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This is to certify that the

thesis entitled

Positive Impacts of Racial Diversity
on Various Measures of Quality of Life in
U. S. Metropolitan Areas

presented by

Eric R. Fahrenkrog

has been accepted towards fulﬁllment
of the requirements for

M.A. Geography

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Major professof/

 

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Date 5—; 7r/777

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1M chlﬁGlDﬁDuopGS—p.“

POSITIVE IMPACTS OF RACIAL DIVERSITY ON VARIOUS MEASURES
OF QUALITY OF LIFE IN US. METROPOLITAN AREAS

By

Eric R. Fahrenkrog

A THESIS

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

MASTER OF ARTS

Department of Geography
1 999

ABSTRACT

POSITIVE IMPACTS OF RACIAL DIVERSITY ON VARIOUS MEASURES OF
QUALITY OF LIFE IN US. METROPOLITAN AREAS

By

Eric R. Fahrenkrog

Racial diversity indices have been used to examine the distribution and
segregation of minority populations within metropolitan areas. Quality of life indices
have identiﬁed and measured the social welfare of populations within these urban areas.
In the last two decades, numerous studies have investigated each type of index but only a
few studies were designed to examine the relationship between racial diversity and
quality of life. Springing from a hypothesis that an increase in racial diversity will result
in an improvement in quality of life, my study used multiple linear regression modeling
to investigate the effects of a diverse urban population on measures of quality of life.
These quality of life measures included an economic, housing, social, and aggregate
component. The hypothesized relationship between racial diversity and quality of life was
not found to be signiﬁcant. Several trends suggested that a more ethnically diverse

commumty has a positive inﬂuence on certain aspects of quality of life.

Cepyright by
ERIC R. FAHRENKROG
1999

Dedicated to Russell and Bernice Crampton, and Paul and Ida Fahrenkrog, who worked
so hard to ensure that following generations could pursue their own interests.

iv

ACKNOWLEDGMENTS

I would like to express my heartfelt gratitude to the Michigan State University
Geography Program class members. These colleagues shared their ideas and insights in
an honest and trusting manner. They helped shape my projects and coursework and more
importantly, my attitude. I wish everyone good luck in their future endeavors.

Dr. Bruce Pi gozzi’s guidance, support, encouragement, and patience (even after my ﬁrst
CATS paper) was inspirational. Thanks to him for being an excellent advisor.

Whenever I walked into his ofﬁce, Dr. Assefa Mehretu treated me like the most
important person in the world. He always greeted me with a smile, encouragement, and
enthusiasm. Many thanks to him for his guidance.

My thanks to Randy Schaetzl, for inviting me to Michigan State University and for
assuming the role of my third committee member. His continual support throughout my
program (as well as my initiation into the Order of St. George) was invaluable. I look
forward to seeing him in Pasadena soon.

Thanks to Dr. Jay Harman and Dr. Jeff Andresen who contributed to my personal and
academic development.

I gratefully acknowledge Rodderick Harrison at the US. Census Bureau, who helped
supply me with the racial diversity index data, as well as a very thorough review of how
each measure was obtained.

My friends, Erin Boydston, Tom Cate, Janet Cochrane, Brett Cohen, Douglas Crocker 111,
Jim Dobbie, Nat Evans, Julia Flagg, Arunas Juska, Lissa Leege, Emily Lyons, Scott
Martin, Deborah Nick, Scott Norman, Paul Rindﬂeisch, Heather Rowe, and Michael
Zuker were all instrumental in my success and development throughout my program. A
special thanks to my friend and roommate Peter Scull who helped turn a difﬁcult and
challenging program into some of the most outrageously fun moments of my life. I would
also like to acknowledge the incredible encouragement, support, and guidance I received
from Elizabeth Capaldi.

Most importantly I would like to thank my family for providing me with love and such a
strong sense of security. They have contributed a foundation to my life that helped
supported me through the process of graduate school. Thanks to my parents for showing
so much interest in my program and Michigan State University. Dad - thanks for making
all the trips up to campus, it meant the world to me! My appreciation and sympathy goes
out to my sister who is going through this painful process (thesis) in Oregon.

vi

TABLE OF CONTENTS

LIST OF TABLES ............................................................................ viii
LIST OF FIGURES ............................................................................ ix
LIST OF EQUATIONS ........................................................................ xi
CHAPTER 1

INTRODUCTION AND LITERATURE REVIEW ........................................ 1

CHAPTER 2

RACIAL DIVERSITY IN METROPOLITAN AREAS .................................... 9
CHAPTER 3

QUALITY OF LIFE IN METROPOLITAN AREAS .................................... 17
CHAPTER 4

METHODS AND PROCEDURES .......................................................... 32
CHAPTER 5

ANALYSIS AND RESULTS ................................................................ 41
CHAPTER 6

CONCLUSION AND FUTURE DIRECTIONS ........................................... 62
APPENDIX A

EQUATIONS FOR POPULATION GROUPS ............................................. 75
APPENDIX B

MIN / MAX VALUES FOR SIGNIFICANT EQUATIONS ............................ 91
APPENDIX C

TREND RESULTS FOR POPULATION GROUPS ..................................... 92
BIBLIOGRAPHY .............................................................................. 96

GENERAL REFERENCES ................................................................. 100

vii

Table 2.1

Table 3.1

Table 3.2

Table 3.3

Table 4.1

Table 4.2

Table 4.3

Table 5.1

Table 5.2

Table 5.3

Table 5.4

Table 5.5

Table 5.6

Table 5.7

LIST OF TABLES

Measures and Dimensions OfRacial Diversity 16
Economic Variables and their Inﬂuence on Quality Of Life ........... 19
Housing Variables and their Inﬂuence on Quality of Life ............ 20
Social Variables and their Inﬂuence on Quality of Life . . . ..22
Metropolitan Statistical Area Population Categories . . . . . . . . ..34
List of the Four Data Groups used in the Analysis ...................... 35
Sample ©SYSTAT Printout of a Quadratic Equation .................. 37
Percentage of Parabolas for all Signiﬁcant Equations that

Adhered to the Research Hypothesis (by Group Size) ................. 43
Constant and Parameters for Two Signiﬁcant Multiple

Regression Equations ....................................................... 46
Constants and Parameters for all Signiﬁcant Multiple

Regression Equations Involving Housing Quality of Life ............ 54
Constants and Parameters for all Signiﬁcant Multiple

Regression Equations Involving the Aggregate Quality

Of Life Measure ............................................................ 55
Constants and Parameters for all Signiﬁcant Multiple

Regression Equations Involving the Isolation Index .................. 58
Constants and Parameters for all Signiﬁcant Multiple

Regression Equations Involving the Spatial Proximity

Index ......................................................................... 59
Constants and Parameters for all Signiﬁcant Multiple

Regression Equations Involving the Dissimilarity and

Absolute Centralization Indexes ......................................... 6O

viii

LIST OF FIGURES

Figure 1.1 Hypothesized Relationship between Racial Diversity
and Quality of Life ........................................................ 7

