"Persistent Homology for Resource Coverage: A Case Study of Access to Polling Sites"

One of my paper was published in final form last week. Here are some details.

Title: Persistent Homology for Resource Coverage: A Case Study of Access to Polling Sites

Authors: Abigail Hickok, Benjamin Jarman, Michael Johnson, Jiajie Luo, and Mason A. Porter

Abstract: It is important to choose the geographical distributions of public resources in a fair and equitable manner. However, it is complicated to quantify the equity of such a distribution; important factors include distances to resource sites, availability of transportation, and ease of travel. We use persistent homology, which is a tool from topological data analysis, to study the availability and coverage of polling sites. The information from persistent homology allows us to infer holes in a distribution of polling sites. We analyze and compare the coverage of polling sites in Los Angeles County and five cities (Atlanta, Chicago, Jacksonville, New York City, and Salt Lake City), and we conclude that computation of persistent homology appears to be a reasonable approach to analyzing resource coverage.

"Topological Data Analysis of Spatial Systems"

A book chapter of ours was just published in final form. It is Chapter 16 in this book. Here are some other details about it.

Title: Topological Data Analysis of Spatial Systems

Authors: Michelle Feng, Abigail Hickok, and Mason A. Porter

Abstract: In this chapter, we discuss applications of topological data analysis (TDA) to spatial systems. We briefly review a recently proposed level-set construction of filtered simplicial complexes, and we then examine persistent homology in two cases studies: street networks in Shanghai and anomalies in the spread of COVID-19 infections. We then summarize our results and provide an outlook on TDA in spatial systems.