Property Crime Clearance in Small Jurisdictions

2018 ◽  
Vol 43 (4) ◽  
pp. 477-493 ◽  
Author(s):  
Jeffrey J. Roth
Keyword(s):  
Negative Binomial ◽  
Property Crime ◽  
Negative Binomial Regression ◽  
The United States ◽  
Police Departments ◽  
Clearance Rates ◽  
Residential Instability ◽  
Large Cities ◽  
Binomial Regression ◽  
Crime Clearance

Research on the factors that influence crime clearance rates has primarily studied violent crimes in large cities. However, property crimes are among the most commonly occurring and least frequently cleared offenses, and the majority of police departments in the United States serve small jurisdictions. Thus, this study undertook an examination of the predictors of clearance rates for burglary, larceny, and vehicle theft in a sample of agencies serving populations of 50,000 people or fewer. Independent variables included both policing factors (e.g., workload, funding, broken windows arrests) and social disorganization indicators (e.g., residential instability, poverty). Negative binomial regression analyses revealed variation in the significance of the predictors across the three crimes. Additionally, many predictors found to be influential in prior work were insignificant in this study, which suggests differences in the nature of crime clearance between large cities and smaller jurisdictions and a need for further research in this area.

scholarly journals Variability of Crime Clearance Among Police Agencies

2018 ◽  
Vol 22 (1) ◽  
pp. 82-111 ◽  
Author(s):  
Thomas L. Scott ◽  
Charles Wellford ◽  
Cynthia Lum ◽  
Heather Vovak
Keyword(s):  
Organizational Factors ◽  
The United States ◽  
Police Departments ◽  
Clearance Rates ◽  
Quantitative And Qualitative Research ◽  
Police Agencies ◽  
Term Variation ◽  
Crime Clearance ◽  
Crime Types

Average crime clearance rates have remained remarkably stable in the United States since the 1980s, despite many advances in investigative technologies or fluctuations in crime. Taking these average trends at face value, some have suggested that this stability indicates that police departments can do little to alter their clearance rates. However, in this study, we find that the average trends mask substantial long-term variation in crime clearance among police agencies. Using group-based trajectory modeling, we test whether large U.S. police departments have reported uniquely different long-term clearance rate trends from 1981 to 2013 and what organizational factors might contribute to different trends. As we discuss, this method has attractive qualities that provide for a more rigorous analysis compared with past comparative work. Our results show diverse levels and patterns of clearance both within individual crime types and across multiple crime types that appear to covary with organizational factors. We explain how finite mixture modeling can advance both quantitative and qualitative research by identifying departmental differences in performance for further study.

Colleges and Community Crime: An Analysis of Campus Proximity and Neighborhood Crime Rates

2020 ◽  
pp. 001112872097431
Author(s):  
Kelsey Cundiff
Keyword(s):  
College Students ◽  
Negative Binomial ◽  
Structural Characteristics ◽  
Property Crime ◽  
Negative Binomial Regression ◽  
Past Research ◽  
College Campuses ◽  
Neighborhood Crime ◽  
Binomial Regression ◽  
Using Data

Property and violent crime have been associated in past research with many of the same lifestyle features typical of college students and the structural characteristics of the neighborhoods where students generally cluster. According to opportunity theory, individuals with the lifestyles and routine activities similar to college students are vulnerable to victimization. Therefore, higher rates of crime can be expected in the neighborhoods that surround college campuses. Using data from the National Neighborhood Crime Survey, this study uses multilevel negative binomial regression to analyze the relationship between proximity to a college campus and rates of violent and property crime. Results show that bordering tracts have higher rates of larceny, burglary, and robbery, controlling for other neighborhood- and city-level indicators of crime.

scholarly journals Spatiotemporal Convergence of Crime and Vehicle Crash Hotspots: Additional Consideration for Policing Places

2017 ◽  
Vol 64 (14) ◽  
pp. 1795-1819 ◽  
Author(s):  
Jeremy G. Carter ◽  
Eric L. Piza
Keyword(s):  
Negative Binomial ◽  
Property Crime ◽  
Negative Binomial Regression ◽  
Spatiotemporal Analysis ◽  
Additional Consideration ◽  
Relationship Strength ◽  
Crash Data ◽  
Vehicle Crash ◽  
Binomial Regression ◽  
Crash Hotspots

