強姦犯の犯罪深度を基にしたベイジアンネットワークモデルによる犯行時期に関する予測

抄録

  The relationship between serial rapist type and the time interval from the first to the second crime was investigated in Study 1. A Bayesian Network (BN) model, included rapists' type derived in Study 1 and the time interval from the first to the second crime, was constructed to predict the time interval till the second crime, and this model was tested in Study 2.
  In Study 1, 147 serial rapists were classified according to the severity of the rapists' criminality by using categorical principal components analysis (CatPCA). Results indicated that the interval from the first to the second crime for rapists with a low severity of the criminality (mean 158.0 days) was longer than that for rapists with a high severity of the criminality (mean 82.1 days). In Study 2, A BN model was constructed from the following variables: rapists' characteristic derived in Study 1 (the severity of the criminality), time interval from the first to the second crime, as well as criminal behaviors. The model was tested by using new data, 20 serial rapists committed, as virtual cases. The results of model estimation indicated that the accuracy of predicting the time interval from the first to the second crime, either within 42 days or later, was 80.0%. In detail, if the model predicted that a rapist would commit the second crime in less than 42 days, the accuracy of the prediction was 75.0%, whereas if the model predicted that a rapist would commit the second crime in over 42 days, the accuracy of the prediction was 87.5%.