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/.

Key Concepts and Techniques in GIS is a concise overview of the fundamental ideas that inform geographic information science. It provides detailed descriptions of the concepts and techniques that anyone using GIS software must fully understand to analyse spatial data. Short and clearly focussed chapters provide explanations of: spatial relationships and spatial data the creation of digital data, the use and access of existing data, the combination of data the use of modelling techniques and the essential functions of map algebra spatial statistics and spatial analysis geocomputation - including discussion of neural networks, cellular automata, and agent-based modelling Illustrated throughout with explanatory figures, the text also includes a glossary, cross referenced to discussion in the text. Written very much from a user′s perspective, Key Concepts and Techniques in GIS is highly readable refresher course for intermediate level students and practitioners of GIS in the social and the natural sciences.

Geographic Information Systems are computer-based systems for geographic analysis. They have been developed over the past twenty five years and are now widely used. A recent research direction has been the development of geocomputation , representing computer-based geographical analysis beyond the traditional bounds of GIS. In geocomputation, the computer is the research environment itself, not merely a tool. A key to geocomputation it that highly powered computing can be used with sufficient data to avaid traditional parametric approaches altogether. The term geocomputation includes the use of computer-based techniques such as artificial neural networks, genetic programming and fuzzy logic, but in a geographical context. This new book in the prestigious Innovations in GIS series, presents the latest research in geocomputational techniques as presented in the GIS UK Annual Conference.

This volume contains selected essays of Manfred M. Fischer in the field of spatial analysis from the perspective of GeoComputation. The volume is structured in four parts, from broad issues in spatial analysis and the role of GIS to computational intelligence technologies such as neural networks. The third part provides the theoretical framework required for adaptive pattern classifiers in remote sensing environments. The final section outlines the latest in neural spatial interaction modeling.

Geocomputation is the use of software and computing power to solve complex spatial problems. It is gaining increasing importance in the era of the ‘big data’ revolution, of ‘smart cities’, of crowdsourced data, and of associated applications for viewing and managing data geographically - like Google Maps. This student focused book: Provides a selection of practical examples of geocomputational techniques and ‘hot topics’ written by world leading practitioners. Integrates supporting materials in each chapter, such as code and data, enabling readers to work through the examples themselves. Chapters provide highly applied and practical discussions of: Visualisation and exploratory spatial data analysis Space time modelling Spatial algorithms Spatial regression and statistics Enabling interactions through the use of neogeography All chapters are uniform in design and each includes an introduction, case studies, conclusions - drawing together the generalities of the introduction and specific findings from the case study application – and guidance for further reading. This accessible text has been specifically designed for those readers who are new to Geocomputation as an area of research, showing how complex real-world problems can be solved through the integration of technology, data, and geocomputational methods. This is the applied primer for Geocomputation in the social sciences.

GIS and Geocomputation for Water Resource Science and Engineering not only provides a comprehensive introduction to the fundamentals of geographic information systems but also demonstrates how GIS and mathematical models can be integrated to develop spatial decision support systems to support water resources planning, management and engineering. The book uses a hands-on active learning approach to introduce fundamental concepts and numerous case-studies are provided to reinforce learning and demonstrate practical aspects. The benefits and challenges of using GIS in environmental and water resources fields are clearly tackled in this book, demonstrating how these technologies can be used to harness increasingly available digital data to develop spatially-oriented sustainable solutions. In addition to providing a strong grounding on fundamentals, the book also demonstrates how GIS can be combined with traditional physics-based and statistical models as well as information-theoretic tools like neural networks and fuzzy set theory.

Geographic information systems (GIS) have become increasingly important in helping us understand complex social, economic, and natural dynamics where spatial components play a key role. The critical algorithms used in GIS, however, are notoriously difficult to both teach and understand, in part due to the lack of a coherent representation. GIS Algorithms attempts to address this problem by combining rigorous formal language with example case studies and student exercises. Using Python code throughout, Xiao breaks the subject down into three fundamental areas: Geometric Algorithms Spatial Indexing Spatial Analysis and Modelling With its comprehensive coverage of the many algorithms involved, GIS Algorithms is a key new textbook in this complex and critical area of geography.

Geographic information science (GIScience) is an emerging field that combines aspects of many different disciplines. Spatial literacy is rapidly becoming recognized as a new, essential pier of basic education, alongside grammatical, logical and mathematical literacy. By incorporating location as an essential but often overlooked characteristic of what we seek to understand in the natural and built environment, geographic information science (GIScience) and systems (GISystems) provide the conceptual foundation and tools to explore this new frontier. The Encyclopedia of Geographic Information Science covers the essence of this exciting, new, and expanding field in an easily understood but richly detailed style. In addition to contributions from some of the best recognized scholars in GIScience, this volume contains contributions from experts in GIS' supporting disciplines who explore how their disciplinary perspectives are expanded within the context of GIScienceâ€"what changes when consideration of location is added, what complexities in analytical procedures are added when we consider objects in 2, 3 or even 4 dimensions, what can we gain by visualizing our analytical results on a map or 3D display? Key Features Brings together GIScience literature that is spread widely across the academic spectrum Offers details about the key foundations of GIScience, no matter what their disciplinary origins Elucidates vocabulary that is an amalgam of all of these fields Key Themes Conceptual Foundations Cartography and Visualization Design Aspects Data Manipulation Data Modeling Geocomputation Geospatial Data Societal Issues Spatial Analysis Organizational and Institutional Aspects The Encyclopedia of Geographic Information Science is an important resource for academic and corporate libraries.

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.

The significance of modeling in managing the environment is well recognized from scientific and engineering perspectives as well as in the political arena. Environmental concerns and issues of sustainability have permeated both public and private sectors, particularly the need to predict, assess and mitigate against adverse impacts that arise from continuing development and use of resources. Students need to be made aware of these issues. Practitioners should enrich their knowledge and skills in these areas. This book focuses on the modeling, rather than on data collection or visualization.