Using Narcotics Arrest Data To Predict Violent Crime

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Research proposal for geographically weighted regression model of crime analysis.

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  • Abstract: The Dallas Police Department (DPD) has the goal of reducing violent crime by targeting guns, gangs, and drugs. The limited resources available to the DPD require strategic planning to maximize the effect of patrol officers. The DPD currently targets violent crime (murder, rape, robbery-individual, robbery-business and aggravated assault) by analyzing criminal offense reports. This crime model is reactive rather than a proactive, intelligence-driven policing model. The DPD needs a statistical tool to predict high crime areas prior to the offenses occurring. This research will develop a geographically weighted regression (GWR) model that utilizes drug arrest data to determine risk for violent crimes at the block-face level. The GWR tool will assist the DPD in determining where to deploy additional resources to prevent violent gun crimes. Key words: crime control, Dallas Police Department, geographic weighted regression, gun crime, narcotics trafficking Chad Smith is an undergraduate student at the University of North Texas in Denton, Texas, pursuing a Bachelors of Science in Geography. He is also a Senior Corporal of Police with the Dallas Police Department, where he has worked as a patrol officer, crime analyst and intelligence analyst. He may be reached at chad@unt.edu
  • During the last decade, DPD has reported a higher crime rate per capita than other large U.S. cities (Dallas Morning News 2008). Individual comparisons are often misleading and disingenuous, but they do occur (Federal Bureau of Investigation 2007). These “high crime” stories make the front page of newspapers and are featured on news casts, leading to immense public pressure to lower the crime rate (Sideman and Couzens 1974). During his inauguration speech, Mayor Tom Leppert declared his determination to get Dallas off “the list” of cities with the highest crime rate (Dallas Morning News 2007). Preventing the offenses is more important than solving existing offenses. Preventing the loss of life, injury and property damage is more critical to lowering the crime rate than finding the person responsible for a past offense.
  • Many major police departments use superficial crime analysis (Willis et al. 2007). While offense location is useful for deployment, crime management requires more detailed analysis of underlying causes and crime risk levels (Craglia, Haining and Wiles 2000). Crime analysis for the DPD is performed by comparing police beats and police reporting areas over various temporal units. This practice makes evaluation of police crime control efforts difficult and doesn’t account for changes in policy or the seasonality of crime.
  • Police officers, from beat cops to the highest executives, have shown to lack a fundamental understanding of crime hot spots and an inability to properly identify hotspots (Ratcliffe and McCullaugh 1999; Willis et al. 2007) Crime perceptions do not match reality. DPD needs a statistical tool to evaluate the spatial relationships between crime place locations at the lowest possible spatial resolution: block-face level.
  • Routing activity theory relates crime to everyday activities. Crime occurs at the intersection of three factors: a suitable target, a motivated offender and an appropriate place (Andersen 2000). Each factor has an intermediary that can prevent the crime from occurring. Suitable targets have capable protectors. Motivated offenders have handlers. Suitable places have place managers (Eck 1997; Mazerolle, Kadleck, and Roehl 1998). The presence of an intermediary does not prevent crime, but reduces the risk of a crime occurring.
  • Social disorganization is defined as “traditions of delinquency are transmitted through successive generations of the same zone in the same way language, roles, and attitudes are transmitted” (Shaw and McKay 1942). Social disorganization is manifested by several factors: residential instability, poverty, and racial/ethnic heterogeneity. Residential instability is a mobile population that does not stay in a neighborhood for long periods of time. Racial/ethnic heterogeneity can lead to social and economic isolation. It can also create feelings of insurmountable obstacles between the neighborhood and success. Poverty, while not a cause of crime, leaves a neighborhood without the resources to deal with common problems. Poverty also influences housing options, leading to concentrations of poverty (Craglia, Haining, and Wiles 2000).
  • The possession and sale of narcotics must occur in a location that will tolerate drug markets (McCord and Ratcliffe 2007). “ Socially disorganized areas are believed to be business-friendly environments for drug markets because they are prone to contain sufficient numbers of drug users in their population, while also lacking the resources or social efficacy to prevent the establishment of the illegal trade.” Violence related to drug trafficking may be explained by three forces (Resignatio 2000): The psychopharmacological effect is the alteration of the neurochemistry which might lead a person to commit violent acts. The economic compulsion is the crime driven by the cost of an increasingly expensive addiction. The black market effect is the violent crime associated with prohibition of narcotics and their distribution. A positive correlation between violent crime and narcotics does exist (Martinez, Rosenfeld, and Mares 2008). Drug activity was a much stronger indicator of violent crime , even more so than alcohol availability (Gorman, Zhu, and Horel 2005). Drug arrests may be an accurate indicator of drug market activity in a neighborhood (Warner and Coomer 2003).
  • Social efficacy is the willingness of individuals within a neighborhood to act in the pursuit of common goals. Social efficacy may prevent drug markets from emerging (McCord and Ratcliffe 2007). Individual activity, such as calling 9-1-1 or confronting offenders, is less successful in preventing crime than collective efforts (Mazerolle, Kadleck, and Roehl 1998).
  • Basic geographic weighted regression model. Distance between location i (arrest) and observation j (crime). Different narcotic types will be modeled with different crime types (approximately 80 combinations).
  • Drug market size will be estimated using a NNA, where the average distance between each arrest and all crime is calculated. The drug market size will limit the GWR, so that each drug market will be weighted according to its size. This should reduce the amount of overlap between two or more adjacent drug markets.
  • Fig. 1. Example A shows four random points, indicated as circles 1–4, dispersed in a study area with two crime distributions, triangles and squares. The table to the right shows the nearest neighbor distances from each random point to the nearest triangle point and the nearest square point. The distances are in arbitrary units, and the relative ranking of the random point within each crime set (triangles and squares) is shown in brackets. Where the crime points are interspersed and share the same general area of the study region it can been seen (example A) that the relative rankings of the random points is the same for both crime sets. When the two distributions are markedly different, as in example B, the relative rankings of the random points is different to the level where the change could be detected with a statistical test. Source: Ratcliffe, J. H. 2005. Detecting Spatial Movement of Intra-Region Crime Patterns Over Time. Journal of Quantitative Criminology 21: 103-123 Using a Spearman’s rank correlation, the movement of crime clusters can be detected. The data will be pre-deployment and post-deployment crimes, measured from random points.
  • DPD collects all of the data needed for this research. The research will utilize a rolling 365 day period. Will include seasonality of crime Will include policy changes which may affect reporting rates. The data will be analyzed at the block-face level to provide greater accuracy of risk assessment and deployment flexibility.
  • The GWR model will be applied to legacy data in order to predict past events. Developing the model independently of real-time police practices will allow for evaluation of the model without having to account for current deployment strategies.
  • DPD will be capable of determining the best deployment locations to prevent offenses. DPD can use this information to mobilize community groups, which may counteract the risk of crime for the neighborhood (Mazerolle, Kadleck, and Roehl 1998). The GWR tool will also reduce time spent by DPD selecting deployment areas, thereby saving money.
  • Using Narcotics Arrest Data To Predict Violent Crime

