Juvenile Arrests and Neighborhood Characteristics

Norris StoughUniversity of Texas at Dallas

Table of ContentsIntroduction................................................................................................................................3Literature Review......................................................................................................................3Project Objective.......................................................................................................................4Hypothesis.................................................................................................................................5

Simple Quantitative Analysis................................................................................................5Nearest Neighbor Indices......................................................................................................5Moran’s I...............................................................................................................................5Local Indications of Spatial Autocorrelation (LISA) Measures............................................5Arrests Per Capita v. Predicted levels of Concentrated Disadvantage and Index of Concentrated Extreme...........................................................................................................6

Data Sources..............................................................................................................................6Arrests....................................................................................................................................6Census Block Groups............................................................................................................6Spatial Data............................................................................................................................6

Data Preparation........................................................................................................................6Arrests and Census Block Group Data..................................................................................6Measures of Concentrated Disadvantage and the Index of Concentrated Extremes.............7

Concentrated Disadvantage...............................................................................................7Index of Concentrated Extremes.......................................................................................8

Nearest Neighbor, Moran’s I and Local Indications of Spatial Autocorrelation (LISA) Measures................................................................................................................................9

Data Analysis.............................................................................................................................9Simple Quantitative Analysis................................................................................................9

Figure 1: Arrests Per Capita v. Index of Concentrated Extremes...................................10Figure 2: Arrests Per Capita v. Concentrated Disadvantage..........................................10

Nearest Neighbor Index.......................................................................................................11Figure 3: Nearest Neighbor Index of Concentrated Extremes........................................11Figure 4: Nearest Neighbor Index of Concentrated Disadvantage.................................12

Moran’s I.............................................................................................................................12Figure 5: Absolute Moran’s I Score...............................................................................13Figure 6: Moran's I Score...............................................................................................14

LISA Relationships..............................................................................................................14Figure 7: LISA Cluster Map of Concentrated Disadvantage..........................................15Figure 8: Arrests Per Capita v. LISA of Concentrated Disadvantage............................15Figure 9: LISA Cluster Map of Index of Concentrated Extremes..................................16Figure 10: Arrests Per Capita v. LISA of Index of Concentrated Extremes..................16Figure 11: Nearest Neighbor Index LISA for Index of Concentrated Extremes............17Figure 12: Nearest Neighbor Index LISA for Concentrated Disadvantage....................17

Proportional Mapping of Arrests Per Capita v. Interpolated Concentrated Disadvantage and Index of Concentrated Extremes Values.......................................................................18

Figure 13: Concentrated Disadvantage v. Proportional Arrests Per Capita...................18Figure 14: Index of Concentrated Extremes v. Proportional Arrests Per Capita............19

Conclusions..............................................................................................................................19References................................................................................................................................21

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Introduction

This project links the home address records of juveniles arrested in Dallas County, Texas between 1997 and 2003 with data for census block groups from the 2000 Census to assess the neighborhood characteristics of those arrested.

The project assesses neighborhood characteristics quantified by measures of Concentrated Disadvantage and the Index of Concentrated Extremes and hypothesizes that if these neighborhood characteristics are indeed indicators of potential criminal activity amongst juveniles then a variety of quantitative, spatial and graphical analyses will show increasing incidences of arrests within theses areas.

Results from the analyses confirm this hypothesis. In every case, the expected outcomes confirm a positive relationship between per capita arrests and increasing levels of Concentrated Disadvantage and a negative relationship between per capita arrests and the Index of Concentrated Extremes.

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.

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 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

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Wilson (1987) published maps revealing the relationship between concentrations of poverty and social problems.

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). 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).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 GIS Workshop project described in the following document also explores these relationships, but with regard to the incidence of juvenile arrests in Dallas County, Texas between 1997 and 2003.

Project ObjectiveIf 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.

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HypothesisIf juvenile arrests cluster in areas with a high degree of Concentrated Disadvantage or low Index of Concentrated Extremes, then the following measures will be true for:

Simple Quantitative Analysis There will be a higher incidence of arrests in areas of high Concentrated

Disadvantage and low Index of Concentrated Extremes score. There will be a positive relationship between arrests per capita and

Concentrated Disadvantage and a negative relationship between arrests per capita and the Index of Concentrated Extremes.

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

the 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.

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.

