Detecting disparities in police deployments using dashcam data
- URL: http://arxiv.org/abs/2305.15210v1
- Date: Wed, 24 May 2023 14:48:59 GMT
- Title: Detecting disparities in police deployments using dashcam data
- Authors: Matt Franchi, J.D. Zamfirescu-Pereira, Wendy Ju, Emma Pierson
- Abstract summary: We show that disparities in police deployment levels can be quantified by detecting police vehicles in dashcam images of public street scenes.
The neighborhood with the highest deployment levels has almost 20 times higher levels than the neighborhood with the lowest.
We discuss the implications of these disparities for policing equity and for algorithms trained on policing data.
- Score: 16.005351901762904
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Large-scale policing data is vital for detecting inequity in police behavior
and policing algorithms. However, one important type of policing data remains
largely unavailable within the United States: aggregated police deployment data
capturing which neighborhoods have the heaviest police presences. Here we show
that disparities in police deployment levels can be quantified by detecting
police vehicles in dashcam images of public street scenes. Using a dataset of
24,803,854 dashcam images from rideshare drivers in New York City, we find that
police vehicles can be detected with high accuracy (average precision 0.82, AUC
0.99) and identify 233,596 images which contain police vehicles. There is
substantial inequality across neighborhoods in police vehicle deployment
levels. The neighborhood with the highest deployment levels has almost 20 times
higher levels than the neighborhood with the lowest. Two strikingly different
types of areas experience high police vehicle deployments - 1) dense,
higher-income, commercial areas and 2) lower-income neighborhoods with higher
proportions of Black and Hispanic residents. We discuss the implications of
these disparities for policing equity and for algorithms trained on policing
data.
Related papers
- Race and Privacy in Broadcast Police Communications [3.034710104407876]
We examine the Chicago Police Department's (CPD's) use of broadcast police communications (BPC) to coordinate activity of law enforcement officers (LEOs) in the city.
From a recently assembled archive of 80,775 hours of BPC associated with CPD operations, we analyze text transcripts of radio transmissions broadcast 9:00 AM to 5:00 PM on August 10th, 2018 in one majority Black, one majority white, and one majority Hispanic area.
We explore the vocabulary and speech acts used by police in BPC, comparing mentions of personal characteristics to local demographics, the personal information shared over BPC, and the
arXiv Detail & Related papers (2024-07-01T21:34:51Z) - Your Car Tells Me Where You Drove: A Novel Path Inference Attack via CAN Bus and OBD-II Data [57.22545280370174]
On Path Diagnostic - Intrusion & Inference (OPD-II) is a novel path inference attack leveraging a physical car model and a map matching algorithm.
We implement our attack on a set of four different cars and a total number of 41 tracks in different road and traffic scenarios.
arXiv Detail & Related papers (2024-06-30T04:21:46Z) - Drone navigation and license place detection for vehicle location in
indoor spaces [55.66423065924684]
This work is aimed at creating a solution based on a nano-drone that navigates across rows of parked vehicles and detects their license plates.
All computations are done in real-time on the drone, which just sends position and detected images that allow the creation of a 2D map.
arXiv Detail & Related papers (2023-07-19T17:46:55Z) - Scalable and Real-time Multi-Camera Vehicle Detection,
Re-Identification, and Tracking [58.95210121654722]
We propose a real-time city-scale multi-camera vehicle tracking system that handles real-world, low-resolution CCTV instead of idealized and curated video streams.
Our method is ranked among the top five performers on the public leaderboard.
arXiv Detail & Related papers (2022-04-15T12:47:01Z) - Real-time smart vehicle surveillance system [0.0]
Vehicle theft is one of the least solved offenses in India.
We propose a real-time vehicle surveillance system, which detects and tracks the suspect vehicle using the CCTV video feed.
Various image processing and deep learning algorithms are employed to meet the objectives of the proposed system.
arXiv Detail & Related papers (2021-11-24T06:15:14Z) - US Fatal Police Shooting Analysis and Prediction [13.569449459014104]
More people in the U.S. think that police use excessive force during law enforcement, especially to a specific group of people.
We proposed a new method to quantify fatal police shooting news reporting deviation of mainstream media.
We analyzed the most comprehensive US fatal police shooting dataset from Washington Post.
arXiv Detail & Related papers (2021-03-24T21:39:32Z) - The effect of differential victim crime reporting on predictive policing
systems [84.86615754515252]
We show how differential victim crime reporting rates can lead to outcome disparities in common crime hot spot prediction models.
Our results suggest that differential crime reporting rates can lead to a displacement of predicted hotspots from high crime but low reporting areas to high or medium crime and high reporting areas.
arXiv Detail & Related papers (2021-01-30T01:57:22Z) - Deep traffic light detection by overlaying synthetic context on
arbitrary natural images [49.592798832978296]
We propose a method to generate artificial traffic-related training data for deep traffic light detectors.
This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds.
It also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state.
arXiv Detail & Related papers (2020-11-07T19:57:22Z) - Multi-officer Routing for Patrolling High Risk Areas Jointly Learned
from Check-ins, Crime and Incident Response Data [6.295207672539996]
We formulate the dynamic crime patrol planning problem for multiple police officers using check-ins, crime, incident response data, and POI information.
We propose a joint learning and non-random optimisation method for the representation of possible solutions.
The performance of the proposed solution is verified and compared with several state-of-art methods using real-world datasets.
arXiv Detail & Related papers (2020-07-31T23:33:14Z) - Train in Germany, Test in The USA: Making 3D Object Detectors Generalize [59.455225176042404]
deep learning has substantially improved the 3D object detection accuracy for LiDAR and stereo camera data alike.
Most datasets for autonomous driving are collected within a narrow subset of cities within one country.
In this paper we consider the task of adapting 3D object detectors from one dataset to another.
arXiv Detail & Related papers (2020-05-17T00:56:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.