GEOG 292: Quantitative and Spatial Analysis

Quant

Professor: Dr. Geiger

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 Content:

  • 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