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Courses : Syllabi : 385

Geography 385 Spatial Data Analysis


Dr. Li An

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.


Statistics 250 or equivilent


Your grade in this course will be based on the following elements:

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

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