Spring 2023 - GEOG 352 D100

Spatial Analysis (4)

Class Number: 2518

Delivery Method: In Person


  • Course Times + Location:

    Jan 4 – Apr 11, 2023: Thu, 2:30–4:20 p.m.

  • Exam Times + Location:

    Apr 18, 2023
    Tue, 12:00–3:00 p.m.

  • Instructor:

    Suzana Dragicevic
    1 778 782-4621
    Office: RCB 6233
  • 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 map data to find the best route to get to the restaurant; we know about clusters of forest insect infestations close to our 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 and computer labs on various spatial analysis methods such as point pattern analysis, cluster analysis, spatial autocorrelation, as well as spatial interpolation, among others. GIS software and geospatial data will be used for laboratory assignments to complement and reinforce theoretical concepts from the lectures.

Format: In-Person delivery for both lectures and computer labs. The course begins in the week of January 9th.

Spring 2023 courses will be delivered in person based on information available at the time of publishing the outline; please note the delivery mode is subject to change following Provincial Health Officer (PHO) and/or SFU recommendations and orders.


  • Depending on the number of students enrolled, available resources and any changing circumstances during the term, the evaluation and course content can be subject to changes on short notice.



Educational Goals: On successful completion of the course, students should be able to:

  • Demonstrate knowledge of spatial analysis methods
  • Use GIS software 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


  • laboratory assignments 50%
  • quizzes 15%
  • final exam 35%



Under SFU's Education Site License, SFU students, staff, and researchers may download the following software to home computers for academic use ONLY. This includes teaching and classroom use and research purposes. 

Software available to download/install on home computers

  • Microsoft 365
  • ESRI Applications such as ArcGIS Desktop, ArcGIS Pro, ArcGIS Online, ESRI CityEngine, etc.
  • Matlab with Named User License
  • Adobe CC with Named User License*

* ONLY those who are in SFU payroll are eligible for Adobe CC with Named User License


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


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