Building spatial databases based on attributes

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).

Resources

These chapters are not ‘prerequisite’ reading for the week, but provide a lot of helpful background for attribute operations in R.

• 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 _join functions in dplyr.

• 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.

Slides

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• Building spatial databases based on attributes
• Workflows for spatial analysis
• Databases and attributes
• Common attribute operations
• Joining (a)spatial data