PowerPoint Overview - Juvenile Arrests and Neighborhood Characteristics - PowerPoint Presentation

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This is the major project required during the completion of my Graduate Certificate in Geographic Information Sciences. …

This is the major project required during the completion of my Graduate Certificate in Geographic Information Sciences.

Please read through it. You will find it interesting as a writing sample and as examples of types of data analysis and research I produce.

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  • 1. Juvenile Arrests and Neighborhood Characteristics GISC 6387 Norris Stough University of Texas at Dallas Summer 2005
  • 2. Literature Review The characteristics of areas in which juvenile criminal activity occurs and develops have long been of interest to sociologists and criminal studies. Much of this interest has focused on a variety of economic and socio-economic measures that characterize areas of high criminal activity in an effort to demonstrate a causal relationship between economic and social status and crime. Theorists such as Wirth (1938) and Banfield (1967) have observed that the traditional social organizations of a rural society break down in increasingly urbanized concentrations of population; suggesting that areas of concentrated population will be characterized by higher levels of social disorganization.
  • 3. Literature Review – cont. In a classic study, Shaw and McKay (1942) argued that low economic status is a primary cause of the social disorganization leading to high rates of juvenile delinquency. The geographic relationship between areas of concentrated poverty and social problems such as delinquency has been offered as evidence of this link between economic status and crime; suggesting that the geographic concentration of poverty causes the concentration of criminal activity in poor neighborhoods (Massey, Condran and Denton 1987). Decades before the development of geographic information systems capable of exploring spatial relationships, individuals such as St. Clair Drake and Horace Cayton (1945) and Wilson (1987) published maps revealing the relationship between concentrations of poverty and social problems such as juvenile delinquency (Massey 1996).
  • 4. Literature Review – cont. Sociologists such as Massey (1996) and Sampson, Raudenbush and Earls (1997) make a convincing argument that the twentieth century has been characterized by increasing urbanization in general, increasing concentrations of the poor within the urban population and more importantly, an increasing spatial segregation of the non-affluent from the affluent. They make the case for a connection between these areas of concentrated populations of the poor with high levels of social disorganization characterized by higher incidences of crime Massey quantifies this spatial separation of the non-affluent from the affluent in a measure called the Index of Concentration of Extremes (2001).
  • 5. Literature Review – cont. Sampson, Raudenbush, and Earls (1997) explored the relationship between the social disorganization expected from increasing urbanized populations and a variety of measures (receipt of public assistance, unemployment, black residents, female-headed households with children, etc.) quantified by a measure referred to as Concentrated Disadvantage. Their research, though purely statistical, demonstrates the clear relationship between this measure and increased incidences of crime (homicides). Morenoff, Sampson and Raudenbush (1999) revisited the 1997 Sampson, Raudenbush and Earls study, replicating their study but with the addition of spatial measures of the correlation between the Index of Concentration of Extremes and Concentrated Disadvantage and areas of increased incidences of crime (homicides).
  • 6. Literature Review – cont. Their research again confirmed the relationship between Concentrated Disadvantage and increased criminal activity. This study included the Index of Concentrated Extremes as well and confirmed the relationship between this measure of economic disadvantage and criminal activity. Additionally, the spatial analysis demonstrated a relationship between areas of Concentrated Disadvantage and the Index of Concentrated Extremes and surrounding areas with similar values. The project explores explores those relationships as well, but with regard to the incidence of juvenile arrests in Dallas County, Texas between 1997 and 2003.
  • 7. Project Objective If Concentrated Disadvantage and the Index of Concentrated Extremes are valid measures of expected juvenile crime activity, then this project will demonstrate that juvenile arrests cluster within areas with high Concentrated Disadvantage scores and low Index of Concentrated Extremes scores.
  • 8. Hypothesis
    • If juvenile arrests are more clustered in areas with a high degree of Concentrated Disadvantage or low Index of Concentrated Extremes, then the following measures will be true:
  • 9.
    • Nearest Neighbor Indices
      • The nearest neighbor index will be lower for areas of high Concentrated Disadvantage score.
      • The nearest neighbor index will be lower for areas with a low Index of Concentrated Extremes score.
      • There will be a negative relationship between the Nearest Neighbor Index and Concentrated Disadvantage score.
      • There will be a positive relationship between the Nearest Neighbor Index and the Index of Concentrated Extremes score.
