Introduction to GIS

An introduction to fundamental techniques for geospatial data science and geoscience using a modern GIS toolkit that includes ArcGIS Pro and the programming language R.

COVID-19 Pandemic

For the Spring 2020 semester, this course has been modified to accomodate remote learning.

Modified Plans for Learning

Course Description

This class introduces both the theoretical and technical skills that constitute the nascent field of geospatial data science. Techniques introduced include map production and cartography, spatial data cleaning and management, and the manipulation of both tabular and spatial data. The course incorporates a wide variety of social, economic, health, urban, meteorological, and environmental data. These data are mapped at a variety of extents, from the City of St. Louis to the St. Louis Metropolian region, Missouri, and the entire United States.

To map these data, we balance an introduction to the industry-standard proprietary software, ArcGIS Pro, with cutting-edge techniques for working with spatial data in the programming language R. Data science tools, including Markdown, Git, and GitHub are also introduced along a brief introduction to the programming language Python.

Resources

Syllabus

policies, assignments, due dates

Docs

general and lecture-specific notes

GitHub Org

course repositories

Slack

ask questions, get help

Office Hours Sign-up

slots available Tuesdays, 9am-11am

R for Data Science

data science in R

Thanks

Like many things in academia, the “final” product here is the result of others’ hard work as well as my own. I owe gratitude and thanks to Hadley Wickham and Edzer Pebesma, whose data science and GIS tools are featured as part of this course. I also draw significant inspiration from Jenny Bryan, whose Stat 545 course has served as a significant resource and a model for my own teaching.

This website itself is the product of Yihui Xie’s work on the blogdown package for R as well as George Cushen’s Academic theme for Hugo.

Finally, I want to acknowledge my now eleven semesters’ worth of students in GIS as well as statistics, whose collective feedback and experiences have helped dramatically reshape the course content and how I teach. For example, the topic and package indexes on this site are direct responses to student suggestions and feedback. I am immensely grateful for each of your contributions to my research methods courses.

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