Lecture 24 Simulation Crime and Crime Pattern Using Cellular Automata and GIS (Cont.)24-2 MIF Example24-3 Implementation of Crime and Crime Pattern Simulation24-3 Implementation of Crime and Crime Pattern Simulation (Cont.)Slide 524-4 Scenario 1 – Offenders all in one cell, with different motivation valuesSlide 724-5 Scenario 2 - Targets all locate at the same neighborhood, with different values of variables. Offenders sparsely distributed.Slide 9Slide 10Slide 1124-6 Targets sparsely distributed, with identical parameter setting. Offenders distribute sparsely.Slide 13Slide 14Slide 1519/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC11Lecture 24 Simulation Crime and Crime Pattern Using Cellular Automata and GIS (Cont.)24-1 Relationships between tension and target variables19/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC2224-2 MIF Example24-2 MIF Example19/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC3324-3 Implementation of Crime and Crime 24-3 Implementation of Crime and Crime Pattern SimulationPattern Simulation19/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC4424-3 Implementation of Crime and Crime 24-3 Implementation of Crime and Crime Pattern Simulation (Cont.)Pattern Simulation (Cont.)19/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC5524-3 Implementation of Crime and Crime 24-3 Implementation of Crime and Crime Pattern Simulation (Cont.)Pattern Simulation (Cont.)The following complexity equation, n is the number of raster data rows. m is the number of raster data columns. x is the number of targets. y is the number of offenders. For a k-loop simulation, the complexity is: W(n,m,x,y) = (19n*m+6x+2xy+10y)k When n=m=100, k=100, x=y=10, it takes 5 minutes on a Pentium 3-500.n=m=1000, k=1000, it takes 3.5 days on the same hardware.19/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC6624-4 Scenario 1 – Offenders all in one cell, with 24-4 Scenario 1 – Offenders all in one cell, with different motivation valuesdifferent motivation valuesThis assumption is designed to test the impacts of offender motivation towards crime & crime patterns, since there is no spatial difference for all offenders – they all locate in the same cell. Motivation values of all offenders are set between 30-45, and the distance decay coefficient is set to 2.19/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC7719/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC8824-5 Scenario 2 - Targets all locate at the same 24-5 Scenario 2 - Targets all locate at the same neighborhood, with different values of variables. neighborhood, with different values of variables. Offenders sparsely distributed.Offenders sparsely distributed.Success Rates Against Desirability0510152025303520 30 40 50 60 70 80 90DesirabilityNumber of success19/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC99Manager effectiveness and offender success times012345620 40 60 80 100Manager EffectivenessOffender Success Times19/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC101019/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC1111Guardian capabilities and offender success times02468101220 30 40 50 60 70 80 90Guardian CapabilityOffender Success Times19/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC121224-6 Targets sparsely distributed, with identical 24-6 Targets sparsely distributed, with identical parameter setting. Offenders distribute sparsely.parameter setting. Offenders distribute sparsely.Repeat offense and repeat victimization.19/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC131319/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC1414Distribution of distance between targets and offenders19/1/1319/1/13Jun Liang, Geography @ UNCJun Liang, Geography @ UNC1515Distance distribution of successful