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Course Details:
Course offered Spring semesters
Class time: M 2:00 - 4:50pm, lecture in 2001 Ag Sci Bldg, lab
in 317 Percival
Office hours: W 2:00 to 5:00pm or by appointment, 317D
Percival
Course
Description:
Over time, Geographic Information Systems (GIS) and its use
have expanded across disciplines. Many have realized that
“location matters” and there is a direct need to store, query, display,
and analyze spatial data to aid in decision making and in managing
natural resources. This course focuses on the approaches that may
be used to analyze spatial relationships using various spatial
technologies and modeling software. The course concentrates on
the use of advanced GIS and spatial analysis techniques to address
natural resource based issues.
For the past eight years, the Division of Resource Management has
offered
applied GIS courses (RESM 440 and RESM 493q) open to undergraduate and graduate
students. This advanced spatial analysis course builds upon the
material presented in the earlier classes to provide an excellent sequence
for students interested in developing advanced analysis and modeling
skills.
Course Goal:
This course is taught to accomplish two main goals. The first is to develop advanced GIS and spatial analysis skills for students to apply for spatial problem solving. And the second objective is to provide an opportunity for students to analyze their spatial data from their research work as part of exercises and a final project.
The course will integrate lectures and lab sessions. Students will have the ability to work with material that is introduced in the lectures in a lab section held the last 90 minutes of the class. Students will be expected to demonstrate comprehension and understanding as part of lab exercises / problem sets assigned throughout the semester.
The specific learning objectives are for students to:
1. Understand the limitations and differences between suitability modeling approaches
2. Create predictive models using appropriate techniques with spatial data
3. Analyze spatial patterns and dependence
4. Derive landscape and terrain variables as inputs for modeling
Prerequisites:
Previous introductory GIS courses as well as GIS software
experience is recommended as well as proficiency in probability theory
and
univariate and
multivariate statistics will be helpful for this course.
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