Name
Abstract | ||
|---|---|---|
We find agglomerations of U.S. counties that are partitioned by commuting patterns by representing inter-county commuting patterns as a weighted network. To do so, we develop a community detection method based on the configuration model to identify significant clusters of nodes in a weighted network that prominently feature self-loops which represent same-county commuting. Application of this method to county level commuting data from 2010 yielded regions that are significantly different from existing delineations such as Metropolitian Statistical Areas and Megaregions. Our method identifies regions with varying sizes as well as highly overlapping regions. Some counties may singularly define a region, while others may be part of multiple regions. Our results offer an alternative way of categorizing economic regions from existing methods and suggest that geographical delineations should be rethought. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Physics and Society | Journal |
Volume | Citations | PageRank |
abs/1903.06029 | 0 | 0.34 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark He | 1 | 0 | 0.68 |
| Joseph Glasser | 2 | 0 | 0.68 |
| Nathaniel Pritchard | 3 | 0 | 0.34 |
| Shankar Bhamidi | 4 | 0 | 0.68 |
| Nikhil Kaza | 5 | 1 | 1.98 |