Hate crimes are a culture phenomenon which is perceived by most as an occurrence that should be uprooted from the society. Yet, to date, we have been unable to do so. Hate crimes are the subject of research and commen...Hate crimes are a culture phenomenon which is perceived by most as an occurrence that should be uprooted from the society. Yet, to date, we have been unable to do so. Hate crimes are the subject of research and comments by experts in various fields. In this regard, most scholars agree that a hate based crime is distinguished from a ＂regular＂ criminal offence by the motive--the attack is aimed at a victim who is part of a differentiated minority group. However, when reading the relevant documents in the area, it seems that the differences between the experts start at the most basic point--what constitutes hate crimes？ This article analyses the concept of ＂hate crimes＂ via an interdisciplinary approach aimed at flashing out the fundamental gaps in the research. We have found that the problems include, inter alia, discrepancies in the definition of hate crimes, methodological difficulties regarding validity and legitimacy （mainly due to the absence of information based on the attacker＇s point of view） and the lack of agreement on the appropriate legal methods required to deal with the ramifications of hate crimes. While part I of this paper revolves around the theoretical aspects of the questions put forth at the centre of this article, part II looks at the same questions from a legal viewpoint. The correlation between the two chapters shows the impact the methodological difficulties have on enforcement endeavors. This relation is further advanced through the examination of test cases from different countries, among them--lsrael. Finally, the article concludes by suggesting a few thoughts on the way to overcome the theoretical problems and making the enforcement efforts more efficient.展开更多
Spatial prediction of any geographic phenomenon can be an intractable problem.Predicting sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data sources.We...Spatial prediction of any geographic phenomenon can be an intractable problem.Predicting sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data sources.We present an innovative approach that combines data in a Discrete Global Grid System(DGGS)and uses machine learning for analysis.A DGGS provides a structured input for multiple types of spatial data,consistent over multiple scales.This data framework facilitates the training of an Artificial Neural Network(ANN)to map and predict a phenomenon.Spatial lag regression models(SLRM)are used to evaluate and rank the outputs of the ANN.In our case study,we predict hate crimes in the USA.Hate crimes get attention from mass media and the scientific community,but data on such events is sparse.We trained the ANN with data ingested in the DGGS based on a 50%sample of hate crimes as identified by the Southern Poverty Law Center(SPLC).Our spatial prediction is up to 78%accurate and verified at the state level against the independent FBI hate crime statistics with a fit of 80%.The derived risk maps are a guide to action for policy makers and law enforcement.展开更多
文摘Hate crimes are a culture phenomenon which is perceived by most as an occurrence that should be uprooted from the society. Yet, to date, we have been unable to do so. Hate crimes are the subject of research and comments by experts in various fields. In this regard, most scholars agree that a hate based crime is distinguished from a ＂regular＂ criminal offence by the motive--the attack is aimed at a victim who is part of a differentiated minority group. However, when reading the relevant documents in the area, it seems that the differences between the experts start at the most basic point--what constitutes hate crimes？ This article analyses the concept of ＂hate crimes＂ via an interdisciplinary approach aimed at flashing out the fundamental gaps in the research. We have found that the problems include, inter alia, discrepancies in the definition of hate crimes, methodological difficulties regarding validity and legitimacy （mainly due to the absence of information based on the attacker＇s point of view） and the lack of agreement on the appropriate legal methods required to deal with the ramifications of hate crimes. While part I of this paper revolves around the theoretical aspects of the questions put forth at the centre of this article, part II looks at the same questions from a legal viewpoint. The correlation between the two chapters shows the impact the methodological difficulties have on enforcement endeavors. This relation is further advanced through the examination of test cases from different countries, among them--lsrael. Finally, the article concludes by suggesting a few thoughts on the way to overcome the theoretical problems and making the enforcement efforts more efficient.
文摘Spatial prediction of any geographic phenomenon can be an intractable problem.Predicting sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data sources.We present an innovative approach that combines data in a Discrete Global Grid System(DGGS)and uses machine learning for analysis.A DGGS provides a structured input for multiple types of spatial data,consistent over multiple scales.This data framework facilitates the training of an Artificial Neural Network(ANN)to map and predict a phenomenon.Spatial lag regression models(SLRM)are used to evaluate and rank the outputs of the ANN.In our case study,we predict hate crimes in the USA.Hate crimes get attention from mass media and the scientific community,but data on such events is sparse.We trained the ANN with data ingested in the DGGS based on a 50%sample of hate crimes as identified by the Southern Poverty Law Center(SPLC).Our spatial prediction is up to 78%accurate and verified at the state level against the independent FBI hate crime statistics with a fit of 80%.The derived risk maps are a guide to action for policy makers and law enforcement.
