Explaining NSW long term trends in property and violent crime Steve Moffatt and Lucy Snowball NSW Bureau of Crime Statistics and Research

Purpose of research Determine the general structure of trends and seasonality Explain some exogenous influences on crime trends, particularly those useful for forecasting Forecasts for state and regions Test scenarios

Background ~ property crime Long term rise (1990s) followed by fall in property crime recorded incidents since 2000 –Motor vehicle theft, steal from motor vehicle, dwelling, retail store, person –Robbery –Break and enter –Receiving/handling stolen goods –Fraud (stabilised after rise)

Property crime (theft + robbery) NSW 95-07

Background ~ violent crime Steep rise (1990s) followed by flattening rise since 2001 in violent crime recorded incidents –Assault –Sexual assault –Harassment –Other offences against the person [Stable or falling murder, attempted murder, manslaughter, blackmail, extortion ]

Violent recorded crime NSW 95-07

Background ~ Summary Fall in property crime incidents Coincided with continuation of upward trend in violent crime incidents Demand for short term forecasting at state and local area level Previous trend research has focused more on property crime Few clues on why violent crime trend persisting, recent focus on alcohol related assaults

Predictors Seasonality and month characteristics Police and Justice –Police activity, incapacitation, deterrence Alcohol and drug use Economic cycles

General Models Trends ( quadratic, cubic ) Seasonality ( months, weekends ) Police and Justice ( POIs by status ) Exogenous influences ( economy, drugs ) First equation: Second equation:

Model characteristics Violent offences model in levels (ARMA) –Quadratic trend Property offences in differences (ARIMA) –Cubic trend Lagged dependent variable or POI variables by status MA(1) error term

Property crime – POI trends

Violent crime – POI trends

Model results (Violent offences)

Forecasts – Violent offences

Model results (Property offences)

Forecasts – Property offences

Model selection and forecast accuracy Stationarity of dependent variable Most appropriate trend MLE ARMA/ARIMA Log likelihood and Wald Chi Sq Error tests and RMSE for forecast

Accuracy vs. Parsimony Over fitting (including non significant variables) improves forecast accuracy However reduction in significance of model Fit for purpose: –Overfitted models useful for forecasting –Parsimonious models useful for determining which factors influence long term trends

Conclusions Can achieve well fitting models for violent and property crime with good forecasting power Majority of trend explained using structure (quadratic or cubic), seasonal (month) terms Weekend dummy and summer months a good proxy for alcohol consumption POIs (clear-up variables) act as a control for autocorrelation

Next steps Report state level trends, seasonal components and influences to NSW Police Project models from state level to regional level –Demand at local area command level Panel data sets for regions Develop models for other crimes, particularly high volume offences that are resilient to police activity –Malicious damage –Assault (domestic violence related and non-domestic violence) –Harassment