Quantitative Methods in Geography

Curriculum guideline

Effective Date:
Return to Course
Print Guideline
Course
GEOG 2251
Discontinued
No
Course code
GEOG 2251
Descriptive
Quantitative Methods in Geography
Department
Geography and the Environment
Faculty
Humanities & Social Sciences
Credits
3.00
Start date
End term
202230
PLAR
No
Semester length
15
Max class size
35
Contact hours
Lecture: 2 hrs. per week / semester Labs: 2 hrs. per week / semester
Method(s) of instruction
Lecture
Lab
Hybrid
Learning activities

The course will employ a variety of instructional methods to accomplish its objectives, including some of the following:  lecture, labs, observation, analysis and interpretation of geographic data, multimedia, individual and/or team projects and small group discussions.

Course description
This course in an introduction to the use of quantitative information in geography, including data collection, management, and analysis. Analytical procedures will include graphical presentation of data, descriptive statistics, application of probability and sampling theory, and inferential statistics. Examples will be taken from both physical and human geography. Computers and data analysis software will be used.
Course content
  1. Introduction
    • quantitative geography
    • statistics
    • nominal, ordinal, interval data
    • primary and secondary data
    • measurement and collection of data                    
  2. Visualization of data
    • tables, graphs and maps
  3. Descriptive statistics
    • central tendency
    • variability
  4. Spatial data analysis
    • areal and point data
    • directional statistics
  5.   Probability theory and distributions
    • random variables
    • discrete probability distributions
    • continuous probability distributions
  6. Sampling and populations
    • types of samples
    • random sampling
    • sampling distributions
    • geographic sampling
  7. Parametric inferential statistics
    • estimation
    • hypothesis testing
    • t-tests
    • confidence intervals
    • statistical significance
  8. Nonparametric statistics
    • comparison of parametric and nonparametric tests
    • examples of nonparametric tests
  9. Correlation
    • Pearson’s product-moment correlation coefficient
    • nonparametric correlation coefficients
    • spatial autocorrelation
  10. Regression
    • simple linear regression model
    • goodness of fit
    • assumptions of  linear regression
    • non-linear regression models
    • multiple regression analysis
  11. Analysis of Variance (ANOVA)
  12. Chi-Square testing
  13. Time series analysis
    • characteristics of time series
    • data homogeneity
    • smoothing
Learning outcomes

At the conclusion of the course the successful student will be  able to:

 

  1. Explain the role of quantitative information in geographic research and applications
  2. Demonstrate an understanding of  basic descriptive statistics and regression methods as they apply to problem solving in Geography
  3. Perform basic data manipulation, statistical calculations and graphical presentation by hand, and using computer spreadsheets or statistical software (e.g. Excel, SPSS)
  4. Evaluate the roles of probability theory and sampling distributions in drawing inferences about populations based on samples
  5. Identify when and where statistical procedures are appropriate

 

Means of assessment

The evaluation will be based on course objectives and will be carried out in accordance with Douglas College policy.  The instructor will provide a written course outline with specific evaluation criteria during the first week of classes.

Evaluation will include some of the following:

  • Laboratory assignments with a combined value of up to 50%.
  • Multiple choice and short answer exams with a combined value of up to 50%.
  • A term project with a value of up to 25%.

 

An example of a possible evaluation scheme would be:

Laboratory Assignments 40%
Midterm Exam 25%
Final Exam 25%
Term Project 10%
Total 100%

 

Textbook materials

Texts will be updated periodically. Typical examples are:

 

  • Shafer, D.S. and Z. Zhang (2012).  Beginning Statistics. Open source textbook: http://2012books.lardbucket.org/
  • Haan, M. (2013). An Introduction to Statistics for Candian Social Scientists.  Oxford.
  • Harris, R. and C. Jarvis (2011).  Statistics for Geography and Environmental Science. Pearson
  • Rogerson, P.A. (2010).  Statistical Methods for Geography:  A Student's Guide.  Sage

 

 

 

Prerequisites

One 1100-level Geography course, or permission of instructor