Crunch Spatial Stats

Overview

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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.

Syllabus

Detecting Spatial Patterns with Global Moran’s I

In this module, you will explore how spatial patterns differ from random distributions and why that difference matters in real-world analysis. Using air-quality sensor data as a motivating example, you will examine how Global Moran’s I quantifies spatial autocorrelation in polygon data and helps analysts identify clustering patterns that might otherwise go unnoticed.

Estimating Surfaces with IDW Interpolation

In this module, you will examine how spatial analysts estimate values between discrete measurement locations. Using air-quality sensor data as a motivating example, you will be introduced to Inverse Distance Weighting (IDW) interpolation and learn how distance-based assumptions are used to generate continuous surfaces from point observations. You will explore how parameter choices influence interpolation results and learn how to interpret estimated surfaces responsibly in real-world spatial analysis contexts.

Understanding Spatial Autocorrelation with Semivariograms

In this module, you will step back from computation to interpretation, focusing on semivariograms as diagnostic tools for spatial structure. By learning how to read range, sill, and nugget, you will gain intuition about spatial dependence, knowledge that informs both analysis choices and communication with non-technical audiences.