This book contains refereed papers from the 13th International Conference on GeoComputation held at the University of Texas, Dallas, May 20-23, 2015. Since 1996, the members of the GeoComputation (the art and science of solving complex spatial problems with computers) community have joined together to develop a series of conferences in the United Kingdom, New Zealand, Australia, Ireland and the United States of America. The conference encourages diverse topics related to novel methodologies and technologies to enrich the future development of GeoComputation research.

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/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds, where he has taught R for geographic research over many years, with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan, where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena, where he develops and teaches a range of geographic methods, with a focus on ecological modeling, statistical geocomputing, and predictive mapping. All three are active developers and work on a number of R packages, including stplanr, sabre, and RQGIS.

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.

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.

"Geosimulation has recently emerged at the intersection of Geographic Information Science, Complex Systems Theory and Computer Science. Geosimulation aims at understanding the dynamics of complex human-driven spatial systems through the use of spatially ex"

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 contributed volume collects cutting-edge research in GeoComputational Analysis of Regional Systems. The contributions emphasize methodological innovations or substantive breakthroughs on many facets of the socio-economic and environmental reality of regional contexts.

A revision of Openshaw and Abrahart’s seminal work, GeoComputation, Second Edition retains influences of its originators while also providing updated, state-of-the-art information on changes in the computational environment. In keeping with the field’s development, this new edition takes a broader view and provides comprehensive coverage across the field of GeoComputation. See What’s New in the Second Edition: Coverage of ubiquitous computing, the GeoWeb, reproducible research, open access, and agent-based modelling Expanded chapter on Genetic Programming and a separate chapter developed on Evolutionary Algorithms Ten chapters updated by the same or new authors and eight new chapters added to reflect state of the art Each chapter is a stand-alone entity that covers a particular topic. You can simply dip in and out or read it from cover to cover. The opening chapter by Stan Openshaw has been preserved, with only a limited number of minor essential modifications having been enacted. This is not just a matter of respect. Openshaw’s work is eloquent, prophetic, and his overall message remains largely unchanged. In contrast to other books on this subject, GeoComputation: Second Edition supplies a state-of-the-art review of all major areas in GeoComputation with chapters written especially for this book by invited specialists. This approach helps develop and expand a computational culture, one that can exploit the ever-increasing richness of modern geographical and geospatial datasets. It also supplies an instructional guide to be kept within easy reach for regular access and when need arises.

Timely and reliable information on natural resources, regarding their potential and limitations, is a prerequisite for sustainable development. Geospatial technologies offer immense potential in providing such information in a timely and cost-effective manner. Using orbital sensors data in conjunction with airborne and proximal sensors data to generate information on soils and agricultural resources, forests, mineral resources, fossil fuel, wetlands, water resources, and marine resources, this book focuses on the advancements in technologies applicable to managing these resources. It addresses global issues like climate change and land degradation neutrality and introduces spatial data infrastructure (SDI) as a mechanism for sharing geospatial data. This book also provides an in-depth discussion on drones, crowdsourcing, cloud computing, Internet of Things, machine learning, and their applications. FEATURES Contains a comprehensive resource on the latest developments in geospatial technologies and their use in monitoring natural resources, productivity mapping, and modeling Explains the geo-computation methods and online algorithm developments Includes clear guidance on how best to use geospatial data for various applications Discusses case studies from a variety of fields and current trends in the management of natural resources Provides future scenarios concerning platforms, sensors, data analysis, and interpretation techniques This book is written for remote sensing and GIS professionals in environmental institutions and government who are involved in natural resource management projects. Senior undergraduate and graduate-level students in Earth sciences, geography, or environmental management can also use this text for supplementary reading.