Figure 1.2 The Inﬂuence of a Change in the Values of the b and c
Parameters on the Shape of the Quadratic Curve ...................... 8

Figure 4.1 Map of the United States and the 288 Metropolitan
Statistical Areas used in this Study ..................................... 32

Figure 4.2 Inﬂuence of the (c — Parameter) on the Shape of the
Resulting Parabola .......................................................... 38

Figure 4.3 Inﬂuence of the (c - Parameter) on the Shape of the
Resulting Parabola ......................................................... 38

Figure 5.1 Quadratic Plot for Native Americans Population Group
Four (Housing Quality Of Life vs. Dissimilarity
Diversity Index.) ........................................................... 42

Figure 5.2 Quadratic Plot for African Americans Population
Group Two (Housing Quality of Life vs. Absolute
Centralization Diversity Index.) ......................................... 45

Figure 5.3 Quadratic Plot for African Americans Population
Group Three (Housing Quality of Life vs. Absolute
Centralization Diversity Index.) ........................................ 45

Figure 5.4 Quadratic Plot for African Americans Population
Group Four (Social Quality of Life vs. Spatial Clustering
Diversity Index.) .......................................................... 47

Figure 5.5 Quadratic Plot for African Americans Population
Group Four (Total Quality of Life vs. Spatial Clustering
Diversity Index.) ......................................................... 47

Figure 5.6 Quadratic Plot for Asians Population Group Two
(Housing Quality of Life vs. Dissimilarity Diversity
Index.) ..................................................................... 49

ix

Figure 5.7 Quadratic Plot for Asians Population Group Three
(Economic Quality of Life vs. Dissimilarity Diversity
Index.) ..................................................................... 49

Figure 5.8 Quadratic Plot for Hispanics Population Group One
(Social Quality of Life vs. Isolation Diversity Index.) .............. 51

Figure 5.9 Quadratic Plot for Hispanics Population Group One
(Social Quality of Life vs. Spatial Proximity
Diversity Index.) ......................................................... 51

Figure 5.10 Quality of Life Types and their Frequencies among
The Signiﬁcant Equations for Population Size Groups ............. 52

Figure 5.11 Racial Diversity Indices and their Frequencies among
The Signiﬁcant Equations for Population Size Groups ............. 57

Equation1.l

Equation1.2

Equation 2.1
Equation 2.2
Equation 2.3
Equation 2.4
Equation 2.5
Equation 2.6
Equation 2.7
Equation 4.1
Equation 4.2
Equation 4.3

Equation 4.4

Equation 4.5

LIST OF EQUATIONS

Basic Linear Regression Equation .................................... 6

Multiple Regression Equation .......................................... 7

Index of Dissimilarity Equation ........................................ 10
Isolation Index Equation ................................................ 11
Relative Concentration Index Equation ............................... 12
Absolute Centralization Index Equation .............................. 13
Average Proximity MinfMaj. Equation .............................. 14

Average Proximity Maj./Total Equation 14

Spatial Proximity Index Equation ...................................... 14
Multiple Regression Equation .......................................... 35
Multiple Regression Equation with Parameters ...................... 36
Example Multiple Regression Used in Study ......................... 36

Equation to Determine the Minimum or Maximum
(Y) Value ................................................................. 39

Equation to Determine the Minimum or Maximum
(X) Value .................................................................. 39

xi

CHAPTER ONE

Introduction and Literature Review

Race, income, household size, and other demographic characteristics form the basis
Of segregation in American residential neighborhoods. A growing Share of minorities is
changing the ethnic composition of the population in the United States and the term
‘racial diversity’ is being heard with increasing frequency. Various academic disciplines
have their own interpretation of the term ‘diversity’, which results in ambiguous and
overlapping deﬁnitions. Recent studies by urban geographers and sociologists have
focused on the impacts of racial composition and diversity measures within spatial areas
(Dahmann 1985; Myers 1987; Blomquist et al.1988; Abrarnson et al.1995; Baldassare
and Wilson 1995). While much of the literature still focuses on aspects of segregation,
the most modern investigations focus on the relationships between racial diversity and the
economic condition of communities and people in our society (Darden 1987; Massey and
Denton 1988, 1988a; Houghton and Mukerjee 1995; Chakravor’ty 1996; Coulton et a1.
1996)

Sociologists and human geographers have been introducing new ideas for
quantifying and measuring the status of members of society. Two of these concepts
include rating occupations in the work force and ranking education attainment levels.
These methods have created a variety of variables and indices for use in constructing a

valid and reliable “social indicator.” Beginning in the mid- 1960’s, various attempts have

2

been made to convince government policy makers of the importance Of social indicators
just as they have accepted the importance of economic indicators (McVeigh and
Dedekind 1995).

Recent studies illustrate the importance of including both economic and social
indicators when determining the quality of life for a group of people (Bumell and Galster
1992; Stover and Leven 1992). For example, a recent study conducted at the University
of Illinois revealed that a person’s overall quality of life improves with that person’s
proximity to trees or other greenery (Sullivan et a1. 1997). Therefore, planting trees in
neighborhoods can help to improve the quality of life for society. Conclusions of this
type increase the likelihood that social welfare research and reform programs will
continue to be helpful. Examples of social welfare components that impact quality of life
include the crime rate, accessibility to public transportation, job availability, and access

to parks and hospitals (Savageau and Loﬁus 1997).

Statement of Problem

Stover and Leven (1992) suggest that knowledge of the relationship between racial
diversity and quality of life might Shed insight into some of the problems that plague
American cities. While an increasing number of studies have begun to investigate each of
the two concepts (Baldassare and Wilson 1995; Haughton and Mukerjee 1995), limited
research has examined the interactions between them. McVeigh and Dedekind (1995)
have brieﬂy examined this relationship. In an attempt to measure the degree and extent
of parity between whites and blacks in the ofﬁce space from 1980 -1990, these authors
explore the interrelations between diversity and quality by utilizing Social Indicators and

a Dissimilarity Index. Their results focus on important aspects of economic welfare and

3

demonstrate the progress made towards increasing adult education and training programs.
The interrelations between racial diversity and quality of life serve as a stepping stone in
this study.

Housing policy makers, local government, and city administrators are not always
able to understand and assess the complex nature of racial diversity issues and quality of
life factors using the conclusions from former studies. In addition to the complexities of
the two concepts, racial diversity and quality of life measures vary across urban areas and
between cities. Therefore, comprehensive research that examines the impacts of both
diversity and quality of life is still needed. There are numerous studies that focus on the
lower quality of life associated with the concentration of certain ethnic groups (Darden
1987; Massey and Denton 1989; Miller 1990; Bickford and Massey 1991; Waldorf 1993;
Denton 1994; Fong 1994; Abramson et a1. 1995). In this research project, I will focus on
examining the improvements of quality of life, which may be attributed to an increase in
the racial diversity among American cities.

