# Network-Based Delineation of Health Service Areas: A Comparative Analysis of Community Detection Algorithms

arXiv: https://arxiv.org/abs/1912.08921 · HTML: https://mlsanity.com/paper/1912.08921/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [GeoAI-Enhanced Community Detection on Spatial Networks with Graph Deep Learning](https://mlsanity.com/paper/2411.15428/) (2024)

## Abstract (arXiv)

A Health Service Area (HSA) is a group of geographic regions served by similar health care facilities. The delineation of HSAs plays a pivotal role in the characterization of health care services available in an area, enabling a better planning and regulation of health care services. Though Dartmouth HSAs have been the standard delineation for decades, previous work has recently shown an improved HSA delineation using a network-based approach, in which HSAs are the communities extracted by the Louvain algorithm in hospital-patient discharge networks. Given the existent heterogeneity of communities extracted by different community detection algorithms, a comparative analysis of community detection algorithms for optimal HSA delineation is lacking. In this work, we compared HSA delineations produced by community detection algorithms using a large-scale dataset containing different types of hospital-patient discharges spanning a 7-year period in US. Our results replicated the heterogeneity among community detection algorithms found in previous works, the improved HSA delineation obtained by a network-based, and suggested that Infomap may be a more suitable community detection for HSA delineation since it finds a high number of HSAs with high localization index and a low network conductance.