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Lecture 23 Simulation Crime and Crime Pattern Using Cellular Automata and GIS

Lecture 23 Simulation Crime and Crime Pattern Using Cellular Automata and GIS. 23-1 Spatial criminology

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Lecture 23 Simulation Crime and Crime Pattern Using Cellular Automata and GIS

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  1. Lecture 23 Simulation Crime and Crime Pattern Using Cellular Automata and GIS 23-1 Spatial criminology Early studies were limited to macro level approaches. Crime facts were aggregated at the neighborhood level. In the 19th century, both offender and target (or place) were aggregated at different levels - neighborhood, cities, counties, or states. At this period, little was done to reveal the relationship between specific places and crime. Since the 1960s, criminologists have developed theories to understand crime events at the micro level – a place, a target, or even a street address. Jun Liang, Geography @ UNC

  2. 23-1 Spatial criminology (Cont.) From the view of environmental criminology, criminal events must be understood as results of offenders, victims or criminal targets, and laws in specific setting at particular times and places. RAT studies crime events at the micro level, which is even more detailed than environmental criminology. Four conditions must be met for a ‘successful’ crime event: a motivated offender; a desirable target; occupying the same place and time; and controllers (such as handlers, guardians and place managers) must be absent. Jun Liang, Geography @ UNC

  3. 23-2 Crime Patterns Basic facts about crime patterns: • Crime events are rare. The chances of a crime at a specific location during a given short time interval is very small. • At any geographic scale, many units have few or no crime events and a few have many crime events. • At any temporal scale, many time intervals have few crime events and a few have many crime events. Jun Liang, Geography @ UNC

  4. 23-2 Crime Patterns (Cont.) • Retrospectively, crime patterns are easy to detect. • The larger the area selected or the longer the time interval selected, the more obvious the crime pattern. • Prospectively, precise crime patterns are difficult to anticipate but the vaguer the pattern that is acceptable, the easier it will be predict. Jun Liang, Geography @ UNC

  5. 23-3 A Probabilistic Approach of RAT Situation S is defined as (t: time, i: place, j: offender, k: offense, v: person). Targets: T Desirability:  Guardians: G Capability:  Offenders: O Motivation: μ Handlers: H Intimacy: β Places: P Accessibility:  Managers: M Effectiveness:  Jun Liang, Geography @ UNC

  6. 23-3 A Probabilistic Approach of RAT (Cont.) The possibility equation can be used to evaluate crime pattern at time t. Limitations: • Static model • Local model • Could not simulate interactions between offenders and targets • Could not reveal relationships between crime patterns Jun Liang, Geography @ UNC

  7. 23-4 Tension To figure out the transition function, we need to understand the changes of interaction between offenders and targets over space and time, and also interaction among targets, and offenders. Jun Liang, Geography @ UNC

  8. 23-4 Tension (Cont.) In order to let targets interact with offenders/other targets over space, the concept of “tension” has been developed in this research. Generally, tension is crime anticipation of targets. Jun Liang, Geography @ UNC

  9. 23-5 Modification of the crime likelihood formula Since only commercial property robbery is considered here, we may simplify the original formula: Offense (k) and person (v) have been removed from likelihood formula. Also victims, guardians, and managers are incorporated into the place, and there is no reason to track individual people. Place accessibility () has also been dropped for simplicity, and because it could be incorporated in the concept of place management. Jun Liang, Geography @ UNC

  10. 23-5 Modification of the crime likelihood formula (Cont.) • Assumptions about offender movement and target reactions to crimes - • Offender movement is assumed to follow Monte Carlo simulation, and • Targets adjust their protection level against offenders according to their expectations of crime. Target reaction follows the CA model. Jun Liang, Geography @ UNC

  11. 23-5 Modification of the crime likelihood formula (Cont.) Thus, the offender factor has been isolated from (3) and simulated separately. Without the offender factor in (3), the likelihood of crime is changed to protection index (PVI) of target. Jun Liang, Geography @ UNC

  12. 23-5 Modification of the crime likelihood formula (Cont.) A new crime likelihood formula (5) - Ln(S)ct is the modified crime likelihood evaluation equation. The modified crime likelihood will compare the value of the motivation and prevention index. If is greater than PVI, then a crime will occur. Jun Liang, Geography @ UNC

  13. 23-6 Offender Movement Simulation The movements of offenders are simulated with a Monte Carlo simulation. The probability of an offender appearing at a particular target is inverse to its distance from the target. The probability matrix of contacts (presence of offenders at targets) between target group and offender group forms the MIF (Mean Information Field). MIF is used to determine offender’s movements. The shorter the distance between a target and an offender the higher the probability of contact. Jun Liang, Geography @ UNC

  14. 23-6 Offender Movement Simulation (Cont.) Two conditions are required for crime to occur at a target: • Presence of at least one offender • Ln(S)ct is positive, otherwise the crime will not occur. Whether a crime occurs successfully or not is determined by another random barrier () and Ln(S)ct. Jun Liang, Geography @ UNC

  15. 23-7 Target Reaction CA Model State Variable Tension serves as the CA state variable. Other target variables, which include desirability, capability, and effectiveness, can be calculated from tension directly or indirectly, based on their relationship with tension. Jun Liang, Geography @ UNC

  16. 23-7 Target Reaction CA Model (Cont.) The transition function f varies under three different situations: • No crime occurs successfully and significant tension difference exists between cell c and its neighbors; • No crime occurs successfully and no significant tension difference exists between cell c and its neighbors; • Crime occurs successfully. Jun Liang, Geography @ UNC

  17. 23-7 Target Reaction CA Model (Cont.) The relationship between TS and δ is assumed to be linear: Jun Liang, Geography @ UNC

  18. 23-7 Target Reaction CA Model (Cont.) Management is assumed as a positive function of target desirability. Greater property value indicates greater desirability, and greater return on investment, indicating higher management effectiveness. Jun Liang, Geography @ UNC

  19. 23-7 Target Reaction CA Model (Cont.) In crime theory, guardianship is defined as a function of victimization of the place.  will increase, as tension increases, in order to prevent the reoccurrence of crime. Jun Liang, Geography @ UNC

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