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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. Purpose of research. Determine the general structure of trends and seasonality

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explaining nsw long term trends in property and violent crime
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
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
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)
background violent crime
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]

background summary
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
Predictors
  • Seasonality and month characteristics
  • Police and Justice
    • Police activity, incapacitation, deterrence
  • Alcohol and drug use
  • Economic cycles
general models

General Models

First equation:

Second equation:

Trends (quadratic, cubic)

Seasonality (months, weekends)

Police and Justice (POIs by status)

Exogenous influences (economy, drugs)

model characteristics
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
model selection and forecast accuracy
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
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
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
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
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