TIME SERIES CRIME PREDICTION ANALYSIS USING RNN: A CASE OF WOLKITE CITY POLICE DEPARTMENT

Abstract

Crime is an undesirable phenomenon and a global concern that impacts both society and individuals. Annually, we observe an increase in the number of criminal incidents, posing a threat to both public safety and the well-being of the community. Demanding facilities at unequal times is one problem observed in police workforce assignment. Our study aims to determine and examine the relationship between Crime date-time and the number of Crime incidents, as well as their types and locations. We collected nine thousand eight hundred twenty (9,820) criminal offenses handled by Wolkite City ranging from 2008-to-2014 E.C and we include seven (7) most frequently occur Crime types and fifty-two (52) Crime locations in our study. Different preprocessing techniques are applied such as label encoder and Minmax scaler. And we employed RNN models, including Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Long Short-Term Memory and Bidirectional Gated Recurrent Unit, alsotrain the models using training dataset and predict Crime type and location, finally evaluate the model’s using metrics like MSE, R2and others by testing dataset. For Crime type prediction LSTM has MSE of 0.0125, 0.0126 and 0.0468, Bi-LSTM has MSE of 0.0126, 0.0125 and 0.0466, GRU has MSE of 0.0127, 0.0128 and 0.0501, Bi-GRU has MSE of 0.0126, 0.026, and 0.0468, for hourly, daily and monthly respectively for each model. For Crime location prediction LSTM has MSE of 0.0108, 0.0109 and 0.0617, Bi- LSTM has MSE of 0.0108, 0.0110and 0.0506, GRU has MSE of 0.0106, 0.0105 and 0.0582, Bi-GRU has MSE of 0.0105, 0.0106, and 0.0513, for hourly, daily and monthly respectively for each model. For Crime type prediction Bi-GRU, Bi-LSTM, LSTM, GRU perform R2of 0.9995, 0.9994, 0.9899, and 0.9811respectively. Fo Crime location prediction Bi-LSTM, LSTM, Bi-Bi-GRU and GRU gained R2of 0.9938 0.9937, 0.9937 and 0.9934, respectively. For hourly Crime type prediction LSTM is slightly better and for daily and monthly Bi-LSTM is better. For hourly and monthly Crime location Bi-GRU is slightly better and for daily, GRU slightly better. In terms of R2, Bi-GRU slightly higher score than others for Crime type and for Crime location Bi-LSTM is slightly higher R2values. In general, Bi-LSTM and Bi-GRU gained better score for Crime prediction with low error for our dataset.