Fall 2022 - GEOG 251 D100

Quantitative Geography (3)

Class Number: 2886

Delivery Method: In Person


  • Course Times + Location:

    Sep 7 – Dec 6, 2022: Mon, 12:30–2:20 p.m.

  • Exam Times + Location:

    Dec 12, 2022
    Mon, 12:00–2:00 p.m.

  • Instructor:

    Peter Keller
    Office: RCB 7135
    Office Hours: I will try and answer e-mails with a 48 hour turn-around during the working week and will post office hours at the beginning of class.
  • Prerequisites:

    GEOG 100 or 111.



An introduction to basic quantitative techniques for the collection of geographic data. Topics include describing data, gathering samples, theoretical distributions, linking samples and populations, testing significance, and exploring spatial relationships all within practical, real-world application contexts. Quantitative.


An introduction to basic quantitative techniques for the collection, analysis and visualization of geographic data, including research design.

Topics include an introduction to types of data, univariate and bivariate data description, gaining familiarity with probability distributions, confidence intervals, difference of means tests, analysis of variance (ANOVA), correlation, and simple linear regression. You will learn about hypothesis statement and their exploration and testing. Examples will cover the breadth of Geography. 

This is an introductory “Q (quantitative)” course that aims to lay a foundation while showing what advanced topics in quantitative geography you might be interested in exploring beyond GEOG 251.

Lectures and Labs:

The course has a weekly two hour lecture introducing theory, explanation and examples. Lectures are complemented by weekly laboratory exercises including two scheduled laboratory hours.

Lectures are divided into learning modules. Key concepts will be reviewed at the beginning and end of each lecture. Laboratory exercises will synchronized to lectures as closely as possible. 

An effort will be made to post a draft of lecture notes on Canvas by 18:00 the evening before each lecture.

You will have the remainder of each week to complete all laboratory material at your own pace.  All laboratory assignments are due the following Monday morning at 08:30 unless otherwise posted. You are expected to abide by due dates for assignments.  Penalty for assignments handed in late but within 24 hours is 10% of the value of the assignment. Assignments handed in more than 24 hours late will be penalized 25% of the value of the assignment. Assignments that are one week late will not be graded.

Lectures:             Monday     12:30 PM – 2:20 PM     WMC 2200, Burnaby

Labs:                   to be finalized

Academic Integrity, Mutual Expectations and Course Agreement

All members of the university are expected to uphold academic integrity introduced and explained at https://www.sfu.ca/students/academicintegrity.html. You are expected to understand and take responsibility for your own learning and academic honesty.  Academic dishonesty is explained at https://www.sfu.ca/students/academicintegrity/what-is-it.html.  Where academic dishonesty is discovered to have occurred, arguing that you weren’t aware of what defines it is not acceptable.

We are a learning community that has a right to expect a learning environment that is safe, respectful and constructive.

Your instructional team is  committed to abide by all academic integrity and teaching and instructional policies (see http://www.sfu.ca/policies/gazette/teaching.html), be on time with lectures, labs and assignments, be good listeners, and engage with concerns and needs you bring to our attention in an un-bias, constructive, reasonable and fair manner. 

Our expectation of you is that you understand academic integrity including the meaning and consequences of cheating, plagiarism and other academic offences under the Code of Student Conduct (see http://www.sfu.ca/policies/gazette/student/s10-05.html), that you are on time, and that you engage in respectful and non-disruptive class behavior and communication.

There will be an opportunity to discuss mutual expectations and a course agreement during class.


You are not permitted to share the course materials (e.g., power point slides, handouts, notes, summaries, etc.) made accessible in this course with others.  They are strictly for your own use as a student registered in this course.  The course materials are protected by law.  These materials may not be copied or distributed in any form or in any medium without explicit permission of the instructor. Note that infringements of copyright can be subject to follow up by the University under the Code of Student Conduct and Disciplinary Procedures.


At the conclusion of the course the successful student will have gained the confidence to understand and interpret the use of numerical data and their basic analysis and interpretation in geographical enquiry.  The successful student will be able to:

  • differentiate different types of numerical data
  • apply appropriate methods to describe, visualize, compare and analyze different data
  • select and run appropriate basic statistical tests using primarily Excel software
  • apply introductory statistics to make inferences, test hypotheses and construct convincing arguments to data in an area of interest
  • know where to learn more about statistics, numerical modelling and analysis in geographical enquiry, should this be of interest
  • explain the role of quantitative information in geographic research and applications


  • Lab Exercises - Ten labs each worth 4%. Lab assignments are due at the beginning of the following lab. Penalty for assignments handed in more than 24 hours late is 10% of the value of the assignment. Assignments that are one week late will not be graded. 40%
  • Mid term - Multiple choice and written answers. Tentatively scheduled for in class Oct 17, 2022 from 12:30 PM – 1:20 PM 20%
  • Final - Multiple choice and written answers. Date to be announced. 40%


The course will use SFU’s standard grading system introduced at https://www.sfu.ca/students/calendar/2021/fall/fees-and-regulations/grading-policy/grading-systems-and-policies.html#standard-grade

Satisfactory performance assumes a grasp of fundamental statistical concepts, capacity to classify and judge data, and ability to differentiate and run the different statistical tests covered.

A good performance assumes satisfactory performance and expects demonstrated judgment and competency applying introductory statistics covered in meaningful ways to research and enquiry, including awareness of underlying assumptions, and capacity to critique.

Truly excellent performance assumes good performance and brings expectation of engagement with some of the recommended readings, ability effectively to argue the role quantitative scientific enquiry can play in advancing knowledge and confirming hypotheses, and awareness of existence and types of advanced quantitative methods in Geography.



The course does not have a required text but additional readings will be suggested and are encouraged.  

Each lecture will finish with a summary of key concepts or statistical tests covered that week. There exists an abundance of websites covering statistical concepts and tests students can access to gain additional insights and explanation.  Students are encouraged to search the Internet and find reference material that suits their own learning style. However, the course was designed using two texts that are available in paperback as well as digital format.  They are:

  • Statistical Analysis of Geographical Data: An introduction by Simon J. Dadson; 2017.  Wiley. ISBN 9780470977040 (paper) and ISBN 9781118525142 (epub)
  • Practical Statistics for Geographers and Earth Scientists by Nigel Walford; 2011. Wiley Blackwell. ISBN 978-0-470-84915-6 (pbk) and ePDF: 978-0-470-67001-9

The first is written in a tight and matter of fact style that can be challenging to follow for a learning style benefitting from detailed explanation and step-by-step introductions.  The second takes more time with explanation and context.  Both texts have been identified as suggested reading.  Three additional books I have found valuable are:

  • Statistical Problem Solving in Geography by J Chapman McGrew, Arthur Lembo Jr. and Charles Monroe; 3rd Edition; Waveland Press, 2014
  • Statistical Methods for Geographers by W.A.V. Clark and P.L Hoskins; published by Wiley; and
  • Elementary Statistics for Geographers by James E. Burt, Gerald M. Barber, and David L. Rigby; published by Guilford Press
For those eager I may occasionally suggest additional readings exploring key subjects. The course requires access to a modern Windows or Mac computer capable of internet browsing and running windows Excel.  Access to required software will be covered during the course.


Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Registrar Notes:


SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html