GEOG5070

GEOG5070/4070 Spatial Analysis and Modeling

The repo hosts lecture slides, practicals, and lab assignments presented in the Spatial Analysis and Modeling course during the spring semester of 2026 at the University of Idaho (UI hereafter). The syllabus and the tentative schedule are both hosted on UI’s canvas site.

Lecture notes

• Lecture 1: Introduction and the role of statistics in GIScience
• Lecture 2: Data types and characteristics of geospatial data
• Lecture 3: Descriptive statistics and central tendency
• Lecture 4: Descriptive spatial statistics
• Lecture 5: Point pattern analysis
• Lecture 6: Probability distribution
• Lecture 7: Sampling, confidence intervals, and hypothesis testing
• Lecture 8: Two-sample t-test, ANOVA, and chi-square
• Lecture 9: Correlation and spatial autocorrelation
• Lecture 10: Spatial interpolation
• Lecture 11: Regression, spatial regression and geographically weighted regression
• Lecture 12: Statistical learning, random forest and spatial prediction

Practicals

• Practical 1: Getting Started with R studio and data wrangling
• Practical 2: Descriptive statistics, plots/maps, coordinate systems
• Practical 3: Spatial data basics and mapping
• Practical 4: Geospatial analysis of spatial data
• Practical 5: Raster analysis
• Practical 6: Imagery classification
• Practical 7: Statistical testing w environmental data
• Practical 8: Statistical interpolation
• Practical 9: Statistical modeling

Lab assignments

• Lab 0: Getting started in R studio and data transformation
• Lab 1: Descriptive statistics, graphing, and choropleth mapping
• Lab 2: Point pattern analysis
• Lab 3: Probability, vector analysis and confidence intervals
• Lab 4: ANOVA, contingency table, raster analysis
• Lab 5: Spatial autocorrelation and correlation analysis
• Lab 6: Spatial and geographically weighted regression
• Lab 7: Statistical learning and spatial prediction (extra credit)