Does a more racially diverse environment result in a better quality of life? Do
humans beneﬁt from exposure to cultures other than their own? These questions are
controversial and emotional subjects throughout the world. Here in the United States, we
hear various opinions in the about whether or not the classroom climate has improved
with increased diversity and “parents’ choice" in the schools their children attend.
Another example of the emotional force behind diversity issues is the heated debate about

books such as The Bell Curve (Hermstein 1994). There will always be critics claiming

 

that integration and diversity have negative effects on society. However, more people

4

will counter with arguments that learning about and sharing different cultures is a beneﬁt
that all citizens can gain from.

Exposure to another race often serves as a catalyst to the acceptance of others. This
attribute can be advantageous throughout one’s life and professional career. From
colleges to the workplace, the acceptance of a member from another ethnic background is
essential to the morale of the “whole.” While the United States still has many racial
issues to work on, it beneﬁts from having members of almost every nation in the world as
citizens. Many Americans have members of different cultures as neighbors, which is
especially evident in the metropolitan areas of the country.

New York City, Chicago, and Los Angeles serve as cultural centers for the whole
USA. In these places, racial identity and cultural traditions almost always overlap and
are often Shared amongst members of communities. A culturally - conscious person most
likely not only accepts members of other racial groups, but Often notices when certain
cultural inﬂuences are lacking or missing from a neighborhood or city. For example,
people who have enjoyed living in a northeastern metropolitan area of the United States
may have difﬁculty moving to a smaller city or town. The lack of ethnic restaurants and
absence of cultural centers would be very obvious to those accustomed to this variety of
social opportunities in a larger city.

Sometimes, however, the exact Opposite situation arises. Throughout the sixties and
seventies, the term “white ﬂight” described the millions of people who left American
cities. Many studies indicate that the majority of these people left the urban areas
because they felt threatened by the increasing African American, Hispanic, and Asian

populations. The peOple who “ﬂed” felt there was no beneﬁt or inherent gain from

5

exposure to a member of another racial group (Darden 1987; Massey and Denton 1988a;
Ginsburg 1990; Bickford and Massey 1991; Massey and Denton 1991; F ong 1994).

The duality of the race issue poses some intriguing questions. Does racial diversity
effect one’s quality of life? What is the extent of this relationship? Are the effects of
racial diversity limited to any particular aspect Of quality of life? Through the expansion
of the ideas and techniques of previous studies, the current research will add to the

geographic literature in this area and examine these questions and issues in detail.

Statement of Purpose

Over the last three decades, policy makers of American cities have been struggling
with issues pertaining to race and the quality of life. Volumes of research literature exist
regarding segregation, racial parity, social indicators, housing quality, and quality of life
during this period of time. Recently, it has become important to eliminate segregation
between communities while simultaneously working to maintain a high quality of life in
cities. The present study examines the inﬂuence of an urban area’s racial diversity on the
quality of life of its populace using tools and quantitative indices derived in previous
research.

The purpose of this research is to determine if any relationships exist between racial
diversity and the quality of life in urban areas. A second more precise purpose is to
determine if an increased amount of diversity adds to a measurable increase in quality of
life. One popular method for establishing and examining these types of relationships is

through multiple linear regression modeling. Under normal conditions a functional

6

relationship is considered linear when pairs of X and Y values fall into a pattern that is
best depicted by a straight line. Hence, we get the linear model:

Y=a+b1X1 (Equ. 1.1)
where:
Y= dependent variable
X= independent variable
a = Y intercept
b = partial coefﬁcient

However, the relationship between racial diversity and quality of life is likely not
linear. Initial regressions that were run in a previous research project I conducted between
both groups in Detroit, MI indicated the lack of any marked linear trend. Hence, quality
of life might be a ﬁrnction of not one but several aspects of racial diversity.

When considering the relationship between racial diversity and quality of life
numerous conditions might exist. Often the highest quality of life values are found in
afﬂuent, majority white areas, and the lowest conditions are found in minority ghettos.
One might also expect that the relationship in areas with varying proportions of both
racial groups would fall somewhere between these other two. If we imagine this
relationship as a curve (Figure 1.1) an initial decline is seen until a minimal quality of life
value is achieved. However, it is unlikely that the quality of life score will remain at the
bottom of the curve. This study hypothesizes that some of the inherent beneﬁts that
accompany a racially diverse area will help create a relationship curve that shows that
quality of life also increases as racial diversity does. In the ﬁgure, this relationship will be
represented by the upswing in the curve resulting in a distinctive “U” shape.

This study expects to ﬁnd a curvilinear relationship between racial diversity and

quality of life, and that to an extent, an increase in racial diversity will add to the quality

 

One Possible Relationship Between Racial
Diversity and Quality of Life

16
14
12
10

Quality oi Lite

 

ONROQ

Racial Diversity

 

 

 

Figure 1.1 - Hypothesized Relationship between Racial Diversity and Quality of Life

of life of that population. I expect to see an initial decline in quality of life, however the
curvilinear hypothesis accounts for the upward swing or eventual improvement in quality
of life I am anticipating. By hypothesizing this increase, this study expects to ﬁnd a
second order polynomial relationship between racial diversity and quality of life.

In order to examine the expected curvilinear relationship a multiple regression

model must be used. Such that,

Y = a + bxi + 6X3, (Equ. 1.2)

where:
Y = dependent variable
X = independent variable
a = constant (Y interval)
b = partial coefﬁcient (b parameter)
c = partial coefﬁcient (c parameter)

8

In this case quality of life is used as the dependent variable Y and racial diversity is
the independent variable X. The parameters can be estimated by the least squares method
in a multiple regression model.

In these relationships the b parameter measures the rate of change in quality of life
at the origin. This partial coefﬁcient can take on negative, zero, or positive values, and
consequently enables the representation of all possible changes in the quality of life near
the origin. The c parameter indicates the degree of curvature of the quality of life surface.
If b is positive and c is positive, the quality of life curve will be similar to the shape
indicated in the upper left corner of Figure 1.2 (Latharn and Yeates 1970). Whether or not

this second order is present and is signiﬁcant will be the test of my research hypothesis.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Y=a+bx.+cx.2 Y=a-bX.+cXi2
60 ' 60
-' 4o -' 4o
8 20 8 20
o o
>0 i\ )- > W ‘3 ‘b :9 i\ k 2‘ ‘ir ‘0 ‘9
Racial Diversity Racial Diversity
Y=a-bx,-cx.2 Y=a+bX.-cX.2
‘zgﬁlwmwll: 42°;HIHWHI11
o-
8 -20 8 -2o
40 4o
:9 :\ JP 5 (Ir <0 % :9 3 P‘ I‘ W ‘3 9
Racial Diversity Racial Diversity

 

 

Figure 1.2 — The Inﬂuence of a Change in the Values of the c Parameters on the Shape of
the Quadratic Curve.

 

 

CHAPTER TWO

Racial Diversity in Metropolitan Statistical Areas

Quantifying racial diversity in an urban area includes many factors. Obviously, the
initial observation is the percentage of minority residents compared to the total
population of the city or metropolitan area. In addition to the total population amount, the
actual physical size and layout of the area being studied will provide a signiﬁcant
inﬂuence.

