Introductory to GIS
This lab-based course provides an introduction to the data management and analysis capabilities of Geographic Information Systems (GIS) and provides a foundation in GIS. Topics include: geospatial data sources, input, attributes, formats, and conversions; projections and coordinate systems; and raster and vector analysis. This course combines data and common practices from natural resource management and social sciences.
There will be no Labs and Tutorials in the first week 8 – 10 Sept, there will be a lecture in the first week.
Syllabus
| Date | Lecture | Tutorial | Lab |
| September | |||
| 8 – 10 | Introduction | ||
| 13 – 17 | GIS Data – Vector & Raster | GeoTagged Photos | Intro to QGIS |
| 20 – 24 | Census Data | Creating shapefiles | Data Management |
| 27 – Oct 1 | National Day for Truth and Reconciliation | Acquiring Census Data | National Day for Truth and Reconciliation |
| October | |||
| 4 – 8 | Spatial Analysis | Working with Census Data | Working with Joins |
| 11-15 | Spatial Analysis | Thanksgiving | Vector Overlays |
| 18-22 | Data Quality | Mid Term & Spatial Analysis | Exploring Raster Data |
| 25-29 | Precision and Accuracy | Lit Review, project outline and Spatial Analysis | Mixed Data Formats |
| November | |||
| 1-5 | History of GIS | Spatial Analysis | Projects |
| 8-12 | Remembrance Day | OpenStreetMap Data | Remembrance Day |
| 15-19 | Data for Decision Support | Projections, Data Quality | Projects |
| 22-26 | Course Review | DataBC and Network Analysis | Projects |
| 29-Dec 3 | Final Exam (29 Nov) | Projects | |
Literature Review Exercise
Below is the grading scheme for the course this year
COURSE GRADING – 2021
| Lab and Tutorial Assignments | 35% | Due weekly or otherwise stated – 6 assignments with one worth 10% |
| Lecture Midterm | 15% | October 20 |
| Literature Review | 5% | November 8 |
| Student Projects | 25% | December 6 |
| Final Exam | 20% | December 2 |
CONTACTS:
Anthony Jjumba (Lectures/Tutorials): jjumba@unbc.ca
Emily Bornestig (Labs): emily.bornestig@unbc.ca
Rulan Xiao (Labs): xiaor@unbc.ca