2023
DOI: 10.1177/23998083231190711
|View full text |Cite
|
Sign up to set email alerts
|

Tabulating and visualizing street-name data in the US and Europe

Abstract: Street names constitute a rich source of data for quantitative analysis in social sciences. We gather and process street-name data from OpenStreetMap to create an accessible and readily analyzable street names database for the US and a large part of Europe. We also develop a web app to visualize the spatial distribution of street names and download the underlying data from users’ queries. These tools will continue to expand its geographic coverage by including additional countries.

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 13 publications
0
1
0
Order By: Relevance
“…1 One of the most exciting aspects of editing Urban Data/Code submissions has been realizing the “demographics” of the submitting population, mostly early career. Another one is the breath of approaches reflected in the section’s pages, from redlining data (Markley, 2022) to a relational reprojection library (Payne and McGynn, 2024), to open data on street names (Carmona-Derqui et al, 2022) or municipal websites (Cai et al, 2023). It is clear to us that these papers would have had a harder time seeing the light of day in a purely traditional research article outlet, but it is also clear that the Urban Analytics and City Science community is better off because these are out, accessible, and creditable.…”
Section: Mediummentioning
confidence: 99%
“…1 One of the most exciting aspects of editing Urban Data/Code submissions has been realizing the “demographics” of the submitting population, mostly early career. Another one is the breath of approaches reflected in the section’s pages, from redlining data (Markley, 2022) to a relational reprojection library (Payne and McGynn, 2024), to open data on street names (Carmona-Derqui et al, 2022) or municipal websites (Cai et al, 2023). It is clear to us that these papers would have had a harder time seeing the light of day in a purely traditional research article outlet, but it is also clear that the Urban Analytics and City Science community is better off because these are out, accessible, and creditable.…”
Section: Mediummentioning
confidence: 99%