This book is designed to serve as an introduction to the fascinating world of maps. It explains how to use maps to obtain information about a wide variety of topics. Throughout the book, maps are viewed in a broad framework. Thus, the discussion includes mental maps, aerial photographs, remotely sensed images, computer-assisted cartography, and geographical information systems, in addition to traditional printed maps. The writing style is neither formalistic nor casual, with an emphasis on clarity of explanation. The discussions assume that the reader has no specific prior knowledge of the topic, so that even novice map users can understand and use the information and techniques presented.
This introductory text presents basic principles of social science research through maps, graphs, and diagrams. The authors show how concept maps and mind maps can be used in quantitative, qualitative, and mixed methods research, using student-friendly examples and classroom-based activities. Integrating theory and practice, chapters show how to use these tools to plan research projects, "see" analysis strategies, and assist in the development and writing of research reports.
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/.
Boots and Getis provide a concise explanation of point pattern analysis - a series of techniques for identifying patterns of clustering or regularity in a set of geographical locations. They discuss quadrat and distance methods of measurement, and consider the problems associated with these methods. The authors also outline and compare other measures of arrangement, suggesting when these techniques should be used.
This is a complete guide to the concept mapping methodology and strategies behind using it for a broad range of social scientists - including students, researchers and practitioners.
How can you establish a customer-centric culture in an organization? This is the first comprehensive book on how to actually do service design to improve the quality and the interaction between service providers and customers. You'll learn specific facilitation guidelines on how to run workshops, perform all of the main service design methods, implement concepts in reality, and embed service design successfully in an organization. Great customer experience needs a common language across disciplines to break down silos within an organization. This book provides a consistent model for accomplishing this and offers hands-on descriptions of every single step, tool, and method used. You'll be able to focus on your customers and iteratively improve their experience. Move from theory to practice and build sustainable business success.
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.
