Spring 2022 - GEOG 352 D100

Spatial Analysis (4)

Class Number: 4756

Delivery Method: Remote


  • Course Times + Location:

    Tu 8:30 AM – 10:20 AM

  • Instructor:

    Hojat Yazdanpanah
    Office Hours: TBA
  • 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.


This course focuses on the use of raster and vector-based geographic information systems to analyze spatial data. For example, for Land suitability mapping, 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. The lab material constitutes an integral part of this course, since this is where students receive hands on work with spatial datasets and must apply the techniques they have learned. Labs must be handed in to the teaching assistant at the beginning of the lab section in the week they are due. Some of the concepts covered in labs may be included on both quizzes and final exam.

Remote delivery: the lectures and labs as well as Quizzes will be asynchronous. The final exam will be synchronous. The course begins in the first week of the term.

The content of the course is subject to minor changes depending on the number of students and available resources.

Spring 2022 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.


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 (asynchronous) 15%
  • • final exam (synchronous) 35%


All marks in the course are absolute and not scaled or assigned based on a curve



Requirements for Remote Learning:
To participate in this course, you will need to have access to a laptop or computer, and Wi-Fi.  You may also want to download the software (ArcGIS) to your computer.The GIS software and some reading materials will be made available for your use during the course.


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)

Manuel Gimond, (2021),Intro to GIS and Spatial Analysis, available online(https://mgimond.github.io/Spatial/index.html)

The Esri Guide to GIS Analysis, Volume 1: Geographic Patterns and Relationships, Second Edition By Andy Mitchell

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


Teaching at SFU in spring 2022 will involve primarily in-person instruction, with safety plans in place.  Some courses will still be offered through remote methods, and if so, this will be clearly identified in the schedule of classes.  You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).

Enrolling in a course acknowledges that you are able to attend in whatever format is required.  You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.

Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112) as early as possible in order to prepare for the spring 2022 term.