active community, not least in the associated Discord group. Links with the vibrant 'R-spatial' community, and other communities such as GeoRust and JuliaGeo, lead to many opportunities for mutual benefit across open-source ecosystems"-- Provided by publisher.
Big Data: Techniques and Technologies in Geoinformatics by
This revised new edition provides up-to-date knowledge on the latest developments related to these three fields for solving geoinformatics problems. There are seven new chapters, and each of them focuses on a separate real-world problem to which deep learning is applied.
Geospatial Data, Information, and Intelligence by
An original framework for interpreting geospatial data and information: the Observe, Analyze, Communicate (OAC) Framework. With a two-level approach to analyzing geospatial data: both a broad information refinement method that transforms geospatial data into information, and more specific tradecraft techniques for both visual and spatial analysis. The book highlights the importance of location for human experience in the everyday world and introduces spatial thinking as a foundation and the location mindset as an approach. Exploring the outsized importance of accurate visual learning in a world in which we are increasingly inundated with visual information and shows how the human eye can be both our most powerful sensor as well as a source of bias and uncertainty. It presents the emergence of digital geospatial data, information, and analysis, and shows how the pairing of visual information with precise locational information anchors human attention and provides an antidote to disorientation. This book is a practical resource for students and practitioners alike. It serves as a textbook for classroom beginners and as a desktop reference for professionals in their day-to-day geospatial efforts.
Recent Advancement in Geoinformatics and Data Science by
"The chapters in this Special Paper present the latest progress and discoveries in both the methodology and technology of geoinformatics. The methodologies, technologies, and best practices will make this volume a useful reference with long-term impacts for data-intensive geoscience in the next decade and beyond"--
Spatiotemporal Analytics by
This book introduces readers to spatiotemporal analytics that are extended from spatial statistics. Spatiotemporal analytics help analysts to quantitatively recognize and evaluate the spatial patterns and their temporal trends of a set of geographic events or objects. Spatiotemporal analyses are very important in geography, environmental sciences, economy, and many other domains. This book explains with very simple terms the concepts of spatiotemporal data and statistics, theories, and methods used. Each chapter introduces a case study as an example application for an in-depth learning process. The software used and the codes provided enable readers to not only learn statistics but also to use them effectively in their projects.
Advances in Scalable and Intelligent Geospatial Analytics: Challenges and Applications by
Geospatial data acquisition and analysis techniques have experienced tremendous growth in the last few years, providing an opportunity to solve previously unsolved environmental- and natural resource-related problems. However, a variety of challenges are encountered in processing the highly voluminous geospatial data in a scalable and efficient manner. Technological advancements in high-performance computing, computer vision, and big data analytics are enabling the processing of big geospatial data in an efficient and timely manner. Many geospatial communities have already adopted these techniques in multidisciplinary geospatial applications around the world. This book is a single source that offers a comprehensive overview of the state of the art and future developments in this domain. FEATURES Demonstrates the recent advances in geospatial analytics tools, technologies, and algorithms Provides insight and direction to the geospatial community regarding the future trends in scalable and intelligent geospatial analytics Exhibits recent geospatial applications and demonstrates innovative ways to use big geospatial data to address various domain-specific, real-world problems Recognizes the analytical and computational challenges posed and opportunities provided by the increased volume, velocity, and veracity of geospatial data This book is beneficial to graduate and postgraduate students, academicians, research scholars, working professionals, industry experts, and government research agencies working in the geospatial domain, where GIS and remote sensing are used for a variety of purposes. Readers will gain insights into the emerging trends on scalable geospatial data analytics.
Spatial Data Science: with Applications in R by
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/.
GIS and Spatial Analysis by
The objective of spatial analysis techniques is to describe the patterns existing in spatial data and to establish, preferably quantitatively, the relationships between different geographic variables. The notion of spatial analysis in a Geographic Information Systems (GIS) environment encompasses the idea of integrating spatial data and alphanumeric attributes and translating it into a series of functions related to selection and data search, on the one hand, and with modelling, on the other. There have been substantial advances in spatial analysis techniques in GIS, mainly in the form of more faithfully apprehending the relationships inherent to the geographic phenomenon, something that was proven impossible to do with non-spatial techniques. Nowadays, spatial analysis involves a set of techniques used to analyse and model variables with distribution in space and/or time. The new era of spatial analysis must also consider the possibilities of integrating artificial intelligence in simulation (geosimulation) processes in computerised environments (geocomputation) in close relationship with models developed in real situations. GIS have emerged as useful tools in geographic modelling processes, helping to answer questions about the time variability of the landscape structure, study the behaviour of fire, predict areas of urban expansion, analyse propagation phenomena, model animal movement and behaviour, and determine periods and areas of high risk of flooding, among other phenomena. GIS and Spatial Analysis is a critical book that provides different methodologies that combine the potential data (including Big Data) analysis with GIS applications. It gives readers a comprehensive overview of the current state-of-the-art methods of spatial analysis, focusing both on the new philosophical and theoretical foundations for spatial analysis and on a flexible framework for analysis in the real world, for problems such as complexity and uncertainty.
Computational Methods and GIS Applications in Social Sciences by
This textbook integrates GIS, spatial analysis, and computational methods for solving real-world problems in various policy-relevant social science applications. Thoroughly updated, the third edition showcases the best practices of computational spatial social science and includes numerous case studies with step-by-step instructions in ArcGIS Pro and open-source platform KNIME. Readers sharpen their GIS skills by applying GIS techniques in detecting crime hotspots, measuring accessibility of primary care physicians, forecasting the impact of hospital closures on local community, or siting the best locations for business. FEATURES Fully updated using the latest version of ArcGIS Pro and open-source platform KNIME Features two brand-new chapters on agent-based modeling and big data analytics Provides newly automated tools for regionalization, functional region delineation, accessibility measures, planning for maximum equality in accessibility, and agent-based crime simulation Includes many compelling examples and real-world case studies related to social science, urban planning, and public policy Provides a website for downloading data and programs for implementing all case studies included in the book and the KNIME lab manual Intended for students taking upper-level undergraduate and graduate-level courses in quantitative geography, spatial analysis, and GIS applications, as well as researchers and professionals in fields such as geography, city and regional planning, crime analysis, public health, and public administration.
