Methods from geographical information science can be used in various ways in spatial planning. The course provides knowledge about the methods from GIS and decision support systems that are most important to planners. The laboratories demonstrate how decision support systems are used within various fields of spatial planning. Emphasis is placed upon urban and rural planning issues and location analysis techniques.
This lab-based course provides an introduction to the data management and analysis capabilities of geographic information systems (GIS) and will provide 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 both natural resource management and social sciences, and has a project component.
This course examines mapping techniques and thematic layers, using GIS software in the labs. Topics include coordinate systems, symbolization, terrain depiction and visualization, aerial photography, satellite images and Global Positioning Systems (GPS). It introduces students to the world of maps and top Geographic Information Systems (GIS) technology.
Geospatial analysis through coding provides the means to address critical questions about our world in an objective and automated way. Large spatial datasets obtained from remote sensing and geophysical models require specialized analytic tools. This course introduces students to geospatial datasets including visualization and analysis techniques using the Python coding language.
This lab-based course builds on the fundamentals of GIS and covers a variety of spatial analysis and data management topics including: vector and raster analysis, network analysis, data structures and formats, creation and management of personal spatial databases, and an introduction to scripting, modelling, and web mapping. A broad range of thematic areas (natural resources, earth science, urban and human environments) are covered. There is a project component to the course.
This course covers digital processing of satellite imagery and integration with raster and vector GIS technology in natural resources and remote sensing of the environment. Topics include sensor platforms and data collection, pre-processing, enhancement, classification, change detection, multi-data integration and vectorization.
This lab- and project-based course expands on the GIS skills acquired in GEOG 300-3. Topics include: enterprise level data management, multi-user versioning, project management, 3D GeoVisualization, and web mapping. Marketable advanced technical GIS skills are developed through a range of subject areas, and members of the GIS community provide hands-on experience and exposure to industry practices.
Students work with and analyze large geospatial remotely-sensed datasets learning and using advanced Python functional programming. In addition to laboratory exercises, students participate in a weekly seminar to critically evaluate research on geospatial algorithms and analyses. Students work together to use geospatial analyses to solve a problem relevant to non-academic stakeholders.
This project-oriented course focuses on advanced classification procedures incorporating digital elevation data, fuzzy and object-oriented classification, and new millenium data sources including ASTER, RADAR, MODIS, LiDAR and high-resolution scenes. Repeat imagery is used to assess local and global changes in land cover, oceanic, atmospheric and/or cryospheric environments.
Concentration on a particular topic agreed upon by a member of the faculty and the student (maximum 6 credit hours).