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| examples of spatial analysis: Geospatial Analysis Michael John De Smith, Michael F. Goodchild, Paul Longley, 2007 Addresses a range of analytical techniques that are provided within modern Geographic Information Systems and related geospatial software products. This guide covers: the principal concepts of geospatial analysis; core components of geospatial analysis; and, surface analysis, including surface form analysis, gridding and interpolation methods. |
| examples of spatial analysis: Spatial Analysis Methods and Practice George Grekousis, 2020-06-11 An introductory overview of spatial analysis and statistics through GIS, including worked examples and critical analysis of results. |
| examples of spatial analysis: An Introduction to Spatial Data Analysis Martin Wegmann, Jakob Schwalb-Willmann, Stefan Dech, 2020-09-14 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 |
| examples of spatial analysis: Applied Spatial Data Analysis with R Roger S. Bivand, Edzer Pebesma, Virgilio Gómez-Rubio, 2013-06-21 Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003. |
| examples of spatial analysis: Geospatial Health Data Paula Moraga, 2019-11-26 Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulate and transform point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches, Create interactive and static visualizations such as disease maps and time plots, Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policy makers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners. |
| examples of spatial analysis: Fundamentals of Spatial Analysis and Modelling Jay Gao, 2021-12-15 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. |
| examples of spatial analysis: Exploring Spatial Analysis in Geographic Information Systems Yue-Hong Chou, 1997 For students and professionals who wish to quickly become proficient with spacial analytical techniques employed in geographic information systems. |
| examples of spatial analysis: Spatial Analysis And GIS S Fotheringham, Peter Rogerson, 2013-04-08 Geographic information systems represent an exciting and rapidly expanding technology via which spatial data may be captured, stored, retrieved, displayed, manipulated and analysed. Applications of this technology include detailed inventories of land use parcels. Spatial patterns of disease, geodemographics, environmental management and macroscale inventories of global resources. The impetus for this book is the relative lack of research into the integration of spatial analysis and GIS, and the potential benefits in developing such an integration. From a GIS perspective, there is an increasing demand for systems that do something other than display and organize data. From a spatial analytical perspective, there are advantages to linking statistical methods and mathematical models to the database and display capabilities of a GIS. Although the GIS may not be absolutely necessary for spatial analysis, it can facilitate such an analysis and moreover provide insights that might otherwise have been missed. The contributions to the book tell us where we are and where we ought to be going. It suggests that the integration of spatial analysis and GIS will stimulate interest in quantitative spatial science, particularly exploratory and visual types of analysis and represents a unique statement of the state-of-the-art issues in integration and interface. |
| examples of spatial analysis: Local Models for Spatial Analysis Christopher D. Lloyd, 2010-10-13 Focusing on solutions, this second edition provides guidance for readers who face a variety of real-world problems. The text presents a complete introduction to key concepts and a clear mapping of the methods. New chapters address spatial patterning in single variables and spatial relations. The author distinguishes between local and global methods and provides detailed coverage of geographical weighting, image texture measures, local spatial autocorrelation, and geographically weighted regression. |
| examples of spatial analysis: Spatial Analysis in Epidemiology Dirk U. Pfeiffer, Timothy P. Robinson, Mark Stevenson, Kim B. Stevens, David J. Rogers, Archie C. A. Clements, 2008-05-29 This book provides a practical, comprehensive and up-to-date overview of the use of spatial statistics in epidemiology - the study of the incidence and distribution of diseases. Used appropriately, spatial analytical methods in conjunction with GIS and remotely sensed data can provide significant insights into the biological patterns and processes that underlie disease transmission. In turn, these can be used to understand and predict disease prevalence. This user-friendly text brings together the specialised and widely-dispersed literature on spatial analysis to make these methodological tools accessible to epidemiologists for the first time. With its focus is on application rather than theory, Spatial Analysis in Epidemiology includes a wide range of examples taken from both medical (human) and veterinary (animal) disciplines, and describes both infectious diseases and non-infectious conditions. Furthermore, it provides worked examples of methodologies using a single data set from the same disease example throughout, and is structured to follow the logical sequence of description of spatial data, visualisation, exploration, modelling and decision support. This accessible text is aimed at graduate students and researchers dealing with spatial data in the fields of epidemiology (both medical and veterinary), ecology, zoology and parasitology, environmental science, geography and statistics. |
