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Showing 1 – 6 of 6 results.

Curated

Anticipating Community Drug Problems in Washington, DC, and Portland, Oregon, 1984-1990 (ICPSR 9924)

Released/updated on: 1994-02-17

Geographic coverage: Oregon, District of Columbia, United States, Portland (Oregon)

Time period: 1984-01-01--1990-01-01

This study examined the use of arrestee urinalysis results as a predictor of other community drug problems. A three-stage public health model was developed using drug diffusion and community drug indicators as aggregate measures of individual drug use careers. Monthly data on drug indicators for Washington, DC, and Portland, Oregon, were used to: (1) estimate the correlations of drug problem indicators over time, (2) examine the correlations among indicators at different stages in the spread of new forms of drug abuse, and (3) estimate lagged models in which arrestee urinalysis results were used to predict subsequent community drug problems. Variables included arrestee drug test results, drug-overdose deaths, crimes reported to the local police department, and child maltreatment incidents. Washington variables also included drug-related emergency room episodes. The unit of analysis was months covered by the study. The Washington, DC, data consist of 78 records, one for each month from April 1984 through September 1990. The Portland, Oregon, data contain 33 records, one for each month from January 1988 through September 1990.

Curated

Simple Crosstabs

Crime Hot Spot Forecasting with Data from the Pittsburgh [Pennsylvania] Bureau of Police, 1990-1998 (ICPSR 3469)

Released/updated on: 2015-08-07

Geographic coverage: United States, Pennsylvania, Pittsburgh

Time period: 1990-01-01--1998-01-01

This study used crime count data from the Pittsburgh, Pennsylvania, Bureau of Police offense reports and 911 computer-aided dispatch (CAD) calls to determine the best univariate forecast method for crime and to evaluate the value of leading indicator crime forecast models.

The researchers used the rolling-horizon experimental design, a design that maximizes the number of forecasts for a given time series at different times and under different conditions. Under this design, several forecast models are used to make alternative forecasts in parallel. For each forecast model included in an experiment, the researchers estimated models on training data, forecasted one month ahead to new data not previously seen by the model, and calculated and saved the forecast error. Then they added the observed value of the previously forecasted data point to the next month's training data, dropped the oldest historical data point, and forecasted the following month's data point. This process continued over a number of months.

A total of 15 statistical datasets and 3 geographic information systems (GIS) shapefiles resulted from this study.

The statistical datasets consist of

Univariate Forecast Data by Police Precinct (Dataset 1) with 3,240 cases
Output Data from the Univariate Forecasting Program: Sectors and Forecast Errors (Dataset 2) with 17,892 cases
Multivariate, Leading Indicator Forecast Data by Grid Cell (Dataset 3) with 5,940 cases
Output Data from the 911 Drug Calls Forecast Program (Dataset 4) with 5,112 cases
Output Data from the Part One Property Crimes Forecast Program (Dataset 5) with 5,112 cases
Output Data from the Part One Violent Crimes Forecast Program (Dataset 6) with 5,112 cases
Input Data for the Regression Forecast Program for 911 Drug Calls (Dataset 7) with 10,011 cases
Input Data for the Regression Forecast Program for Part One Property Crimes (Dataset 8) with 10,011 cases
Input Data for the Regression Forecast Program for Part One Violent Crimes (Dataset 9) with 10,011 cases
Output Data from Regression Forecast Program for 911 Drug Calls: Estimated Coefficients for Leading Indicator Models (Dataset 10) with 36 cases
Output Data from Regression Forecast Program for Part One Property Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 11) with 36 cases
Output Data from Regression Forecast Program for Part One Violent Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 12) with 36 cases
Output Data from Regression Forecast Program for 911 Drug Calls: Forecast Errors (Dataset 13) with 4,936 cases
Output Data from Regression Forecast Program for Part One Property Crimes: Forecast Errors (Dataset 14) with 4,936 cases
Output Data from Regression Forecast Program for Part One Violent Crimes: Forecast Errors (Dataset 15) with 4,936 cases.
The GIS Shapefiles (Dataset 16) are provided with the study in a single zip file: Included are polygon data for the 4,000 foot, square, uniform grid system used for much of the Pittsburgh crime data (grid400); polygon data for the 6 police precincts, alternatively called districts or zones, of Pittsburgh(policedist); and polygon data for the 3 major rivers in Pittsburgh the Allegheny, Monongahela, and Ohio (rivers).

