# Demarcating Geographic Regions using Community Detection in Commuting Networks with Significant Self-Loops

## TL;DR

This paper introduces a novel community detection method for weighted commuting networks with many self-loops, revealing overlapping regional structures in the U.S. that challenge traditional boundary definitions.

## Contribution

It presents a new statistical approach to identify significant communities in networks with high self-loop weights, applied to U.S. counties to uncover complex regional overlaps.

## Key findings

- Identifies three types of communities: non-nodal, nodal, and monads.
- Reveals that traditional regional boundaries overlook significant long-distance connections.
- Shows that many regions extend beyond conventional metropolitan or megaregion borders.

## Abstract

We develop a method to identify statistically significant communities in a weighted network with a high proportion of self-looping weights. We use this method to find overlapping agglomerations of U.S. counties by representing inter-county commuting as a weighted network. We identify three types of communities; non-nodal, nodal and monads, which correspond to different types of regions. The results suggest that traditional regional delineations that rely on ad hoc thresholds do not account for important and pervasive connections that extend far beyond expected metropolitan boundaries or megaregions.