Full text of Graduate Certification project

1. Juvenile Arrests
and Neighborhood Characteristics
Norris Stough
University of Texas at Dallas

2. Table of Contents
Introduction.....................................................................................................................................3
Literature Review...........................................................................................................................3
Project Objective............................................................................................................................4
Hypothesis......................................................................................................................................5
Simple Quantitative Analysis.....................................................................................................5
Nearest Neighbor Indices...........................................................................................................5
Moran’s I....................................................................................................................................5
Local Indications of Spatial Autocorrelation (LISA) Measures................................................5
Arrests Per Capita v. Predicted levels of Concentrated Disadvantage and Index of
Concentrated Extreme................................................................................................................6
Data Sources...................................................................................................................................6
Arrests.........................................................................................................................................6
Census Block Groups.................................................................................................................6
Spatial Data.................................................................................................................................6
Data Preparation.............................................................................................................................6
Arrests and Census Block Group Data......................................................................................6
Measures of Concentrated Disadvantage and the Index of Concentrated Extremes................7
Concentrated Disadvantage....................................................................................................7
Index of Concentrated Extremes............................................................................................8
Nearest Neighbor, Moran’s I and Local Indications of Spatial Autocorrelation (LISA)
Measures.....................................................................................................................................9
Data Analysis..................................................................................................................................9
Simple Quantitative Analysis.....................................................................................................9
Figure 1: Arrests Per Capita v. Index of Concentrated Extremes......................................10
Figure 2: Arrests Per Capita v. Concentrated Disadvantage..............................................10
Nearest Neighbor Index............................................................................................................11
Figure 3: Nearest Neighbor Index of Concentrated Extremes...........................................11
Figure 4: Nearest Neighbor Index of Concentrated Disadvantage....................................12
Moran’s I..................................................................................................................................12
Figure 5: Absolute Moran’s I Score....................................................................................13
Figure 6: Moran's I Score....................................................................................................14
LISA Relationships..................................................................................................................14
Figure 7: LISA Cluster Map of Concentrated Disadvantage.............................................14
Figure 8: Arrests Per Capita v. LISA of Concentrated Disadvantage................................15
Figure 9: LISA Cluster Map of Index of Concentrated Extremes.....................................16
Figure 10: Arrests Per Capita v. LISA of Index of Concentrated Extremes......................16
Figure 11: Nearest Neighbor Index LISA for Index of Concentrated Extremes...............17
Figure 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.......................18
Figure 14: Index of Concentrated Extremes v. Proportional Arrests Per Capita...............19
Conclusions...................................................................................................................................19
References.....................................................................................................................................21
2

3. 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 Wilson
3

4. (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 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.
4

5. Hypothesis
If 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.
5

6. • 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.
Data Sources
Arrests
Dallas County Juvenile Services, through Dr. Kimberly Kempf-Leonard, provided the address
data for juveniles arrested in Dallas County from 1997 through 2003.
Census Block Groups
Demographic 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 Data
Census Tract, Zip Code, County and Roads shape files come from the North Central Texas
Council of Governments at www.dfwinfo.com
Data Preparation
Arrests and Census Block Group Data
The 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
6

7. 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 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 Extremes
Concentrated Disadvantage
Concentrated 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.
7

8. 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 Number
Percentage of families
below the poverty line SF30007 P090002
Percentage of families
receiving public
assistance SF30006 P064002
Percentage of
unemployed individuals
in the civilian labor force SF30004 P043007 and P043014
Percentage of female-
headed families with
children SF30002 P015016
Percentage 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 Extremes
The 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 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 Number
Number of affluent
families SF30007 P076011-17
Number 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.
8

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

10. Figure 1: Arrests Per Capita v. Index of Concentrated Extremes
Arrests Per Capita v. Index of Concentrated Extremes
3.50%
3.00%
2.50%
Arrests Per Capita
2.00%
1.50%
1.00%
0.50%
0.00%
-1.0 to -0.5 -0.5 to 0 0 to 0.5 0.5 to 1.0
ICE Index
Figure 2: Arrests Per Capita v. Concentrated Disadvantage
Arrests Per Capita v. Concentrated Disadvantage Score
6.00%
5.00%
4.00%
Arrests Per Capita
3.00%
2.00%
1.00%
0.00%
0.0 to 15.00 15.00 to 30.00 30.00 to 45.00 45.00 to 60.49
Concentrated Disadvantage Score
10

