Attribute Data Operations

Content for Monday, September 22, 2025

At this point, you should be (somewhat) familiar with the dplyr approach to manipulating tabular data. No only that, but you can get spatial data into R!! Now it’s time to start bringing different datasets together to answer our questions. For the next 3 sessions, we’ll be working towards this. Today, the focus is on creating and manipulating attribute data, with a special focus on using spatial location as the foundation for doing that.

Readings

Setting the Stage

Technical Details

Objectives

By the end of today, you should be able to:

  • Define Spatial Analysis and distinguish it from making maps.

  • Recognize the role of database structure and spatial geometry in generating attributes

  • Develop appreciation for the link between attribute operations and analysis validity.

  • Perform attribute operations on vector and raster data.

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.

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References

Boyce, G., S. N. Chambers, T. Plath, and D. E. Martı́nez. 2025. Manufacturing desolation: Unauthorized border crosser mortality, disappearance, and the sociopolitical construction of remoteness in US boundary enforcement. Annals of the American Association of Geographers:1–18.
Lovelace, R., J. Nowosad, and J. Muenchow. 2019. Geocomputation with R. CRC Press.
Yanosky, J. D., C. J. Paciorek, F. Laden, J. E. Hart, R. C. Puett, D. Liao, and H. H. Suh. 2014. Spatio-temporal modeling of particulate air pollution in the conterminous united states using geographic and meteorological predictors. Environmental Health 13:63.