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Geocomputation
Geocomputation is essentially the follow-on revolution from Geographic Information Science and is expected to gather speed and momentum in the first decade of the 21st century. It comes into use once a GIS database has been set up, with a digital data library, and expanded and linked to a global geographical two or three dimensional co-ordinate system. It exploits developments in IT and new data gathering and earth observing technologies, and takes the notion of GIS beyond data and towards its analysis, modelling, and use in problem solving. This book provides pointers on how to harness these technologies in tandem and in the context of multiple different subjects and problem areas. It seeks to establish the principles and set the foundations for subsequent growth. L
An Introduction to R for Spatial Analysis and Mapping
"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...
Geocomputation
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 s...
Geographically Weighted Regression
Geographical Weighted Regression (GWR) is a new local modelling technique for analysing spatial analysis. This technique allows local as opposed to global models of relationships to be measured and mapped. This is the first and only book on this technique, offering comprehensive coverage on this new 'hot' topic in spatial analysis. * Provides step-by-step examples of how to use the GWR model using data sets and examples on issues such as house price determinants, educational attainment levels and school performance statistics * Contains a broad discussion of and basic concepts on GWR through to ideas on statistical inference for GWR models * uniquely features accompanying author-written software that allows users to undertake sophisticated and complex forms of GWR within a user-friendly, Windows-based, front-end (see book for details).
Quantitative Geography
Integrating a discussion of the application of quantitative methods with practical examples, this book explains the philosophy of the new quantitative methodologies and contrasts them with the methods associated with geography′s `Quantitative Revolution′ of the 1960s. Key issues discussed include: the nature of modern quantitative geography; spatial data; geographical information systems; visualization; local analysis; point pattern analysis; spatial regression; and statistical inference. Concluding with a review of models used in spatial theory, the authors discuss the current challenges to spatial data analysis. Written to be accessible, to communicate the diversity and excitement of r...
Classics from IJGIS
The past 20 years can be regarded as the adolescence of geographic information science (GIS), as it grew from a burgeoning area of study into a mature and thriving field. During those two decades, the International Journal of Geographic Information Science (formerly Systems) (IJGIS) was one of the most prominent academic guiding forces in GIScience
Geographical Data Science and Spatial Data Analysis
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.
Local Models for Spatial Analysis
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.
The Handbook of Geographic Information Science
This Handbook is an essential reference and a guide to the rapidly expanding field of Geographic Information Science. Designed for students and researchers who want an in-depth treatment of the subject, including background information Comprises around 40 substantial essays, each written by a recognized expert in a particular area Covers the full spectrum of research in GIS Surveys the increasing number of applications of GIS Predicts how GIS is likely to evolve in the near future