Computational Methods and GIS Applications in Social Sciences: Lab Manual by
"A companion to the third edition of the textbook Computational Methods and GIS Applications in Social Sciences, this lab manual uses an open-source platform, KNIME to illustrate a step-by-step implementation of each case study in the book. KNIME is a workflow-based platform supporting visual programming and multiple scripting language such as R, Python, and Java. The intuitive, structural workflow helps students understand the methodology of each case study and enables them to easily replicate, transplant and expand the workflow for further exploration with new data or models. Advanced users of spatial analysis can also use this as a reference for GIS automation"-- Provided by publisher.
Spatial Statistics Illustrated by
Spatial statistics empowers you to go beyond visual analysis to answer questions confidently and make data-driven decisions. Thanks to the data and computational power now at our fingertips, data science is in every aspect of our lives. But with so many algorithms and buzzwords floating around, where do you start to solve complex problems or figure out where to go next? There has never been a more exciting time to learn about spatial statistics. Spatial statistics uses an aspect of geography that helps you quantify patterns and relationships so that you can feel confident in your analysis. Spatial Statistics Illustrated is an introductory book for learning the concepts behind the powerful spatial statistics tools in ArcGIS. With approachable explanations and uncomplicated drawings, Spatial Statistics Illustrated gives readers an accessible understanding of some of the most widely used spatial statistics methods, including how they work and when to use them. In a friendly, conversational tone, the authors share techniques that can help you explore your data in meaningful ways; quantify patterns and relationships; understand trends, and make informed, impactful decisions. This book has something for everyone analyzing data, including: seasoned data scientists looking to explore the value that spatial analysis offers GIS analysts looking to expand their spatial statistics skill set new GIS users discovering the value of spatial statistics Spatial Statistics Illustrated is a perfect complement to more traditional, technical statistics and spatial statistics texts and is also ideal as supplemental reading for academic courses. Based on the popular series of Spatial Statistics workshops presented by the authors at the annual Esri User Conference, Spatial Statistics Illustrated welcomes readers into the unparalleled world of spatial statistics.
Multiscale Geographically Weighted Regression: Theory and Practice by
Multiscale geographically weighted regression (MGWR) is an important method that is used across many disciplines for exploring spatial heterogeneity and modeling local spatial processes. This book introduces the concepts behind local spatial modeling and explains how to model heterogeneous spatial processes within a regression framework. It starts with the basic ideas and fundamentals of local spatial modeling followed by a detailed discussion of scale issues and statistical inference related to MGWR. A comprehensive guide to free, user-friendly, software for MGWR is provided, as well as an example of the application of MGWR to understand voting behavior in the 2020 US Presidential election. Multiscale Geographically Weighted Regression: Theory and Practice is the definitive guide to local regression modeling and the analysis of spatially varying processes, a very cutting-edge, hands-on, and innovative resource. Features Provides a balance between conceptual and technical introduction to local models Explains state-of-the-art spatial analysis technique for multiscale regression modeling Describes best practices and provides a detailed walkthrough of freely available software, through examples and comparisons with other common spatial data modeling techniques Includes a detailed case study to demonstrate methods and software Takes a new and exciting angle on local spatial modeling using MGWR, an innovation to the previous local modeling 'bible' GWR The book is ideal for senior undergraduate and graduate students in advanced spatial analysis and GIS courses taught in any spatial science discipline as well as for researchers, academics, and professionals who want to understand how location can affect human behavior through local regression modeling.
Spatial Statistics for Data Science: Theory and Practice with R by
Spatial data is crucial to improve decision-making in a wide range of fields including environment, health, ecology, urban planning, economy, and society. Spatial Statistics for Data Science: Theory and Practice with R describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. The book provides a comprehensive overview of the varying types of spatial data, and detailed explanations of the theoretical concepts of spatial statistics, alongside fully reproducible examples which demonstrate how to simulate, describe, and analyze spatial data in various applications. Combining theory and practice, the book includes real-world data science examples such as disease risk mapping, air pollution prediction, species distribution modeling, crime mapping, and real state analyses. The book utilizes publicly available data and offers clear explanations of the R code for importing, manipulating, analyzing, and visualizing data, as well as the interpretation of the results. This ensures contents are easily accessible and fully reproducible for students, researchers, and practitioners. Key Features: Describes R packages for retrieval, manipulation, and visualization of spatial data. Offers a comprehensive overview of spatial statistical methods including spatial autocorrelation, clustering, spatial interpolation, model-based geostatistics, and spatial point processes. Provides detailed explanations on how to fit and interpret Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches.
Spatial Analysis by
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 models Comprehensive exploration of the estimation of spatial structure Practical 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.
Python for Geospatial Data Analysis: Theory, Tools, and Practice for Location Intelligence by
In spatial data science, things in closer proximity to one another likely have more in common than things that are farther apart. With this practical book, geospatial professionals, data scientists, business analysts, geographers, geologists, and others familiar with data analysis and visualization will learn the fundamentals of spatial data analysis to gain a deeper understanding of their data questions. Author Bonny P. McClain demonstrates why detecting and quantifying patterns in geospatial data is vital. Both proprietary and open source platforms allow you to process and visualize spatial information. This book is for people familiar with data analysis or visualization who are eager to explore geospatial integration with Python. This book helps you: Understand the importance of applying spatial relationships in data science Select and apply data layering of both raster and vector graphics Apply location data to leverage spatial analytics Design informative and accurate maps Automate geographic data with Python scripts Explore Python packages for additional functionality Work with atypical data types such as polygons, shape files, and projections Understand the graphical syntax of spatial data science to stimulate curiosity
The Esri Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics by
Learn how to get better answers in map analysis when you use spatial measurements and statistics. Spatial measurements and statistics give you a powerful way to analyze geospatial data, but you don't need to understand complex mathematical theories to apply statistical tools and get meaningful results in your projects. The Esri Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics, second edition, builds on Volume 1 by taking you to the next step of GIS analysis. Learn to answer such questions as, how are features distributed? What is the pattern created by a set of features? Where can clusters be found? This book introduces readers to basic statistical concepts and some of the most common spatial statistics tasks: measuring distributions, identifying patterns and clusters, and analyzing relationships. Updated with the latest and most useful software tools and revised explanations, each chapter in The Esri Guide to GIS Analysis, Volume 2 is organized to answer basic questions about the topic. Explore how spatial statistical tools can be applied in a range of disciplines, from public health to habitat conservation. Learn how to quantify patterns beyond visualizing them in maps. Examine spatial clusters through an updated chapter on identifying clusters. Use The Esri Guide to GIS Analysis, Volume 2, second edition, to understand the statistical methods and tools that can move your work past mapping and visualization to more quantitative statistical assessment.
PostGIS in Action, Third Edition by
PostGIS in Action, Third Edition shows you how to solve real-world geodata problems. You'll go beyond basic mapping, and explore custom functions for your applications. Summary In PostGIS in Action, Third Edition you will learn: An introduction to spatial databases Geometry, geography, raster, and topology spatial types, functions, and queries Applying PostGIS to real-world problems Extending PostGIS to web and desktop applications Querying data from external sources using PostgreSQL Foreign Data Wrappers Optimizing queries for maximum speed Simplifying geometries for greater efficiency PostGIS in Action, Third Edition teaches readers of all levels to write spatial queries for PostgreSQL. You'll start by exploring vector-, raster-, and topology-based GIS before quickly progressing to analyzing, viewing, and mapping data. This fully updated third edition covers key changes in PostGIS 3.1 and PostgreSQL 13, including parallelization support, partitioned tables, and new JSON functions that help in creating web mapping applications. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology PostGIS is a spatial database extender for PostgreSQL. It offers the features and firepower you need to take on nearly any geodata task. PostGIS lets you create location-aware queries with a few lines of SQL code, then build the backend for mapping, raster analysis, or routing application with minimal effort. About the book PostGIS in Action, Third Edition shows you how to solve real-world geodata problems. You'll go beyond basic mapping, and explore custom functions for your applications. Inside this fully updated edition, you'll find coverage of new PostGIS features such as PostGIS Window functions, parallelization of queries, and outputting data for applications using JSON and Vector Tile functions. What's inside Fully revised for PostGIS version 3.1 and PostgreSQL 13 Optimize queries for maximum speed Simplify geometries for greater efficiency Extend PostGIS to web and desktop applications About the reader For readers familiar with relational databases and basic SQL. No prior geodata or GIS experience required. About the author Regina Obe and Leo Hsu are database consultants and authors. Regina is a member of the PostGIS core development team and the Project Steering Committee. Table of Contents PART 1 INTRODUCTION TO POSTGIS 1 What is a spatial database? 2 Spatial data types 3 Spatial reference systems 4 Working with real data 5 Using PostGIS on the desktop 6 Geometry and geography functions 7 Raster functions 8 Spatial relationships PART 2 PUTTING POSTGIS TO WORK 9 Proximity analysis 10 PostGIS TIGER geocoder 11 Geometry and geography processing 12 Raster processing 13 Building and using topologies 14 Organizing spatial data 15 Query performance tuning PART 3 USING POSTGIS WITH OTHER TOOLS 16 Extending PostGIS with pgRouting and procedural languages 17 Using PostGIS in web applications
GIS for science : maps for saving the planet by
GIS for Science: Maps for Saving the Planet, Volume 3, highlights real-world examples of scientists creating maps about saving life on Earth and preserving biodiversity. With Earth and the natural world at risk from various forces, geographic information system (GIS) mapping is essential for driving scientifically conscious decision-making about how to protect life on Earth. In volume 3 of GIS for Science, explore a collection of maps from scientists working to save the planet through documenting and protecting its biodiversity. In this volume, learn how GIS and data mapping are used in tandem with global satellite observation forestry marine policy artificial intelligence conservation biology, and environmental education to help preserve and chronicle life on Earth. This volume also spotlights important global action initiatives incorporating conservation, including Half-Earth, 30 x 30, AI for Earth, the Blue Nature Alliance, and the Sustainable Development Solutions Network. The stories presented in this third volume are ideal for the professional scientist and conservationist and anyone interested in the intersection of technology and the conservation of nature. The book's contributors include scientists who are applying geographic data gathered from the full spectrum of remote sensing and on-site technologies. The maps and data are brought to life using ArcGIS® software and other spatial data science tools that support research, collaboration, spatial analysis, and science communication across many locations and within diverse communities. The stories shared in this book and its companion website present inspirational ideas so that GIS users and scientists can work toward preserving biodiversity and saving planet Earth before time runs out.
Essential Graphical Techniques in Geography by
Representation of geographical data using graphs, diagrams and mapping techniques is a key for geographers and for researchers in other disciplines to explore the nature of data, the pattern of spatial and temporal variations and their relationships, and formulation of principles to accurately understand and analyze features on or near the earth's surface. These modes of representation also enable the development of spatial understanding and the capacity for technical and logical decision making. The book depicts all types of graphs, diagrams and maps, explained in detail with numerous examples. The emphasis is on their appropriate data structure, the relevance of selecting the correct technique, methods of their construction, advantages and disadvantages of their use, and applications of these techniques in analyzing and realizing the spatial pattern of various geographical features and phenomena. This book is unique in that it reflects a perfect correlation between theoreticalknowledge of geographical events and phenomena and their realistic implications, with relevant examples using appropriate graphical methods. The book serves as a valuable resource for students, researchers, cartographers and decision makers to analyze and represent various geographical data for a better, systematic and scientific understanding of the real world.