The location or distribution of minorities throughout a given city has a great impact
on the interaction between various ethnic groups. Large and/or small clusters of minority
residents and their relation to the center of the city are another important factor in the
identiﬁcation process. In many older cities, neighborhoods adjacent to the downtown area
are densely populated with minorities. While the close proximity to the city center is in
one sense a positive factor, it is negated due to poorly maintained and overcrowded
housing. In contrast, many cities are experiencing renewal of neighborhoods surrounding
the central core. Racial and ethnic diversity in these renewed neighborhoods will be
assessed when the next US census is taken.

Because racial segregation or diversity is a multi-dimensional phenomenon that is
best measured by a battery of indices rather than one. Over twenty measures for
segregation are available; this study will utilize the ﬁve best known measures (Table 2.1).
This decision is based upon a previous study that ran a principle components analysis on

all 20 segregation measures and determined the ﬁve most inﬂuential and reliable

9

10

measures (Massey and Denton 1988). These ﬁve indices each measure a different
dimension of racial diversity. The values for each diversity index have been calculated by
the US. Census Bureau for each Metropolitan Statistical Area, and are recalculated every
5 years (US. Census Bureau, 1997). This study uses the pre-calculated 1990 values for 5
different indices, obtained from Roderick Harrison at the US Census Bureau. Three of the
ﬁve MSA diversity indices are aggregate measures calculated by combining the census
tract unit values for each city.

The ﬁrst dimension, evenness, refers to each city having the same proportion of a
minority group as the nation as a whole. The index chosen to measure this dimension of
racial diversity is the index of dissimilarity,

D = Z LIP.- - Pl

,., 2TP(1- P).
(Equ. 2.1)

Where:
i, = total population of areal unit (MSA) i
pi = total minority proportion of areal unit (MSA) i
T = total population size of the study area (US)
P = total minority proportion of the study area (US)
11 = number of areal units

This equation measures the departure from evenness by taking the weighted mean
absolute deviation of every unit’s minority proportion from the MSA’s minority
proportion, and expressing this quantity as a proportion of its theoretical maximum
(James & Taeuber 1985). Conceptually, the index of dissimilarity measures the
proportion of minorities that would have to change their area of residence to achieve a

uniform population distribution in a speciﬁc area.

11

In this index, the number of minority members moving are expressed as a proportion
of the number that would have to move under conditions of total segregation (Harrison
and Weinberg 1992). The index ranges from 0.0 to 1.0. On this scale a 0.0 represents
minimum levels of segregation or a highly diverse community or city. A score of 1.0
represents an extremely segregated metro area with very low levels of diversity.

The second dimension, exposure, “measures the degree of potential contact, or
possibility of interaction, between minority and majority group members” (Massey and
Denton 1988). Exposure is dependent upon the extent to which two groups Share
common residential areas, and hence on the degree to which the average minority group
member encounters segregation. The most widely used and recognized measure of

exposure is the isolation index,

P*x = 2ni=l [Xi / XIIXi / ti] (Equ. 23)

where:
t, = total population of areal unit i
x, = total minority population of areal unit i
X = total minority population of study area
n = number of areal units

Unlike the dissimilarity index that examines proportions, the isolation index focuses
on probability. This index reﬂects the probabilities that a minority person lives in the
same residential area as a majority person (Harrison and Weinberg 1992). My study uses
the isolation index to measure the lack of exposure the four minority groups have to other
inhabitants in each city. The isolation index measures the amount to which minority
members are exposed only to one another, and is calculated as the minority — weighted

average of the minority proportion in each area. On this scale that also ranges from 0.0 to

12

1.0, a low score represents a low level of segregation or a high level of diversity. A high
score would reﬂect a city with a low level of diversity.

The third diversity dimension, concentration, refers to the relative amount of
physical space occupied by a minority group or groups in the metropolitan area. Minority
groups of the same relative Size occupying less space would be considered more
concentrated and consequently more segregated. To measure this dimension the study

uses the relative concentration index:

(Equ. 2.3)
RCO = {[Zni=| (xiai/X)]/[2"i=1(yia,/Y)] —1}/{[2",=1(t.-ai/T1)]/[£",=nz(tia,/T2)]-l}

where:

a, = land area of unit I

n = number of areal units

n. = rank size of the MSA where the cumulative total population of areal units equals the
total minority population of the study area

n; = rank of the MSA where the cumulative total population of units equals the minority
population totaling from the largest MSA down

t, = total population of area i

T. = total population of units from 1 to n.

T2 = total population of units ﬁ'om n; to n

x, = total number of minority members in unit i

X = total number of minority members in study area

y, = total number of majority members in unit i

Y = total number of majority members in study area

This index takes the ratio of X minority members’ to Y majority members’
concentration and compares it with the maximum possible ratio that would be
obtained if X were maximally concentrated and Y minimally concentrated,
standardizing the quotient so that the index varies between negative one (-1 .0) and
one (1.0). A score of zero (0) means that the two groups are equally concentrated in
urban space. A score of—1.0 means that Y’s concentration exceeds X’s to the
maximum extent possible, and a score of 1.0 means the converse. The relative
concentration index (RCO) measures the share of urban space occupied by group X
compared to group Y (Massey and Denton 1988, p. 291.).

13

The fourth dimension, centralization, refers to the proximity a racial group’s
population has to the central business district or downtown area. As previously
mentioned, in many older and larger urban areas, much of the housing surrounding the
downtown area is rundown, and neglected. It is often considered disadvantageous to live
in these areas; as a result, minorities because of the poor infrastructure often inhabit
centralized areas. The index chosen for measuring centralization is the absolute

centralization index,

ACE = (2ni=l Xi-lAi) - (2ni=l XiAi-I) (Equ. 2-4)

where:
A, = the cumulative proportion of land area through unit 1
X, = the cumulative proportions of minorities in unit i
n = number of areal units

An absolute centralization index measures a group’s spatial distribution compared
to the distribution of land area around the city center. The area] units are ordered by
increasing distance from the central business district and A, refers to the cumulative
proportion of land area through unit i (Massey and Denton 1988, p. 291).

Varying between —1 .0 and 1.0, the absolute centralization index examines only the
distribution of the minority group around the downtown area. Positive values of this
index suggest a tendency for minority group members to reside close to the city center,
while negative values indicate a tendency to live in outlying suburbs (Hanison and

Weinberg 1992). On this index a score close to 0.0 would indicate an even distribution of

a racial group throughout a city, or a high level of uniformity.

