Teaching

GEOGRAPH 491P/691P GIS Programming

Syllabus: Fall 2024

Course description: This course will cover a number of programming methods and applications in GIS. Beginning in the (familiar) ArcGIS environment, this course will explore fundamentals of programming in Python while learning the Model Builder interface. By exploring basic automation methods of repetitive or complex tasks, this course will also introduce foundations of computer science and computational thinking. While gaining proficiency in Model Builder, this course will expand to other python scripting applications, both within ArcGIS and on other platforms. By exploring many applications of programming to advance GIS analysis and improve workflows, students will build a strong base of knowledge and capacity for future learning and flexibility with programming in GIS.

GEOGRAPH 493A/693A Cartography and Geovisualization

Syllabus: Fall 2024

Course description: Students in Cartography and Geovisualization will understand and implement principles of good design in cartography along with understanding the human vision and how it influences perception and cognition. The course will also cover the scope of contemporary thematic cartography and web mapping. Students will gain hands-on experience in designing and improving web-based maps.

GEOG 264 Programming for Environmental Sciences

Syllabus: Fall 2023

Calendar description: This course is an introduction to the fundamentals of computer programming relevant for environmental sciences. It presents the basic building blocks of computer programming, including data types, variables and constants; expressions and operators; assignments, control structures, simple library functions and programmer‑defined functions. Students learn how to develop algorithms and how to convert algorithms/pseudo codes into a programming language — specific syntax (e.g. R, Python) — to collect, query, preprocess, visualize and analyze environmental datasets.

GEOG 202 Statistics and Spatial Analysis

Syllabus: Fall 2022

Calendar description: Exploratory data analysis, univariate descriptive and inferential statistics, non-parametric statistics, correlation and simple regression. Problems associated with analysing spatial data such as the ‘modifiable areal unit problem’ and spatial autocorrelation. Statistics measuring spatial pattern in point, line and polygon data.

Strategies and Resources for Teaching GIS: Insights from AAG 2024