Fundamentals of Spatial Analysis and Modelling by
This textbook provides comprehensive and in-depth explanations of all topics related to spatial analysis and spatiotemporal simulation, including how spatial data are acquired, represented digitally, and spatially aggregated. Also features the nature of space and how it is measured. Descriptive, explanatory, and inferential analyses are covered for point, line, and area data. It captures the latest developments in spatiotemporal simulation with cellular automata and agent-based modelling, and through practical examples discusses how spatial analysis and modelling can be implemented in different computing platforms. A much-needed textbook for a course at upper undergraduate and postgraduate levels.
Modelling spatial and spatial-temporal data : a Bayesian approach by
Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach is aimed at statisticians and quantitative social, economic and public health students and researchers who work with spatial and spatial-temporal data. It assumes a grounding in statistical theory up to the standard linear regression model. The book compares both hierarchical and spatial econometric modelling, providing both a reference and a teaching text with exercises in each chapter. The book provides a fully Bayesian, self-contained, treatment of the underlying statistical theory, with chapters dedicated to substantive applications. The book includes WinBUGS code and R code and all datasets are available online. Part I covers fundamental issues arising when modelling spatial and spatial-temporal data. Part II focuses on modelling cross-sectional spatial data and begins by describing exploratory methods that help guide the modelling process. There are then two theoretical chapters on Bayesian models and a chapter of applications. Two chapters follow on spatial econometric modelling, one describing different models, the other substantive applications. Part III discusses modelling spatial-temporal data, first introducing models for time series data. Exploratory methods for detecting different types of space-time interaction are presented followed by two chapters on the theory of space-time separable (without space-time interaction) and inseparable (with space-time interaction) models. An applications chapter includes: the evaluation of a policy intervention; analysing the temporal dynamics of crime hotspots; chronic disease surveillance; and testing for evidence of spatial spillovers in the spread of an infectious disease. A final chapter suggests some future directions and challenges.
Spatial analysis with R : statistics, visualization, and computational methods by
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 mining This 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.
Introduction to spatial data analysis : remote sensing and GIS with open source software by
This is a book about how ecologists can integrate remote sensing and GIS in their research. It will allow readers to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. An Introduction to Spatial Data Analysis introduces spatial data handling using the open source software Quantum GIS (QGIS). In addition, readers will be guided through their first steps in the R programming language. The authors explain the fundamentals of spatial data handling and analysis, empowering the reader to turn data acquired in the field into actual spatial data. Readers will learn to process and analyse spatial data of different types and interpret the data and results. After finishing this book, readers will be able to address questions such as "What is the distance to the border of the protected area?", "Which points are located close to a road?", "Which fraction of land cover types exist in my study area?" using different software and techniques. This book is for novice spatial data users and does not assume any prior knowledge of spatial data itself or practical experience working with such data sets. Readers will likely include student and professional ecologists, geographers and any environmental scientists or practitioners who need to collect, visualize and analyse spatial data. The software used is the widely applied open source scientific programs QGIS and R. All scripts and data sets used in the book will be provided online at book.ecosens.org. This book covers specific methods including: what to consider before collecting in situ data how to work with spatial data collected in situ the difference between raster and vector data how to acquire further vector and raster data how to create relevant environmental information how to combine and analyse in situ and remote sensing data how to create useful maps for field work and presentations how to use QGIS and R for spatial analysis how to develop analysis scripts
Analyzing Spatial Models of Choice and Judgment by
With recent advances in computing power and the widespread availability of preference, perception and choice data, such as public opinion surveys and legislative voting, the empirical estimation of spatial models using scaling and ideal point estimation methods has never been more accessible.The second edition of Analyzing Spatial Models of Choice and Judgment demonstrates how to estimate and interpret spatial models with a variety of methods using the open-source programming language R. Requiring only basic knowledge of R, the book enables social science researchers to apply the methods to their own data. Also suitable for experienced methodologists, it presents the latest methods for modeling the distances between points. The authors explain the basic theory behind empirical spatial models, then illustrate the estimation technique behind implementing each method, exploring the advantages and limitations while providing visualizations to understand the results. This second edition updates and expands the methods and software discussed in the first edition, including new coverage of methods for ordinal data and anchoring vignettes in surveys, as well as an entire chapter dedicated to Bayesian methods. The second edition is made easier to use by the inclusion of an R package, which provides all data and functions used in the book. David A. Armstrong II is Canada Research Chair in Political Methodology and Associate Professor of Political Science at Western University. His research interests include measurement, Democracy and state repressive action. Ryan Bakker is Reader in Comparative Politics at the University of Essex. His research interests include applied Bayesian modeling, measurement, Western European politics, and EU politics. Royce Carroll is Professor in Comparative Politics at the University of Essex. His research focuses on measurement of ideology and the comparative politics of legislatures and political parties. Christopher Hare is Assistant Professor in Political Science at the University of California, Davis. His research focuses on ideology and voting behavior in US politics, political polarization, and measurement. Keith T. Poole is Philip H. Alston Jr. Distinguished Professor of Political Science at the University of Georgia. His research interests include methodology, US political-economic history, economic growth and entrepreneurship. Howard Rosenthal is Professor of Politics at NYU and Roger Williams Straus Professor of Social Sciences, Emeritus, at Princeton. Rosenthal's research focuses on political economy, American politics and methodology.
Applied Spatial Statistics and Econometrics: Data Analysis in R by
This textbook is a comprehensive introduction to applied spatial data analysis using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. The author team showcases key topics, including unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity and bootstrapping. It is accompanied by a suite of data and R code on Github to help readers practise techniques via replication and exercises. This text will be a valuable resource for advanced students of econometrics, spatial planning and regional science. It will also be suitable for researchers and data scientists working with spatial data.
GIS : research methods by
This book provides a non-technical overview of the science and tools behind geographic information systems and geographic information science for researchers, students and academics who do not have a GIS or Geography background. The book covers the history of GIS, from John Snow's Cholera map (1854) right up to today's software and data and cutting-edge analysis techniques. Bearman goes on to cover how to find, use and evaluate the latest data sets to critiquing existing maps, highlighting limitations and common mistakes. A variety of different GIS methods including Google Maps, GPS, big data, context and choropleth maps are discussed and the pros and cons of each are highlighted allowing you to choose the appropriate method or piece of software for your own research. This is the ideal book for anyone thinking about using GIS in their own research.
