GIS 435 Statistics and Spatial Data Management is a two part course that starts with a review of essential statistical techniques and secondly focuses on statistical approaches used in spatial analyses. Emphasis will be placed on integrating practical examples into course exercises and projects. Basic statistical concepts of exploring data, probability distributions, hypothesis testing, one sample, two sample tests, regression, ANOVA, and model building will be addressed using real data and a variety of computer software. Students will explore geostatistical functions such as interpolation, point pattern analysis, kernel density estimation, kriging and trend surface analysis in the second part of the course.
Prerequisites: Acceptance to ADGIS Program/ Bachelor Degree
Corequisite: GIS 302, GIS 303, GIS 310, and GIS 318
Accessibility Services Notice
Students who would like an academic accommodation and who have a documented disability should contact Accessibility Services, if they have not already done so.Course Details| Total number of weeks | 15 |
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| Total Credits | |
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| Total Hours | 45 |
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Typical hours per week breakdown| Lecture | 3 |
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Learning Outcomes
Upon successful completion of this course, the learner will be able to:
- Recognize different data types, scales, and distributions
- Identify the appropriate statistical test to use for a given situation
- Understand the concept of data variability and how this is central to the use of statistics
- Manipulate datasets in different software programs enabling error checking and outlier identification
- Develop and use scripts to complete data exploration and analysis
- Produce professional quality reports that answer real statistical questions, highlighting the students’ statistical knowledge
- Understand geostatistical concepts, positional uncertainty of point samples and spatial analysis
Teaching and Learning Approach
The course is delivered in-person will include a mix of theory, discussion, demonstration, guided application, and
independent study.Learning Resources
Required:
Crawley, M.J. 2015. Statistics: an introduction using R. West Sussex, United Kingdom: John Willey & Sons, Ltd.
Available in the bookshop.
Handouts and assigned readingsDetailed Course Content, Topics, and Sequence Covered
1. Course intro/ Basics/ Data manipulation/ Descriptive statistics
2. Using R/ Central tendency/ Variance
3. Hypothesis testing/ Probability distributions
4. One sample tests/ Determining sample size
5. Two sample tests
6. ANOVA tests
7. Reading Week – No Class
8. Linear regression/model building
9. Midterm test
10. Descriptive spatial statistics/ Exploratory spatial data analysis
11. Point patterns analysis/ Deterministic interpolation
12. Kriging and Kernel density estimation
13. Introduction to Final Assignment
14. Final Assignment dueAssessment
| Title | Learning Outcomes | Value |
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| Task 1 Lab assignments | 1-7 | 70% |
| Task 2 Midterm test | 1-6 | 15% |
| Task 3 Final project | 1-7 | 15% |
| Total | 100% |
Grading Table
Standard Academic and Career Programs Grading Table
| Percentage | Letter Grade | GPA |
| 90-100 | A+ | 4.33 |
| 85-89 | A | 4.00 |
| 80-84 | A- | 3.67 |
| 76-79 | B+ | 3.33 |
| 72-75 | B | 3.00 |
| 68-71 | B- | 2.67 |
| 64-67 | C+ | 2.33 |
| 60-63 | C | 2.00 |
| 55-59 | C- | 1.67 |
| 50-54 | P | 1.00 |
| 0-49 | F | 0.00 |
| | DNW | 0.00 |
See the Academic Calendar for General Information including how to withdraw from course(s) and other regulations.
Disclaimer
Information contained in this course outline is correct at the time of publication. Content of the course is revised on an ongoing basis to ensure relevance to changing educational, employment and market needs. The instructor will endeavor to provide notice of changes to students as soon as possible. The instructor reserves the right to add or delete material from courses.