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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 ExtremeA 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.Data SourcesArrestsDallas County Juvenile Services, through Dr. Kimberly Kempf-Leonard, provided the address data for juveniles arrested in Dallas County from 1997 through 2003. Census Block GroupsDemographic data necessary to calculate Concentrated Disadvantage and the Index of Concentrated Extremes comes from the 2000 Census files available through the North Central Texas Council of Governments (NCTCOG) at www.dfwinfo.com Spatial DataCensus Tract, Zip Code, County and Roads shape files come from the North Central Texas Council of Governments at www.dfwinfo.comData PreparationArrests and Census Block Group DataThe original address data for arrested juveniles was in DBF format. The quality of the data was very poor, with very little consistency in either content or format. There were a considerable number of records missing the attribute data necessary for geocoding and within the attributes of the records there was very little consistency in street naming or address standardizing conventions. “Cleansing” the data required converting the DBF file to MS Excel format. Within Excel, the records were parsed to separate the address record fields, invalid data characters were stripped from the records, attribute values, such apartment numbers and non-text characters, unnecessary for the geocoding process were removed. The address record fields were then concatenated to provide a complete address within one field, as required by the geocoding process.Cleaning up the data eliminated several thousand of the original records. Because these are addresses for those arrested and not crime locations, many thousands of the addresses were outside of the Dallas County study area. Additionally, Dallas County Juvenile Services had already identified many hundreds of the records as “bad addresses”. A check of the “bad addresses” found nothing wrong with the physical address itself, and it is assumed that the designation of “bad address” indicates the juvenile provided an address that later turned out not to be theirs. Eliminating these records reduced the total number of addresses to be geocoded by approximately 12 percent; from approximately 43,000 to approximately 38,000. The Excel file was converted back to DBF format and the 38,000 records were geocoded in ArgGIS; resulting in approximately 32,000 matches within Dallas County - approximately 84% of all the attempted records and 74% of the original

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records.Geocoding produced a “points” file of addresses. Once the characteristics of Concentrated Disadvantage and the Index of Concentrated Extremes was calculated for the census block groups (outlined below) the points file was spatially joined to the block group polygon file. Block group characteristics were attributed to each of the address point records and the block group records were summarized by block group, producing sum counts of the number of arrests per block group.In order to account for the unequal distribution of population of juveniles across Dallas County, arrests were normalized to the number of arrests per capita per block group, as an attribute of the block group polygon file. Since the arrests records covered a six year period and the census data only one year’s population, the 2000 population totals were annualized to a six-year total by multiplying by six.Measures of Concentrated Disadvantage and the Index of Concentrated ExtremesConcentrated DisadvantageConcentrated Disadvantage represents “economic disadvantage in racially segregated urban neighborhoods. It is defined by 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” (Morenoff et al., 527). Equally weighting the factors and dividing by the number of items calculates the Concentrated Disadvantage score. These calculations were done within an MS Excel file of the census block group data and then joined to the block group shape files. The following table provides the census tract file and field number for the necessary demographic variables used to calculate Concentrated Disadvantage.

Demographic Variable Census File Field NumberPercentage of families below the poverty line SF30007 P090002Percentage of families receiving public assistance SF30006 P064002Percentage of unemployed individuals in the civilian labor force SF30004 P043007 and P043014Percentage of female-headed families with children SF30002 P015016Percentage of residents who are black SF30001 P006003

The site http://census.nctcog.org/sf3econ_readme.html contains a link to the list of field number descriptions by file.Index of Concentrated ExtremesThe Index of Concentrated Extremes “is defined for a given neighborhood by the following formula: [(number of affluent families – number of poor families) / total number of families], where “affluent” is defined as families with income above $50,000 and “poor” is defined as families below the poverty line. The ICE index ranges from a theoretical value of –1 (which represents extreme poverty, namely, that all families

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are poor) to +1 (which signals extreme affluence, namely, that all families are affluent). A value of zero indicates that an equal share of poor and affluent families live in the neighborhood.” (Morenoff et al., 529). Again, these calculations were done within an MS Excel file of the census block group data and then joined to the block group shape files. The following table provides the census tract file and field number for the necessary demographic variables used to calculate the Index of Concentrated Extremes. Demographic Variable Census File Field NumberNumber of affluent families SF30007 P076011-17Number of poor families (below poverty line) SF30007 P090002