GIS for science : applying mapping and spatial analytics. Volume 2 by
Merging the rigor of the scientific method with the technologies of GIS GIS for Science: Applying Mapping and Spatial Analytics, Volume 2 brings to life a continuing collection of current, real-world examples of scientists using geographic information systems (GIS) and spatial data science to expand our understand of the world. Co-edited by Esri Chief Scientist Dawn Wright and Esri Technology Writer and Information Designer Christian Harder and with a foreword by Pulitzer Prize winner Jared Diamond, these case studies are part of a global effort to find ways to sustain a livable environment for all life on this planet. The contributors of GIS for Science: Applying Mapping and Spatial Analytics, Volume 2 represent a cross section of scientists who employ data gathered from satellites, aircraft, ships, drones, and myriad other remote-sensing and on-site technologies. This collected data is brought to life with GIS and the broader realm of spatial data science to study a range of issues relevant to our understanding of planet Earth--including epidemiology in light of the COVID-19 pandemic; sustainable precision agriculture; predicting geological processes below the surface of the earth; leveraging GIS near-realtime disaster response, recovery, resilience and reporting; the latest innovations in monitoring air quality; and more. Their stories also show in very practical terms how ArcGIS software and the ArcGIS Online cloud-based system work as a comprehensive geospatial platform to support research, collaboration, spatial analysis, and science communication across many settings and communities. A rich supplementary web site--gisforscience.com--includes actual data along with additional maps, videos, web apps, story maps, workflows and snippets of computer code, including Python notebooks, for readers curious to learn more. Written for professional scientists, the swelling ranks of citizen scientists, and anyone interested in science and geography, GIS for Science: Applying Mapping and Spatial Analytics, Volume 2 offers wonderful examples of our impulse to dream, discover, and understand, as coupled with the rigor and discipline of the scientific method and the foundation of geography. See how scientists from a variety of disciplines are solving some of the world's most pressing problems using geographic information systems--GIS.
Learning geospatial analysis with Python : perform GIS processing tasks and remote sensing data analysis using Python 3.7 by
Geospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. With this systematic guide, you'll get started with geographic information system (GIS) and remote sensing analysis using the latest features in Python.
This book will take you through GIS techniques, geodatabases, geospatial raster data, and much more using the latest built-in tools and libraries in Python 3.7. You'll learn everything you need to know about using software packages or APIs and generic algorithms that can be used for different situations. Furthermore, you'll learn how to apply simple Python GIS geospatial processes to a variety of problems, and work with remote sensing data.
By the end of the book, you'll be able to build a generic corporate system, which can be implemented in any organization to manage customer support requests and field support personnel.
Spatio-temporal statistics with R by
The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these "big data" that are available are spatio-temporal in nature, meaning that they can be indexed by their spatial locations and time stamps. Spatio-Temporal Statistics with R provides an accessible introduction to statistical analysis of spatio-temporal data, with hands-on applications of the statistical methods using R Labs found at the end of each chapter. The book: Gives a step-by-step approach to analyzing spatio-temporal data, starting with visualization, then statistical modelling, with an emphasis on hierarchical statistical models and basis function expansions, and finishing with model evaluation Provides a gradual entry to the methodological aspects of spatio-temporal statistics Provides broad coverage of using R as well as "R Tips" throughout. Features detailed examples and applications in end-of-chapter Labs Features "Technical Notes" throughout to provide additional technical detail where relevant Supplemented by a website featuring the associated R package, data, reviews, errata, a discussion forum, and more The book fills a void in the literature and available software, providing a bridge for students and researchers alike who wish to learn the basics of spatio-temporal statistics. It is written in an informal style and functions as a down-to-earth introduction to the subject. Any reader familiar with calculus-based probability and statistics, and who is comfortable with basic matrix-algebra representations of statistical models, would find this book easy to follow. The goal is to give as many people as possible the tools and confidence to analyze spatio-temporal data.
Google Earth Engine Applications by
In a rapidly changing world, there is an ever-increasing need to monitor the Earth's resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth's surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.
Geospatial Data Science Quick Start Guide : Effective Techniques for Performing Smarter Geospatial Analysis Using Location Intelligence. by
Discover the power of location data to build effective, intelligent data models with Geospatial ecosystems Key Features Manipulate location-based data and create intelligent geospatial data models Build effective location recommendation systems used by popular companies such as Uber A hands-on guide to help you consume spatial data and parallelize GIS operations effectively Book 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 learn Learn how companies now use location data Set up your Python environment and install Python geospatial packages Visualize spatial data as graphs Extract geometry from spatial data Perform spatial regression from scratch Build web applications which dynamically references geospatial data Who 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.
GIS for Science, Volume 1, Applying Mapping and Spatial Analytics by
GIS for Science presents a collection of real-world stories about modern science and a cadre of scientists who use mapping and spatial analytics to expand their understanding of the world. The accounts in this book are written for a broad audience including professional scientists, the swelling ranks of citizen scientists, and people generally interested in science and geography. Scientific data are brought to life with GIS technology to study a range of issues relevant to the functioning of planet Earth in a natural sense as well as the impacts of human activity. In a race against the clock, the scientists profiled in this volume are using remote sensing, web maps within a geospatial cloud, Esri StoryMaps, and spatial analysis to document and solve an array of issues with a geographic dimension, ranging from climate change, natural disasters, and loss of biodiversity, to homelessness, loss of green infrastructure, and resource shortages. These stories present geospatial ideas and inspiration that readers can apply across many disciplines, making this volume relevant to a diverse scientific audience. See how scientists working on the world's most pressing problems apply geographic information systems--GIS.