14

The ﬁfth and ﬁnal dimension, clustering, addresses the issue of contiguity among a
minority group’s neighborhoods. To calculate this measure, one must ﬁrst estimate the
average proximity between members of the same group, and between members of
different groups. The average proximity between group X members can be approximated

by:

Pxx = Zni=1 an=1 Xiijij/XZ (Equ. 2.5)
while the average proximity between members of X and Y is estimated as:

ny = 2ni=1 213:1 Xiijij/XY (Equ. 2.6)

Here, the average proximities between Y members (ny) and among all members of
the population (Pu) are calculated by analogy with Equation 2.5. The index of spatial
proximity is the average of intra-group proximities, Pxx/Pn and ny/Pn, weighted by the

fraction of each group in the population:

SP = (XPXX + Yny)/TPu (Equ. 2.7)

where:
XP,“ = average weighted proximity between minority members
Yny = average weighted proximity between majority and minority members
TPn = average weighted proximity among all members of the population

The spatial proximity index (SPC) measures what can be thought of as the
checkerboard problem: diversity is negatively impacted if red squares are adjacent to

other red squares while black squares are similarly grouped together. If neighborhoods

15

are individually segregated but are mixed up like the squares on a checkerboard like
alternating black and red squares, diversity is improved. Integration and interaction
between racial groups in the entire metropolitan area are more difﬁcult for people living
in a large, contiguous ghetto, rather than in an isolated neighborhood (Denton 1994).

According to White’s (1986) index of spatial proximity, the average intragroup
clustering index for the minority and majority populations results from a weighted
proportion of each group in the total population. Spatial proximity equals 1.0 if there is
no differential clustering between minority and majority group members. The index is
greater than 1.0 when members of each group live nearer to one another than to members
of the other group. If minority and majority members lived nearer to members of the
other group than to members of their own group, then the index is less than 1.0 (Harrison
and Weinberg 1992). For this measure, a low score represents a more diverse

metropolitan area.

16

Table 2.1 — Measures and Dimensions of Racial Diversity.

 

 

INDEX WON FORMULA
n tilpi _P|
Diss' ' - s Evenness D= .
”"1“” (D1 ’ E 2TP(1- P)
Isolation (ISO) Isolaion 130:2“... (xi/rowing
Spaial proximity (SPC) Clmtaing SPC=(XP,, +Yp,,)/TP,

[(aniDO/(EYWI-l
Rm:
[mammalian-1

 

Relative Comemmion (RCD) (brmﬂation

Alsolute Canalimtion (ME) Ocrlralization ACE=OIXH A0 -(XX, AH)

Synbols:
x,=mnrberofnurbersofgrupxinMSAi.
yi=murbaofnurbersofgrmpyinWAi
X=netropolitanwidetotalnmbaofnmbasofgmpx
Y=nmwolitanwidetotalnurbaofnurbasofgoqu
q=taalporxﬂatimofmi.

TFtaalpopﬂaionofb/SA

ai=minsquarenils,ofareai.
A.=taalaea,insquarerrilcs.ofMSA

Puamlearleu refcrtoaveragcproxim'ty betweargroupsx,y.andt.

 

 

 

CHAPTER THREE

Quality of Life in Metropolitan Statistical Areas

As in identifying racial diversity, assessing the quality of life in an urban community
is a complex study involving a multitude of factors. Complicating the study are variables
that are difﬁcult to compare on an equal basis. For example, median family income is a
factor that relates to all areas whereas climate does not. Greatly inﬂuencing lifestyle,
climate varies among and within most cities.

Size of a city impacts life quality in a number of ways (see ch.2). Cities of large
population may contain sizable clusters of impoverished and environmentally depressed
areas. At the same time, these large cities may provide cultural amenities accessible to
low-income residents that would not be available in smaller communities. The number of
automobiles per family is often considered a positive economic factor that may indicate a
good transportation network. However, jammed expressways causing commuting delays
and carbon monoxide pollution is a serious negative factor. An efﬁcient public
transportation network is a positive quality of life factor for all income levels.

Median family income is the economic standard of quality of life in an urban area.
However, quality of life must be thoroughly analyzed in a manner similar to that utilized
in determining racial diversity in MSAS (see chapter 2). Type and condition of housing

assert a major inﬂuence on a city’s inhabitant’s, life quality. Housing characteristics, like

17

18

economic factors, can be compared to those of urban areas everywhere. As such,
variables associated with economic quality and housing quality are listed as two of the
three special groups or components for quality of life as it pertains to this study. The third
component is comprised of social quality of life measures that includes variables relating
to health, climate, education, transportation, culture, recreation, etc.

The data for the quality of life measurements came from three sources: the 1990 US
Census Bureau report on Social and Economic Characteristics for Metropolitan Areas,
the 1990 US Census Bureau report of Detailed Housing Characteristics for Metropolitan
Areas, and the 1990 Ijl_aces Rged AlrLanag (Savageau and Loftus, 1993). The quality of
life variables were selected based upon the availability of the data at the MSA level and
their use in previous studies of economic/social geography. In McVeigh and Dedekind’s
study (1995), 106 variables of social description and racial parity demonstrated that
certain measures are better at distinguishing different aspects of quality of life. Several of
the variables the authors found to be highly reliable in their study have been used here,
including: Labor Force Participation, Unemployment Rate, Families Below Poverty
Level, Exposure to Crime, Median Family Income, and Access to Education and Health

Care. The present study utilizes 27 variables that were available at the MSA level.

Economic Quality of Life (EQOL)

The quality of life variables were developed in three groups. The ﬁrst group,
‘economic quality of life,’ was made up of six variables (Table 3.1) that reveal various

economic characteristics of the inhabitants of the MSAS.

19
“Median Family Income” (MFI) and “Percentage Families Below Poverty” (FBP)

were chosen because they indicate different aspects of economic well being. MFI

represents the median dollar income per family for a given MSA, while F BP is a

Table 3.1 — Economic Variables and their Inﬂuence on Quality of Life.

 

 

 

Variables Inﬂuence
1. Median Family Income (MFI) Positive (+)
2. % F arnilies Below Poverty Level (FBP) Negative (-)
3. % Persons 16 and over in Labor Force (PLF) Positive (+)
4. % Persons Unemployed (PUE) Negative (-)
5. % Persons Graduated from High School or GED (PGH) Positive (+)
6. % Persons Attaining their Bachelors Degree (PBG) Positive (+)

 

percentage of the total population in each MSA that is below poverty level. The
distinction between measures of these variables is important because a low median family
income does not necessarily mean that it is below the national poverty level or vice-versa.

The variables “Percentage Persons 16 and over in Labor Force” (PLF) and
“Percentage Persons Unemployed” (PUE) were used in order to examine the strengths
and weaknesses of the labor situation within each MSA.

The last two variables examine the educational backgrounds within the MSAS.
These factors, Percentage Persons having Graduated from High School or Equivalence
Degree (PGH) and Percentage Persons having attained their Bachelors Degree (PBG), are
predictors of the economic beneﬁts attributed to higher education. For example, a high
school graduate may have an advantage over someone who does not have a high school

diploma. The same reasoning applies for the advantages of obtaining a University degree.

20

Housing Quality of Life (HQOL)

The second quality of life component is based on housing characteristics. Housing
plays a large role in the quality of life of a city’s inhabitants (Denton and Massey 1991;
Suﬁan 1993; Lawrence 1995). The present study uses eight housing variables to deﬁne

housing quality.

Table 3.2 — Housing Variables and their Inﬂuence on Quality of Life.

