GGR 272 F  Geographic Information and Mapping I

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 COURSE DESCRIPTION

This course provides an introduction to digital mapping and spatial analysis using geographic information systems (GIS). Students learn how to create their own maps, and how to use GIS software to analyze geographic problems and learn techniques that can be applied to a wide variety of subject areas within geography as well as in other disciplines. The lectures discuss the underlying theory, and how it is implemented in GIS software.  The lab sessions give students the opportunity to learn for themselves how to put that theory into practice, gaining hands-on experience with ESRI ArcInfo 9 software, the latest version of the most popular GIS and an industry standard in many fields.  A basic understanding of computers and familiarity with the Microsoft Windows operating system is assumed.

GGR 272 TA's

Martin Danyluk
  danylukm@geog.utoronto.ca

Tamar Fuhrer
  fuhrert@geog.utoronto.ca

Brian Greguol
  greguolb@geog.utoronto.ca

Beiping Zhang
  zhangb@geog.utoronto.ca

Quick Notes

Check this space for current reminders (updated throughout the course)

Final Exam (Information)
Wednesday, December 19, 2-5pm
Location: Brennan Hall 200 (BR 200)

Free Geomatics Info Session
Tuesday  Nov. 13, 11am-12pm
Room 5064, Sidney Smith Hall
See poster for more information.

LECTURES
Tue & Thu, 10:00am-11:00am, SS 2118

LABS
All labs are in Room 620, Sidney Smith Hall
Note: There are NO lab sessions in the first week.

P0101 Tue, 11:00am-1:00pm
  (Martin: Weeks 2-13)
P0301 Wed, 10:00am-12:00pm
  (Brian: Weeks 2-7 / Tamar: Weeks 8-13)
P0401 Wed, 1:00pm-3:00pm
  (Tamar: Weeks 2-13)
P0501 Thu, 1:00pm-3:00pm
  (Beiping: Weeks 2-13)
P0601 Thu, 4:00pm-6:00pm
  (Martin: Weeks 2-7 / Beiping: Weeks 8-13)
P5102 Tue, 6:00pm-8:00pm
  (Brian: Weeks 2-13)
 

TEXT
Lo, C.P. and Albert K.W. Yeung, 2007.
Concepts and Techniques of Geographic Information Systems, 2nd ed., Pearson Education Canada, Inc., Toronto.

PREREQUISITES
There are no prerequisites for this course.

EVALUATION
Lab assignments: 35%
Midterm exam: 25%
Final exam: 40%

Download Handouts

Course handouts  in the list below will be activated here at the time needed throughout the course.

• Course Outline
   
• Data Classification Methods

• Lab and TA hours
   
• Midterm Format
   
• Vector Analysis (Queries...)
   
• EXTRA CREDIT