Spatial analysis using big data : methods and urban applications by
Spatial Analysis Using Big Data: Methods and Urban Applications helps readers understand the most powerful, state-of-the-art spatial econometric methods, focusing particularly on urban research problems. The methods represent a cluster of potentially transformational socio-economic modeling tools that allow researchers to capture real-time and high-resolution information to potentially reveal new socioeconomic dynamics within urban populations. Each method, written by leading exponents of the discipline, uses real-time urban big data to solve research problems in spatial science. Urban applications of these methods are provided in unsurpassed depth, with chapters on surface temperature mapping, view value analysis, community clustering and spatial-social networks, among many others. Reviews some of the most powerful and challenging modern methods to study big data problems in spatial science Provides computer codes written in R, MATLAB and Python to help implement methods Applies these methods to common problems observed in urban and regional economics
Manual of Digital Earth by
This open access book offers a summary of the development of Digital Earth over the past twenty years. By reviewing the initial vision of Digital Earth, the evolution of that vision, the relevant key technologies, and the role of Digital Earth in helping people respond to global challenges, this publication reveals how and why Digital Earth is becoming vital for acquiring, processing, analysing and mining the rapidly growing volume of global data sets about the Earth. The main aspects of Digital Earth covered here include: Digital Earth platforms, remote sensing and navigation satellites, processing and visualizing geospatial information, geospatial information infrastructures, big data and cloud computing, transformation and zooming, artificial intelligence, Internet of Things, and social media. Moreover, the book covers in detail the multi-layered/multi-faceted roles of Digital Earth in response to sustainable development goals, climate changes, and mitigating disasters, the applications of Digital Earth (such as digital city and digital heritage), the citizen science in support of Digital Earth, the economic value of Digital Earth, and so on. This book also reviews the regional and national development of Digital Earth around the world, and discusses the role and effect of education and ethics. Lastly, it concludes with a summary of the challenges and forecasts the future trends of Digital Earth. By sharing case studies and a broad range of general and scientific insights into the science and technology of Digital Earth, this book offers an essential introduction for an ever-growing international audience.
Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools by
Geospatial Analysis (6th edition) provides a compehensive guide to concepts, methods and tools, with many examples being provided using a variety of software tools such as ArcGIS, Idrisi, Grass, Surfer and many others to clarify the concepts discussed. Topics covered in detail include: Geospatial analysis concepts Analytical methodologies and model building Core components of geospatial analysis, including distance and directional analysis, geometrical processing, map algebra, and grid models Exploratory Spatial and Spatio-temporal Data Analysis (ESDA, ESTDA) and spatial statistics, including spatial autocorrelation and spatial regression Surface analysis, including surface form and flow analysis, gridding and interpolation methods, and visibility analysis Network and locational analysis, including shortest path calculation, travelling salesman problems, facility location and arc routing Geocomputational methods, including agent-based modelling, artifical neural networks and evolutionary computing Big Data - lessons for researchers
Data Science for Dummies by
Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here's what to expect: Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate It's a big, big data world out there--let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.
Statistical Analysis of Geographical Data : An Introduction by
Statistics Analysis of Geographical Data: An Introduction provides a comprehensive and accessible introduction to the theory and practice of statistical analysis in geography. It covers a wide range of topics including graphical and numerical description of datasets, probability, calculation of confidence intervals, hypothesis testing, collection and analysis of data using analysis of variance and linear regression. Taking a clear and logical approach, this book examines real problems with real data from the geographical literature in order to illustrate the important role that statistics play in geographical investigations. Presented in a clear and accessible manner the book includes recent, relevant examples, designed to enhance the reader's understanding.
Adjustment computations : spatial data analysis by
The definitive guide to bringing accuracy to measurement, updated and supplemented Adjustment Computations is the classic textbook for spatial information analysis and adjustment computations, providing clear, easy-to-understand instruction backed by real-world practicality. From the basic terms and fundamentals of errors to specific adjustment computations and spatial information analysis, this book covers the methodologies and tools that bring accuracy to surveying, GNSS, GIS, and other spatial technologies. Broad in scope yet rich in detail, the discussion avoids overly-complex theory in favor of practical techniques for students and professionals. This new sixth edition has been updated to align with the latest developments in this rapidly expanding field, and includes new video lessons and updated problems, including worked problems in STATS, MATRIX, ADJUST, and MathCAD. All measurement produces some amount of error; whether from human mistakes, instrumentation inaccuracy, or environmental features, these errors must be accounted and adjusted for when accuracy is critical. This book describes how errors are identified, analyzed, measured, and corrected, with a focus on least squares adjustment--the most rigorous methodology available. Apply industry-standard methodologies to error analysis and adjustment Translate your skills to the real-world with instruction focused on the practical Master the fundamentals as well as specific computations and analysis Strengthen your understanding of critical topics on the Fundamentals in Surveying Licensing Exam As spatial technologies expand in both use and capability, so does our need for professionals who understand how to check and adjust for errors in spatial data. Conceptual knowledge is one thing, but practical skills are what counts when accuracy is at stake; Adjustment Computations provides the real-world training you need to identify, analyze, and correct for potentially crucial errors.
Geospatial Data Science Techniques and Applications by
Data science has recently gained much attention for a number of reasons, and among them is Big Data. Scientists (from almost all disciplines including physics, chemistry, biology, sociology, among others) and engineers (from all fields including civil, environmental, chemical, mechanical, among others) are faced with challenges posed by data volume, variety, and velocity, or Big Data. This book is designed to highlight the unique characteristics of geospatial data, demonstrate the need to different approaches and techniques for obtaining new knowledge from raw geospatial data, and present select state-of-the-art geospatial data science techniques and how they are applied to various geoscience problems.
Risk terrain modeling : crime prediction and risk reduction by
Imagine using an evidence-based risk management model that enables researchers and practitioners alike to analyze the spatial dynamics of crime, allocate resources, and implement custom crime and risk reduction strategies that are transparent, measurable, and effective. Risk Terrain Modeling (RTM) diagnoses the spatial attractors of criminal behavior and makes accurate forecasts of where crime will occur at the microlevel. RTM informs decisions about how the combined factors that contribute to criminal behavior can be targeted, connections to crime can be monitored, spatial vulnerabilities can be assessed, and actions can be taken to reduce worst effects. As a diagnostic method, RTM offers a statistically valid way to identify vulnerable places. To learn more, visit http://www.riskterrainmodeling.com and begin using RTM with the many free tutorials and resources.
