Table · dataset · 2026
<b>A dataset of inter-county distances along the real-world highway and railway networks in the contiguous United States</b>
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<p dir="ltr">This dataset provides <b>transportation mode-specific inter-county shortest-path distances along the real-world highway and railway networks in the contiguous United States</b>.
Description
The dataset was generated using a <b>QGIS–RStudio crosswalk</b> that integrates geographic information system processing with network-based analysis to transform transportation infrastructure shapefiles into spatial networks and compute shortest-path distances between counties.</p><p dir="ltr">The released dataset contains <b>15,213,970 origin–destination records</b> across the two transportation modes.
Among the <b>3,109 counties</b> in the study area, <b>2,863 counties</b> have an available representative network node on the highway network and <b>2,649 counties</b> have an available representative network node on the railway network. Accordingly, the dataset contains <b>8,196,769 highway records</b> and <b>7,017,201 railway records</b>. For each transportation mode, the dataset contains all ordered origin–destination combinations among counties with available representative network nodes, including same-county records.
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Because the highway and railway network graphs are undirected, the reported inter-county distances are symmetric, although both origin–destination directions are retained as separate records.</p><p dir="ltr">In the construction procedure, the <i>adjusted geometric centroid</i> of each county is used as the spatial reference for representative-node selection. Only network nodes assigned to the same county GEOID and belonging to the <b>largest connected component</b> of the corresponding transportation network are retained as <i>qualifying network nodes</i>.
The qualifying node nearest to the adjusted geometric centroid is selected as the <b>representative network node</b> for that county. The adjusted geometric centroid itself is not used directly as an origin or destination in the shortest-path calculation. Instead, inter-county shortest-path distances are computed between the selected representative network nodes using edge-length weights along the corresponding real-world highway or railway network.</p><p dir="ltr">Each record contains the origin county GEOID (<i>ori_geoid</i>), origin county and state name (<i>ori_name</i>), origin adjusted geometric centroid latitude and longitude (<i>ori_centroid_lat</i>, <i>ori_centroid_lon</i>), origin mode-specific representative network-node latitude and longitude (<i>ori_node_lat</i>, <i>ori_node_lon</i>), destination county GEOID (<i>des_geoid</i>), destination county and state name (<i>des_name</i>), destination adjusted geometric centroid latitude and longitude (<i>des_centroid_lat</i>, <i>des_centroid_lon</i>), destination mode-specific representative network-node latitude and longitude (<i>des_node_lat</i>, <i>des_node_lon</i>), transportation mode (<i>mode</i>), and inter-county shortest-path distance in miles (<i>distance</i>).
All latitude and longitude coordinates are reported in <b>WGS 84 (EPSG:4326)</b>.</p><p dir="ltr">The highway network is constructed from highway features classified as <b>Classes 1–2–3</b>, corresponding to freeways, primary highways, and secondary highways or municipal arterials, respectively. The railway network is constructed from railway features categorized as <b>M, I, or O</b>, corresponding to the main sub-network, major industrial leads, and other tracks (minor industrial leads), respectively.
The transportation infrastructure data are obtained from datasets maintained by the U.S. Department of Transportation Bureau of Transportation Statistics.</p><p dir="ltr">The dataset can support applications in <b>transportation planning, infrastructure investment, freight transportation, and supply chain and logistics analysis</b>. The accompanying reproducibility code is available separately as a Figshare Software item at DOI: <b>10.6084/m9.figshare.33936526</b>.</p>
Links
Where it is published
- DOI doi.org/10.6084/m9.figshare.32211066.v1 ↗
DOI / persistent id · from figshare com
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from figshare com
Topics
- From keywords
- Astronomy & Astrophysics · Chemistry · Computer Science & AI · Data engineering and data science · Earth & Environmental Science · Economics & Finance · Engineering · Humanities · Life Sciences · Medicine & Health · Ocean & Atmospheric Science · Rail transportation and freight services · Road transportation and freight services · Social Science · Transport geography · Transport planning
- Inferred from text
- Genome sequencing 65%
Provenance · 1 source records, 22 field assertions
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|---|---|---|---|
| figshare | oai:figshare.com:article/32211066 | 7 d ago | JSON v1 |
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| concepts[field].anzsrc:field:350907 | mapping · figshare com | vocabulary-mapper@1.0.0 | keywords['Rail transportation and freight services'] |
| concepts[field].anzsrc:field:350908 | mapping · figshare com | vocabulary-mapper@1.0.0 | keywords['Road transportation and freight services'] |
| concepts[field].anzsrc:field:440611 | mapping · figshare com | vocabulary-mapper@1.0.0 | keywords['Transport geography'] |
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| concepts[field].local:field:economics-finance | mapping · figshare com | connector:figshare_com@1.0.0 | |
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| license | source · figshare com | connector:figshare_com@1.0.0 | /metadata/dc/rights |
| publication_date | source · figshare com | connector:figshare_com@1.0.0 | |
| title | source · figshare com | connector:figshare_com@1.0.0 | /metadata/dc/title |