Geographical Data Science and Spatial Data Analysis: An Introduction in R

The book discusses the challenges of identifying analytical insight from trends and patterns in spatial data, and provides a practical guide for addressing these challenges with confidence. It is a 'learning by doing textbook' that builds on the previous book by the same authors, and is designed for students and professionals in the field of spatial analysis and mapping.

\n Format: Hardback
\n Length: 360 pages
\n Publication date: 29 December 2020
\n Publisher: SAGE Publications Ltd
\n

In the era of big data, where our daily interactions and transactions generate vast amounts of data, a significant portion of this data possesses spatial characteristics. This spatial data is collected at various locations, presenting unique challenges when it comes to extracting analytical insights from the trends and patterns within these rich digital footprints. While other books explore different aspects of data analytics in R and other programming languages, there is a notable gap in the literature regarding spatial data and its integration with big data, geography, GIS, mapping, and spatial analytics.

To address this gap, we present a comprehensive textbook titled "An Introduction to R for Spatial Analysis and Mapping." This book builds upon the previous work of the same authors and aims to provide a practical and hands-on approach to analyzing big spatial data. It delves into the theoretical foundations of spatial analysis and mapping, equipping readers with the necessary knowledge to confidently tackle these challenges.

The book begins by introducing the fundamental concepts and principles of spatial data analysis, including spatial autocorrelation, spatial heterogeneity, and spatial regression. It then explores the various techniques and methods available in R for handling spatial data, such as spatial interpolation, spatial clustering, and spatial mapping. Readers will learn how to apply these techniques to real-world spatial data sets and gain practical experience in data visualization, analysis, and interpretation.

Furthermore, the book emphasizes the importance of integrating spatial data with other data sources, such as demographic information, environmental data, and social media data. It discusses the challenges and opportunities associated with data integration and provides practical examples of how to combine spatial and non-spatial data to address complex research questions.

In addition to its theoretical foundations, the book also includes numerous hands-on exercises and case studies to reinforce the learning process. These exercises cover a wide range of spatial analysis and mapping applications, including urban planning, environmental monitoring, and public health analysis. By working through these exercises, readers will develop their skills and apply their knowledge to real-world scenarios.

Whether you are a researcher, analyst, or practitioner interested in spatial data analysis and mapping, "An Introduction to R for Spatial Analysis and Mapping" is an essential resource for you. It provides a comprehensive and up-to-date introduction to the field, covering both the theoretical and practical aspects of spatial data analysis. With its clear and concise writing style, engaging exercises, and real-world examples, this textbook is designed to help you build the skills and knowledge necessary to thrive in the era of big data and spatial analytics.

\n Weight: 840g\n
Dimension: 176 x 248 x 22 (mm)\n
ISBN-13: 9781526449351\n \n