Policing strategies that seek to simultaneously combat crime and vehicle crashes operate under the assumption that these two problems have a corollary relationship—an assumption that has received scant empirical attention and is the focus of the present study. Geocoded vehicle crash, violent crime, and property crime totals across were aggregated to Indianapolis census blocks over a 36-month period (2011-2013). Time series negative binomial regression and local indicators of spatial autocorrelation analyses were conducted. Results indicate that both violent and property crime are significantly related to vehicle crash counts, both overall and during the temporal confines of patrol tours. Relationship strength was modest. Spatiotemporal analysis of crime and crash data can identify places for police intervention and improved scholarly evaluation.

scholarly journals Application of Discrete Regression Models for Analyzing K-8 Students Nonchronic Absenteeism in the United States

2021 ◽  
Vol 10 (4) ◽  
pp. 127
Author(s):  
Khairul Islam ◽  
Tanweer J. Shapla
Keyword(s):  
United States ◽  
Regression Models ◽  
Negative Binomial ◽  
Descriptive Analysis ◽  
Negative Binomial Regression ◽  
The United States ◽  
Chronic Absenteeism ◽  
Factors Affecting ◽  
National Crisis ◽  
Binomial Regression

Absenteeism is a national crisis in the United States, and must be addressed adequately at the early stages or at its onset, to prevent consequential disaster and burden due to absenteeism. A pervasive and persuasive nonchronic absenteeism results in chronic absenteeism, and causes severe damage to students’ life, schools and societies. While a good number of articles address various issues relating to chronic absenteeism, no evidence of research exists investigating nonchronic absenteeism. The aim of this article is to investigate factors affecting nonchronic absenteeism in K-8 students in the United States by applying discrete regression models. Initially, we investigate K-8 students nonchronic absenteeism discrepancies due to socio-demographic and parental involvement factors via descriptive analysis and then employ Poisson and negative binomial regression models for exploring significant factors of K-8 nonchronic absenteeism. The findings of this study will be of great use to stakeholders in developing appropriate incentive measures for reducing nonchronic absenteeism early and thereby reducing chronic absenteeism.

Identification of Critical Intersection Angle through Crash Modification Functions

2019 ◽  
Vol 2673 (2) ◽  
pp. 531-543 ◽  
Author(s):  
Wesley Kumfer ◽  
David Harkey ◽  
Bo Lan ◽  
Raghavan Srinivasan ◽  
Daniel Carter ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Skew Angle ◽  
Intersection Angle ◽  
Crash Data ◽  
Highway Safety Manual ◽  
Intersection Safety ◽  
Binomial Regression ◽  
Safety Effect

A significant portion of both fatal and total crashes occurs at intersections in the United States. Skew angle may be a significant contributor to these crashes. This paper examines the effects of intersection angle on intersection safety performance. With seven years of crash data from Minnesota and five years of crash data from Ohio, random forest regression data mining and negative binomial regression models were developed to estimate crash modification functions at three-leg and four-leg stop-controlled intersections with two-lane and multilane major legs. Where possible, the results were compared between the two states and used to develop average crash modification function curves. This study shows that over half of the intersection types experience the highest number of predicted crashes when the intersection angle between roadway legs is between 50 degrees and 65 degrees. These results have practical implications for engineers and safety professionals. First, the crash modification function curves supplement and revise the guidance for intersection angle in the Highway Safety Manual and Policy on Geometric Design of Highways and Streets. Second, the functions offer new guidance to agencies planning intersection improvements. Third, the crash modification functions can be used to determine the safety effect of changes in intersection angle.