    1. 1. Using Narcotics Arrest Data to Predict Violent Crime Locations in Dallas, Texas Chad Smith [email_address]
    2. 2. The crime rate is high in Dallas. More than 15,000 gun crimes per year.
    3. 3. Dallas PD uses superficial crime analysis. Percent change by week, month, & year to determine crime trends.
    4. 4. Dallas PD targets crime hot spots. DPD needs better tools to predict crime locations to lower the crime rate.
    5. 5. Routine activities increase victimization potential. Crime occurs when a motivated offender, a suitable victim, and an appropriate place intersect.
    6. 6. Social disorganization increases victimization potential. Social efficacy is a willingness to take action to achieve common goals for a neighborhood.
    7. 7. Narcotics and violent crime are related. Drugs and crime are most often related due to the Black Market Effect of prohibition.
    8. 8. Social efficacy can overcome most crime factors. Socioeconomic factors of crime are not guarantees crime.
    9. 9. The GWR will model crime from arrest locations.
    10. 10. The arrests will be weighted by to limit predictions to the drug market.
    11. 11. Evaluation of outcomes will be based on a NNA. When two distributions are different, the relative rankings are different.
    12. 12. Data for this research will come from Dallas PD records. The data will include arrest and offense reports for drugs and violent crime.
    13. 13. The GWR model evaluation will compare predictions to reality. The GWR model will assist in resource deployment for greater efficiency.
    14. 14. This research will extend our understanding of narcotics and violent crime. This research will lead to better crime prevention strategies for law enforcement and communities.

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