Courses : Syllabi : 585
Geography 585 Quantitative Methods in Geographic Research
Instructor
Course Description
This course introduces concepts and techniques of a set of quantitative methods, emphasizing their applications in geographic research. Quantitative methods have connections to many fields (such as ecology, commerce, marketing, and redistricting of political jurisdictions) and your daily life. Using several San Diego related spatial datasets (rather than lifeless numbers alone), we will explore questions, test hypotheses, and solve problems throughout the course, aiming to achieve the following goals:
- Foundational knowledge: You will understand and remember a set of significant concepts, terms, and relationships (see the list)
- Application: Given research questions, you will be able to collect and visualize data of necessity using multiple ways (e.g., graphs), make inferences and test hypotheses, and establish relationships among varying geographic phenomena through building your own regression models
- Integration: You will be able to relate the methods in this class to patterns and processes over varying spatial scales in other disciplines of your interests
- Human dimension: You will be more confident about your ability when you confront lifeless numbers, able to inform or educate other people using real-world spatial data and statistics. You will find that quantitative reasoning is fun, not boring or intimidating
- Learn how to learn: You will be able to identify useful resources (e.g., datasets, web sites), expose to multiple data collection and analysis methods, and find your preferred learning style
Computational resources: Though this course is not specifically designed for software training, participants are encouraged to get your hands wet to explore the powerful capacities of SAS and ArcGIS in geographic data processing and analysis. These two software packages are available in all the SAL and CESAR computers.
Conduct: Attendance and attentiveness in class pays off on your home assignments and exams. Reading materials (e.g., newspapers) or other distracting behavior during class will not be permitted. Lateness to class disrupts the activities and is not appreciated by either the instructor or your fellow students. You are responsible to know the elements of, and penalties for, academic misconduct, including dishonesty, plagiarism, cheating, etc. The penalty for violating these SDSU policies in this class is an “F” for the exam, assignment, or in-class work where the violation occurs.
Other notes: Students with disabilities should talk to me for any possible facilities or assistance. Go to http://www.sa.sdsu.edu/dss/dss_home.html for more information. By the end of the second week of classes, students should notify the instructor of planned absences in this class for religious observances.
Prerequisites
All participants are expected to have taken either GEOG 385 (Spatial Data Analysis) or a course in probability and statistics, or have the working knowledge of the materials presented in these two courses. Otherwise, seek permission from the instructor.
Grading
Your scores of the class (1000 points) will be comprised of the following components:
- 200 points (20%) for in-class work, such as group discussions, individual-group quizzes, and in-class short assignments (e.g., essays)
- 200 points (20%) for the two open-book and open-notes midterm exams, 100 points each
- 300 points (30%) for the six home assignments, 40, 80, 30, 50, 40, and 60 points for each. Undergraduate students are only required to work on FOUR assignments; if more than four assignments are turned in, the top four scores will be used in grading
- 300 points (30%) for the final comprehensive exam
Lateness: All the assignments should be turned in on time. Late assignments will be docked 20% per day, beginning effective on the due date, unless a pre-permission is granted from the instructor for special reasons such as sickness. Lateness over three days will not be accepted. The valid excuses for missing the exam or failing to turn in an assignment on time are illness requiring medical care, university responsibilities, or personal emergency of a serious nature. Documentation is required, or permission from the instructor. Excuses such as a time conflict, oversleeping, and forgetting are not accepted. In case that a makeup exam is justified and needed, contact the instructor as soon as possible.
Books and Materials
Required text: A bound collection of chapters from multiple sources, available at Montezuma Publishing, around $50 each.
Optional text: Statistical Methods for Geographers (Clark and Hosking 1986, John Wiley & Sons: New York. ISBN 0-471-81807-0), available at SDSU bookstore; you can also buy online (e.g., amazon.com) at a possibly lower cost.
Weekly Topics
| Week | Topic |
|---|---|
| Week One | Intro to class |
| Week Two | Data and Intro to SAS & GIS Computation in SAS and Excel Visualizing data I |
| Week Three | Visualizing data II Descriptive statistics |
| Week Four | Statistical inference |
| Week Five | Statistics for spatial data Spatial autocorrelation |
| Week Six | Spatial autocorrelation Simple regression analysis |
| Week Seven | Simple regression analysis Matrix algebra |
| Week Eight | Midterm One Multiple regression analysis |
| Week Nine | Issues in MRA Extensions of MRA |
| Week Ten | Advanced topics in MRA Logistic regressions I |
| Week Eleven | Logistic linear regressions II Logistic linear regressions III |
| Week Twelve | Midterm Two Geographically weighted regressions (GWR) I |
| Week Thirteen | Geographically weighted regressions (GWR) II |
| Week Fourteen | Cluster analysis) Factor analysis |
| Week Fifteen | Review for Final Exam |