Instructor: Dr. Clio Andris
See Canvas for official syllabus.
Introduction
This class is a natural next step for those who have had a GIS class and are interested in learning more about Geographic Information Systems and Geographic Information Science. This class will go over advanced topics such as point pattern analysis, compactness and characterizing geometries, distance decay functions, location/allocation functions, routing, basic non-planar network metrics, geographically-weighted parameterization, and geovisualization. It will have a project component.
Class meets Tuesdays and Thursdays 12:30 pm – 1:45 pm. It will be taught remotely through webex. There are no expectations for meeting in person. There will be lecture days and lab days as well as guest lectures and practical assignments. Lectures will be taught synchronously. Attendance on lab days (dates to be announced with the syllabus) is encouraged but lab days will be recorded and the recordings can be watched later.
Introduction to GIS, or a prior GIS class. Students will have a skills assessment on the first week to ensure that they have the prerequisite skills for the class.
Students are expected to attend all lectures, read readings, and complete all assignments. There will be no official final exam. Late submissions will be graded down by 10% for each day of delay past the due time starting at 12:30 pm. All assignments should be handed in on Canvas.
| Content | Number of Assignments | Total Points |
| GIS labs | 5 | 45 |
| Practical assignments | 4* (R review counts for 2 pts) | 15 |
| Quizzes | 4 | 20 |
| Project | 1 | 10 |
| Contribution to discussion and professionalism | 10 |
Course Objectives
- Learn about polygon encapsulation and description methods
- Create GIS analyses that measure environmental exposure and vulnerability for different demographic groups
- Download and use GIS data sources from the web
- Compare different methods of computing distance between points
- Quantitatively demonstrate issues with edge effects
- Use an API through the R statistical computing environment
- Find locations for facilities and evaluate disparities in emergency response times
- Quantify point pattern distributions and distance decay
- Demonstrate use of geographically-weighted regression models
- Understand principles of geovisualization and interaction with data
Tentative Schedule (subject to change)
Check Canvas for Official Deadlines
| Unit | Date | Topic | Reading | Due |
| Th 1/14 | Introduction to Course, GIS and R Demo | None | ||
| Tu 1/19 | On your own: GIS and R Review (help available) | None | ||
| Polygons | Th 1/21 | Lecture 1: Capturing Polygonal Geometries Breakout activity: finding irregular polygons | GIST-Redistricting Opt[Wu] | GIS Review due (1/21) |
| Polygons | Tu 1/26 | Lab 1: Compactness | Gerrymandering | PPI: 343-353 iRedistrict Opt[MacEachren] | R Review due (1/26) |
| Polygons | Th 1/28 | Lecture 2: Social Vulnerability and Social Capital Indexes Breakout activity: exploring indices | Chakraborty | |
| Polygons | Tu 2/2 | Lab 2: Environmental Justice and Social Vulnerability: Does Unit Matter | None/TBD | Lab 1 due (2/2) |
| Polygons | Th 2/4 | Guest Speaker: Dr. Jayajit Chakraborty (University of Texas-El Paso) | None/TBD Opt[ZipCodes] | |
| Networks | Tu 2/9 | Lecture 3: Networks 1: Road Networks, Location/Allocation | Networks | Lab 2 due (2/9) |
| Networks | Th 2/11 | Guest Speaker-Practitioner | Opt[Berke] | |
| Networks | Tu 2/16 | Lab 3.1: Network Analysis | None/TBD | Polygon quiz (2/16) |
| Networks | Th 2/18 | Lecture 4: Networks 2: More on Travel Time, Travel Cost, Data Sources Breakout activity: your route (hypotheses) | None/TBD | |
| Networks | Tu 2/23 | Lab 3.2: Network Analysis: Response Analysis | Boscoe | |
| Th 2/25 | Guest Speaker: Sarah Williams (Massachusetts Institute of Technology) | None/TBD | ||
| Networks | Tu 3/2 | Practical 1: APIs for Travel Time, Travel Cost, Euclidean Distance in R. | None/TBD | Lab 3 due (3/2) |
| Th 3/4 | Practical 2: Fieldwork on Data/Landscape | Paper 4 | Practical 1 due (3/4) | |
| Points | Tu 3/9 | Lecture 5: Point Patterns 1: Capturing Distributions | Point Patterns | |
| Points | Th 3/11 | Lecture 6: Point Patterns 2: Point Patterns: Capturing Distributions and Geographically-Weighted Regression Breakout activity: Patterns in interactive visualizations | None/TBD | |
| Tu 3/16 | MIDSEMESTER BREAK: NO CLASS | None/TBD | ||
| Th 3/18 | Project Introduction Data Search and Project Workshop | Andris (Sports) | Networks quiz (3/18) | |
| Points | Tu 3/23 | Lab 4: Part 1 University Sports Teams: Calculating Distributions | None/TBD | Practical 2 due (3/23) |
| Points | Th 3/25 | Lab 4: Part 2 Restaurant Analysis: Predictive Factors | None/TBD | |
| Geovis | Tu 3/30 | Lecture 7: Principles of Good Maps and Elements of Design | None/TBD | |
| Geovis | Th 4/1 | Practical 3: Pair Activity: Practical Assessment: COVID 19 Visualization Tools (Same day: in-class assignment). | Cartography Topics | Practical 3 due (4/1) |
| Geovis | Tu 4/6 | Lecture 8: Symbology and Gestalt Psychology | None/TBD | Points quiz (4/6) |
| Geovis | Th 4/8 | Lab 5: Small Multiples in R (tiny lab)–map your project data. | None/TBD | Lab 4 due (4/8) |
| Geovis | Tu 4/13 | Lecture 9: Geovisualization Interaction, Tasks and HCI Principles | None/TBD | |
| Project | Th 4/15 | Quiz. Project Help (at school or virtual) | None/TBD | Lab 5 due GeoVis quiz (4/15). |
| Project | Tu 4/20 | Project Help (at school or virtual) | None/TBD | |
| Project | Th 4/22 | Project Presentations | None/TBD | |
| Project | Tu 4/27 | Project Presentations | None/TBD | |
| Th 4/29 | READING DAY/FINALS: NO CLASS | Project Due Date on Canvas |