| examples of spatial analysis: Theory of Spatial Statistics M.N.M. van Lieshout, 2019-03-19 Theory of Spatial Statistics: A Concise Introduction presents the most important models used in spatial statistics, including random fields and point processes, from a rigorous mathematical point of view and shows how to carry out statistical inference. It contains full proofs, real-life examples and theoretical exercises. Solutions to the latter are available in an appendix. Assuming maturity in probability and statistics, these concise lecture notes are self-contained and cover enough material for a semester course. They may also serve as a reference book for researchers. Features * Presents the mathematical foundations of spatial statistics. * Contains worked examples from mining, disease mapping, forestry, soil and environmental science, and criminology. * Gives pointers to the literature to facilitate further study. * Provides example code in R to encourage the student to experiment. * Offers exercises and their solutions to test and deepen understanding. The book is suitable for postgraduate and advanced undergraduate students in mathematics and statistics. |
| examples of spatial analysis: The ESRI Guide to GIS Analysis: Geographic patterns & relationships Andy Mitchell, Environmental Systems Research Institute (Redlands, Calif.), 1999 Backed by the collective knowledge and expertise of the worlds leading Geographic Information Systems company, this volume presents the concepts and methods unleashing the full analytic power of GIS. |
| examples of spatial analysis: Geocomputation with R Robin Lovelace, Jakub Nowosad, Jannes Muenchow, 2019-03-22 Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), bridges to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/. |
| examples of spatial analysis: Spatial Analysis of Coastal Environments Sarah M. Hamylton, 2017-04-13 This book covers the spatial analytical tools needed to map, monitor and explain or predict coastal features, with accompanying online exercises. |
| examples of spatial analysis: The SAGE Handbook of Spatial Analysis A Stewart Fotheringham, Peter A Rogerson, 2008-12-22 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. |
| examples of spatial analysis: The ArcGIS Book Christian Harder, Clint Brown, 2017 This is a hands-on book about ArcGIS that you work with as much as read. By the end, using Learn ArcGIS lessons, you'll be able to say you made a story map, conducted geographic analysis, edited geographic data, worked in a 3D web scene, built a 3D model of Venice, and more. |
| examples of spatial analysis: GIS and Spatial Analysis for the Social Sciences Robert Nash Parker, Emily K. Asencio, 2009-09-10 This is the first book to provide sociologists, criminologists, political scientists, and other social scientists with the methodological logic and techniques for doing spatial analysis in their chosen fields of inquiry. The book contains a wealth of examples as to why these techniques are worth doing, over and above conventional statistical techniques using SPSS or other statistical packages. GIS is a methodological and conceptual approach that allows for the linking together of spatial data, or data that is based on a physical space, with non-spatial data, which can be thought of as any data that contains no direct reference to physical locations. |
| examples of spatial analysis: GIS and Geostatistical Techniques for Groundwater Science Senapathi Venkatramanan, Prasanna Mohan Viswanathan, Sang Yong Chung, 2019-05-28 GIS and Geostatistical Techniques for Groundwater Science provides a detailed synthesis of the application of GIS and geostatistics in groundwater studies. As the book illustrates, GIS can be a powerful tool for developing solutions for water resource problems, assessing water quality, and managing water resources. Beginning with an introduction to the history of GIS and geostatistical techniques in groundwater studies, the book then describes various spatial techniques, including case studies for various applications, from quality assessment, to resource management. This book assembles the most up-to-date techniques in GIS and geostatistics as they relate to groundwater, one of our most important natural resources. - Provides details on the application of GIS and statistics in groundwater studies - Includes practical coverage of the use of spatial analysis techniques in groundwater science - Bridges the gap between geostatistics and GIS as it relates to groundwater science and management - Offers worldwide case studies to illustrate various techniques and applications in addressing groundwater issues |
