Abstract

The current study investigates the effects of coronavirus restrictions on family violence in the seventh largest city in the country, San Antonio, Texas. Two streams of data were used to evaluate the potential change between what occurred during the lockdown period versus what would have been expected, including the COVID-19 Government Response Stringency Index and police calls for service from the San Antonio Police Department. The methodological approach used takes advantage of feature engineering, various machine learning time series forecasting techniques commonly leveraged in financial technical analysis, as well as cross-validation for optimized model selection. These techniques have not been considered in previous domestic or family violence-related research. During the lockdown period in San Antonio, we observed a larger than expected increase in calls to police for family violence incidents. Specifically, an increase of over fourteen percent of police calls for family incidents was observed. The findings of the current study suggest that social service and social welfare agencies consider and plan for how future pandemics or other major disasters will affect the incidence of family violence and take appropriate steps now to bolster resources and scale up for the future.

Full Text
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