In this module of the MATCH Workshop Series, students will learn how to prepare and clean claims data, define spatial relationships, and compute measures of spatial dependence. The module emphasizes both theoretical understanding and practical implementation using R and RStudio, guiding learners through preprocessing workflows, spatial weights construction, and interpretation of clustering patterns in healthcare outcomes. Hands-on exercises provide experience applying global and local Moran’s I to real-world datasets, while discussions highlight common pitfalls, such as the Modifiable Areal Unit Problem (MAUP) and data quality issues, ensuring critical evaluation of results.

By the end of this module, students will be able to:

1. Define geospatial analysis and explain the role of spatial autocorrelation in healthcare and public health research

2. Differentiate between global and local measures of spatial autocorrelation, and interpret the outcomes of Moran’s I

3. Execute a complete Moran’s I workflow by preparing data, defining spatial neighbors, calculating the statistic, and interpreting results

4. Preprocess healthcare claims data, construct spatial weights matrices, and implement Moran’s I analysis using appropriate libraries using R & RStudio.