 

 

Variables Inﬂuence
1. % Living in Different House than in 1985 (PDH) Positive (+)
2. % Housing Units that are Condominiums (PUC) Positive (+)

3. % of Housing lacking complete Plumbing Facilities (HLP) Negative (-)
4. % of Housing lacking complete Kitchen Facilities (HLK) Negative (-)
5. Median Year the Housing Structure was Built (YSB) Positive (+)
6. Median Monthly costs (in Dollars) with Mortgage (MCM) Negative (-)
7. Median Monthly costs (in Dollars) not Mortgaged (MCN) Negative (-)
8. Median Gross Rent (in Dollars) (MGR) Negative (-)

 

 

While some correlation exists between the housing and economic quality of life
variables, the eight housing variables measure distinctly different conditions in a person’s
quality of life. For example, the quality of a person’s housing is in part dependent upon
their income, but it is also reliant upon housing availability.

In metropolitan areas, both the rate of ‘recycling’ of available units and the type of
units available are important housing factors. Housing built more recently is considered a
positive indicator of an area’s growth. In order to capture these aspects of housing

quality, the variables “Percentage of People living in Different Housing than in 1985”

21
(PDH), “Percent Units that are Condominiums” (PUC), and the “Median Year the

Housing Structure was Built” (YSB), were chosen.

Two detrimental aspects Of housing quality included in this study are the
“Percentage of Housing Units that Lack Complete Plumbing” or “Kitchen Facilities”
(HLP and HLK). The presence of these facilities is generally considered important to
maintaining optimal living conditions; housing with complete facilities may promote
better personal hygiene and attention to nutrition - factors that indicate a higher quality of
life.

Three median cost measures were utilized to examine housing costs for homeowners
and renters in the metropolitan areas: “Median Monthly Owner Costs with Mortgage”
(MCM), “Median Monthly Owner Costs Not Mortgaged” (MCN), and “Median Gross
Rent” (MGR). These variables indicate the demand for housing within the MSAS as well

as its affordability.

Social Quality of Life (SQOL)

The ﬁnal quality of life component is ‘social welfare.’ This group is composed of
thirteen different social factors that reﬂect quality of life (Table 3.3). Six of these focus
on the social characteristics of the population of the metro areas. The other eight
variables examine characteristics of the cities themselves and how they affect a person’s
quality of life in that area. By including the social dimensions for both the metropolitan
areas and the populations, this component thoroughly examines numerous aspects Of

social quality of life.

22

The ﬁrst ﬁve population variables were obtained from the US. Census Bureau —
(Social and Economic characteristics). “Percentage of Persons Without a Telephone”
(PWT) was selected as an indicator of quality of life because communication is essential
in modern society. Whether someone uses the phone for an emergency or to use a dial-in
server for the Internet, the telephone has been, and will continue to be, an important

instrument for outside contact.

Table 3.3 — Social Variables and their Inﬂuence on Quality of Life.

 

 

 

Variables Inﬂuence
l. % of Persons without a Telephone (PWT) Negative (-)
2. % of Persons using Public Transportation (PPT) Positive (+)
3. Mean Travel Time to Work (TTW) Negative (-)
4. % of Persons age 18-24 at a College or University (PEC) Positive (+)
5. % of Persons under age 18 Living with two Parents (PLP) Positive (+)
6. Access to Various Methods of Transportation (MT) Positive (+)
7. Access to Various types of Employment (MJ) Positive (+)
8. Access to Secondary and Higher Education (ME) Positive (+)
9. Exposure to Various Climate Conditions (MCL) Positive (+)
10. Exposure to Various types of Crime (MCR) Negative (-)
11. Access to Culture and the Arts (MA) Positive (+)
12. Access to Various types of Healthcare (MH) Positive (+)
13. Access to Lakes, Parks, and Recreational Activities (MR) Positive (+)

 

“Percentage of Persons who use Public Transportation in a metro area” (PPT) often
reveals whether an inhabitant must endure trafﬁc to get to work. In addition, numerous
city areas have carbon monoxide problems. Hence, the use of public transportation can
positively inﬂuence a person’s quality of life in more than one way. Another commuting
variable included is the Mean Travel Time to Work (TTW). This measure is the average

travel time it takes a city resident to get to work from the front door of their home. A

23

longer travel time has a negative impact or inﬂuence on the quality of life because people
consider their time to be valuable.

The next two variables help depict the social quality of life for children and young
adults within an urban area. The Percentage of Eighteen to Twenty-four years oldS
Enrolled in a College or University (PEC) does a good job of explaining how well
prepared this age group will be educationally. Another important aspect of childhood is
the Percentage of Children Under the Age of Eighteen that are Living with Two Parents
(PLP). With both parents present in a household, role models of both genders can add a
sense of “self-security” as well as have a positive inﬂuence on a child’s quality of life.

The next eight variables for social quality of life were taken from the Places Rated
Almanac (Savageau and Loftus 1993). The data for these eight variables have already
been collected and calculated by the authors. The raw data are unavailable, but the ms
Rated Almanac scores are widely used for research purposes and are therefore deemed
acceptable for use in this thesis. These variables are composite measures because they
combine several quality of life characteristics to make one aggregate score. Rather than
incorporating information from a particular characteristic of the population, these
measures focus on the resources each metropolitan area has to offer its residents. The
methodology used by Savageau and Loftus rates each metropolitan area using 50
aggregate variables from their own research in 1990 or in 1993. They rank each area’s
quality of life score by summing the ordinal rankings over the nine categories, which
include cost of living, transportation, job availability, higher education, climate, crime,
the arts, health care, and recreation. The present study did not use the Places Rated

variable entitled ‘cost of living’ because several of the variables used to create that

24

category were already used in this study’s economic quality of life component. To avoid
collinearity between variables, this category was omitted. What follows is a brief
description of each of the eight variables and how Savageau and Loftus created the
metropolitan statistical scores.

Although these aggregate variables are deemed acceptable and widely used in
quality of life research, several urban geographers and sociologists argue that the
methods Savageau and Loftus use to calculate these measures are biased. For example, in
their article “Quality of Life Measurements and Urban Size: An Empirical Note,” James
Bumell and George Galster (1992) suggest that certain social amenities and disamenities
are often associated with city size. Their main criticism towards Savageau and Loftus’
method centers on the use of the ad hoc weighting scheme that may bias the rankings
since they may not reﬂect the actual value or importance the residents place on the
quality of life components. In addition, Bumell and Galster are critical of the
appropriateness and reliability of some of the variables chosen to measure the quality of
life components. Their main concern is that the methodology biases the quality-of-life
scores to favor larger areas.

Although Bumell and Galster’s (1992) concerns are valid and well argued, they
shouldn’t apply to this research project. The ﬁrst reason their argument doesn’t pertain to
this paper is that since Bumell and Galster’s article, Savageau and Loftus (1993) made a
concerted effort to address these issues, and improve their ‘ad hoc’ weighting scheme. In
addition, my research project will group the metropolitan areas examined into four

population categories to guard against a “big city bias. What follows is a brief description

25

of each of the eight variables and how Savageau and Loftus created the metropolitan
statistical scores.

