Title: Measuring the unmeasurable - a project of domestic violence risk prediction and management,
Abstract: The prevention of domestic violence (DV) have aroused serious concerns in
Taiwan because of the disparity between the increasing amount of reported DV
cases that doubled over the past decade and the scarcity of social workers.
Additionally, a large amount of data was collected when social workers use the
predominant case management approach to document case reports information.
However, these data were not properly stored or organized.
To improve the efficiency of DV prevention and risk management, we worked
with Taipei City Government and utilized the 2015 data from its DV database to
perform a spatial pattern analysis of the reports of DV cases to build a DV
risk map. However, during our map building process, the issue of confounding
bias arose because we were not able to verify if reported cases truly reflected
real violence occurrence or were simply false reports from potential victim's
neighbors. Therefore, we used the random forest method to build a repeat
victimization risk prediction model. The accuracy and F1-measure of our model
were 96.3% and 62.8%. This model helped social workers differentiate the risk
level of new cases, which further reduced their major workload significantly.
To our knowledge, this is the first project that utilized machine learning in
DV prevention. The research approach and results of this project not only can
improve DV prevention process, but also be applied to other social work or
criminal prevention areas.