The site http://census.nctcog.org/sf3econ_readme.html contains a link to the list of field number descriptions by file.Nearest Neighbor, Moran’s I and Local Indications of Spatial Autocorrelation (LISA) MeasuresNearest neighbor calculations were completed using CrimeStat. Separate points files for the various categories of analysis (ranges of Concentrated Disadvantage and Index of Concentrated Extremes values and LISA relationships) were generated within ArcGIS from the overall arrests points file by selecting points based on attributes. These points files were there imported into CrimeStat for analysis.Moran’s I and LISA measures were calculated using GeoDA (version 0.95-i5). Separate polygon files for the various categories of analysis (ranges of Concentrated Disadvantage and Index of Concentrated Extremes values and LISA relationships) were generated within ArcGIS from the overall census block group polygons file polygons based on attributes. These points files were there imported into GeoDA for analysis.The LISA relationships for High to High and Low to Low values of Concentrated Disadvantage and the Index of Concentrated Extremes were generated within GeoDA. The LISA Cluster maps were then georeferenced using ArcGIS so that the LISA relationship could be attributed to the block group polygon files. Polygons were then selected by their LISA relationship and used to clip the arrests points files for areas of High to High and Low to Low relationships. These points files were again imported into CrimeStat in order to obtain the nearest neighbor indices for areas of High to High and Low to Low LISA relationships.Data AnalysisSimple Quantitative AnalysisBefore performing any exploratory spatial data analysis, a simple quantitative analysis of arrest address data was performed to quantify arrests by category. The analysis provided an overall understanding of the data and a general idea of the relationships that additional spatial analysis might reveal.Categorizing arrests per capita by the Index of Concentrated Extremes (Figure 1) and Concentrated Disadvantage (Figure 2) in equal intervals, immediately reveals the positive relationship between Concentrated Disadvantage and arrests and the negative relationship between the Index of Concentrated Extremes.Figure 1: Arrests Per Capita v. Index of Concentrated Extremes

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Figure 2: Arrests Per Capita v. Concentrated Disadvantage

Nearest Neighbor IndexThe 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,

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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 (Levine 2004).The nearest neighbor index is a global measure of the spatial relationship between each point and every other point in the study area. When the nearest neighbor index is calculated based on equal interval ranges of the Index of Concentrated Extremes Score (Figure 3) and Concentrated Disadvantage (Figure 4) the results show an increasing density of arrests within areas of higher Concentrated Disadvantage and lower Index of Concentrated Extremes values. This confirms the positive relationship between Concentrated Disadvantage and arrests per capita and the negative relationship between the Index of Concentrated Extremes and arrests per capita, across Dallas County. Figure 3: Nearest Neighbor Index of Concentrated Extremes

Figure 4: Nearest Neighbor Index of Concentrated Disadvantage

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Moran’s IThe 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.The absolute value of the Moran’s I score (Figure 5) measures the relationship between arrests per capita in an, relative to the level of arrests per capita, the level of Concentrated Disadvantage, or the degree of the Index of Concentrated Extremes for surrounding areas. The higher values for the Concentrated Disadvantage and Index of Concentrated Extremes variable as compared to the arrests per capita variable confirms that arrests are more highly clustered in these areas than overall.Figure 5: Absolute Moran’s I Score

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A Moran’s I scatter plot with the Index of Concentrated Extremes on the X axis and arrests per capita on the Y axis produces a downward sloping line. As the Index of Concentrated Extremes moves from –1.0 to 1.0, arrests per capita fall, thereby producing the negative value of the Bivariate Moran’s I score (Figure 6) for arrests per capita versus the Index of Concentrated Extremes. The again confirms the negative relationship between arrests per capita and the Index of Concentrated Extremes and the higher incidence or arrests in areas with lower Index of Concentrated Extremes scores.Figure 6: Moran's I Score

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LISA RelationshipsLISA 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.LISA Cluster Maps for Concentrated Disadvantage and the Index of Concentrated Extremes (Figures 7 & 9) clearly indicate significant clustering of these values. Extracting the arrests per capita for High to High and Low to Low areas and comparing the relative arrests per capita (Figures 8 & 10) and Nearest Neighbor Indices (Figures 11 & 12) shows a higher incidence of arrests per capita in areas of High to High Concentrated Disadvantage and Low to Low Index of Concentrated Extremes than for arrests overall. The incidence of arrests is also lower than overall for areas with Low to Low levels of Concentrated Disadvantage and High to High levels for the Index of Concentrated Extremes. Additionally, the Nearest Neighbor Indices reflect increased clustering of arrests in areas with High to High Concentrated Disadvantage and Low to Low Index of Concentrated Extremes levels, compared to arrests overall.Figure 7: LISA Cluster Map of Concentrated Disadvantage