| examples of spatial analysis: Spatial Data Analysis Robert P. Haining, 2003-04-17 Spatial Data Analysis: Theory and Practice, first published in 2003, provides a broad ranging treatment of the field of spatial data analysis. It begins with an overview of spatial data analysis and the importance of location (place, context and space) in scientific and policy related research. Covering fundamental problems concerning how attributes in geographical space are represented to the latest methods of exploratory spatial data analysis and spatial modeling, it is designed to take the reader through the key areas that underpin the analysis of spatial data, providing a platform from which to view and critically appreciate many of the key areas of the field. Parts of the text are accessible to undergraduate and master's level students, but it also contains sufficient challenging material that it will be of interest to geographers, social and economic scientists, environmental scientists and statisticians, whose research takes them into the area of spatial analysis. |
| examples of spatial analysis: Geographical Data Science and Spatial Data Analysis Lex Comber, Chris Brunsdon, 2020-12-02 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. |
| examples of spatial analysis: Applications of Spatial Statistics Ming Hung, 2016-11-02 Spatial statistics has been widely used in many environmental studies. This book is a collection of recent studies on applying spatial statistics in subjects such as demography, transportation, precision agriculture and ecology. Different subjects require different aspects of spatial statistics. In addition to quantitative statements from statistics and tests, visualization in forms of maps, drawings, and images are provided to illustrate the relationship between data and locations. This book will be valuable to researchers who are interested in applying statistics to spatial data, as well as graduate students who know statistics and want to explore how it can be applied to spatial data. With the processing part being simplified to several mouse clicks by commercial software, one should pay more attention to justification of using spatial statistics, as well as interpretation and assessment of the results. GIScience proves to be a useful tool in visualization of spatial data, and such useful technology should be utilized, as part, for the interpretation and assessment of the results. |
| examples of spatial analysis: Historical GIS Ian N. Gregory, Paul S. Ell, 2007-12-13 Historical GIS is an emerging field that uses Geographical Information Systems (GIS) to research the geographies of the past. Ian Gregory and Paul Ell's study, first published in 2007, comprehensively defines this field, exploring all aspects of using GIS in historical research. A GIS is a form of database in which every item of data is linked to a spatial location. This technology offers unparalleled opportunities to add insight and rejuvenate historical research through the ability to identify and use the geographical characteristics of data. Historical GIS introduces the basic concepts and tools underpinning GIS technology, describing and critically assessing the visualisation, analytical and e-science methodologies that it enables and examining key scholarship where GIS has been used to enhance research debates. The result is a clear agenda charting how GIS will develop as one of the most important approaches to scholarship in historical geography. |
| examples of spatial analysis: Spatial Data Analysis Christopher Lloyd, 2010 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 the analysis of spatial data. |
| examples of spatial analysis: Spatial Analysis for the Social Sciences David Darmofal, 2015-11-12 This book shows how to model the spatial interactions between actors that are at the heart of the social sciences. |
| examples of spatial analysis: Spatial Analysis in Karst Geomorphology: An Example from Krk Island, Croatia Ela Šegina, 2020-11-20 This book presents (i) a comprehensive methodological approach in analyzing karst surface features using contemporary GIS tools of high resolution applied on a large study area and extensive dataset, (ii) a revision and evaluation of the existing concepts valid in the modern karst geomorphology, (iii) a detailed elaboration of overlooked and yet undefined karst surface features, and (iv) an innovation in the discussion on origin and evolution of karst surface features. The book contains the first example of such comprehensive application of the modern technologies in spatial analysis in the field of karst geomorphology, and it offers several new methodological and theoretical perspectives and presents numerous starting points for further research. It contributes both to the understanding of the local karst evolution and to the knowledge of karst surface features globally. |
