Analyzing spatial relationships is crucial for environmental, social, and political research as well as decision-making. In this course, students will learn the essential elements of reproducible spatial analysis, including data types,projections, geoprocessing, and introductory spatial statistics. The course also highlights the use of open-source data and effective communication of research findings to interdisciplinary audiences. Although case studies will center on environmental topics such as environmental justice, conservation, climate, and energy, the skills acquired will transfer to a variety of disciplinary questions. This course assumes no prior experience with data science and will utilize both RStudio and ArcGIS.

Units: 1

Max Enrollment: 20

Prerequisites: ES 100, ES 101, ES 102, GEOS 101/ES 111, or permission of the instructor.

Degree Requirements: DL - Data Literacy (Formerly QRDL)

Typical Periods Offered: Spring

Semesters Offered this Academic Year: Spring

Notes: