Quantitative Methods in Geography

Curriculum Guideline

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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
Not Specified
PLAR
No
Semester Length
15 Weeks
Max Class Size
35
Course Designation
None
Industry Designation
None
Contact Hours

Lecture: 2 hours per week

and

Lab: 2 hours per week

Method(s) Of Instruction
Lecture
Lab
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 statistics and the use of quantitative information in geography, including data collection, management, and analysis. Analytical procedures include graphical presentation of data, descriptive statistics, application of probability and sampling theory, inferential statistics, and spatial statistics. Examples will be taken from both physical and human geography. Computers and data analysis software will be used.
Course Content
  • Introduction
    • quantitative geography
    • statistics
    • types of data a levels of measurement
    • measurement and collection of data                    
  • Visualization of data
    • tables, graphs and maps
  • Descriptive statistics
    • central tendency
    • variability
  • Spatial data analysis
    • areal and point data
    • directional statistics
    • geographic centres
    • point pattern analysis
  • Probability theory and distributions
    • random variables
    • discrete probability distributions
    • continuous probability distributions
  • Sampling and populations
    • types of samples
    • sources of error
    • sampling distributions
    • geographic sampling
  • Parametric inferential statistics
    • Central Limit Theorem
    • estimation
    • hypothesis testing
    • t-tests
    • confidence intervals
    1. statistical significance
  • Nonparametric statistics
    • comparison of parametric and nonparametric tests
    • Chi-Square tests
  • Correlation
    • Pearson’s product-moment correlation coefficient
    • nonparametric correlation coefficients
    • spatial autocorrelation
  • Regression
    • simple linear regression model
    • goodness of fit
    • assumptions of  linear regression
    • non-linear regression models
    • multiple regression analysis
  • Analysis of Variance (ANOVA)
  • 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 descriptive statistics and regression methods as they apply to problem solving in Geography.
  3. Perform 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

Assessment will be based on course objectives and will be carried out in accordance with the Douglas College Evaluation 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:

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

Texts will be updated periodically. Typical examples are:

  • Haan, M. and Godley, J. (2017). An Introduction to Statistics for Canadian 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.
  • Shafer, D.S. and Z. Zhang (2012).  Beginning Statistics. Open source textbook: http://2012books.lardbucket.org/

 

Prerequisites

GEOG 1100 or GEOG 1110 or GEOG 1120 or GEOG 1130 or GEOG 1140 or GEOG 1150 or GEOG 1160 or GEOG 1170 or GEOG 1180 or GEOG 1190 or EAES/GEOL 1120 or permission of the instructor