    • Moran’s I
      • The Moran’s I score for Concentrated Disadvantage and the Index of Concentrated Extremes measured against arrests per capita will be higher than for arrests per capita alone.
      • The value of the Concentrated Disadvantage score will be positive, indicating a positive relationship between arrests per capita and Concentrated Disadvantage.
      • The value of the Index of Concentrated Extremes score will be negative, indicating a negative relationship between arrests per capita and Concentrated Extreme.
  • 10.
    • Local Indications of Spatial Autocorrelation (LISA) Measures
      • For Concentrated Disadvantage, arrests per capita will be higher in areas with a High to High LISA relationship than for areas with a Low to Low LISA relationship.
      • For the Index of Concentrated Extremes, arrests per capita will be lower in areas with a High to High LISA relationship than for areas with a Low to Low LISA relationship.
      • For Concentrated Disadvantage, the Nearest Neighbor Index will be lower in areas of High to High LISA relationships than Low to Low LISA relationships.
      • For the Index of Concentrated Extremes, the Nearest Neighbor Index will be higher in areas of High to High LISA relationships than for Low to Low LISA relationships.
    • Arrests Per Capita v. Predicted levels of Concentrated Disadvantage and Index of Concentrated Extreme
      • A proportional mapping of arrests per capita against a background of the predicted level of Concentrated Disadvantage will show clustering of arrests within areas of increasing Concentrated Disadvantage.
      • A proportional mapping of arrests per capita against a background of the predicted Index of Concentrated Extremes will show clustering of arrests within areas of decreasing Index of Concentrated Extreme.
  • 11. Project Variables
    • Juvenile arrests:
      • Normalized and expressed in terms of arrests per capita to remove the effect of unequal population distribution
      • Geocoded from Dallas County arrest records (over 32,000 total records) from 1997 to 2003
    • Concentrated Disadvantage:
      • The sum of the percentage of families below the poverty line, percentage of families receiving public assistance, percentage of unemployed individuals in the civilian labor force, percentage of female-headed families with children, and percentage of residents who are black, equally weighted, and divided by the number of items (derived from 2000 Census Block Groups)
      • Values in this study range from 0.0 to 60.49
      • Generally, the higher the number the lower the area’s socio-economic status
    • Index of Concentrated Extremes:
      • The number of affluent families less the number of poor families divided by the total families -affluent and poor (derived from 2000 Census Block Groups)
      • Values range from –1.0 to 1.0
      • Generally, the lower the number the lower the area’s socio-economic status
  • 12. Analysis
  • 13. Nearest Neighbor Indices
    • The nearest neighbor index is the ratio of the observed nearest neighbor distance to the mean random distance.
    • The index compares the average distance from the closest neighbor to each point with a distance that would be expected on the basis of chance.
    • If the observed average distance is about the same as the mean random distance, then the ratio will be about 1.0. On the other hand, if the observed average distance is smaller than the mean random distance, that is, points are actually closer together than would be expected on the basis of chance, then the nearest neighbor index will be less than 1.0. This is evidence for clustering.
    • Conversely, if the observed average distance is greater than the mean random distance, then the index will be greater than 1.0. This would be evidence for dispersion, that points are more widely dispersed than would be expected on the basis of chance.
    • Ned Levine (2004). CrimeStat : A Spatial Statistics Program for the Analysis of Crime Incident Locations (v 3.0).
    • Ned Levine & Associates, Houston, TX, and the National Institute of Justice, Washington, DC.
  • 14. Chart NNI Concentrated Disadvantage Less Clustered – More Clustered
  • 15. Chart NNI ICE Less Clustered – More Clustered
  • 16. Moran’s I
    • The univariate Moran’s I is a measure of the correlation of a variable with itself in space, the bivariate Moran’s I is a measure of the correlation of one variable with another variable in space.
    • This project compares the relative Moran’s I scores of arrests per capita alone and then arrests per capita when measured against the variables of Concentrated Disadvantage and the Index of Concentrated Extremes.
    • Values closer to zero indicate less clustering, while values closer to one indicate more clustering.
  • 17. Chart Absolute Moran’s I Score More Clustered – Less Clustered
  • 18. Chart Moran’s I Score More Clustering – Less Clustering – More Clustering
  • 19. Local Indications of Spatial Autocorrelation
    • LISA statistics detect local spatial autocorrelation and identify local clusters where adjacent areas have similar values.
