1. Lin Liu, Ph.D. Department of Geography University of Cincinnati Cincinnati, OH 45221-0131 Email: lin.liu@uc.edu The Role of Street Network in Crime Analysis and Simulation

2. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA2 Who am I? Education: Education: –Geomorphology –Remote Sensing and Mapping –GIS, Economic Geography –Computer Science A Barbarian by the Eck definition? A Barbarian by the Eck definition? –Haven’t invaded the CJ territory far enough How did I get into this? How did I get into this?

3. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA3 Motivation - Geographic Scale Matters Recent attempt on modeling aggressive policing by a graduate advisee Recent attempt on modeling aggressive policing by a graduate advisee –Hypothesis: a higher rate of arrests on minor offense (A) leads to reduction on serious crime rate (C) –Previous studies suggest that the correlation between A and C, r(A,C), is negative, when individual cities are used as the unit of analysis –However, r(A,C) is positive if the unit of analysis is neighborhood areas in a city –Why is one positive while the other negative? »Models variant of geographic scale? »Perhaps the neighborhood model should be:

4. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA4 Outline The impact of spatial infrastructure The impact of spatial infrastructure –street network in a “racial profiling” study –water pipeline network in epidemiological study »Implication to crime studies Simulation of street robbery Simulation of street robbery Typology of crime simulation Typology of crime simulation –To be used in the crime simulation book Issues and discussions Issues and discussions

5. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA5 “Racial Profiling” in Traffic Stops  Hypothesis: are vehicles driven by minorities disproportionately stopped by police?  A pattern of disproportionality does not show the process that gives arise to the pattern. Liu, L. and J. Eck. Forthcoming. “Analyzing traffic stops in Cincinnati: A Geographic Perspective,” Geography Research Forum.

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8. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA8 Disproportionality Index (DI)  Ratio of Blacks stopped to Blacks base divided by total stopped to total base (B s/ B b / T s/ T b )  Ratio of actual proportion of stops over expected proportion (B s/ T s / B b/ T b )  This is what we use  One (1) indicates no disproportionality

9. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA9 DI Calculation  The numerator is calculated from the data on contact cards  For the denominator, many studies use Census residential population  Assumes people drive where they live  Not reasonable  We use estimated vehicle miles by race in each of the 52 neighborhoods

10. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA10 Vehicle Miles by Race  Data used  Average daily traffic counts  Field observation for the race of driver during rush hours  Trip table: # of people traveling from one TAZ to work in another TAZ  Vehicle miles  Rush hours: 23% of the average daily traffic count, race by observation and extrapolation. Equilibrium traffic assignment is used.  Work hours: 38.5% of the average daily traffic count, race by work hour population distribution  Nighttime: 38.5% of the average daily traffic count, race by census population distribution

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12. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA12 DI Calculation (Continued)  DI based on driving population  0 to 17.8 in the 52 neighborhoods  1.23 for the entire city  DI based on vehicle miles  0 to 3.24 in the 52 neighborhoods  1.36 for the entire city  The spatial distribution of these two sets of DI is very different.

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15. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA15 Summary Network model improve the estimate of the denominator Network model improve the estimate of the denominator –Cannot assess its accuracy because the truth is unknown »Can simulation help? The disproportionality index is highly variant of the denominator The disproportionality index is highly variant of the denominator A single globe measure does not help improve policing A single globe measure does not help improve policing

16. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA16 The Impact of Pipeline Network on Spatial Aggregations Study the correlation between drinking water quality and gastrointestinal illness Study the correlation between drinking water quality and gastrointestinal illness Implication on crime analysis Implication on crime analysis Swift, A., L. Liu*, J. Uber. “Reducing MAUP Bias of Correlation Statistics between Water Quality and GI Illness,” submitted to Computers, Environment and Urban Systems.

17. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA17 EPANET Hydraulic Simulation of Water Quality

18. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA18 1 Control Layer, 8 Aggregation Layers Consider both scaling and zoning effects

19. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA19 Experiment Design Question: if there were a perfect correlation between water quality (WQ) and GI illness (GI), could we assess the MAUP effect? Question: if there were a perfect correlation between water quality (WQ) and GI illness (GI), could we assess the MAUP effect? Control polygon layer, where r(WQ, GI) = 1 Control polygon layer, where r(WQ, GI) = 1 –Scenario I: point-based WQ and polygon-based GI »WQ is constrained by the pipeline network »GI incidences are randomly placed in each polygon (as if we only know the count of GI for each polygon) –Scenario II: point-based WQ and point-based GI »Both constrained by the pipeline network –r1=r2=1 Re-aggregate using different polygon layers Re-aggregate using different polygon layers –Compare r1 and r2, with the expectation of r2>r1

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21. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA21 Summary Network constrained aggregation help reduce the MAUP bias Network constrained aggregation help reduce the MAUP bias Increased r Increased r Reduced deviation of r Reduced deviation of r To what degree is the extent of bias reduction generalizeable? To what degree is the extent of bias reduction generalizeable?

22. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA22 Agent-Based Crime Simulation Integrate agent based modeling and cellular automaton to simulate street robbery events Integrate agent based modeling and cellular automaton to simulate street robbery events Spatial Adaptive Crime Event Simulation (SPACES) Spatial Adaptive Crime Event Simulation (SPACES) Liu, L. X. Wang, J. Eck and J. Liang. 2005. Simulating Crime Events And Crime Patterns In a RA/CA Model, Geographic Information Systems and Crime Analysis, edited by F. Wang. 2005. Reading, PA: Idea Publishing. Pp. 197-213. Wang, X., L. Liu and J. Eck. Forthcoming. “Crime Patterns Simulation Using GIS and Artificial Intelligent Agents,” Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems. Edited by Liu, L. and J. Eck. Idea Publishing. Liu, L. and J. Eck. Forthcoming. Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems. Idea Publishing.

23. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA23 The Routine Activity Theory For a crime to occur, a motivated offender must meet a desirable target at an accessible place OffendersTargets Places

24. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA24 Model Components Input Input –Properties/methods of agents –Environment Simulation Simulation –Interaction among agents and environment –Dynamic, stochastic, and adaptive Output Output –Crime events and crime patterns –Activities and behavior of the agents

25. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA25 The Simulation Component Integration of agent based simulation and Cellular automaton Integration of agent based simulation and Cellular automaton Cellular automaton (CA) Cellular automaton (CA) –A space-time simulation framework –Micro (local) rules lead to macro (global) changes Interaction among agents and environment Interaction among agents and environment –Agents placed on a CA –Adaptation through feedback and experience

26. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA26 A Crime CA Model State variable – Tension (or fear) State variable – Tension (or fear) –A surrogate measure of people’s reaction to crime »Global (via news media), and local (via personal experience) –A crime event increases the tension at the crime location »Decays in space and time through a wave like function Tension surface Tension surface –A crime event generates a tension wave, just like a stone generating a water wave in a pond –Multiple crime events generate multiple tension waves, which mix just like multiple water waves mixing in a pond –A tension surface consists one tension wave or multiple mixed tension waves

27. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA27 Agents in Crime Simulation Types of agents: offender, target, place manager, police Types of agents: offender, target, place manager, police Properties: characteristics of an agent Properties: characteristics of an agent –Desirability of a target, motivation of an offender, etc. –Adaptation rate: how fast does an agent learn from the past experience Methods: what an agent can do Methods: what an agent can do –Update properties –Routine activities: home  shopping  home, etc. –Adaptive learning: »Adaptation rates determine the speed of learning »Offenders and targets adjust movement patterns »Place managers adjust security measures

28. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA28 Target Agents Each crime event imposes a cost to target Each crime event imposes a cost to target –Saved at the crime location on a cost map, shared by all target agents –Cost decays in time Target cognitive map Target cognitive map –Guides spatial movement of all target agents –Generated and dynamically updated by re-enforcement learning of all target agents »Based from travel cost and crime cost – a local model »The goal is to minimize overall cost

29. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA29 Offender Agents Each crime event gives a reward to offender Each crime event gives a reward to offender –Saved at the crime location on a reward map, shared by all offender agents –Reward decays in time Offender cognitive map Offender cognitive map –Guides spatial movement of all offender agents –Generated and dynamically updated by re-enforcement learning of all offender agents »Based from travel cost and crime reward – a local model »The goal is to minimize travel cost and maximize reward

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31. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA31 What For? Not for: Not for: –Prediction For For –Examination of processes –Hypothesis generation and testing –What-if scenarios analysis –What do we learn from agent’s behavior? –What do we learn from spatial-temporal distribution of crime events?

32. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA32 Experiments Adaptive learning of targets Adaptive learning of targets –Shifting spatial movement patterns Adaptive learning of offender Adaptive learning of offender –Shifting spatial movement patterns Impact of different adaptation rates Impact of different adaptation rates –Fast learner v.s. slow learner Distribution and accumulation of crime events as a result of the agents’ adaptation Distribution and accumulation of crime events as a result of the agents’ adaptation –Power law

33. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA33 AspatialSpatial Data DrivenTheory Driven ContinuousDiscrete StationaryMobile ArtificialReal Aspatial/Spatial Data/Theory Driven Stationary/Mobile Agents Continuous/ Discrete Geographic Environment DeterministicProbabilistic Deterministic/ Probabilistic Typology of Crime Simulation Models 12 2222x2 SPACES 121211 Note: 0 Use 0 if a criteria does not apply, 0 i.e., 121022 is not an agent based model. 3 Use 3,4…9 to expand the def for each criteria, 3 i.e., 223222 6 digit code: ? ?

34. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA34 Key Issues Calibration and validation Calibration and validation –Theory driven models that address agent behavior are difficult, if not impossible, to calibrate »Too many parameters »No data, or bad data –Data driven models that do not explicitly address the process are easier to calibrate –Must use real geographic environment How to compare two spatial layers? How to compare two spatial layers? –Similarity measure –A single (global) measure is not enough

35. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA35 Key Issues Prediction Prediction –Total count (aspatial) can be predictable »What will be next year’s over all crime rate for the entire city? –Individual events (spatial) is impossible to predict »When will the next burglary occur at a specific location? –Accumulation of individual events at a location during a period of time is difficult, but possible, to predict »What is threshold temporal resolution to produce meaningful patterns? Varies by crime, more frequent crime needs less time Varies by crime, more frequent crime needs less time –Un-calibrated models cannot be used to prediction »Be careful with calibrated models as well, as they tend to over- fit the data

36. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA36 Summary Use model type 2222x2 (spatial, theory driven, discrete, mobile agents, real/virtual geographic environment, probabilistic) Use model type 2222x2 (spatial, theory driven, discrete, mobile agents, real/virtual geographic environment, probabilistic) –as virtual lab –to study process, mechanism, causal effect –not for prediction Use model x1xxxx (data driven) Use model x1xxxx (data driven) –for prediction –but not for making causable statement

37. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA37 Challenges Create hybrid models that are both theory and data driven Create hybrid models that are both theory and data driven –For causal analysis and prediction Design sound experiments for theory driven models Design sound experiments for theory driven models –Explore what’s new

38. Jan. 29 - Feb. 2, 2007Liu, at IPAM, UCLA38 Thank You!