Spatial data is everywhere, but maps alone can be misleading. In Crunch Spatial Stats, you will move beyond visual patterns and use spatial statistics to make defensible, evidence-based conclusions from location-based data. Working with realistic air-quality examples, you will develop practical skills to test whether patterns are meaningful, estimate conditions between measurements, and explain how spatial relationships change with distance. The course emphasizes clear reasoning and interpretation, not complex mathematics, so you will confidently explain results to both technical and non-technical audiences.

By the end of this course, you will be able to compute Global Moran’s I for a polygon layer, perform IDW interpolation for point observations, and interpret semivariograms to assess spatial autocorrelation. Throughout the course, you will practice skills commonly used in environmental monitoring, public health, and spatial analysis roles, focusing on understanding the assumptions and limitations behind each method. This course is designed for beginners. You will need basic familiarity with maps, tabular datasets, and simple descriptive statistics. No prior experience with spatial statistics or geostatistical modeling is required.