Courses : Syllabi : 385
Geography 385 Spatial Data Analysis
Instructor
Course Description
This course is designed to introduce you to the use of statistical methods in geographic research. This is a second course in statistics. Geographers make extensive use of the same statistical methods utilized in other social and natural sciences, but also employ techniques that are specifically designed to quantify data that are spatially arranged. Our goals in this class are (1) to understand the underlying logic of the basic statistical tools used in Geography; (2) to learn how to identify the appropriate statistical techniques to apply in specific research settings; and (3) to correctly calculate and interpret those statistics. In doing so, the course will provide a building block for more advanced courses in spatial statistical analysis.
Attendance and conduct: Attendance is critical to classes. Missing lectures, coming late, or leaving early can be costly to one’s performance on the exams and assignments. 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.
Prerequisites
Statistics 250 or equivilent
Grading
Your grade in this course will be based on the following elements:
- A set of 6 homework assignments, which will count for a total of 20 percent of your grade. Homework assignments will be made available on the internet via Blackboard on the dates shown in the course calendar. They must be turned in (in person, not over the internet) on or before the due date. No late assignments will be accepted. We will discuss the homework in class on the day that it is handed back.
- Three midterm exams. The first midterm will be worth 10 percent of your grade, the second midterm
will be worth 10 percent of your grade, and the third midterm will be worth 20 percent of your grade,
so the midterms will total 40 percent of your grade. The problems on the midterm will be similar to
the homework. All exams will be open-book. Please note that there will be no makeup exams. If you
absolutely have to miss an exam for a legitimate reason, the weight of that exam will be added to
the next exam.
The only 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. - A comprehensive final exam will count for 30 percent of your grade. It will be similar to the midterms, except that it will be lengthier.
- Class participation will count for 10 percent of your grade. I assume that you will attend every class and that you will be prepared to ask and answer questions related to the course material.
Books and Materials
Required: J. Chapman McGrew, Jr., and Charles B. Monroe, An Introduction to Statistical Problem Solving in Geography, 2nd Edition (Boston: McGraw-Hill), 2000
You should also own a hand-held calculator–you will need it for homework and exams. You will also need an account on the Geography Network to work in the Geography Department computer labs, but those will be assigned in class, if you don’t already have one.
Weekly Topics
| Week | Topic |
|---|---|
| Week One | Intro to class |
| Week Two | Overview of non-spatial descriptive statistics Visualization of data |
| Week Three | Descriptive spatial statistics |
| Week Four | Probability distributions and quadrat analysis |
| Week Five | Probability distributions Review for Midterm One |
| Week Six | Midterm One Basic elements of sampling Estimation in sampling |
| Week Seven | Elements of inferential statistics Hypothesis testing, one sample case |
| Week Eight | Hypothesis testing, two sample case |
| Week Nine | Review for Midterm Two Midterm Two Point pattern analysis |
| Week Ten | Non-spatial correlation and regression |
| Week Eleven | Review for Midterm Three Midterm Three |
| Week Twelve | Chi-square tests for goodness of fit Weight matrices and Introduction to ArcView |
| Week Thirteen | Moran's I and other measures of spatial autocorrelation (I) |
| Week Fourteen | Moran's I and other measures of spatial autocorrelation (II) Applications of spatial statistics |
| Week Fifteen | Review for Final Exam |