Geography 13 seeks to provide a broad exposure to the techniques, methods, and opportunites in geoinformatics. Topics span the nature of geographic information models, digital envioronments, and basic data science practices. Labs provide hands-on experience with spatial data.
| Name | Role | Office Hours | |
|---|---|---|---|
| Mike Johnson | Instructor | jmj00@ucsb.edu | T 2-3:30 |
| Jiwon Baik | Teaching Assistant | jiwon.baik@ucsb.edu | TR 4-6 |
Prerequisite: Geography 12
Credit hours: 4 units
Course website: https://github.com/mikejohnson51/spds
The class meets 4 times a week for a 65-minute discussion (M-R). Lecture material will be a mix of live coding and presentation depending on the daily topic. All work will be done in R - teaching the basics of data wrangling, manipulation, visualization and analysis for spatial data. By the end of this course you will be comfortable with:
The general outline can be seen below:
In this course we will use R which is an open source programming environment. You will interact with R through the RStudio IDE. Your projects and code will be turned into through GitHub as Rmarkdown reports. Through the course you will build an online portfolio hosted as a static website using a personal GitHub account.
A total of 1,000 points can be earned in this course. Points are cumulative and the overall breakdown of assignments and types of work are given below:
Each week will have a 80 minute hands-on lab section focused on applied topics such as data visualization, cartography, and applying code to practical problems With each lab, there will be an associated assignment that will be are released on the Sunday of each week and are due the following Sunday at 11:59PM.
The final will be the submission of a personal website (built in lab 1) complete with links to all work completed through the quarter. If you keep it up to date through the course this will be easy, if you don’t, this could be painful!
Each day (of class) will conclude with a daily assignment that will be due before the following class period. These should take ~30 minutes and are either coding or reading based, Daily assignments are graded pass/fail based on whether an honest attempt was made. These are intended to encourage you to code each day, and to solidify the concepts learned in an applied way. There will be 22 opportunities to complete an assignment. Each is worth 15 points and 20 of them are required. The remaining two will count as extra credit if completed, or as a free “opps” pass for days you have other obligations/commitments.
Everything in this course from the software we use, to the texts we read are free! You will not need to purchase anything.
| Activity | Applied Topic | Points | Assigned | Due Date |
|---|---|---|---|---|
| Lab 01 | Rmarkdown | 100 | June 21st | June 27th |
| Lab 02 | Wrangling & Visualization | 100 | July 6th | July 13th |
| Lab 03 | Distance & Projections | 100 | July 13th | July 20th |
| Lab 04 | Tessellations & Predicates | 100 | July 20th | July 27th |
| Lab 05 | Data Retrieval & Remote Sensing | 100 | July 25th | July 31st |
| Final | Final Portfolio w/ CV | 200 | July 31st | |
| Daily Exercises | 300 | |||
| Total: | 1,000 |
This year there are holidays on June 28th (Juneteenth) and July 5th (Independence Day). There will be no lecture on these days.
A four-credit course will require, for the average undergraduate student, twelve hours of academic work per week, averaged over a 10 week term. Since this is a summer course we have to cover these 120 hours over 6 weeks (~20 hours a week). Generally I don’t think you will spend this long but should anticipate budgeting the following each week:
Total: ~10-13 hours a week
Each lecture will introduce new topics or expand on existing content. Therefore if you miss a section or lab, you will miss material. It is highly encouraged (but not mandatory) that you attend all lectures and the lab sections.
This class will have a steep learning curve. Jiwon and I will do everything we can to help you along. If you stick with the course and do the work (particularly the daily assignments!), you will get a good grade, learn a lot, and be prepared to serve as a spatial data scientist.
This class, and all derivative material, is owned by Mike Johnson and released under the Creative Commons Attribution-NonCommercial 2.0 Generic (CC BY-NC 2.0) license. This means any individual can use this material to learn, explore, and study but it is not permitted for commercial use. I consider universities charging tuition to be “commercial use”. If you would like to use the material for a university class please seek permission first.