Schedule (Refined)

This schedule was updated on October 3, 2022.

How is this schedule organized

Each session includes information on:

• Assignments due: Assignments are due by 11:59 PM on the day they’re listed unless otherwise noted.

• Topics: A listing of the topics, questions, or skills that we expect to focus on during each course session.

• Readings: Readings for this class include both material on how to work with spatial data in R and QGIS and material on broader considerations around working with data and how spatial data is collected, organized, and shared. Most readings are free and online but a few may require you to use the UMBC Library Books and Media Search or AOK Article Search to locate.

• Additional references: Optional material (including readings and videos) that you are encouraged to use as a reference but you are not required to review before class.

Setup

To participate in this class, you need to have access to a laptop with a few applications installed: R, RStudio, and QGIS. You also need to set up a GitHub account. I’ve gathered a few resources with step-by-step instructions on how to install these applications along with some introductory material for any students who has no or limited prior experience with a desktop GIS or a programming language like R.

To install QGIS and get an introduction to the interface (if you do not have prior experience with a desktop GIS application), please review:

• 5. Getting started in QGIS project “QGIS Desktop User Guide/Manual (QGIS 3.22),” August 24, 2022, https://docs.qgis.org/3.22/en/docs/user_manual/index.html.
• Ch. 1 Where Do I Start? in Andrew Cutts and Anita Graser Learn QGIS: Your Step-by-Step Guide to the Fundamental of QGIS 3.4, Fourth edition. (Birmingham: Packt, 2018). (available via UMBC Library)

To install R and RStudio and get an introduction to how R works, please review:

• Ch. 1 Install R and RStudio and Ch. 2 R Basics and Workflows in Jenny Bryan and The STAT 545 TAs STAT 545: Data Wrangling, Exploration, and Analysis with R, 2019, https://stat545.com/.

To set up GitHub account and learn how to connect your account to RStudio, please review:

• Installation and Connect Git, GitHub, RStudio from Jenny Bryan, the STAT 545 TAs, and Jim Hester Happy Git and GitHub for the useR, accessed August 28, 2022, https://happygitwithr.com/.

Part 1. Getting started with spatial data

1. Taking care with data – Aug. 31

Topics

• Course overview
• How is spatial data structured
• How can we take a critical approach to working with data
• Demonstration on using GitHub to submit weekly log

Readings

• Ch. 1 Local Origins in Yanni Alexander Loukissas All Data Are Local: Thinking Critically in a Data-Driven Society, 2019, doi:10.7551/mitpress/11543.001.0001.
• Ch. 1 Introduction in Robin Lovelace, Jakub Nowosad, and Jannes Muenchow Geocomputation with R, 2nd (WIP)., 2022, https://geocompr.robinlovelace.net/.

Additional references

• Jer Thorp “Turning Data Around,” November 18, 2016, https://medium.com/memo-random/turning-data-around-7acea1f7479c.
• Dawn J. Wright, Michael F. Goodchild, and James D. Proctor “Demystifying the Persistent Ambiguity of GIS as ‘Tool’ Versus ‘Science’,” Annals of the Association of American Geographers 87, no. 2 (June 1997): 346–362, doi:10.1111/0004-5608.872057.

2. Getting started using spatial data with R – Sept. 7

Topics

• Using best practices for file naming and organization
• Using RStudio to support project management
• Reading and writing common spatial data file formats in R
• Converting tabular data into spatial data

Readings

• Ch. 2 A Place for Plant Data in Loukissas All Data Are Local.
• Catherine D’Ignazio and Lauren Klein “Who Collects the Data? A Tale of Three Maps,” MIT Case Studies in Social and Ethical Responsibilities of Computing no. Winter 2021 (February 5, 2021), doi:10.21428/2c646de5.fc6a97cc.
• Ch. 2 Geographic data in R in Lovelace, Nowosad, and Muenchow Geocomputation with R.
• Jennifer Bryan “How to Name Files” (May 14, 2015), https://speakerdeck.com/jennybc/how-to-name-files.

