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.
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.
Sixteen years ago, Franklin estimated that about 80% of data contain geo-referenced information. To date, the availability of geographic data and information is growing, together with the capacity of users to operate with IT tools and instruments. Spatial data infrastructures are growing and allow a wide number of users to rely on them. This growth has not been fully coupled to an increase of knowledge to support spatial decisions. Spatial analytical techniques, geographical analysis and modelling methods are therefore required to analyse data and to facilitate the decision process at all levels. Old geographical issues can find an answer thanks to new methods and instruments, while new issues are developing, challenging researchers towards new solutions. This volume aims to contribute to the development of new techniques and methods to improve the process of knowledge acquisition. The Geocomputational expression is related to the development and the application of new theories, methods and tools in order to provide better solutions to complex geographical problems. The geocomputational analysis discussed in this volume, could be classified according to three main domains of applications; the first one related to spatial decision support system and to spatial uncertainty, the second connected to artificial intelligence, the third based on all spatial statistics techniques.
Keeping abreast of new developments, this new edition provides a comprehensive and up-to-date review of the entire field of classification methods applied to remotely sensed data. It provides seven fully revised chapters and two new chapters covering support vector machines (SVM) and decision trees.
This handbook explores how systematic research methods could aid in understanding traditional questions in public administration as well as emerging theoretical and practical questions. A supporting website provides datasets, unpublished research examples and actual published research articles.
Spatial dimensions need to be properly captured if modeling and engineering techniques are to be successfully applied in addressing environmental problems. The links between the geographical information systems (GIS) that capture this data, simulation modeling, and engineering offer tremendous possibilities for building versatile support systems fo
Economists, geographers and surveyors are beginning to recognise the powerful tool which a Geographical Information System (GIS) offers in effective property management. It provides a means of managing land and property information digitally and in a geographical context, and allows for rapid access to information and a means of analyzing that information in a geographical context. GIS in Land and Property Management shows how to use GIS, both in principle and in practice. It introduces digital mapping and GIS, along with a brief history of the development of GIS and LIS, all with an emphasis on property. In presenting the spectrum of GIS applications in property management it gives a number of case studies from a variety of market sectors, and it analyzes the issues to provide guidance and a number of recommendations for the implementation of GIS. At the same time common themes and issues are drawn out to present a coherent message for students and practitioners. The book is useful for undergraduate and postgraduate students on land management, built environment, economics and geography courses, and for property professionals, in both public and private sectors, looking to GIS as a property management decision aid.
The Handbook of Regional Science is a multi-volume reference work providing state-of-the-art information, prepared by respected scientists in the field. This second edition includes new sections on the history of regional science, and on regional policy. It has been thoroughly updated to reflect new developments, including new chapters on R&D collaboration networks, knowledge spillovers, web-based tools for exploratory spatial data analysis, fuzzy modeling, multivariate spatial process models, heterogeneous coefficient spatial regression panel models, and endogeneity in spatial models, among others. The multi-volume handbook covers the field of regional science comprehensively, including topics such as location and interaction, regional housing and labor markets, regional economic growth, innovation and regional economic development, regional policy in emerging markets, new economic geography and evolutionary economic geography, environmental and natural resources, spatial analysis and geocomputation, as well as spatial statistics and spatial econometrics. The book is intended to serve the needs of graduate students, beginning and experienced scientists in regional science and related fields with an interest in exploring local and regional socio-economic issues.