scholarly journals Victimization, Social Structure and Psychosocial Variables: The Case of Spain in 1999 and 2016

2019 ◽  
Vol 8 (3) ◽  
pp. 102
Author(s):  
Gonzalo Herranz de Rafael ◽  
Juan Sebastián Fernández-Prados
Keyword(s):  
Comparative Analysis ◽  
Social Structure ◽  
Crime Prevention ◽  
Regression Models ◽  
Significant Contribution ◽  
Environmental Design ◽  
Psychosocial Variables ◽  
Independent Variables ◽  
Subjective Perceptions ◽  
Factors Influencing

This article reviews the research on the factors influencing victimization and explores the case of Spain. The first section presents a comparative analysis of the data for 1999 and 2016 in terms of perceptions, profiles and the most significant sociodemographic and socioeconomic variables. The second section shows an explanatory analysis based on multivariate logistical regression models, using as independent variables sociodemographic and psychosocial items, and the dependent variable is whether one is described as one is described as a victim or no in the 2016 survey. The results point towards an explanatory model of victimization in which sociodemographic variables play a less important role, whereas psychosocial variables related to lifestyle and subjective perceptions make a significant contribution to greater understanding of the nature of being the victim of a crime and offer suggestions on how to improve Crime Prevention Through Environmental Design (CPTED).

scholarly journals Victimization and Social Structure: The Case of Spain in 1999 and 2016

2018 ◽  
Author(s):  
Gonzalo Herranz de Rafael ◽  
Juan Sebastián Fernández-Prados
Keyword(s):  
Comparative Analysis ◽  
Regression Model ◽  
Social Structure ◽  
Significant Contribution ◽  
Explanatory Model ◽  
Sociodemographic Variables ◽  
Socioeconomic Variables ◽  
Subjective Perceptions ◽  
Dependent Variables ◽  
Independent Variable

After brings about a brief review of the theoretical explanations and researches on the reasons for being a victim, this article is organized into two sections. The first presents a comparative analysis of the data for 1999 and 2016 in terms of perceptions, profiles and most significant sociodemographic and socioeconomic variables. The second one shows an explanatory analysis based on a multivariate logistical regression model using as an independent variable lifestyle of the population and socioeconomic variables, and as dependent variables individual’s susceptibility to becoming a victim of certain crimes. The results points towards an explanatory model of victimization in which sociodemographic variables play an increasingly less important role while variables related to lifestyle and subjective perceptions make a significant contribution to greater understanding of the nature of being the victim of a crime.

scholarly journals Empirical α–β runout modelling of snow avalanches in the Catalan Pyrenees

2021 ◽  
pp. 1-12
Author(s):  
Pere Oller ◽  
Cristina Baeza ◽  
Glòria Furdada
Keyword(s):  
Comparative Analysis ◽  
Regression Model ◽  
Extreme Values ◽  
Multiple Linear Regression Model ◽  
Present Knowledge ◽  
Snow Avalanches ◽  
Independent Variables ◽  
Horizontal Length ◽  
Length And Area ◽  
Runout Modelling

Abstract A variation in the α−β model which is a regression model that allows a deterministic prediction of the extreme runout to be expected in a given path, was applied for calculating avalanche runout in the Catalan Pyrenees. Present knowledge of major avalanche activity in this region and current mapping tools were used. The model was derived using a dataset of 97 ‘extreme’ avalanches that occurred from the end of 19th century to the beginning of 21st century. A multiple linear regression model was obtained using three independent variables: inclination of the avalanche path, horizontal length and area of the starting zone, with a good fit of the function (R2 = 0.81). A larger starting zone increases the runout and a larger length of the path reduces the runout. The new updated equation predicts avalanche runout for a return period of ~100 years. To study which terrain variables explain the extreme values of the avalanche dataset, a comparative analysis of variables that influence a longer or shorter runout was performed. The most extreme avalanches were treated. The size of the avalanche path and the aspect of the starting zone showed certain association between avalanches with longer or shorter runouts.

