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Measuring racial and ethnic disparities in traffic enforcement with large-scale telematics data
Past studies have found that racial and ethnic minorities are more likely than White drivers to be pulled over by the police for alleged traffic infractions, including a combination of speeding and equipment violations. It has been difficult, though, to measure the extent to which these disparities...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9802422/ https://www.ncbi.nlm.nih.gov/pubmed/36714855 http://dx.doi.org/10.1093/pnasnexus/pgac144 |
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author | Cai, William Gaebler, Johann Kaashoek, Justin Pinals, Lisa Madden, Samuel Goel, Sharad |
author_facet | Cai, William Gaebler, Johann Kaashoek, Justin Pinals, Lisa Madden, Samuel Goel, Sharad |
author_sort | Cai, William |
collection | PubMed |
description | Past studies have found that racial and ethnic minorities are more likely than White drivers to be pulled over by the police for alleged traffic infractions, including a combination of speeding and equipment violations. It has been difficult, though, to measure the extent to which these disparities stem from discriminatory enforcement rather than from differences in offense rates. Here, in the context of speeding enforcement, we address this challenge by leveraging a novel source of telematics data, which include second-by-second driving speed for hundreds of thousands of individuals in 10 major cities across the United States. We find that time spent speeding is approximately uncorrelated with neighborhood demographics, yet, in several cities, officers focused speeding enforcement in small, demographically nonrepresentative areas. In some cities, speeding enforcement was concentrated in predominantly non-White neighborhoods, while, in others, enforcement was concentrated in predominately White neighborhoods. Averaging across the 10 cities we examined, and adjusting for observed speeding behavior, we find that speeding enforcement was moderately more concentrated in non-White neighborhoods. Our results show that current enforcement practices can lead to inequities across race and ethnicity. |
format | Online Article Text |
id | pubmed-9802422 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98024222023-01-26 Measuring racial and ethnic disparities in traffic enforcement with large-scale telematics data Cai, William Gaebler, Johann Kaashoek, Justin Pinals, Lisa Madden, Samuel Goel, Sharad PNAS Nexus Social and Political Sciences Past studies have found that racial and ethnic minorities are more likely than White drivers to be pulled over by the police for alleged traffic infractions, including a combination of speeding and equipment violations. It has been difficult, though, to measure the extent to which these disparities stem from discriminatory enforcement rather than from differences in offense rates. Here, in the context of speeding enforcement, we address this challenge by leveraging a novel source of telematics data, which include second-by-second driving speed for hundreds of thousands of individuals in 10 major cities across the United States. We find that time spent speeding is approximately uncorrelated with neighborhood demographics, yet, in several cities, officers focused speeding enforcement in small, demographically nonrepresentative areas. In some cities, speeding enforcement was concentrated in predominantly non-White neighborhoods, while, in others, enforcement was concentrated in predominately White neighborhoods. Averaging across the 10 cities we examined, and adjusting for observed speeding behavior, we find that speeding enforcement was moderately more concentrated in non-White neighborhoods. Our results show that current enforcement practices can lead to inequities across race and ethnicity. Oxford University Press 2022-07-30 /pmc/articles/PMC9802422/ /pubmed/36714855 http://dx.doi.org/10.1093/pnasnexus/pgac144 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of National Academy of Sciences. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Social and Political Sciences Cai, William Gaebler, Johann Kaashoek, Justin Pinals, Lisa Madden, Samuel Goel, Sharad Measuring racial and ethnic disparities in traffic enforcement with large-scale telematics data |
title | Measuring racial and ethnic disparities in traffic enforcement with large-scale telematics data |
title_full | Measuring racial and ethnic disparities in traffic enforcement with large-scale telematics data |
title_fullStr | Measuring racial and ethnic disparities in traffic enforcement with large-scale telematics data |
title_full_unstemmed | Measuring racial and ethnic disparities in traffic enforcement with large-scale telematics data |
title_short | Measuring racial and ethnic disparities in traffic enforcement with large-scale telematics data |
title_sort | measuring racial and ethnic disparities in traffic enforcement with large-scale telematics data |
topic | Social and Political Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9802422/ https://www.ncbi.nlm.nih.gov/pubmed/36714855 http://dx.doi.org/10.1093/pnasnexus/pgac144 |
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