Tchabal Mbabo Faro et Deo SRTM

Geographic Data Analysis


Course Information:

Detailed Course Outline (pdf): Download the detailed course outline which includes assessment activities, course policies, and a course calendar.

Professor: Arthur Gill Green
Contact Info: agreen@okanagan.bc.ca
Office Hours: Monday 14h00-15h00 or by appointment
Office: C145
Lecture Times and Locations: Monday 830-950 (E212) & Wednesday  830-950 (E401)
Lab Time and Location: Wednesday 1530-1820 (E401)
Course Website: http://greengeographer.com/teaching/geographicdataanalysis/
Course ID: Okanagan College GEOG 270
Student grades are available on Moodle.


Course Description:

Introduction to descriptive and inferential statistical analysis in geography. Topics include descriptive statistics, elementary probability, statistics for spatial analysis, hypothesis testing, analysis of variance, correlation and regression. We also evaluate the ethics of geographic data (what is geographic data in a world of changing technology), visualization of geographic data, and communication of geographic data analysis results.


Learning Outcomes:

The course is intended to introduce students to basic statistical methods used in spatial and non-spatial analyses. The course focuses on helping students understand their data, describe their data, select an appropriate statistical test, use common software packages to run that test, and interpret results. On completion of this course, the student will be able to:

  1. Apply basic statistical and spatial analysis methods.
  2. Design research questions.
  3. Identify appropriate data types.
  4. Gather sample data.
  5. Select appropriate tests for parametric, non-parametric, and spatial data.
  6. Conduct basic analyses using different statistical software packages.
  7. Interpret statistical output.
  8. Produce analytical reports that answer simple research questions.
  9. Present results in a professional format.

Course Resources

Below you can access required readings, lecture notes,  and other materials for each week of the course. All materials posted below (videos, podcasts, etc.) may be on the exams. We will be drawing primarily from open textbooks in addition to materials that the instructor creates. The primary readings are:

If you would like to see a more detailed course outline (policies, calendar, etc.) you can download it from the top of this page as a pdf.

Week 01
Week 02
Week 03
Week 04
Week 05
Week 06
Week 07
Week 08
Week 09
Week 10
Week 11
Week 12
Week 13
Week 14
Final Lab Project