Introduction to Mapping and Spatial Analysis
Last edited on 2024-11-05
Getting Started
This book is a compilation of notes for for GIS and spatial analysis in R courses and modules. Most pages can be read in any order. This book is split into two parts: data manipulation & visualization and exploratory spatial data analysis. The first part of this book is usually conducted using ArcGIS Desktop, the latter part of the book is conducted in R. ArcGIS was chosen as the GIS environment because it is the most common GIS graphical user interface in academic and non-academic applications.
ArcGIS Pro Tutorials (Google Site)
Other GIS software environments, like the open source software QGIS, could be adopted in lieu of ArcGIS–and R can be used to perform many spatial data analysis operations, for exmaple clipping, buffering and projecting, and digital cartography. Even though some of the chapters of this book make direct reference to ArcGIS techniques, most chapters are applicable to learning GIS and spatial analysis in any computing environment.
The latter part of this book is focused on the use of R because of
its wide use and community of users in the world of data science
its rich (if not richest) array of spatial analysis and spatial statistics packages
its scripting environment which facilitates reproducibility
it’s completely free and open source with a large community of users, free learning materials, and specialized packages allowing users to implement analyses not readily supported in any GUI.
R can be used for many traditional GIS applications that involve data manipulation operations–the main benefits of using a full-blown GIS environment like ArcGIS or QGIS are:
creating/editing spatial data,
rendering/designing complex maps and map annotations (text, graphics)
hands-on manipulation of spatial data.
managing large amounts of spatial data easily in a geodatabase
Tutorials for mapping and spatial analysis in R.
0.0.1 Giving Credit
The following websites were used in the construction of this workbook. My gratitude to the people who invested their time and effort in developing and offering these valuable resources to the public.
Manuel Gimond’s Intro to GIS and Spatial Analysis
Noli Brazil’s Spatial Methods in Community Research
Claudia Engel’s Using Spatial Data in R
Ben Baumer’s Introduction to Data Science Course
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Website created and maintained by Jordan Ayala