# GEOG 292: Quantitative and Spatial Analysis

### Course Description:

Advanced spatial analytical techniques in a computer environment. Data collection methods and sources are reviewed. Descriptive and inferential statistical methods are surveyed. Prerequisite: Map Interpretation and Analysis (GEOG 281).

### Required Textbook:

J. Chapman McGrew, Jr. & Charles B. Monroe: An Introduction to Statistical Problem Solving in Geography, 2nd edition, Waveland Press, 2009.

• ### Course Objectives:

• Students will identify and describe (using appropriate terminology) types and characteristics of both non-spatial and spatial data, and will demonstrate an ability to collect such data. Assessed via assignments, exams, the major project, and in-class discussions.
• Students will present collected data using appropriate terminology and formats. Assessed via assignments, the major project, and in-class discussions.
• Students will construct hypotheses which call for inferential statistical analysis, and select appropriate statistical tests. Assessed via assignments, exams, the major project, and in-class discussions.
• Students will manage data and conduct statistical tests in a computer context. Assessed via assignments, the major project, and in-class activities.
• Students will draw appropriate conclusions from conducted statistical tests, and clearly present and explain the conclusions. Assessed via assignments, exams, the major project, and in-class discussions.
• ### Session Topics:

• Introduction to Statistics & Geography
• Statistics: Terms & Concepts
• Geographic Data: Measurement
• Geographic Data: Classification & Graphing
• Descriptive Statistics: Central Tendency
• Descriptive Statistics: Dispersion
• Other Descriptive Statistics
• Statistical Probability & Prob. Distributions
• Normal Distribution & Probability Mapping
• Sampling
• Parameter Estimation
• Hypothesis Testing
• One-Sample Tests
• Two-Sample Difference Tests
• Matched Pairs Difference Tests
• 3+-Sample Difference Tests
• Categorical Diff. Tests: Goodness of Fit
• Categorical Diff. Tests: Contingency Tables
• Nearest Neighbor & Quadrat Analyses
• Correlation
• Correlation Issues
• Regression Basics
• Regression Residuals Analysis
• Extensions to Multi-variate Regression
• Course Conclusion