Apply quantitative methods to uncover hidden patterns, clusters, and relationships in geographic data.
Welcome to Spatial Statistics & Analysis. Traditional statistics assume that data points are independent of one another. In geography, this is rarely true. As Tobler's First Law of Geography states: "Everything is related to everything else, but near things are more related than distant things."
This advanced course introduces the quantitative methods required to analyze spatial data. Students will learn to measure spatial autocorrelation, analyze point patterns, perform spatial interpolation, and conduct geographically weighted regression. We move beyond simply mapping data to statistically proving whether spatial patterns are significant or merely random.
Proficiency in spatial statistics is highly sought after in fields ranging from epidemiology and criminology to environmental modeling and retail site selection. This course requires a foundational understanding of basic statistics and GIS.
Advanced GIS Students
10-12 Hours / Week
Intro GIS & Basic Statistics
Quantitative Labs
Upon successful completion of this course, students will be able to demonstrate proficiency in the following core competencies:
Master the foundational pillars that drive this discipline.
Measuring the degree to which a set of spatial features and their associated data values tend to be clustered together in space.
Evaluating the spatial distribution of points to determine if they are clustered, dispersed, or random.
Estimating unknown values at specific locations based on known values at surrounding locations.
Modeling spatial relationships where the relationship between variables changes across the study area.
Discover how these concepts are actively used to solve critical challenges across various industries.
Using Hot Spot Analysis to identify statistically significant clusters of criminal activity for resource allocation.
Tracking disease outbreaks and modeling spatial diffusion patterns to identify sources of infection.
Using Kriging to interpolate pollution levels or rainfall across a continuous surface from limited sample points.
Move beyond visual interpretation. Use these tools to statistically prove spatial patterns and relationships.
Calculate Global Moran's I on sample datasets. Visualize how changing the spatial weights matrix affects the index.
Compare Inverse Distance Weighting (IDW) and Kriging. Adjust parameters to see how they change the predicted surface.
Analyze crime or disease point data. Run Nearest Neighbor and Quadrat analysis to determine if points are clustered.
Run Ordinary Least Squares (OLS) vs Geographically Weighted Regression (GWR) to see how relationships vary across space.
Run Hot Spot Analysis (Getis-Ord Gi*) to find statistically significant spatial clusters of high and low values.
Curated materials to support your academic journey and professional development.
Detailed information regarding our college-level curriculum.