Fall 2019 - GEOG 352 D100

Spatial Analysis (4)

Class Number: 4327

Delivery Method: In Person


  • Course Times + Location:

    Th 10:30 AM – 12:20 PM
    AQ 3154, Burnaby

  • Exam Times + Location:

    Dec 10, 2019
    3:30 PM – 6:30 PM
    SWH 10041, Burnaby

  • Instructor:

    Shivanand Balram
    1 778 782-2003
    Office: RCB 6143
  • Prerequisites:

    GEOG 251 or one of STAT 201, 203 (formerly 103), 205, or 270.



Advanced quantitative techniques for spatial analysis of geographic data and patterns. Topics include geostatistics, spatial interpolation, autocorrelation, kriging, and their use in geographic problem solving with spatial analysis software. Quantitative.


Course Description:
In many decisions and observations in the real world, spatial analysis plays an important role. For example: expensive land is concentrated in the core of the city; we explore the campus map layout to find the best route to get to lectures; we know some diseases cluster in closed communities; and we are concerned about the distribution of point-source industrial pollutants in our environment. In each of these, we mentally process observations in space and time to arrive at an understanding. But this mental processing is inadequate for large volumes of data. We need to depend on spatial data analysis methods and GIS tools to support our planning and decision-making.

The course will be an integration of lectures, discussions and computer labs on various spatial analysis topics such as exploratory spatial data analysis, point pattern analysis, cluster and factor analysis, spatial autocorrelation, as well as analysis of spatially continuous data among others. The GIS software and geospatial data will be used for laboratory assignments to complement and reinforce theoretical concepts from the lectures.

Lectures, Labs and Office Hours begin in the first week of the term

This course may be applied towards the SIS Certificate Program.
The content of the course is subject to minor changes depending on the number of students and available resources.


Learning Objectives:
On successful completion of the course, students should be able to:
- Demonstrate knowledge of spatial analysis methods and when to use them
- Use software tools for spatial data analysis and management
- Apply spatial analysis methods to solve problems in geography and related disciplines
- Pursue further advanced study in spatial analysis and modeling


  • Course Participation 2%
  • Assignments 48%
  • Quizzes 15%
  • Final Exam 35%


All marks in the course are absolute and hence NOT scaled or assigned based on a curve.



McGrew, J.C., Lembo, A.J. and Monroe, C.B. (2014). An Introduction to Statistical Problem Solving in Geography. Waveland Press, Inc.

The textbook will be on reserve in the Bennett Library at the beginning of the term.

Registrar Notes:

SFU’s Academic Integrity web site 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