| examples of spatial analysis: Handbook of Applied Spatial Analysis Manfred M. Fischer, Arthur Getis, 2009-12-24 The Handbook is written for academics, researchers, practitioners and advanced graduate students. It has been designed to be read by those new or starting out in the field of spatial analysis as well as by those who are already familiar with the field. The chapters have been written in such a way that readers who are new to the field will gain important overview and insight. At the same time, those readers who are already practitioners in the field will gain through the advanced and/or updated tools and new materials and state-of-the-art developments included. This volume provides an accounting of the diversity of current and emergent approaches, not available elsewhere despite the many excellent journals and te- books that exist. Most of the chapters are original, some few are reprints from the Journal of Geographical Systems, Geographical Analysis, The Review of Regional Studies and Letters of Spatial and Resource Sciences. We let our contributors - velop, from their particular perspective and insights, their own strategies for m- ping the part of terrain for which they were responsible. As the chapters were submitted, we became the first consumers of the project we had initiated. We gained from depth, breadth and distinctiveness of our contributors’ insights and, in particular, the presence of links between them. |
| examples of spatial analysis: Perspectives on Spatial Data Analysis Luc Anselin, Sergio J. Rey, 2009-12-24 Spatial data analysis has seen explosive growth in recent years. Both in mainstream statistics and econometrics as well as in many applied ?elds, the attention to space, location, and interaction has become an important feature of scholarly work. The methodsdevelopedto dealwith problemsofspatialpatternrecognition,spatialau- correlation, and spatial heterogeneity have seen greatly increased adoption, in part due to the availability of user friendlydesktopsoftware. Throughhis theoretical and appliedwork,ArthurGetishasbeena majorcontributing?gureinthisdevelopment. In this volume, we take both a retrospective and a prospective view of the ?eld. We use the occasion of the retirement and move to emeritus status of Arthur Getis to highlight the contributions of his work. In addition, we aim to place it into perspective in light of the current state of the art and future directions in spatial data analysis. To this end, we elected to combine reprints of selected classic contributions by Getiswithchapterswrittenbykeyspatialscientists.Thesescholarswerespeci?cally invited to react to the earlier work by Getis with an eye toward assessing its impact, tracing out the evolution of related research, and to re?ect on the future broadening of spatial analysis. The organizationof the book follows four main themes in Getis’ contributions: • Spatial analysis • Pattern analysis • Local statistics • Applications For each of these themes, the chapters provide a historical perspective on early methodological developments and theoretical insights, assessments of these c- tributions in light of the current state of the art, as well as descriptions of new techniques and applications. |
| examples of spatial analysis: Spatial Analytics with ArcGIS Eric Pimpler, 2017-04-26 Pattern Analysis and cluster mapping made easy About This Book Analyze patterns, clusters, and spatial relationships using ArcGIS tools Get up to speed in R programming to create custom tools for analysis Sift through tons of crime and real estate data and analyze it using the tools built in the book Who This Book Is For This book is for ArcGIS developers who want to perform complex geographic analysis through the use of spatial statistics tools including ArcGIS and R. No knowledge of R is assumed. What You Will Learn Get to know how to measure geographic distributions Perform clustering analysis including hot spot and outlier analysis Conduct data conversion tasks using the Utilities toolset Understand how to use the tools provided by the Mapping Clusters toolset in the Spatial Statistics Toolbox Get to grips with the basics of R for performing spatial statistical programming Create custom ArcGIS tools with R and ArcGIS Bridge Understand the application of Spatial Statistics tools and the R programming language through case studies In Detail Spatial statistics has the potential to provide insight that is not otherwise available through traditional GIS tools. This book is designed to introduce you to the use of spatial statistics so you can solve complex geographic analysis. The book begins by introducing you to the many spatial statistics tools available in ArcGIS. You will learn how to analyze patterns, map clusters, and model spatial relationships with these tools. Further on, you will explore how to extend the spatial statistics tools currently available in ArcGIS, and use the R programming language to create custom tools in ArcGIS through the ArcGIS Bridge using real-world examples. At the end of the book, you will be presented with two exciting case studies where you will be able to practically apply all your learning to analyze and gain insights into real estate data. Style and approach Filled with live examples that you can code along with, this book will show you different methods and techniques to effectively analyze spatial data with ArcGIS and the R language. The exciting case studies at the end will help you immediately put your learning to practice. |
| examples of spatial analysis: Spatial Analysis with R Tonny J. Oyana, 2020-08-31 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. |
