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 Analysis of Nonsevere Crashes on Two- and Four-Lane Urban and Rural Highways: Effects of Wet Pavement Surface Condition

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Aschalew Kassu ◽  
Michael Anderson
Keyword(s):  
Negative Binomial ◽  
Surface Condition ◽  
Negative Binomial Regression ◽  
Segment Length ◽  
Mean Values ◽  
Rural Highways ◽  
Explanatory Variables ◽  
Crash Data ◽  
Pavement Surface ◽  
Binomial Regression

This study examines the effects of wet pavement surface conditions on the likelihood of occurrences of nonsevere crashes in two- and four-lane urban and rural highways in Alabama. Initially, sixteen major highways traversing across the geographic locations of the state were identified. Among these highways, the homogenous routes with equal mean values, variances, and similar distributions of the crash data were identified and combined to form crash datasets occurring on dry and wet pavements separately. The analysis began with thirteen explanatory variables covering engineering, environmental, and traffic conditions. The principal terms were statistically identified and used in a mathematical crash frequency models developed using Poisson and negative binomial regression models. The results show that the key factors influencing nonsevere crashes on wet pavement surfaces are mainly segment length, traffic volume, and posted speed limits.

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.

Effects of Ramp Spacing on Freeway Mainline Crashes

2011 ◽  
Vol 97-98 ◽  
pp. 95-99
Author(s):  
Yong Qing Guo
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Model Estimation ◽  
Vehicle Crashes ◽  
Crash Data ◽  
Binomial Regression ◽  
Single Vehicle ◽  
Exit Ramp

This research applies Negative Binomial regression models to investigate safety effects of ramp spacing. Data for model estimation was collected in 112 freeway segments where each entrance ramp is followed by an exit ramp. Three years (2005-2007) of freeway crash data were examined by the NB model in this study. The modeling results suggest that the frequencies of total crashes, fatal-plus-injury crashes, single-vehicle crashes and multiple-vehicle crashes increase as ramp spacing decreases, and the frequencies of total crashes and multiple-vehicle crashes increase at significant rates. The modeling result has been geared into the development of accident modification factors (AMFs) for ramp spacing that can be used safety prediction of freeways.

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.

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.

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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