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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...

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Autores principales: Cai, William, Gaebler, Johann, Kaashoek, Justin, Pinals, Lisa, Madden, Samuel, Goel, Sharad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
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.
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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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