| examples of spatial analysis: Spatial Analysis and Modeling in Geographical Transformation Process Yuji Murayama, Rajesh Bahadur Thapa, 2011-02-26 Currently, spatial analysis is becoming more important than ever because enormous volumes of spatial data are available from different sources, such as GPS, Remote Sensing, and others. This book deals with spatial analysis and modelling. It provides a comprehensive discussion of spatial analysis, methods, and approaches related to human settlements and associated environment. Key contributions with empirical case studies from Iran, Philippines, Vietnam, Thailand, Nepal, and Japan that apply spatial analysis including autocorrelation, fuzzy, voronoi, cellular automata, analytic hierarchy process, artificial neural network, spatial metrics, spatial statistics, regression, and remote sensing mapping techniques are compiled comprehensively. The core value of this book is a wide variety of results with state of the art discussion including empirical case studies. It provides a milestone reference to students, researchers, planners, and other practitioners dealing the spatial problems on urban and regional issues. We are pleased to announce that this book has been presented with the 2011 publishing award from the GIS Association of Japan. We would like to congratulate the authors! |
| examples of spatial analysis: GIS Research Methods Sheila L. Steinberg, Steven J. Steinberg, 2015 This book presents a spatially-based multiple methods approach to research serving academic and organizational researchers from across a wide variety of disciplines. For many, consideration of spatial relationships is an important component of their research questions, including those who may not have yet recognized GIS as a valuable tool. The book will provide readers essential steps to conceptualize and implement research and analysis, develop meaningful quantitative and qualitative geographic results and to communicate their findings using the visualization capabilities of GIS to assist decision-makers and affect policy. Furthermore it offers researchers a deeper understanding of social, economic and environmental questions considering spatial relationships in their data.The broad subject area of the project is the integration of spatial analysis as a research methodology. More specifically the book provides practical guidance for the identification, collection and analysis of appropriate research data for analysis in an Esri/ArcGIS context without being specific to a particular version of the software. The objective is to present ArcGIS with an eye towards incorporating spatial analysis as a fundamental component of mixed methods research. Because GIS is, by nature, an integrative technology which can draw together multiple data sources via a common spatial attribute, it is a natural fit for mixed-methods research. GIS provides the researcher an unparalleled ability to enhance their research incorporating a geographic perspective. |
| examples of spatial analysis: Modern Data Science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, 2021-03-31 From a review of the first edition: Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice. |
| examples of spatial analysis: Comprehensive Geographic Information Systems , 2017-07-21 Geographical Information Systems, Three Volume Set is a computer system used to capture, store, analyze and display information related to positions on the Earth’s surface. It has the ability to show multiple types of information on multiple geographical locations in a single map, enabling users to assess patterns and relationships between different information points, a crucial component for multiple aspects of modern life and industry. This 3-volumes reference provides an up-to date account of this growing discipline through in-depth reviews authored by leading experts in the field. VOLUME EDITORS Thomas J. Cova The University of Utah, Salt Lake City, UT, United States Ming-Hsiang Tsou San Diego State University, San Diego, CA, United States Georg Bareth University of Cologne, Cologne, Germany Chunqiao Song University of California, Los Angeles, CA, United States Yan Song University of North Carolina at Chapel Hill, Chapel Hill, NC, United States Kai Cao National University of Singapore, Singapore Elisabete A. Silva University of Cambridge, Cambridge, United Kingdom Covers a rapidly expanding discipline, providing readers with a detailed overview of all aspects of geographic information systems, principles and applications Emphasizes the practical, socioeconomic applications of GIS Provides readers with a reliable, one-stop comprehensive guide, saving them time in searching for the information they need from different sources |