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Figure 8: Arrests Per Capita v. LISA of Concentrated DisadvantageFigure 9: LISA Cluster Map of Index of Concentrated Extremes

Figure 10: Arrests Per Capita v. LISA of Index of Concentrated ExtremesFigure 11: Nearest Neighbor Index LISA for Index of Concentrated ExtremesFigure 12: Nearest Neighbor Index LISA for Concentrated DisadvantageProportional Mapping of

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Arrests Per Capita v. Interpolated Concentrated Disadvantage and Index of Concentrated Extremes ValuesEconomic and social characteristics, such as

Concentrated Disadvantage and the Index of Concentrated Extremes are not typically

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well defined by the imposition of artificial boundaries such as census block groups. Change between these areas is more gradual and better envisioned as a transition across the areas than as an absolute change at the block group boundary. An interpolation of the predicted value for a location captures this transition by producing an expected value for an area based on the values of the surrounding areas.The predicted measures of Concentrated Disadvantage and the Index of Concentrated Extremes versus a proportional symbology of the arrests per capita for each census block group (Figures 13 & 14) captures the transitional change in these measures while reflecting the degree to which arrests per capita varies in these block groups. Clearly, arrests per capita are proportionally higher in those areas where the predicted value for Concentrated Disadvantage are higher and the Index of Concentrated Extremes is lower.Figure 13: Concentrated Disadvantage v. Proportional Arrests Per Capita

Figure 14: Index of Concentrated Extremes v. Proportional Arrests Per Capita

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ConclusionsPoverty is nothing new. The Gospels say, “for you always have the poor with you” (John 12:8). As the literature review suggests though, “with” is an increasingly relative measure. Indeed, there are increasing numbers of the “poor.” What is new is the degree to which the poor are concentrated in areas separate from the rich and the extent to which this concentration and separation results in increased criminal activity.This study hypothesized that if the neighborhood characteristics quantified by two measures – Concentrated Disadvantage and the Index of Concentrated Extremes – were valid indicators of expected increased levels of crime, then juvenile arrest records would show a pattern of increased clustering within areas of high Concentrated Disadvantage and low Index of Concentrated Extremes scores. The results show exactly that.Measured quantitatively, the incidence of per capita arrests has a positively relationship with Concentrated Disadvantage values and a negative relationship the Index of Concentrated Extremes values. An overall measure of the spatial distribution of arrests across Dallas County, the Nearest Neighbor Indices, indicates increased clustering in areas of high Concentrated Disadvantage and low indices of Concentrated Extremes. Comparing the degree to which arrests per capita in one area is related to arrests per capita, Concentrated Disadvantage or the Index of Concentrated Extremes for surrounding areas - the Bivariate Moran’s I measure - found a stronger relationship with the degree of Concentrated Disadvantage and the Index of Concentrated Extremes than for arrests per capita in general. Analyzing areas of High to High or Low to Low clustering, the LISA relationships,

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for Concentrated Disadvantage and the Index of Concentrated Extremes produces the same results; higher incidences of arrests in areas of high Concentrated Disadvantage and low Indices of Concentrated Extremes. Finally, a proportional mapping of arrests per capita against the background of predicted values of Concentrated Disadvantage and the Index of Concentrated Extremes, depicts increasing arrest rates in areas where theses measures are predicted to be their worst. The evidence supports the hypothesis. Juvenile arrests in Dallas County do 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. Finally, Concentrated Disadvantage and the Index of Concentrated Extremes are valid indicators for expected higher incidences of juvenile arrests

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ReferencesBanfield, 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.Levine, Ned. 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, D.C.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 66:29-57Morenoff, 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 MichiganShaw, Clifford and Henry McKay. 1942. (1969, 2nd edition). Juvenile Delinquency and Urban Areas. Chicago: University of Chicago Press.Sampson, Robert J. Stephen Raudenbush and Felton Earls. 1997. “Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy.” Science 277:918-924Wilson, William Julius. 1987. The Truly Disadvantaged: The Inner City, the Underclass,and Public Policy. Chicago: University of Chicago PressWirth, L. 1938. “Urbanism as a Way of Life.” American Journal of Sociology 44:3-24.

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