Combining Spatial and Tabular Data
Content for Monday, October 9, 2023
Today we’ll begin exploring typical workflows for spatial analysis by working with attribute data. Attributes generally provide additional information about a location that we can use for visualization and analysis. Unlike spatial operations that we’ll explore next week, attribute data do not all require geographic information (but they do need some means of relating to a geography). These chapters are not ‘prerequisite’ reading for the week, but provide a lot of helpful background for attribute operations in R.
Resources
The Tidy Data and Relational Data sections from R For Data Science (Wickham and Grolemund 2016) provide a great overview to data cleaning and manipulation functions available in the
tidyverse.Doing things with multiple tables has a lot of nice visual examples of for using the
_joinfunctions indplyr.This article (Di Minin et al. 2021) provides a recent recap of a variety of reasons why we may need to combine data from multiple, often disparate, sources.
The Spatial Data Operations Chapter in (Lovelace et al. 2019) makes the concepts of a network concrete (literally) by using a transportation route example to illustrate the various components of a network analysis in
R.
Objectives
By the end of today, you should be able to:
Define spatial analysis
Describe the steps in planning a spatial analysis
Understand the structure of relational databases
Use attributes and topology to subset data
Generate new features using geographic data
Join data based on attributes and location
Slides
The slides for today’s lesson are available online as an HTML file. Use the buttons below to open the slides either as an interactive website or as a static PDF (for printing or storing for later). You can also click in the slides below and navigate through them with your left and right arrow keys.