"Analysis of Spatial and Spatiotemporal Anomalies Using Persistent Homology: Case Studies with COVID-19 Data"

I'm posting about one of my papers that was published in journal form a couple of months ago. I waited for a while because the journal made surprise, unwanted changes after the galley-proof stage — and unsurprisingly I objected very strongly to what they did — and I tried and failed to get those surprise changes addressed. They are very small, but they annoy me (and, as a matter of principle, they should not have made surprise wording changes between the version that we approved and the version that we published). Anyway, here are some details about the article.

Title: Analysis of Spatial and Spatiotemporal Anomalies Using Persistent Homology: Case Studies with COVID-19 Data

Authors: Abigail Hickok, Deanna Needell, and Mason A. Porter

Abstract: We develop a method for analyzing spatial and spatiotemporal anomalies in geospatial data using topological data analysis (TDA). To do this, we use persistent homology (PH), which allows one to algorithmically detect geometric voids in a data set and quantify the persistence of such voids. We construct an efficient filtered simplicial complex (FSC) such that the voids in our FSC are in one- to-one correspondence with the anomalies. Our approach goes beyond simply identifying anomalies; it also encodes information about the relationships between anomalies. We use vineyards, which one can interpret as time-varying persistence diagrams (which are an approach for visualizing PH), to track how the locations of the anomalies change with time. We conduct two case studies using spatially heterogeneous COVID-19 data. First, we examine vaccination rates in New York City by zip code at a single point in time. Second, we study a year-long data set of COVID-19 case rates in neighborhoods of the city of Los Angeles.