Curated

Dissociating Affect and Deliberation in Choice Processes, 2001 (ICPSR 26281)

Released/updated on: 2010-01-25

Geographic coverage: Oregon, United States

This study was conducted to examine hypotheses derived from an emotion-based model of stigma responses to radiation sources. A model of stigma susceptibility was proposed in which affective reactions and cognitive worldviews activate predispositions to appraise and experience events in systematic ways that result in the generation of negative emotion, risk perceptions, and stigma responses. For this study, a total of 198 respondents were asked about a series of 15 objects and activities: sun-tanning, radiation therapy for cancer control, microwave ovens, nuclear power plants, radiation from air travel, death of a favorite pet, medical x-rays, the upcoming spring break, natural background radiation, final exams for the term, radiation from nuclear weapons testing, radiation to prevent bacteria in food, a series of thefts or crimes in their neighborhoods, cosmic radiation, and radioactive waste from nuclear power plants. Providing ratings on 17 scales, respondents gave their feelings about each object or activity, offered their opinions on situations wherein the object or activity would or would not be of concern, the impact of the object or activity in their lives, and their adjustment to situations involving the object or activity. Queries also included how angry and afraid the object or activity made respondents, and how risky, disgraceful, moral, acceptable, and stigmatized they felt it was. Finally, participants provided self-report ratings of affective reactivity and worldviews.

Curated

Evaluation of Waiver Effects in Maryland, 1998-2000 (ICPSR 4077)

Released/updated on: 2005-03-04

Geographic coverage: Maryland

Time period: 1998-01-01--2000-01-01

The purpose of this research was to assist policymakers in determining if the targeted youths affected by the waiver laws passed by the Maryland legislature in 1994 and 1998 were being processed as intended. The waiver laws were enacted to ensure that a youth who was unwilling to comply with treatment and/or committed a serious offense would have a serious consequence to his/her action and, therefore, would be processed in the adult system. As a result of the legislation, four pathways of court processing emerged which created four groups of youths to study: at-risk of waiver (not waived), waiver, legislative waiver, and reverse waver. A variety of data sources in both the juvenile and adult systems were triangulated to obtain the necessary information to accurately describe the youths involved. The triangulation of data from multiple file sources happened in a variety of formats (automated, hardcopy, and electronic files) from a variety of agencies to compare and contrast youths processed in the juvenile and adult systems. The five legislative criteria (age, mental and physical condition, amenability to treatment, crime seriousness, and public safety) plus extra-legal data were used as a framework to profile the youths in this study. Many of the variables chosen to explore each domain were included in previous studies. Other variables, such as those designed to operationalize mental health issues (not defined by the legislation) were chosen to extend the literature and to generate the most complete profile of youths processed in each system. The study includes variables pertinent to the five legislative criteria in addition to demographic and family information variables such as gender, race, and socioeconomic status, information on school expulsions, school suspensions, gang involvement, drug history, health, and hospitalization.

Curated

Using Machine Learning to Identify High-Risk Domestic Violence Offenders in New York City, New York, 2006-2017 (ICPSR 38540)

Released/updated on: 2024-02-12

Geographic coverage: New York City, United States, New York (state)

Time period: 2006-01-01--2019-05-30

To address the relative difficulty in predicting domestic violence incidents and effectively targeting resources, the University of Chicago Crime Lab and the New York Police Department (NYPD) collaborated to develop and test a machine learning-based statistical model to predict the risk of domestic violence victimization in New York City.

Phase 1 of the project was to develop a statistical model using machine learning techniques. NYPD administrative records dated between January 2006 and January 2017 were used as input data to build and refine the tool. Due to the lack of unique identifiers for victims in the records, the research team also used data from the Chicago Police Department to create a probabilistic record linkage toolkit (Name Match) to identify which records belonged to the same person within and across data sources.

In Phase 2, the researchers aimed to field test the tool's capability to identify individuals at risk of repeated domestic violence through a large-scale randomized control trial. Measuring the effects of regular home visits of high-priority individuals thought to be at risk of serious domestic assault, the test intended to compare the selections of individuals made by officers versus those predicted by the tool.

This collection contains only the machine learning code files (R and Python) created during secondary analysis, which have been released as a zipped package. Please refer to the Data Roadmap for instructions on how to obtain the original NYPD data. To access the Name Change algorithm and documentation, please visit the Github repository.

Curated

Validation of Risk Assessment Tools for Predicting Re-offending at Different Developmental Periods, 1951-2010 (ICPSR 32761)

Released/updated on: 2014-02-26

Geographic coverage: North Carolina, Canada, Netherlands, United States, Connecticut

Time period: 1951-01-01--2010-01-01

The study was a secondary data analysis examining the accuracy of risk assessment tools in predicting re-offending during early adulthood (age 18 to 25 years) compared to their accuracy in predicting re-offending during adolescence (age 12-17 years; youth tools only) or in later adulthood (older than 25 years, adult tools only). The investigators combined datasets that involved the same risk assessment tools. The adolescent risk assessment tools included the North Carolina Assessment of Risk (NCAR), the Youth Level of Service/Case Management Inventory (YLS/CMI), and the Structured Assessment of Violence Risk for Youth (SAVRY). The adult risk assessment tools included the Historical Clinical Risk Management-20 items (HCR-20) and the Violence Risk Appraisal Guide (VRAG). Using the datasets, the study examined the following recidivism outcomes: (1) any type of re-offending (excluded status offenses), and (2) violent re-offending specifically.