Additional references

• Tom MacWright “More Than You Ever Wanted to Know about GeoJSON,” March 23, 2015, https://macwright.com/2015/03/23/geojson-second-bite.html.
• Jakub Nowosad and Martijn Tennekes Elegant and Informative Maps with tmap, 2021, https://r-tmap.github.io/tmap-book/.
• Introduction to R for Geospatial Data (Data Carpentry)

3. Exploring and visualizing attribute data with R – Sept. 14

Assignments due

• Find spatial data
• Set up a GitHub account

Topics

• Subsetting or filtering based on attributes
• Creating new attributes based on existing attributes
• Joining tables by attributes with {dplyr}
• Basics of visualizing attribute data with {ggplot2}

Readings

• Ch. 3 Collecting Infrastructures in Loukissas All Data Are Local.
• Ch. 3 Attribute data operations in Lovelace, Nowosad, and Muenchow Geocomputation with R.
• Ch. 5 Data transformation in Hadley Wickham and Garrett Grolemund R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2nd edition (WIP). (Sebastopol, CA: O’Reilly Media, 2022), https://r4ds.had.co.nz.

Additional references

• Ch. 6 Efficient data carpentry in Colin Gillespie and Robin Lovelace Efficient R Programming (O’Reilly Media, 2021), https://csgillespie.github.io/efficientR/.
• Ch. 9 Making maps with R in Lovelace, Nowosad, and Muenchow Geocomputation with R.
• Mel Moreno and Mathieu Basille “Drawing Beautiful Maps Programmatically with R, sf and ggplot2 — Part 1: Basics,” October 25, 2018, https://r-spatial.org/r/2018/10/25/ggplot2-sf.html. (continue with Part 2: Layers and Part 3: Layouts)
• Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen Ggplot2: Elegant Graphics for Data Analysis, 3rd (WIP)., Use R! (Springer, 2022), https://ggplot2-book.org/index.html.

4. Exploring and visualizing attribute data with QGIS – Sept. 21

Topics

• Using QGIS to support project management
• Reading and writing common spatial data file formats with QGIS
• Using the Style Manager and Print Layout features in QGIS

Additional references

• An Introductory QGIS Workshop for Beginners from Michele Tobias (UC Davis DataLab)
• “An Introductory QGIS Workshop for Beginners” (July 17, 2020), https://www.youtube.com/watch?v=-7v5qfJYWxA. (workshop repository on GitHub)
• QGIS Uncovered by Steven Bernard (a design editor with the Financial Times)
• “QGIS Expressions, Variables, Data Defined Settings: Putting It All Together!” (June 12, 2020), https://www.youtube.com/watch?v=h-mpUkwDdOQ.
• Ch. 2 Data Creation and Editing in Cutts and Graser Learn QGIS. (available via UMBC Library)

5. Documenting spatial data – Sept. 28

This session features a guest speaker, Reina Chano Murray, Geospatial Data Curator and Applications Administrator, Johns Hopkins Sheridan Libraries, sharing her perspective on documentation and archiving spatial data.

Thanks to Reina for sharing her slides and related resources in a GitHub repository for this session.

Assignments due

• Read spatial data and make a map with QGIS
• Read spatial data and make a map with R and ggplot2

Topics

• Introduction to metadata and spatial metadata
• Writing metadata, READMEs, and other documentation
• Documentation for data and data analysis workflows
• Planning for reproducible spatial data analysis

Readings

• Ch. 7 Show Your Work in Catherine D’Ignazio and Lauren F. Klein Data Feminism (The MIT Press, 2020), doi:10.7551/mitpress/11805.001.0001.
• Greg Wilson et al. “Good Enough Practices in Scientific Computing,” PLOS Computational Biology 13, no. 6 (June 22, 2017): e1005510, doi:10.1371/journal.pcbi.1005510.