Longitudinal Analysis of Light Rail and Streetcar Safety in the United States

2020 ◽  
Vol 2674 (9) ◽  
pp. 83-95
Author(s):  
Abubakr Ziedan ◽  
Candace Brakewood
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Descriptive Statistics ◽  
Light Rail ◽  
Right Of Way ◽  
Binomial Regression ◽  
Using Data ◽  
American Cities

Many American cities have launched or expanded light rail or streetcar services recently, which has resulted in a 61% increase in light rail and streetcar revenue miles nationwide during the period 2006–2016. Moreover, light rail and streetcars exhibit higher fatality rates per passenger mile traveled compared with other transit modes. In light of these trends, this study explores light rail and streetcar collisions, injuries, and fatalities using data obtained from the National Transit Database. This study applies a two-part methodology. In the first part, descriptive statistics are calculated for light rail and streetcar collisions, injuries, and fatalities, and a comparative analysis of light rail and streetcars is performed. In the second part, multilevel negative binomial regression models are used to analyze light rail and streetcar collisions and injuries. Three key findings have emerged from this study. First, the results generally align with findings from prior studies that show the majority of light rail and streetcar collisions occur in mixed right-of-way or near at-grade crossings. Second, this analysis revealed an issue predominantly at stations: 42% of light rail injuries were people waiting or leaving. Third, suicide was the leading cause of light rail fatalities, which represents 28% of all light rail fatalities. The implications of this study are important for cities that currently operate these modes or are planning to introduce new light rail or streetcar service to improve safety.

Hate Groups and Muslim Population Changes in the Fifty States: Does the Presence of Muslims Encourage Hate Group Formation?

2018 ◽  
Vol 25 (2) ◽  
pp. 317-332
Author(s):  
Daniel Hummel
Keyword(s):  
Negative Binomial ◽  
Group Formation ◽  
Negative Binomial Regression ◽  
The United States ◽  
Future Research ◽  
Group Status ◽  
Hate Groups ◽  
Muslim Population ◽  
Binomial Regression ◽  
Positive Correlations

The size of the Muslim population in the United States is growing, while the number of hate groups also continues to increase across the states. Based on the social dominance theory and group threat theory, there may be a link between these two dynamics as social dominators become concerned about their group status, i.e. white Christian males. This potential relationship is explored in this article. Although there were significant positive correlations between the number of hate groups and the size of the Muslim population, a panelled negative binomial regression with a number of relevant control variables found that there was not a significant relationship between these variables at the 0.05 probability level. The article further explores these findings and future research in this area.

Self-Reported Inmate Conduct

2016 ◽  
Vol 97 (1) ◽  
pp. 79-93
Author(s):  
Lisa Degiorgio Worthy
Keyword(s):  
Negative Binomial ◽  
Violent Behavior ◽  
Area Under The Curve ◽  
Negative Binomial Regression ◽  
The United States ◽  
Female Inmates ◽  
Operating Characteristics ◽  
Dynamic Factors ◽  
Binomial Regression ◽  
Improved Model

This article applies Walters’s criteria of dynamic factors in prison assessment to assess the predictive capabilities of the Prison Inmate Inventory (PII) in a sample of offenders from a southeastern state in the United States. Nested negative binomial regression was used, as well as ROC/AUC (receiver operating characteristics/area under the curve) to evaluate accuracy. The results show that beyond the effects of demographic and static variables, dynamic factors incrementally improved model fit and estimated counts of female inmates, non-White inmates, and inmates reporting more disciplinary actions and violent behavior. All PII scales demonstrated accuracy above .50; however, only two were within the range considered acceptable for criminal justice instruments. Practical implications have been discussed.

An Examination of Schools, Social Ecological Factors, and Neighbourhood Crime

2020 ◽  
Vol 60 (4) ◽  
pp. 851-872 ◽  
Author(s):  
James C Wo ◽  
Jihye Park
Keyword(s):  
Negative Binomial ◽  
Ecological Factors ◽  
Property Crime ◽  
Negative Binomial Regression ◽  
Crime Rates ◽  
Social Ecological ◽  
Binomial Regression ◽  
Block Level ◽  
The City ◽  
Level Analysis

Abstract Although theories suggest that schools are associated with higher neighbourhood crime rates, it is not clear what particular measures of schools have a crime-producing impact when controlling for a diversity of social-ecological factors. We therefore address this question by performing a block-level analysis of schools and crime in the city of Chicago. Negative binomial regression models reveal that the presence of any school in the focal block is associated with higher violent and property crime rates and that these associations are largely robust to differing measures of schools. We also determine that concentrated disadvantage moderates the effect of school presence in the block on property crime. The implications of these findings for criminology and public policy are discussed.

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