    • LISA maps identify four types of spatial autocorrelation based on a weights matrix that defines their contiguity: Spatial clusters (negative near negative values and positive near positive values) and spatial outliers (negative near positive values and positive near negative values).
    • This project focuses on the comparison of arrests per capita for areas of High to High (positive near positive values) and Low to Low (negative near negative) LISA relationships for Concentrated Disadvantage and Index of Concentrated Extremes scores.
  • 20. LISA Cluster Map of Concentrated Disadvantage
  • 21. Chart Concentrated Disadvantage
  • 22. Chart Nearest Neighbor Concentrated Disadvantage LISA Relationship
  • 23. LISA Cluster Map of Index of Concentrated Extremes
  • 24. Chart ICE
  • 25. Chart Nearest Neighbor ICE LISA Relationship
  • 26. Arrests Per Capita v. Predicted Levels of Concentrated Disadvantage and Index of Concentrated Extremes
    • Surface generated using Inverse Distance Weighting (IDW)
    • Symbology represents quantity (arrests per capita) proportionally for each block group
  • 27. Arrests Per Capita v. Concentrated Disadvantage Score
  • 28. Arrests Per Capita v. Index of Concentrated Extremes
  • 29. Results
    • Nearest Neighbor Indices
      • More clustered in area with high Concentrated Disadvantage and low Index of Concentrated Extremes scores
      • Positive relationship between arrests per capita and Concentrated Disadvantage
      • Negative relationship between arrests per capita and Index of Concentrated Extremes
    • Moran’s I
      • More clustered in area with high Concentrated Disadvantage and low Index of Concentrated Extremes scores
      • Positive relationship between arrests per capita and Concentrated Disadvantage
      • Negative relationship between arrests per capita and Index of Concentrated Extremes
    • Local Indications of Spatial Autocorrelation (LISA) Relationships
      • Arrests per capita higher in areas of High to High Concentrated Disadvantage and Low to Low Index of Concentrated Extremes
      • Nearest Neighbor Indices lower in areas of High to High Concentrated Disadvantage and Low to Low Index of Concentrated Extremes LISA relationships
    • Arrests Per Capita v. Prediction Map
      • Proportional representation of arrests per capita shows clustering according to the predicted levels of increasing Concentrated Disadvantage and decreasing Index of Concentrated Extremes
  • 30. Conclusions
    • Juvenile arrests in Dallas County cluster within areas of high Concentrated Disadvantage and low Index of Concentrated Extremes.
    • Arrests per capita increase as the level of Concentrated Disadvantage increases.
    • Arrests per capita increase as the Index of Concentrated Extremes decreases.
    • Concentrated Disadvantage and the Index of Concentrated Extremes are valid indicators for expected higher incidences of juvenile arrests.
  • 31. References Banfield, E.C. 1967. The Moral Basis of a Backward Society. New York: Free Press. Drake, St.C. and H.R. Cayton. 1945. Black Metropolis: A Study of Life in a Northern City. New York: Harcourt, Brace. Massey, Douglas S. 1996. “The Age of Extremes: Concentrated Affluence and Poverty in the Twenty-First Century.” Demography 33:395-412. Massey, Douglas S. 2001. “The Prodigal Paradigm Returns: Ecology Comes Back to Sociology.” Pp. 41-48 in Does It Take a Village? Community Effects on Children, Adolescents, and Families , edited by Alan Booth and Ann Crouter. Mahway, New Jersey: Lawrence Erlbaum Associates, Publishers. Massey, Douglas S., G.A Condran, and N.A. Denton. 1987. “The Effect of Residential Segregation on Black Social and Economic Well-Being.” Social Forces 66:29-57 Morenoff, Jeffrey D. Robert J. Sampson and Stephen W. Raudenbush. 2001. “Neighborhood Inequality, Collective Efficacy, and the Spatial Dynamics of Urban Violence.” Ann Arbor: Population Studies Center, University of Michigan Shaw, Clifford and Henry McKay. 1942. (1969, 2 nd edition). Juvenile Delinquency and Urban Areas . Chicago: University of Chicago Press.
  • 32. References (continued) Sampson, Robert J. Stephen Raudenbush and Felton Earls. 1997. “Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy.” Science 277:918-924 Wilson, William Julius. 1987. The Truly Disadvantaged: The Inner City, the Underclass,and Public Policy. Chicago: University of Chicago Press Wirth, L. 1938. “Urbanism as a Way of Life.” American Journal of Sociology 44:3-24.