Analyzing Spatial Clusters for Multiple Attributes

Public health concerns are often multi-faceted, complex problems. Any exploratory analysis conducted to support public health concerns should be scalable to this multi-attribute nature. This activity pairs with a chapter focused on spatial clustering techniques for multivariate analysis, which can reveal the locations with unusually high or low occurrences of multiple diseases. We will use obesity and insufficient sleep, which often occur together, as conditions to analyze considering their serious impacts on public health. Once contributing factors are determined, policymakers can be informed so that they can begin to address the negative impacts.

In the activity, students will use SaTScan to find spatiotemporal hotspots and coldspots in obesity and insufficient sleep data representing American children. After a brief Q&A session, students will follow the walkthrough to complete the spatial clustering with SaTScan (Version 9.4). (SaTScan is a free software designed to detect clusters of spatial, temporal, or spatiotemporal data using scan statistics. This allows for complex relationships within the data to be revealed and explored. You can download SaTScan at https://www.satscan.org/.) The lecture can be closed with a discussion session where students will evaluate the statistically significant hotspots where they are found.

LEARNING OUTCOMES
By the end of this lesson, students will be able to
• analyze multivariate spatiotemporal data on obesity and insufficient sleep among children by using SaTScan;
• interpret and evaluate hotspots and coldspots regions; and
• create spatial cluster maps by handling spatial data files.

This activity is part of Spatial Literacy in Public Health: Faculty-Librarian Teaching Collaborations (ACRL, 2024).