This advanced GIS course focuses on frequently used quantitative methods in spatial analysis within the R programming environment. These methods help answer questions such as:

• Are features of interest in my study area spatially clustered or independently distributed?
• How can I interpolate between field-collected point data to create a spatially continuous map?
• Is my data spatially autocorrelated, and how does this affect my analysis?
• What underlying variables best explain or predict land cover patterns in my study area?

Course Topics Include:

• Use of R in spatial analysis
• Assessment of spatial autocorrelation
• Spatial point pattern analysis and clustering analysis
• Spatial interpolation
• Spatial regression analysis

Course Format:

• Emphasizes hands-on lab work, utilizing R (and some ArcGIS).
• Labs (homework) will require additional time to finish outside of scheduled lab sessions.

Required Prerequisites:

• One GIS course (e.g., EAS 531 or similar)
• One introductory statistics course (e.g., EAS 538 or similar)
• Basic familiarity with the R environment (If you have no previous R experience, contact the instructor for permission to enroll; some preparatory work may be required).