This Model showcases the interplay between the Youth and Crime in Bourke with a monopoly of contributing factors.
The Groups and factors are explained in depth below.
With a total population of 3000 the the total youth population is estimated to 2000.
Youth: Initialing this encompasses 2000 individuals aged 15-25 years that are currently living in Bourke.
Crime: this denotes the number of youth involved in crime
Conviction: this includes youth that have been caught after committing a crime.
Youth in detention: the number of youth that have been detained for a period of time after being convicted of a crime.
Community program involvement: The number of youth that participate in community programs.
FACTORS (affecting the groups above)
Crime Rate: the rate at which youth commit a crime, this variable based upon 'alienation'.
Caught: the ratio of youth committing crime are caught. This is variable based upon the 'policing expenditure'
Alienation: a ratio reflecting the division between Bourke's youth and law enforcement. This variable is based upon the level of policing expenditure and community program expenditure.
Policing Expenditure: The respective amount of money (000's of dollars) allocated to tackle youth crime.
SLIDER
min: 5 ($5,000)
max: 50 ($50,000)
Community Program Expenditure: The respective amount of money (000's of dollars) allocated running community programs aimed to help Bourke's youth population.
SLIDER
min: 0 ($0)
max: 40 ($40,000)
By adjusting the policing and community program expenditure parameters using the sliders shows fluctuations between the groups. For example, by increasing policing expenditure will increase alienation, subsequently the crime rate will increase. Furthermore an increase in the proportion of youth committing crime that are caught, subsequently increasing the youth being convicted and in detention.
On the other using the slider to increase the expenditure on community programs, will see increase in the the number of youth involved in the programs. Also the level of alienation will decrease and subsequently reflecting in a reduced crime rate.
Time variable used in this model is Months. This is shown in the relative simulations.