Course Description
Descriptive and inferential techniques used in quantitative geographic analysis. Probability distributions, sampling techniques, parametric and nonparametric inference, analysis of variance, spatial autocorrelation measures and regression procedures. Applications of statistical methods to spatial analysis and geographic research design. Exercises develop knowledge of statistical programming with computer software.
Additional Requirements for Graduate Students:
Graduate students in the course prepare and present a brief (5-6 page) literature review on statistical methods in their chosen area of interest, which is done in addition to the other required coursework. There are also additional test questions and assignments.
Athena Title
Data Science in Geography
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Course Objectives
Learning Objectives: 1. Understand basic concepts and methods of data collection and description 2. Understand basic concepts of mathematical probability theory 3. Understand the logic and practice of simple inferential statistics 4. Understand correlation and regression analysis 5. Ability to use appropriate statistical techniques in tackling geographical problems. 6. Ability to comprehend academic literature that uses statistical techniques. This course meets the following General Education Abilities by accomplishing the specific learning objectives listed below: Communicate effectively through writing. This is met by a series of writing assignments associated with supplemental reading and data analysis. Computer Literacy is addressed through course administration, student-faculty electronic interaction, data analysis activities and assignments, and exposure to GIS technologies. Critical Thinking is central to the learning objectives of this class, and is developed through homework assignments, lecture, classroom discussion, and inquiry- based learning efforts. Moral Reasoning (Ethics) is addressed by consideration of ethical guidelines for use of statistical data and for interpretation and application of spatial statistical outcomes. Moral reasoning is developed through lectures, writing assignments, classroom discussion, and inquiry-based learning activities.
Topical Outline
Geographical Data and Measurement Geography, Data, and Statistics (Chapter 1 & 2) Descriptive Statistics Non-Spatial Descriptive Statistics (Chapter 3) Spatial Descriptive Statistics (Chapter 4) Statistical Relationships Between Variables Correlation (Chapter 13) Regression (Ch 14, pp. 210-220) Building Blocks of Inferential Statistics Probability Theory & Distributions (Chapter 5) Sampling (Chapter 6) Estimation (Chapter 7) Statistical Inference Basic Parametric Hypothesis Testing (Chapter 8) Multiple Sample Parametric Tests (Chapter 9) ANOVA (Chapter 10) Goodness-of-Fit, Categorical Tests