scholarly journals Block-Level Analysis of the Attractors of Robbery in a Downtown Area

SAGE Open ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 215824402096367
Author(s):  
Kingsley U. Ejiogu
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Geographic Mobility ◽  
Negative Binomial Regression Model ◽  
American Time Use Survey ◽  
Pattern Theory ◽  
Crime Pattern Theory ◽  
Time Use Survey ◽  
Block Level ◽  
Street Robbery

This article examines the predictions of crime pattern theory in a unique neighborhood type. It tested potential crime attracting facilities against street robbery data from 2009 to 2013 in the Police Districts I & II in Downtown Houston. The analysis modeled the four daily human routine periods described in the American Time Use Survey (ATUS). Generalized linear simultaneous negative binomial regression model was used to determine the size of the influence of the variables (beta coefficients) and their significance for each model outcome. The findings show some distinct patterns of street robbery due to the immediate and lagged effects of the variables relatable to the study environment’s unique setting. Two variables, geographic mobility, and barbershops were particularly significant across three of the outcome models. The results suggest that the physical and social structure of neighborhoods determined by land-use regulations would enhance understanding of the time-based influence on robbery patterns due to crime-attracting facilities.

A Socioeconomic Edge Effect: Rational Crime in Small Areas of Sharp Socioeconomic Contrast

2020 ◽  
pp. 002242782094500
Author(s):  
Robert Drew Heinzeroth
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Economic Incentive ◽  
Future Research ◽  
Census Block ◽  
Pattern Theory ◽  
Crime Pattern Theory ◽  
Dependent Variables ◽  
Block Groups ◽  
Crime Pattern

Objectives: To determine whether criminogenic “edges,” as defined by crime pattern theory, exist at points of sharp contrast of socioeconomic status (SES). Methods: The study uses a quasi-experimental design with pattern matching logic. A series of negative binomial regression models separately examine five different crimes with an economic incentive as dependent variables, and five crimes without an economic incentive as nonequivalent dependent variables, to determine whether census block groups of predominantly and comparatively higher SES than the wider surrounding area experience greater reported rational crime than would otherwise be expected. Results: The census block groups of comparatively higher SES located within and/or near areas of predominantly lower SES experienced one of the five crimes with an economic incentive, robberies by firearm, 40 percent more frequently than would otherwise be expected. Conclusions: The study’s findings are partially consistent with its hypothesis, which is grounded in crime pattern, rational choice, routine activities, and social disorganization theories. The findings encourage future research that may extend the definition of an “edge” under crime pattern theory as well as research at the intersection of criminological theories.

scholarly journals Socioeconomic Status and Housework: Cultural Differences in Participation in Routine Housework in Japan, Canada, and the US

2019 ◽  
Author(s):  
Kamila Kolpashnikova ◽  
Ryota Chiba ◽  
Kiyomi Shirakawa
Keyword(s):  
Socioeconomic Status ◽  
Time Use ◽  
Negative Binomial ◽  
Leisure Activities ◽  
General Social Survey ◽  
Negative Binomial Regression ◽  
American Time Use Survey ◽  
The Us ◽  
Time Use Survey ◽  
Global North

The assumption about socioeconomic status (SES) and participation in housework are based on the empirical results in Western countries. As such, SES is assumed to work in a similar way in other regions as it does in the countries of the global north. This assumption can often lead to misguided interpretations of the effects of SES on housework participation in other cultural contexts. One such exception is Japan. We analyze time-use diaries from the American Time Use Survey for the period from 2003 to 2016, 1986-2010 Canadian General Social Survey, and the 2006 Japan Survey on Time Use and Leisure Activities (社会生活基本調査). Using the negative binomial regression, we test whether SES is associated with less time spent on housework as the outsourcing hypothesis predicts. The findings show that this hypothesis stands only for Canadian and American women, whereas married Japanese women are unlikely to reduce their participation in housework with the increase of their SES.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

Evaluation of Strategies to Mitigate Culvert-Involved Crashes

2021 ◽  
pp. 036119812199207
Author(s):  
Hitesh Chawla ◽  
Megat-Usamah Megat-Johari ◽  
Peter T. Savolainen ◽  
Christopher M. Day
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
Negative Binomial Regression Model ◽  
Promising Alternative ◽  
Roadside Safety ◽  
Adverse Conditions ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Binomial Regression

The objectives of this study were to assess the in-service safety performance of roadside culverts and evaluate the potential impacts of installing various safety treatments to mitigate the severity of culvert-involved crashes. Such crashes were identified using standard fields on police crash report forms, as well as through a review of pertinent keywords from the narrative section of these forms. These crashes were then linked to the nearest cross-drainage culvert, which was associated with the nearest road segment. A negative binomial regression model was then estimated to discern how the risk of culvert-involved crashes varied as a function of annual average daily traffic, speed limit, number of travel lanes, and culvert size and offset. The second stage of the analysis involved the use of the Roadside Safety Analysis Program to estimate the expected crash costs associated with various design contexts. A series of scenarios were evaluated, culminating in guidance as to the most cost-effective treatments for different combinations of roadway geometric and traffic characteristics. The results of this study provide an empirical model that can be used to predict the risk of culvert-involved crashes under various scenarios. The findings also suggest that the installation of safety grates on culvert openings provides a promising alternative for most of the cases where the culvert is located within the clear zone. In general, a guardrail is recommended when adverse conditions are present or when other treatments are not feasible at a specific location.

Bike Count Forecast Model with Multimodal Network Connectivity Measures

2021 ◽  
pp. 036119812110218
Author(s):  
Bingqing Liu ◽  
Divya Bade ◽  
Joseph Y. J. Chow
Keyword(s):  
New York ◽  
Negative Binomial ◽  
Network Connectivity ◽  
Negative Binomial Regression ◽  
Forecast Model ◽  
Negative Binomial Regression Model ◽  
Significance Level ◽  
Commute Time ◽  
Improve Model ◽  
Subway Stations

With the rise of cycling as a mode choice for commuting and short-distance delivery, as well as policy objectives encouraging this trend, bike count models are increasingly critical to transportation planning and investment. Studies have found that network connectivity plays a role in such models, but there remains a lack of measure for the connectivity of a link in a multimodal trip context. This study proposes a connectivity measure that captures the importance of a link in connecting the origins of cyclists and nearby subway stations, and incorporates it in a negative binomial regression model to forecast bike counts at links. Representative bike trips are generated with regard to bike-friendliness using the New York City transit trip planner and used to determine the deviation from the shortest path via the designated link. The measure is shown to improve model fitness with a significance level within 10%. Insights are also drawn for income levels, bike lanes, subway station availability, and average commute time of travelers.

A Negative Binomial Regression Model for Accuracy Tests

2012 ◽  
Vol 36 (2) ◽  
pp. 88-103 ◽  
Author(s):  
Lai-Fa Hung
Keyword(s):  
Negative Binomial ◽  
Social Science Research ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Science Research ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Negative Binomial Regression Model ◽  
Marginal Maximum Likelihood ◽  
The Mean

Rasch used a Poisson model to analyze errors and speed in reading tests. An important property of the Poisson distribution is that the mean and variance are equal. However, in social science research, it is very common for the variance to be greater than the mean (i.e., the data are overdispersed). This study embeds the Rasch model within an overdispersion framework and proposes new estimation methods. The parameters in the proposed model can be estimated using the Markov chain Monte Carlo method implemented in WinBUGS and the marginal maximum likelihood method implemented in SAS. An empirical example based on models generated by the results of empirical data, which are fitted and discussed, is examined.

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