Sentiment and Emotions of Olympic Themed Tweets for Data in Brief companion article

For description and associated code files please view companion paper titled (under review): "Data on sentiments and emotions of Olympic-themed Tweets" in journal Data in Brief For interpretation and analysis, please view research article (under review): "Good Games, bad host? Using big data to measure public attention and imagery of the Olympic Games

Animal Cruelty and Neighborhood Conditions

Background: Animal cruelty appears to be widespread. Competing theories have been posed regarding the causes of animal cruelty leading to conflicting findings and little direction for public policies to combat it. Objective: To assess the applicability of extant theories of the causes of animal cruelty: domestic violence; deviance; perpetrator traits; and social disorganization. Methods: Data are drawn from police department reports of animal cruelty in the City of Detroit from 2007 to 2015; 302 incidences of animal cruelty were reported. Multiple regression is used to determine the theory which best appears to account for animal cruelty. Results: Common types of animal cruelty in Detroit are shooting; blunt force trauma; neglect; and dogfighting. While most incidents involve unknown persons; cruelty by owners; neighbors; and domestic partners is also common. Neighborhood conditions in terms of economic stress; vacancy and blight; and crime appear to have the greatest impact on animal cruelty. Conclusions: The findings from Detroit support deviance and social disorganization theories of animal cruelty. Neighborhood conditions in terms of economic stress, vacancy and blight, and crime appear to have the greatest impact on animal cruelty in this urban area