Additional references

• Andrew Battista et al. “GeoDatabase Data Curation Primer,” 2019, https://github.com/DataCurationNetwork/data-primers/blob/c6ec438e76fea49eaaf2806bc79ec2c8c12de7f3/Geodatabase%20Data%20Curation%20Primer/Geodata-Primer.md.
• Tahu Kukutai and Maggie Walter “Indigenous Data Sovereignty: Implications for Data Journalism,” in The Data Journalism Handbook: Towards a Critical Data Practice, ed. Liliana Bounegru and Jonathan Gray, 2nd ed. (Amsterdam University Press, 2021), 65–73, doi:10.5117/9789462989511_ch09.
• Natalia Mazotte “Working Openly in Data Journalism,” in The Data Journalism Handbook: Towards a Critical Data Practice, ed. Liliana Bounegru and Jonathan Gray, 2nd ed. (Amsterdam University Press, 2021), 138–142, doi:10.5117/9789462989511_ch20.

6. Geometric and spatial data operations with R – Oct. 5

This session is the same day as the Jewish holiday of Yom Kippur. Class will take place remotely on October 2. A full recording for this session will be made available to students who are unable to attend the remote session.

Topics

• Using geometry operations for buffering or simplifying features
• Using geometric operations like union, intersection, and difference
• Using related functions including spatial joins and filters
• Using spatial and geometric operations in exploratory data analysis

Readings

• Ch. 4 Spatial data operations in Lovelace, Nowosad, and Muenchow Geocomputation with R.
• Ch. 5 Geometry operations in Lovelace, Nowosad, and Muenchow Geocomputation with R.

7. Tidying data in R – Oct. 12

Topics

• Using {stringr} functions to tidy messy address data
• Recoding categorical attribute data with {forcats}
• Converting between wide and long data formats with dplyr::pivot_longer() and dplyr::pivot_wider()
• Working with date-time attributes using {lubridate}

Readings

• Ch. 5 Market, Place, Interface in Loukissas All Data Are Local.
• Ch. 12 Tidy data in Wickham and Grolemund R for Data Science.
• The Quartz Guide to Bad Data (Quartz, 2022), https://github.com/Quartz/bad-data-guide.

Additional references

• Manipulating, analyzing and exporting data with tidyverse from Data Analysis and Visualisation in R for Ecologists (Data Carpentry)
• Emily Riederer “Column Names as Contracts,” September 6, 2020, https://emilyriederer.netlify.app/post/column-name-contracts/.
• Yanqi Xu “My Data Cleaning in r Cheat Sheet,” October 24, 2019, https://www.yanqixu.com/My_R_Cheatsheet/data_cleaning_cheatsheet.html.
• Katie Rawson and Trevor Muñoz “Against Cleaning” (July 6, 2016), http://www.curatingmenus.org/articles/against-cleaning/.

8. Summarizing and analyzing data with R – Oct. 19

Topics

• Visualizing distribution of values of attribute data
• Visualizing spatial distribution of features
• Creating crosstabs with janitor::tabyl() (see vignette on tabyls)
• Summarizing or aggregating attribute data with dplyr::summarize()
• Creating summary tables with {gtsummary}

Readings

• Ch. 7 Exploratory Data Analysis in Wickham and Grolemund R for Data Science.
• Ch. 6 Models of Local Practice and Ch. 7 Local Ends in Loukissas All Data Are Local.

Additional references

• Roger Beecham “Exploratory Data Analysis: Using Colour and Layout for Comparison,” August 11, 2022, https://www.roger-beecham.com/class/04-class/.
• Ch. 11 Scripts, algorithms and functions in Lovelace, Nowosad, and Muenchow Geocomputation with R.
• Roger Beecham and Robin Lovelace “A Framework for Inserting Visually Supported Inferences into Geographical Analysis Workflow: Application to Road Safety Research,” Geographical Analysis n/a, no. n/a (n.d.), doi:10.1111/gean.12338.