To determine a transportation measure (MT) for each urban area, the authors take
three broad factors into consideration. The ﬁrst factor is the overall connectivity of the
city to other areas. This portion is calculated from the number of non-stop jet and
commuter airline destinations from that city, as well as the number of passenger rail
departures from that area. These measures help determine the access, and options, a
person living in a metro area has for traveling to another city. The second part of the
transportation score is the commuting involved for the metro area’s inhabitants. The
transit revenue miles and the average time (in minutes) it takes to get to and from work is
used to construct this factor. The third part of the transportation score is the centrality of
the metropolitan area. This is a measurement that examines one metro area’s proximity
to all other US metro areas; a combination of latitude and longitude measurements, the
distance connecting cities by national highways, and passenger rail directions are
included.

Savageau and Loftus (1993) weight all three transportation factors differently, based
on what they feel is more important. Connectivity constitutes for 60 percent of the ﬁnal
score, while commute and centrality make up for the other 30 and 10 percent,
respectively. The sum of these weighted scores for each metro area is then normalized
such that the 50th percentile point is the mean for all metro areas (Savageau and Loftus,
1993). For instance, the authors indicate that the MT variable for Chicago, Illinois was
ranked number one among all metro areas with a score of 98.92, indicating that the

people who live in Chicago have a high quality of life when it comes to transportation.

26

The next variable chosen from the P_laces Rated Almm for use in the present study
was Jobs (MJ). This variable examines the near-future job growth rates for each metro
area and evaluates the prospects for future employment in that area, which is a major
quality of life factor for a city’s population. Two criteria are used here to create this
score: the percent increase in new jobs by the year 2000, and the total number of new
jobs created between now and that date.

In the case of jobs, factor analysis assigns a weight of 74 percent to number of new

jobs and 26 percent to percent growth. A metro area’s ﬁnal score is its percentile on a
scale of 0 to 100 corresponding to its weighted average scores for new jobs and for
percent growth. Atlanta, Georgia’s score is 100 and New York City, New York’s is
0.00. They are respectively the best and worst US metro areas for jobs between now
and the millenium (Savageau and Loftus 1993, p.109.).

The Education (ME) value measures the Opportunities for higher education available
to residents of a given area. Because it reveals how many options an individual has for
pursuing or continuing a higher education, MB is an important aspect Of an area’s social
quality of life. To obtain the higher education score, two major criteria are used. The ﬁrst,
‘college town,’ is the collegiate enrollment weighted by number of typical attendance
years needed to get the highest degree offered (i.e. Associate degree = 2, baccalaureate by
4, comprehensive by 6 and doctoral enrollment by 9.) This number is then divided by the
city’s population to get the ‘college town’ score (Ibid., 134.) The other ME component is
‘available institution,’ or the total number of institutions at any level that are available in
each metropolitan area. The ‘college town’ and ‘available institution’ details are
combined through a differential weighting procedure that uses one-third of ‘college town’

and two-thirds for ‘available institutions’ to produce the ﬁnal score. All of the overall

ME scores are then normalized such that the 50th percentile is the average. A high score

27

denotes an area’s educational Options past high school; lower scores indicate fewer
options for an area’s residents (Ibid., 134.)

The Climate (MCL) category is clearly not a social variable itself, but the
physicalclimate of a metropolitan area does have an impact on a person’s social quality of
life. This variable considers how various weather elements and climatic conditions
inﬂuence quality of life. This variable contends that a better climate results in a better
quality of life. Twelve data elements were used to calculate the MCL: monthly maximum
and minimum temperatures, wind speeds, humidity, darkness, clear days, and
precipitation in the form of rain and snow. Savageau and Loftus (1993) reduce this
weather information into three general parameters. Winter wind-chill temperatures,
summer humidity levels and other discomfort descriptors created the “mildness”
parameter. “Brightness,” which embraces the number of clear days and wet days
mediated by latitude, is an indicator of potential sunlight in the area. The third factor is
“stability” which incorporates weather extremes such as thunderstorms, snow
accumulation, and the difference between summer and winter mean temperatures. To get
a ﬁnal score, the scores for mildness, brightness and stability in each area were weighted
by their relative importance. A metro area’s ﬁnal score is its percentile on a scale of 0 to
100 corresponding to its weighted average (Savageau and Loftus, 1993).

Among all previous urban studies, crime (MCR) is unanimously considered to have
a major negative inﬂuence on a city’s quality of life (Johnston 1988; Bumell and Galster
1990; Stover and Leven 1992; F ong 1994; McVeigh and Dedekind 1994; Baldassare and
Wilson 1995). In the present study, this variable was used to indicate the level of both

personal and property safety in each city. To create a score for this factor, Savageau and

28
Loftus (1993) averaged the rates for violent and property crimes for the last ﬁve years for

each metro area. The overall ‘violent crime rate’ is a combination of the murder, robbery,
and aggravated assault rate. Because property crime is considered less threatening to
human nature than crime against people, property crimes are given one-tenth the weight
of violent personal crimes in the calculation of this category. The ‘property crime rate’
includes the rates for burglary, larceny-theft, and motor vehicle theft. The sum of the
property and violent crime rates were scaled against a standard where the average sum for
all metro areas is set at 50. Cities with lower crime rates than the metro area average
earned standard scores higher than 50. Likewise, places with higher crime rates than the
average get standard scores lower than 50. In other words, if a city received a high score
for MCR, then its inhabitants are exposed to more crime.

The “renaissance” ﬂavor, or positive enlightenment, in a city is often attributed to
exposure to culture and the arts. Savageau and Loftus (1993), using 14 different
descriptors of the formal cultural aspects of each city created an Arts variable (MA). The
analysis resulted in three broad components: bigness, reading popularity, and museum
popularity. The ﬁrst of these three components, ‘bigness,’ takes into consideration the
number of art museums, the total museum attendance, the number of dance and theatrical
performances (such as ballet, touring artists, operas, and symphony performances), as
well as the number of people served by libraries, the total library books, and the total
library circulation. The second component, ‘reading popularity’, is made up of the
percent of total population served by libraries, the number of library books per capita, and
the library circulation per capita. ‘Museum popularity’ measures per capita museum

attendance as an indicator of the attractiveness of that city’s culture. To arrive at a ﬁnal

29

number, each city’s scores for bigness, reading popularity, and museum popularity are
weighted by their relative importance. The score for each metro area is its percentile on a
scale from 0 to 100, corresponding with its weighted average. New York City’s score is
100 while Las Vegas, Nevada’s score is 0.00 which are respectively the best and worst
scores for (MA) in US metropolitan areas.

