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

TL;DR

This paper introduces a topological data analysis method using persistent homology to detect and analyze spatial and spatiotemporal anomalies in COVID-19 data, capturing their relationships and evolution over time.

Contribution

The paper develops an efficient filtered simplicial complex approach and employs vineyards to analyze the dynamics of anomalies in geospatial COVID-19 data.

Findings

Identified spatial anomalies in vaccination rates and COVID-19 case rates.

Tracked the evolution of anomalies over time using vineyards.

Provided a novel topological framework for geospatial anomaly analysis.

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.