| examples of spatial analysis: An Introduction to R for Spatial Analysis and Mapping Chris Brunsdon, Lex Comber, 2014-04-30 In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using ′out of the box′ software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research. Providing practical tips as well as fully working code, this is a practical ′how to′ guide ideal for undergraduates as well as those using R for the first time. It will be required reading on my own courses. - Richard Harris, Professor of Quantitative Social Science, University of Bristol R is a powerful open source computing tool that supports geographical analysis and mapping for the many geography and ‘non-geography’ students and researchers interested in spatial analysis and mapping. This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for researchers collecting and using data with location attached, largely through increased GPS functionality. Brunsdon and Comber take readers from ‘zero to hero’ in spatial analysis and mapping through functions they have developed and compiled into R packages. This enables practical R applications in GIS, spatial analyses, spatial statistics, mapping, and web-scraping. Each chapter includes: Example data and commands for exploring it Scripts and coding to exemplify specific functionality Advice for developing greater understanding - through functions such as locator(), View(), and alternative coding to achieve the same ends Self-contained exercises for students to work through Embedded code within the descriptive text. This is a definitive ′how to′ that takes students - of any discipline - from coding to actual applications and uses of R. |
| examples of spatial analysis: Remote Sensing and GIS for Ecologists Martin Wegmann, Benjamin Leutner, Stefan Dech, 2016-02-08 This is a book about how ecologists can integrate remote sensing and GIS in their daily work. It will allow ecologists 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. All practical examples in this book rely on OpenSource software and freely available data sets. Quantum GIS (QGIS) is introduced for basic GIS data handling, and in-depth spatial analytics and statistics are conducted with the software packages R and GRASS. Readers will learn how to apply remote sensing within ecological research projects, how to approach spatial data sampling and how to interpret remote sensing derived products. The authors discuss a wide range of statistical analyses with regard to satellite data as well as specialised topics such as time-series analysis. Extended scripts on how to create professional looking maps and graphics are also provided. This book is a valuable resource for students and scientists in the fields of conservation and ecology interested in learning how to get started in applying remote sensing in ecological research and conservation planning. |
| examples of spatial analysis: Handbook of Spatial Statistics Alan E. Gelfand, Peter Diggle, Peter Guttorp, Montserrat Fuentes, 2010-03-19 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 |
| examples of spatial analysis: Using ArcGIS Spatial Analyst Steve Kopp, Jill McCoy, Kevin Johnston, Environmental Systems Research Institute (Redlands, Calif.), 2001 |
| examples of spatial analysis: Spatial Point Patterns Adrian Baddeley, Ege Rubak, Rolf Turner, 2015-11-11 Modern Statistical Methodology and Software for Analyzing Spatial Point PatternsSpatial Point Patterns: Methodology and Applications with R shows scientific researchers and applied statisticians from a wide range of fields how to analyze their spatial point pattern data. Making the techniques accessible to non-mathematicians, the authors draw on th |
| examples of spatial analysis: Urban Analytics Alex D. Singleton, Seth Spielman, David Folch, 2017-11-27 The economic and political situation of cities has shifted in recent years in light of rapid growth amidst infrastructure decline, the suburbanization of poverty and inner city revitalization. At the same time, the way that data are used to understand urban systems has changed dramatically. Urban Analytics offers a field-defining look at the challenges and opportunities of using new and emerging data to study contemporary and future cities through methods including GIS, Remote Sensing, Big Data and Geodemographics. Written in an accessible style and packed with illustrations and interviews from key urban analysts, this is a groundbreaking new textbook for students of urban planning, urban design, geography, and the information sciences. |
| examples of spatial analysis: Spatial Analysis Tonny J. Oyana, Florence Margai, 2015-07-28 An introductory text for the next generation of geospatial analysts and data scientists, Spatial Analysis: Statistics, Visualization, and Computational Methods focuses on the fundamentals of spatial analysis using traditional, contemporary, and computational methods. Outlining both non-spatial and spatial statistical concepts, the authors present p |
Examples - Apache ECharts
Apache ECharts，一款基于JavaScript的数据可视化图表库，提供直观，生动，可交互，可个性化定制的数据可视化图表。
Examples - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code Standard; …
Examples - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code Standard; …
Apache ECharts
ECharts: A Declarative Framework for Rapid Construction of Web-based Visualization. 如果您在科研项目、产品、学术论文、技术报告、新闻报告、教育、专利以及其他相关活动中使用了 Apache …
Events - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code Standard; …
Examples - Apache ECharts
Apache ECharts，一款基于JavaScript的数据可视化图表库，提供直观，生动，可交互，可个性化定制的数据可视化图表。
Examples - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
Examples - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
Apache ECharts
ECharts: A Declarative Framework for Rapid Construction of Web-based Visualization. 如果您在科研项目、产品、学术论文、技术报告、新闻报告、教育、专利以及其他相关活动中使用了 …
Events - Apache ECharts
Examples; Resources. Spread Sheet Tool; Theme Builder; Cheat Sheet; More Resources; Community. Events; Committers; Mailing List; How to Contribute; Dependencies; Code …
