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Curated
Simple Crosstabs

Applying Artificial Intelligence to Person-Based Policing Practices, 2019-2023 (ICPSR 39074)

Released/updated on: 2024-09-26
Time period: 2019-01-01--2023-12-31
In this project, the research team developed and evaluated an artificial intelligence (AI) tool using agent-based modeling methods for crime analysis and risk evaluation (CARE): CAREsim. The purpose of this tool was to improve the effectiveness of person-based patrol strategies, where police take preemptive actions upon selected high-risk individuals (determined based on factors known to police such as violent crime history) when predicted risks of committing crimes are high. CARESim was developed and tested with a simulated randomized controlled experiment within the jurisdiction of Hampton, Virginia. 240 high-risk individuals (120 in each group) were followed for a 12-month period, with the simulation lasting 23 months. The treatment group received additional crime analyses using the AI tool and more focused patrols, while the control group received analyses as usual and random patrols in the simulated environment. The tool was evaluated on a series of outcomes (e.g., number of crimes and arrests) comparing the control and treatment groups. This collection contains the simulated high-risk individual data (DS1) and the simulated crimes data (DS2) used for the experiment.
Curated
Simple Crosstabs

Improving Officer Decision-Making: Can Personality Predict Outcomes in Use of Force Decisions? North Carolina and South Carolina, 2018-2020 (ICPSR 38687)

Released/updated on: 2024-04-11
Geographic coverage: North Carolina, United States, South Carolina
Time period: 2018-01-01--2020-09-30

The current study sought to examine the impact of select psychological, cognitive, professional experience and social network factors on police officers' decisions to use force. Additionally, the study examined the impact of a brief citizen education intervention (i.e. the completion of police officer training simulations) on citizens' attitudes toward police and use of force. All participants completed three training scenarios inside a firearms training simulator.

A sample of law enforcement officers and civilians took part in the study. Participants completed a series of questionnaires designed to measure, among other things:

  • Positive and Negative Emotionality
  • Need for Cognition
  • Cognitive Reflection
  • Professional experiences as a police officer (law enforcement participants only)
  • Size of friendship networks within the workplace (law enforcement participants only)
  • Perceptions of how their friendship networks would be impacted if the participant were to use excessive force (law enforcement participants only)
  • Pre-post measures of attitudes toward police (civilian participants only)
Self-published
Restricted

COVID-19 aerosol transmission simulation-based risk analysis for in-person learning (ICPSR 172081)

Released/updated on: 2022-06-07
As educational institutions begin a school year following a year and a half of disruptionfrom the COVID-19 pandemic, risk analysis can help to support decision-making forresuming in-person instructional operation by providing estimates of the relative riskreduction due to different interventions. In particular, a simulation-based risk analysisapproach enables scenario evaluation and comparison to guide decision making andaction prioritization under uncertainty. We develop a simulation model to characterizethe risks and uncertainties associated with infections resulting from aerosol exposure inin-person classes. We demonstrate this approach by applying it to model a semester ofcourses in a real college with approximately 11,000 students embedded within a largeruniversity. To have practical impact, risk cannot focus on only infections as the endpoint of interest, we estimate the risks of infection, hospitalizations, and deaths ofstudents and faculty in the college. We incorporate uncertainties in disease transmission,
the impact of policies such as masking and facility interventions, and variables outsideof the college’s control such as population-level disease and immunity prevalence. Weshow in our example application that universal use of masks that block 40% of aerosolsand the installation of near-ceiling, fan-mounted UVC systems both have the potentialto lead to substantial risk reductions and that these effects can be modeled at theindividual room level. These results exemplify how such simulation-based risk analysiscan inform decision making and prioritization under great uncertainty.open-source code available here with generic data: https://github.com/tlswan/in-class_covid_transmission
Self-published

Estimating Money Laundering Flows with a Gravity Model-Based Simulation (ICPSR 122401)

Released/updated on: 2020-09-23
Geographic coverage: Earth
Time period: 2009-01-01--2014-12-31
It is important to understand the amounts and types of money laundering flows, since they have very different effects and, therefore, need different enforcement strategies. Countries that mainly deal with criminals laundering their proceeds locally, need other measures than countries that mainly deal with foreign illegal investments or dirty money just flowing through the country. This paper has two main contributions. First, we unveil the country preferences of money launderers empirically in a systematic way. Former money laundering estimates used assumptions on which country characteristics money launderers are looking for when deciding where to send their ill-gotten gains. Thanks to a unique dataset of transactions suspicious of money laundering, provided by the Dutch Institute infobox Criminal and Unexplained Wealth (iCOV), we can empirically test these assumptions with an econometric gravity model estimation. We use this information for our second contribution: iteratively simulating all money laundering flows around the world. This allows us, for the first time, to provide estimates that distinguish between three different policy challenges: the laundering of domestic crime proceeds, international investment of dirty money and money just flowing through a country.
Curated
Restricted

