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Predictors of firearm violence in urban communities: A machine-learning approach
- Publication Year :
- 2018
-
Abstract
- Interpersonal firearm violence is a leading cause of death and injuries in the United States. Identifying community characteristics associated with firearm violence is important to improve confounder selection and control in health research, to better understand community-level factors that are associated with firearm violence, and to enhance community surveillance and control of firearm violence. The objective of this research was to use machine learning to identify an optimal set of predictors for urban interpersonal firearm violence rates using a broad set of community characteristics. The final list of 18 predictive covariates explain 77.8% of the variance in firearm violence rates, and are publicly available, facilitating their inclusion in analyses relating violence and health. This list includes the black isolation and segregation indices, rates of educational attainment, marital status, indicators of wealth and poverty, longitude, latitude, and temperature.
- Subjects :
- Male
Firearms
Health (social science)
Adolescent
Urban Population
Geography, Planning and Development
Ethnic group
Interpersonal communication
030204 cardiovascular system & hematology
Violence
Machine learning
computer.software_genre
Article
Machine Learning
03 medical and health sciences
0302 clinical medicine
Age Distribution
Cause of Death
Poverty Areas
Ethnicity
Humans
030212 general & internal medicine
Sex Distribution
Set (psychology)
Poverty
business.industry
Public Health, Environmental and Occupational Health
Variance (land use)
Educational attainment
United States
Marital status
Female
Artificial intelligence
Psychology
business
Inclusion (education)
computer
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....1070bb35fa5147956c4161e32fd4d373