Statistics for Spatial Data by
The Wiley Classics Library consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. Spatial statistics -- analyzing spatial data through statistical models -- has proven exceptionally versatile, encompassing problems ranging from the microscopic to the astronomic. However, for the scientist and engineer faced only with scattered and uneven treatments of the subject in the scientific literature, learning how to make practical use of spatial statistics in day-to-day analytical work is very difficult. Designed exclusively for scientists eager to tap into the enormous potential of this analytical tool and upgrade their range of technical skills, Statistics for Spatial Data is a comprehensive, single-source guide to both the theory and applied aspects of spatial statistical methods. The hard-cover edition was hailed by Mathematical Reviews as an "excellent book which will become a basic reference." This paper-back edition of the 1993 edition, is designed to meet the many technological challenges facing the scientist and engineer. Concentrating on the three areas of geostatistical data, lattice data, and point patterns, the book sheds light on the link between data and model, revealing how design, inference, and diagnostics are an outgrowth of that link. It then explores new methods to reveal just how spatial statistical models can be used to solve important problems in a host of areas in science and engineering. Discussion includes: Exploratory spatial data analysis Spectral theory for stationary processes Spatial scale Simulation methods for spatial processes Spatial bootstrapping Statistical image analysis and remote sensing Computational aspects of model fitting Application of models to disease mapping Designed to accommodate the practical needs of the professional, it features a unified and common notation for its subject as well as many detailed examples woven into the text, numerous illustrations (including graphs that illuminate the theory discussed) and over 1,000 references. Fully balancing theory with applications, Statistics for Spatial Data, Revised Edition is an exceptionally clear guide on making optimal use of one of the ascendant analytical tools of the decade, one that has begun to capture the imagination of professionals in biology, earth science, civil, electrical, and agricultural engineering, geography, epidemiology, and ecology.
GIS research methods : incorporating spatial perspectives by
Few if any books have been written about social science research methods and GIS in an ArcGIS environment. This books claims that niche.
Scale in Spatial Information and Analysis by
Now ubiquitous in modern life, spatial data present great opportunities to transform many of the processes on which we base our everyday lives. However, not only do these data depend on the scale of measurement, but also handling these data (e.g., to make suitable maps) requires that we account for the scale of measurement explicitly. Scale in Spatial Information and Analysis describes the scales of measurement and scales of spatial variation that exist in the measured data. It provides you with a series of tools for handling spatial data while accounting for scale. The authors detail a systematic strategy for handling scale issues from geographic reality, through measurements, to resultant spatial data and their analyses. They also explore a process-pattern paradigm in approaching scale issues. This is well reflected, for example, in chapters dealing with terrain analysis, in which scale in terrain derivatives is described in relation to the processing involved in the derivation of specific terrain variables from elevation data, and area classes, which are viewed as driven by class-forming covariates. Lastly, this book provides coverage of some of the issues related to scale that are relatively under-represented in the literature, such as the effects of scale on information content in remotely sensed images, and the interaction between scale and uncertainty that is increasingly important for spatial information and analysis. By taking a rigorous, scientific approach to scale and its various meanings in relation to the geographic world, the book alleviates some of the frustration caused by dealing with issues of scale. While past research has led to an increasing number of journal articles and a few books dedicated to scale modeling and change of scale, this book helps you to develop coherent strategies for scale modeling, highlighting applicability for a variety of fields, from geomatic engineering and geoinformatics to environmental modeling.
Mathematical Techniques in GIS by
The second edition of a bestseller, Mathematical Techniques in GIS demystifies the mathematics used in the manipulation of spatially related data. The author takes a step-by-step approach through the basics of arithmetic, algebra, geometry, trigonometry and calculus that underpin the management of such data. He then explores the use of matrices, de
Computing in Geographic Information Systems by
Capable of acquiring large volumes of data through sensors deployed in air, land, and sea, and making this information readily available in a continuous time frame, the science of geographical information system (GIS) is rapidly evolving. This popular information system is emerging as a platform for scientific visualization, simulation, and computa
Quantitative Methods and Socio-Economic Applications in GIS by
The second edition of a bestseller, Quantitative Methods and Socio-Economic Applications in GIS (previously titled Quantitative Methods and Applications in GIS) details applications of quantitative methods in social science, planning, and public policy with a focus on spatial perspectives. The book integrates GIS and quantitative (computational) me
Learning R for geospatial analysis : leverage the power of R to elegantly manage crucial geospatial analysis tasks by
This book is intended for anyone who wants to learn how to efficiently analyze geospatial data with R, including GIS analysts, researchers, educators, and students who work with spatial data and who are interested in expanding their capabilities through programming. The book assumes familiarity with the basic geographic information concepts (such as spatial coordinates), but no prior experience with R and/or programming is required. By focusing on R exclusively, you will not need to depend on any external software-a working installation of R is all that is necessary to begin.
Spatial mathematics : theory and practice through mapping by
In terms of statistics, GIS offers many connections. With GIS, data are gathered, displayed, summarized, examined, and interpreted to discover patterns. Spatial Mathematics: Theory and Practice through Mapping uses GIS as a platform to teach mathematical concepts and skills through visualization of numbers. It examines theory and practice from disp
Progress in Geospatial Analysis by
This book examines current trends and developments in the methods and applications of geospatial analysis and highlights future development prospects. It provides a comprehensive discussion of remote sensing- and geographical information system (GIS)-based data processing techniques, current practices, theories, models, and applications of geospatial analysis. Data acquisition and processing techniques such as remote sensing image selections, classifications, accuracy assessments, models of GIS data, and spatial modeling processes are the focus of the first part of the book. In the second part, theories and methods related to fuzzy sets, spatial weights and prominence, geographically weighted regression, weight of evidence, Markov-cellular automata, artificial neural network, agent-based simulation, multi-criteria evaluation, analytic hierarchy process, and a GIS network model are included. Part three presents selected best practices in geospatial analysis. The chapters, all by expert authors, are arranged so that readers who are new to the field will gain an overview and important insights. Those readers who are already practitioners will gain from the advanced and updated materials and state-of-the-art developments in geospatial analysis.
