Explaining NSW long term trends in property and violent crime

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

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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)

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Property crime (theft robbery) NSW 95-07
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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

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Violent recorded crime NSW 95-07
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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

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Predictors

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

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General Models
First equation
Second equation

• Trends (quadratic, cubic)
• Seasonality (months, weekends)
• Police and Justice (POIs by status)
• Exogenous influences (economy, drugs)

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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

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Property crime POI trends
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Violent crime POI trends
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Model results (Violent offences)
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Forecasts Violent offences
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Forecasts Violent offences
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Forecasts Violent offences
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Model results (Property offences)
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Forecasts Property offences
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Forecasts Property offences
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Forecasts Property offences
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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

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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

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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

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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