Simulated Data From a Known Covariance Matrix of Advanced Placement Course Data (ICPSR 36953)

Released/updated on: 2017-11-07
Propensity score analysis is widely used for simulating random assignment in observational studies where true random assignment is not possible. In propensity score modeling, a number of covariates are used to estimate the probability that an individual will belong to one of two groups. Prospective participants are then matched on their probabilities of belonging to the two groups rather than on the exact set of covariate values (as in traditional matching methods). However, traditional propensity score analysis can only be used in studies with two groups, such as an experimental and control group. In this study a new method is introduced called piecewise propensity score analysis (PPSA) for ordinal polytomous grouping variables. PPSA was compared with another method of conducting propensity score analysis with ordered categories, marginal mean weighting through stratification (MMW-S) in a 3 x 5 x 4 study across three model misspecification conditions, five matching methods, and four sample sizes (1000, 5000, 10000, 21753). No significant difference were found between PPSA and MMW-S methods across conditions. Linear regression, simple mean difference, or propensity stratification methods are recommended for simulating causal inference.
Curated
Restricted

Investigating the Impact of In-car Communication on Law Enforcement Officer Patrol Performance in an Advanced Driving Simulator in Mississippi, 2011 (ICPSR 34922)

Released/updated on: 2016-12-21
Geographic coverage: Mississippi, United States
Time period: 2011-06-01--2011-11-30

These data are part of NACJD's Fast Track Release and are distributed as they there received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except of the removal of direct identifiers. Users should refer to the accompany readme file for a brief description of the files available with this collections and consult the investigator(s) if further information is needed.

This study used an experimental design to evaluate law enforcement officers' driving, visual attention, and situation awareness during patrol driving. The conditions were varied to determine the impact of information presentation formats on officers' ability to execute patrols. In addition, the effectiveness of in-vehicle technologies that may provide additional support to the officer and reduce the impact of information overload were investigated.

Curated
Restricted

Alabama Sentencing Simulation Model, 1998-2003 (ICPSR 34671)

Released/updated on: 2014-09-30
Geographic coverage: United States, Alabama
Time period: 1970-01-01--2003-12-31

Prior to 2003, the State of Alabama had no formal methodology to forecast prison populations, including a simulation model or statistical time-series and forecasting methods. Instead, the Alabama Department of Corrections relied on percent growth models, using the existing prison population to forecast future statewide prison populations. As Alabama moved toward a structured sentencing system, more precision was needed to investigate the impact statewide sentencing reform would have on the prison population. Adding to the need for more precise forecast methods, the Alabama Sentencing Commission intended to incorporate Virginia worksheet-style sentencing guidelines into its sentencing reform efforts. The Virginia sentencing guidelines uses offender and offense factors identified with statistical models and weights to guide sentence recommendations. Alabama require an analytical tool to guide the Commission during development of such a complicated sentencing system. To shepherd this process, the simulation model development project was undertaken which consisted of three phases;

  • The development of a baseline projection of current practices for later comparison with projections made following implementation of the sentencing standards;
  • Incorporating the initial sentencing standards into the simulation model; and
  • Integrating disparate modules together into a user-friendly model interface.
Curated

Empirical Investigation of "Going to Scale" in Drug Interventions in the United States, 1990, 2003 (ICPSR 26101)

Released/updated on: 2009-08-26
Geographic coverage: United States
Time period: 1990-01-01--1990-12-31, 2003-01-01--2003-12-31
Despite a growing consensus among scholars that substance abuse treatment is effective in reducing offending, strict eligibility rules have limited the impact of current models of therapeutic jurisprudence on public safety. This research effort was aimed at providing policy makers some guidance on whether expanding this model to more drug-involved offenders is cost-beneficial. Since data needed for providing evidence-based analysis of this issue were not readily available, micro-level data from three nationally representative sources were used to construct a 40,320 case synthetic dataset -- defined using population profiles rather than sampled observation -- that was used to estimate the benefits of going to scale in treating drug involved offenders. The principal investigators combined information from the NATIONAL SURVEY ON DRUG USE AND HEALTH, 2003 (ICPSR 4138) and the ARRESTEE DRUG ABUSE MONITORING (ADAM) PROGRAM IN THE UNITED STATES, 2003 (ICPSR 4020) to estimate the likelihood of drug addiction or dependence problems and develop nationally representative prevalence estimates. They used information in the DRUG ABUSE TREATMENT OUTCOME STUDY (DATOS), 1991-1994 (ICPSR 2258) to compute expected crime reducing benefits of treating various types of drug involved offenders under four different treatment modalities. The project computed expected crime reducing benefits that were conditional on treatment modality as well as arrestee attributes and risk of drug dependence or abuse. Moreover, the principal investigators obtained estimates of crime reducing benefits for all crimes as well as select sub-types. Variables include age, race, gender, offense, history of violence, history of treatment, co-occurring alcohol problem, criminal justice system status, geographic location, arrest history, and a total of 134 prevalence and treatment effect estimates and variances.
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