Prédiction de la composition corporelle post-mortem de cerfs de Virginie (Odocoileus virginianus) à partir de mesures morphologiques et massiques

1997 ◽  
Vol 75 (11) ◽  
pp. 1790-1795 ◽  
Author(s):  
Chantal Bois ◽  
Michel Crête ◽  
Jean Huot ◽  
Jean-Pierre Quellet
Keyword(s):  
Odocoileus Virginianus ◽  
Regression Models ◽  
Carcass Composition ◽  
Chemical Analyses ◽  
Linear Regression Models ◽  
Mass Measurements ◽  
Composition Corporelle ◽  
Independent Variables ◽  
Multiple Linear Regression Models ◽  
Field Stations

Morphologic and mass measurements were taken on 24 complete white-tailed deer (Odocoileus virginianus) carcasses of varying ages and both sexes in southern Quebec. Each carcass was divided into three parts (skin, viscera, rest) to determine water, protein, fat, and ash content by chemical analyses. Fat content of carcasses varied between 0.8 and 17.4%. Multiple linear regression models were selected to predict carcass composition from morphologic and mass measurements. Two situations were considered: measurements taken at the laboratory on whole animals and measurements taken at field stations on eviscerated carcasses provided by hunters. All selected models can be applied to any deer without taking into account age or sex; they include 1 – 4 independent variables. For whole animals, adjusted R2 of models varied between 0.99 (water) and 0.89 (ash); models developed for field stations were less precise, the lowest R2 values being 0.82 and 0.73 for ash and fat, respectively. These models can be useful for research and management purposes.

scholarly journals Sonographic Cervical Shortening after Labor Induction is a Predictor of Vaginal Delivery

2016 ◽  
Vol 38 (12) ◽  
pp. 585-588 ◽  
Author(s):  
Ugo Indraccolo ◽  
Gennaro Scutiero ◽  
Pantaleo Greco
Keyword(s):  
Logistic Regression ◽  
Vaginal Delivery ◽  
Regression Models ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Cervical Length ◽  
Labor Induction ◽  
Logistic Regression Models ◽  
Independent Variables ◽  
Sonographic Evaluation

Objective Analyzing if the sonographic evaluation of the cervix (cervical shortening) is a prognostic marker for vaginal delivery. Methods Women who underwent labor induction by using dinoprostone were enrolled. Before the induction and three hours after it, the cervical length was measured by ultrasonography to obtain the cervical shortening. The cervical shortening was introduced in logistic regression models among independent variables and for calculating receiver operating characteristic (ROC) curves. Results Each centimeter in the cervical shortening increases the odds of vaginal delivery in 24.4% within 6 hours; in 16.1% within 24 hours; and in 10.5% within 48 hours. The best predictions for vaginal delivery are achieved for births within 6 and 24 hours, while the cervical shortening poorly predicts vaginal delivery within 48 hours. Conclusion The greater the cervical shortening 3 hours after labor induction, the higher the likelihood of vaginal delivery within 6, 24 and 48 hours.

Social Structure and Self-Direction: A Comparative Analysis of the United States and Poland

1993 ◽  
Vol 44 (2) ◽  
pp. 347
Author(s):  
Geoff Evans ◽  
Melvin L. Kohn ◽  
Kazimierz M. Slomcynski
Keyword(s):  
United States ◽  
Comparative Analysis ◽  
Social Structure ◽  
The United States

Spatial Distribution of Snow Storage and Snowmelt Dynamics in Central Yenisei Siberia

2021 ◽  
pp. 82-92
Author(s):  
I. V. Danilova ◽  
◽  
A. A. Onuchin ◽  
◽  
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Sea Level ◽  
Thermal Regime ◽  
Regression Models ◽  
Weather Station ◽  
Digital Maps ◽  
Independent Variables ◽  
Snow Storage

In this paper the spatial distribution of water reserves in the snow cover and the dynamics of snow cover melting due to the peculiarity of the thermal regime were analyzed for the central part of Yenisei Siberia. To create digital maps of water reserves in the snow cover, regression models were developed. The geographic coordinates, elevation above sea level and the distance from the orographic boundaries were used as independent variables in regression models. Based on the created maps, the dynamics of snow cover melting was obtained in the study area, taking into account the thermal regime at a key weather station.

Sign in / Sign up

Export Citation Format

Share Document