11. Nearest Neighbor Index
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 (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
Relative Nearest Neighbor Indices for Index of Concentrated Extremes
0.3
0.2491
0.25
0.2317
0.2
0.15
0.1359
0.1286
0.1
0.05
0
ICE -1.0 to -0.50 ICE -0.50 to 0.0 ICE 0.0 to 0.50 ICE 0.50 to 1.0
ICE Score Range
11

12. Figure 4: Nearest Neighbor Index of Concentrated Disadvantage
Nearest Neighbor Indices for Concentrated Disadvantage
0.3500
0.3000
0.2500
0.2000
0.1500
0.1000
0.0500
0.0000
0 - 15 15 - 30 30 - 45 45 - 60
Concentrated Disadvantage Score
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.
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.
12

13. Figure 5: Absolute Moran’s I Score
Relative Absolute Moran's I Score
0.3500
0.3009
0.3000
0.2601
0.2500
Moran's I Score
0.2000
0.1460
0.1500
0.1000
0.0500
0.0000
Arrests Concentrated Disadvantage Score Index of Concentrated Extreme Score
Arrests Per Capita v. Measurement Category
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.
13

14. Figure 6: Moran's I Score
Relative Moran's I Score
Index of Concentrated Extreme
-0.3009
Score
Arrests Per Capita v. Measurement Category
Negative relationship between Index of Concentrated Extremes
score and arrests per capita
Concentrated Disadvantage Score 0.2601
Postive relationship between Concentrated Disadvantage
score and arrests per capita
0.1460
Arrests
-0.4000 -0.3000 -0.2000 -0.1000 0.0000 0.1000 0.2000 0.3000
Moran's I Score
LISA Relationships
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.
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
14

15. Figure 8: Arrests Per Capita v. LISA of Concentrated Disadvantage
Arrests Per Capita v. LISA of Concentrated Disadvantage
4.00%
3.50%
3.00%
Arrests Per Capita
2.50%
2.00%
1.50%
1.00%
0.50%
0.00%
High to High Low to Low Overall Arrests Per Capita
LISA Relationship
15

16. Figure 9: LISA Cluster Map of Index of Concentrated Extremes
Figure 10: Arrests Per Capita v. LISA of Index of Concentrated Extremes
Arrests Per Capita v. LISA of Index of Concentrated Extremes Score
3.50%
3.00%
2.50%
Arrests Per Capita
2.00%
1.50%
1.00%
0.50%
0.00%
High to High Low to Low Overall Arrests Per Capita
LISA Relationship
16

17. Figure 11: Nearest Neighbor Index LISA for Index of Concentrated
Extremes
Relative Nearest Neighbor Indices
0.3
0.2455
0.25
More Clustered - Less Clustered
0.2 0.1923
0.15
0.1
0.05
0
ICE Low To Low LISA ICE High to High LISA
Index of Concentrated Extremes LISA Relationship
Figure 12: Nearest Neighbor Index LISA for Concentrated Disadvantage
Relative Nearest Neighbor Indices
0.3000
0.2507
0.2500
More Clustered - Less Clustered
0.2000 0.1890
0.1500
0.1000
0.0500
0.0000
Concentrated Disadvantage High to High LISA Concentrated Disadvantage Low to Low LISA
17
Concentrated Disadvantage LISA Relationship

18. Proportional Mapping of Arrests Per Capita v. Interpolated
Concentrated Disadvantage and Index of Concentrated
Extremes Values
Economic and social characteristics, such as Concentrated Disadvantage and the Index of
Concentrated Extremes are not typically 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
18

19. Figure 14: Index of Concentrated Extremes v. Proportional Arrests Per
Capita
Conclusions
Poverty 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.
19

20. 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, 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
20

21. 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.
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 66:29-57
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