Macquarie-University Models

These models and simulations have been tagged “Macquarie-University”.

Bourke Youth Crime Model This simple model is designed to simulate crime amongst youth in the country town of Bourke, Australia. Bourke has a youth population (under 24 years of age) of 998 (ABS, 2015). These 998 persons begin arbitrarily split between the general population [Bourke Youth]
Bourke Macquarie University Crime
Bourke Youth Crime Model

This simple model is designed to simulate crime amongst youth in the country town of Bourke, Australia.

Bourke has a youth population (under 24 years of age) of 998 (ABS, 2015). These 998 persons begin arbitrarily split between the general population [Bourke Youth], youth in the football club [Football Club], youth engaged in criminal activity [Criminals] and incarcerated youth [Prisoners]

The rates of transfer between these blocks are determined by several logical mechanisms which are explained below. All calculations are rounded for the obvious reason that populations are necessarily integer values. To facilitate investigation into the interaction between variables, only Police and Funding are adjustable. Any other inputs would unnecessarily complicate the model, and degrade its usability and usefulness.

Observations:
Police and Funding have an interaction that determines the outcome for criminals in the simulation. At a funding multiplier of 1 (standard) and with minimum police, Criminals outnumber youths by the end of the simulation. As the funding is decreased, this threshold increases until a funding multiplier of 0.2, where even the maximum number of police cannot control the criminal population.

Perhaps most interestingly, the equilibrium prisoner population depends on the sports club funding multiplier, not the number of police.

An interesting comparison can be found between setting the funding multiplier to 1.5 and police to 100, and setting funding to 0.3 with police at 225. This comparison is an ideal use for this model, as it reflects the benefits from community engagement seen in the case study.

Rates:
Commit Crime: The crime rate in Bourke is modelled to be dependent on several factors, principally the number of police in Bourke (a greater police presence will reduce crime). It is also assumed that a greater general youth population will increase the rate of crime, and that participation in the football club (or interaction with other engaged community members) will discourage crime. For these reasons, the rate of criminalisation is modelled with the equation: 
Round([Bourke Youth]^2/([Football Club]*[Police]+1))

Arrested: The arrest rate is determined by a factor of the number of police available to charge and arrest suspects, as well as the number of criminals eligible for arrest. A natural logarithm is taken for police, as police departments should see diminishing returns in adding more officers. A logarithm is also taken of criminals to allow it to factor into the rate without swamping the effect of police. Thus, the rate is calculated with:
Round(ln([Police]+1)*5*log([Criminals]+1))

Released: The release rate is a straightforward calculation; it is set to increase with the square of the number of prisoners to keep the maximum number of inmates low. This is because Bourke is a small town with a small gaol and it would have to prematurely release inmates as the inmate population overflowed. Thus it is calculated with:
Round(0.001*[Prisoners]^2)

Recruited: The Football recruitment rate is assumed to be dependent on the population available for recruitment, and the funding received for the football club. A better funded club would recruit youths in greater numbers. Consequently, the recruitment rate is calculated with:
Round(ln([Bourke Youth]+1)*[Funding Modifier]+1)

Dropout Rate: The dropout rate from the football club is assumed to be dependent on the number of players (a proportion should quit every season) and the funding of the club (a well funded club should retain more players. Thus it is calculated with:
Round(1+ln([Football Club]*10/([Funding Modifier]+5)))

Self Adjust: A small leak flow to represent those criminals that cease their criminal activity and return to the general population.

Enjoy!
- Sam
Clone of 44910304 - Crime in Bourke
Andy West
Bourke Youth Crime Model This simple model is designed to simulate crime amongst youth in the country town of Bourke, Australia. Bourke has a youth population (under 24 years of age) of 998 (ABS, 2015). These 998 persons begin arbitrarily split between the general population [Bourke Youth]
Crime Macquarie University Bourke
Bourke Youth Crime Model

This simple model is designed to simulate crime amongst youth in the country town of Bourke, Australia.

Bourke has a youth population (under 24 years of age) of 998 (ABS, 2015). These 998 persons begin arbitrarily split between the general population [Bourke Youth], youth in the football club [Football Club], youth engaged in criminal activity [Criminals] and incarcerated youth [Prisoners]

The rates of transfer between these blocks are determined by several logical mechanisms which are explained below. All calculations are rounded for the obvious reason that populations are necessarily integer values. To facilitate investigation into the interaction between variables, only Police and Funding are adjustable. Any other inputs would unnecessarily complicate the model, and degrade its usability and usefulness.

Observations:
Police and Funding have an interaction that determines the outcome for criminals in the simulation. At a funding multiplier of 1 (standard) and with minimum police, Criminals outnumber youths by the end of the simulation. As the funding is decreased, this threshold increases until a funding multiplier of 0.2, where even the maximum number of police cannot control the criminal population.

Perhaps most interestingly, the equilibrium prisoner population depends on the sports club funding multiplier, not the number of police.

An interesting comparison can be found between setting the funding multiplier to 1.5 and police to 100, and setting funding to 0.3 with police at 225. This comparison is an ideal use for this model, as it reflects the benefits from community engagement seen in the case study.

Rates:
Commit Crime: The crime rate in Bourke is modelled to be dependent on several factors, principally the number of police in Bourke (a greater police presence will reduce crime). It is also assumed that a greater general youth population will increase the rate of crime, and that participation in the football club (or interaction with other engaged community members) will discourage crime. For these reasons, the rate of criminalisation is modelled with the equation: 
Round([Bourke Youth]^2/([Football Club]*[Police]+1))

Arrested: The arrest rate is determined by a factor of the number of police available to charge and arrest suspects, as well as the number of criminals eligible for arrest. A natural logarithm is taken for police, as police departments should see diminishing returns in adding more officers. A logarithm is also taken of criminals to allow it to factor into the rate without swamping the effect of police. Thus, the rate is calculated with:
Round(ln([Police]+1)*5*log([Criminals]+1))

Released: The release rate is a straightforward calculation; it is set to increase with the square of the number of prisoners to keep the maximum number of inmates low. This is because Bourke is a small town with a small gaol and it would have to prematurely release inmates as the inmate population overflowed. Thus it is calculated with:
Round(0.001*[Prisoners]^2)

Recruited: The Football recruitment rate is assumed to be dependent on the population available for recruitment, and the funding received for the football club. A better funded club would recruit youths in greater numbers. Consequently, the recruitment rate is calculated with:
Round(ln([Bourke Youth]+1)*[Funding Modifier]+1)

Dropout Rate: The dropout rate from the football club is assumed to be dependent on the number of players (a proportion should quit every season) and the funding of the club (a well funded club should retain more players. Thus it is calculated with:
Round(1+ln([Football Club]*10/([Funding Modifier]+5)))

Self Adjust: A small leak flow to represent those criminals that cease their criminal activity and return to the general population.

Enjoy!
- Sam
Clone of 44910304 - Crime in Bourke
JAMES SAVAGE