Part 2. Creating and sharing spatial data

9. Editing and creating spatial data with web-based tools, spreadsheets, QGIS, or R – Oct. 26

Topics

• Creating and editing point features using CSV or Google Sheets
• Creating and editing features using geojson.io or other web-based tools
• Creating and editing features using the {mapedit} package in R
• Creating and editing vector data in QGIS

Additional references

• Data Organization in Spreadsheets for Social Scientists (Data Carpentry)

10. Getting data from public web services – Nov. 2

Topics

• Downloading data from ArcGIS Feature Services with {esri2sf} (I recommend installing my fork)
• Downloading data from Socrata open data portals with {RSocrata}
• Working with data dictionaries and the {labelled} package
• Interpreting administrative data and other public sources

Readings

• Ch. 8 Geographic data I/O in Lovelace, Nowosad, and Muenchow Geocomputation with R.
• Dan Bouk, Kevin Ackermann, and danah boyd A Primer on Powerful Numbers: Selected Readings in the Social Study of Public Data and Official Numbers, March 23, 2022, https://datasociety.net/library/a-primer-on-powerful-numbers-selected-readings-in-the-social-study-of-public-data-and-official-numbers/.

11. Working on collaborative data projects – Nov. 9

This session features a guest speaker, Elliott Plack, Technical Project Manager, Whitney Bailey Cox & Magnani, LLC, sharing an introduction to OpenStreetMap, his experience as an OSM admin, and perspective on spatial data as a member of the Maryland Council on Open Data.

Readings

• Emily Talen “Bottom-up GIS,” Journal of the American Planning Association 66, no. 3 (2000): 279, doi:10.1080/01944360008976107.
• C. E. Dunn “Participatory GIS - a People’s GIS?” Progress in Human Geography 31 (January 1, 2007): 616–637.
• Rob Kitchin and Tracey P. Lauriault “Small Data in the Era of Big Data,” GeoJournal 80, no. 4 (2015): 463–475, doi:10.1007/s10708-014-9601-7.
• Elliot Bentley “The Web as Medium for Data Visualization,” in The Data Journalism Handbook: Towards A Critical Data Practice, ed. Liliana Bounegru and Jonathan Gray, 2nd ed. (Amsterdam University Press, 2021), 138–142, doi:10.2307/j.ctv1qr6smr_ch26.

Assignments due

• Final project proposal

12. Contributing to OpenStreetMap – Nov. 16

This session (tentatively) features a workshop on editing OpenStreetMap led by Elliott Plack and a short exercise on accessing OSM data with R using the {osmdata} package.

Readings

• Geoff Boeing “The Right Tools for the Job: The Case for Spatial Science Tool-Building,” Transactions in GIS 24, no. 5 (October 2020): 1299–1314, doi:10.1111/tgis.12678.
• Peter Mooney and Marco Minghini “A Review of OpenStreetMap Data,” in Mapping and the Citizen Sensor, ed. Peter Mooney et al. (Ubiquity Press, 2017), 37–60, http://www.jstor.org/stable/j.ctv3t5qzc.6.

13. Project work session – Nov. 23 (scheduled Nov. 21)

This session is the day before the Thanksgiving holiday and may conflict with travel for class participants. Instead of a session at the scheduled time, I plan to host a remote session at a different date and time (to be determined). This session will focus on sharing updates and support for the final project. The session will not be recorded but notes will be shared with any students who are unable to participate.

14. Special topics in spatial data – Nov. 30

There are a wide range of special topics that we could cover in this course but we don’t have time to cover them all. This session is a place-holder to dig deeper into a special topic based on the interests of students in the course. Possible topics could include working with spatial network data using sfnetworks, working with time series data using the QGIS temporal controller feature, visualizing elevation data using the rayshader package, or something else.

15. Final project review – Dec. 7

This session will be dedicated to students sharing individual and collaborative work completed for the final project. Additional details on the final project will be shared with students prior to the end of the first section of the course in early October.

Assignments due

• Final project presentation

• Final project materials (due Dec. 16)