Spatial Statistics : GeoSpatial Information Modeling and Thematic Mapping. by
Geospatial information modeling and mapping has become an important tool for the investigation and management of natural resources at the landscape scale. Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping reviews the types and applications of geospatial information data, such as remote sensing, geographic information systems (GIS), and GPS as well as their integration into landscape-scale geospatial statistical models and maps. The book explores how to extract information from remotely sensed imagery, GIS, and GPS, and how to combine this with field data¿vegetation, soil, and environmental¿to produce a spatial model that can be reconstructed and displayed using GIS software. Readers learn the requirements and limitations of each geospatial modeling and mapping tool. Case studies with real-life examples illustrate important applications of the models. Topics covered in this book include: An overview of the geospatial information sciences and technology and spatial statistics Sampling methods and applications, including probability sampling and nonrandom sampling, and issues to consider in sampling and plot design Fine and coarse scale variability Spatial sampling schemes and spatial pattern Linear and spatial correlation statistics, including Moran¿s I, Geary¿s C, cross-correlation statistics, and inverse distance weighting Geospatial statistics analysis using stepwise regression, ordinary least squares (OLS), variogram, kriging, spatial auto-regression, binary classification trees, cokriging, and geospatial models for presence and absence data How to use R statistical software to work on statistical analyses and case studies, and to develop a geospatial statistical model The book includes practical examples and laboratory exercises using ArcInfo, ArcView, ArcGIS, and other popular software for geospatial modeling. It is accessible to readers from various fields, without requiring advanced knowledge of geospatial information sciences or quantitative methods.
Spatial Data Analysis : Models, Methods and Techniques by
The availability of spatial databases and widespread use of geographic information systems has stimulated increasing interest in the analysis and modelling of spatial data. Spatial data analysis focuses on detecting patterns, and on exploring and modelling relationships between them in order to understand the processes responsible for their emergence. In this way, the role of space is emphasised , and our understanding of the working and representation of space, spatial patterns, and processes is enhanced. In applied research, the recognition of the spatial dimension often yields different and more meaningful results and helps to avoid erroneous conclusions. This book aims to provide an introduction into spatial data analysis to graduates interested in applied statistical research. The text has been structured from a data-driven rather than a theory-based perspective, and focuses on those models, methods and techniques which are both accessible and of practical use for graduate students. Exploratory techniques as well as more formal model-based approaches are presented, and both area data and origin-destination flow data are considered.
Spatial data analysis : an introduction for GIS users by
What is the shortest route between one point and another in a road network? Where is the incidence of disease the highest? How does rainfall correlate with altitude? How does the concentration of a pollutant vary in space, and where do high concentrations correlate with densely populatedareas?Geographical or spatial data play a vital role in many parts of daily life. We are dependent on information about where things are located and about the attributes of those things, either directly, as in the use of a map for navigating around a city, or indirectly, where we use resources like wateror gas.Making use of spatial data requires a whole set of approaches to extract information from those data and make them useful. Underpinning these approaches is the analysis of data.Spatial Data Analysis introduces key principles about spatial data and provides guidance on methods for their exploration; it provides a set of key ideas or frameworks that will give the reader knowledge of the kinds of problems that can be tackled using the tools that are widely available for theanalysis of spatial data.The approach is gradual and systematic; the initial focus is on themes that follow through the rest of the book. These key ideas are introduced, illustrated, and restated to ensure that readers develop a clear understanding of them.Beyond careful explanations, a clear understanding is fostered still further by numerous worked examples and case studies. In short, the stress is on first principles and reinforcement of key ideas throughout - on education rather than simply training, based on the conviction that users of spatialdata analysis tools should know something about how the approaches work rather than simply how to apply them.The Online Resource Centre to accompany Spatial Data Analysis features:For registered adopters of the book:* Figures from the book, available to download.For students:* Synthetic data and worked examples to enable readers to experiment with the methods described in the book.
Handbook of Spatial Statistics by
Assembling a collection of very prominent researchers in the field, the Handbook of Spatial Statistics presents a comprehensive treatment of both classical and state-of-the-art aspects of this maturing area. It takes a unified, integrated approach to the material, providing cross-references among chapters.The handbook begins with a historical intro
Local Models for Spatial Analysis by
Written in recognition of developments in spatial data analysis that focused on differences between places, the first edition of Local Models for Spatial Analysis broke new ground with its focus on local modelling methods. Reflecting the continued growth and increased interest in this area, the second edition describes a wide range of methods which
Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses by
When compared to classical sciences such as math, with roots in prehistory, and physics, with roots in antiquity, geographical information science (GISci) is the new kid on the block. Its theoretical foundations are therefore still developing and data quality and uncertainty modeling for spatial data and spatial analysis is an important branch of t
The Sage Handbook of Spatial Analysis by
The widespread use of Geographical Information Systems (GIS) has significantly increased the demand for knowledge about spatial analytical techniques across a range of disciplines. As growing numbers of researchers realise they are dealing with spatial data, the demand for specialised statistical and mathematical methods designed to deal with spatial data is undergoing a rapid increase. Responding to this demand, The Handbook of Spatial Analysis is a comprehensive and authoritative discussion of issues and techniques in the field of Spatial Data Analysis. Its principal focus is on: why the analysis of spatial data needs separate treatment the main areas of spatial analysis the key debates within spatial analysis examples of the application of various spatial analytical techniques problems in spatial analysis areas for future research Aimed at an international audience of academics, The Handbook of Spatial Analysis will also prove essential to graduate level students and researchers in government agencies and the private sector."