The access people have to health care is another very important aspect of quality of
life. Health (MH) was selected from the P_laces Rated Almanﬁ to measure the
availability and choice of medical facilities the residents have in a given city. Five criteria
are used to rate the supply of health care in a metro area on a per capita basis: the
numbers of general / family practitioners, medical specialists, surgical specialists, short-
terrn general hospital beds, and hospitals with physician teaching programs certiﬁed by
the AMA. The size of the patient base generally reﬂects the type of physicians who
practice in an area. Typically, residents of a city with a larger population have better
access to medical specialists. In smaller metropolitan areas, general / family practitioners
are usually the primary health care providers, while in larger cities, the medical
specialists per 100,000 people (such as specialists in speciﬁc medical disciplines such as
pediatrics or cardiovascular diseases) are more common. In addition, the number of
surgical specialists per 100,000 people would reﬂect greater access to surgery (based on
the number of physicians operating regularly in a given week).

The number of hospitals in a city that earn accreditation by the Joint Commission on
Accreditation of Healthcare Organizations indicates the quality of health care in a
community. The number of beds located in each facility that is accredited is also a

reliable indicator of medical accessibility in a city. While the lack of accreditation

30

doesn’t necessarily mean that a facility is substandard, the presence of such accreditation
demonstrates that the hospital has passed rigorous and periodic reviews. Because of cost-
containment policies in health care and the shift towards outpatient services, bed
availability still reﬂects a city’s health-care supply, even though the number of hospital
beds is dropping throughout North America.

Another component to the MH category is the size of the local physician residency
programs. This measure is the number general hospitals that have approved physician-
training programs. Institutions without teaching programs aren’t necessarily lagging in
quality, but facilities with such programs tend to be larger urban hospitals where the
interaction between students and faculty encourages the development and use of the latest
techniques, equipment, and therapy.

A total score for MH comes from standardizing and combining the totals from all
ﬁve factors. The ﬁnal measures are then scaled against a standard where the average sum
for all metro areas is set at 50. A low score in (MH) indicates that the health care
emphasis in the particular city is probably centered on basic health care which would
indicate that the latest techniques, equipment, and personnel trained to implement new
advances in health care are most likely to be found elsewhere.

The ﬁnal social variable used from the Places Rated AlmaLag in this study is
Recreation (MR). Quality of life is certainly affected by the options and quantity of
recreational activities available in a metropolitan area. Recreation opportunities that are
accessible during evening hours and on weekends reﬂect how leisure time can be Spent.
In order to measure the quantity of activities available to each resident twelve

recreational elements were examined and grouped into three clusters. One is called

31

‘common denominators’ and includes the number of public golf holes, movie screens,
and restaurant quality stars, including per capita measures of each of these. A cluster
entitled ‘crowd pleasers’ accounted for seats for major and minor-league professional
sports games as well as seats for college sports home games. The last cluster, ‘outdoor
assets,’ includes the number of acres of protected recreation land as a percent of total land
area, protected land area per capita, circumference of inland lakes, and the length of
ocean or Great Lakes coastlines. Using factor analysis, Savageau and Loftus (1993) were
able to separate the clustered groups to produce a score for each metro area on each of the
three parts. These scores were then weighted by their relative importance, ‘bigness’ at 60
percent, ‘recreation land’ at 22 percent and golf/movies/ good food per capita at 18
percent. A city’s ﬁnal MR score is its percentile on a scale of 0 to 100 corresponding to

its weighted average.

CHAPTER FOUR

Methods and Procedures

This study focuses on 288 major Metropolitan Statistical Areas (MSAS) designated
by the US Census Bureau (Figure 4.1). The areas were selected to include as many
United States cities as possible with populations over 50,000 people. This requirement
guarantees that the sample size would be more than adequate and ensures the inclusion of

areas with varying and diverse ethnic populations. In addition, this study required data

 

 

 

 

Figure 4.1 — Map of the United States and the 288 Metropolitan Statistical Areas used in
this Study.

that documented the racial diversity of these US. cities. These data were collected from
the US. Bureau of the Census for Housing, and are available for four minority groups

classiﬁed as Native Americans, Asians and Paciﬁc Islanders, African Americans,

32

33

and Hispanics. The study examines all four minority groups’ diversity, and that
diversity’s impact on the quality of life in the 288 metropolitan areas.

In the study, transforming the aforementioned data into normalized z-scores created
four quality of life indices. The z-scores were calculated for each of the quality of life
variables by measuring the difference between the mean of each variable and the raw
scores for each MSA. The resulting number was then divided by the standard deviation
of that variable which produces the z-score for that variable and MSA. For this project
the z-scores were ﬁrst calculated for all 288 metropolitan areas, then grouped into the
four population groups. The relative value Of the resulting z-score depends upon the
index. For instance, one assumes that the higher an urban area’s median family income
is, the better the economic conditions are within that city. As a result, the indices Median
Income, and Labor Force were considered “positive” indicators. The variables %
Families Below Poverty and % Unemployment were both considered “negative”
indicators, because higher rates of poor families or unemployed reﬂects a lower quality of
life. To ensure that the distributions correspond with the other “positive” variables, the z-
scores of “negative” indicators were multiplied by (-—1.0).

The z-scores for the economic, housing, and social variables were totaled and
divided by the number of variables to yield a mean number. The resulting values were
then summed to create an over-all quality Of life index value for each variable. The fourth
quality of life index, an aggregate total of all the quality of life z-scores for each MSA, is
intended to reﬂect the overall quality of life of residents in each city. For all four
categories, a high score represented a high quality of life while a low score illustrated a

poor quality of life.

34

The next step was to break the 288 metropolitan statistical areas into four population
size groups (Table 4.1). This helps guard against comparing the larger cities with small
ones. This is important because the big urban areas often have larger minority
populations, a larger volume of housing units, and also receive more social funding from
the federal government. In addition, most people living in larger metro areas have more
cultural amenities and social qualities than people living in smaller urban areas do. For
these reasons the study breaks down the metropolitan areas into groups that are

comparable in size.

Table 4.1 — Metropolitan Statistical Area Population Categories.

 

 

 

MSA Category Example MSA Populations
Group one Chicago, IL 600,000 - 8,000,000 (+)
Group two Lansing / East Lansing, MI 260,000 - 599,999

Group three Champaign / Urbana, IL 142,193 — 259,999
Group four Bangor, ME 56,735 - 142,192

 

 

In order to group the cities by size, I rank transformed the variable ‘Population.’
The ranking created four categories and each contained 72 metropolitan areas. The ﬁrst
group contains the major US. cities with populations over Six hundred thousand people.
This division is made up of the largest metropolitan areas including the “big three:” Los
Angeles, New York City, and Chicago. The second group is made up of metro areas with
populations between 260,000 and 590,000. This category includes metro areas similar in
size to El Paso, Texas or Lansing/East Lansing, Michigan. The third group is made up of

cities with populations similar to Reno, Nevada or Champaign / Urbana, Illinois. These

35
urban areas all have residents within the 140,000 to 259,000 ranges. The last category,

group four, contains the smaller metropolitan areas of the US. These cities have
populations between ﬁfty thousand and one hundred and thirty nine thousand people.
Examples within this group are Kenosha, Wisconsin and Bangor, Maine.

Table 4.2 summarizes the four essential factors used in the analysis, including 289
MSAS split into four population groups, ﬁve racial diversity measures for each of the four

minority groups, and four different quality of life indexes.

Table 4.2 — Four Data Groups used in the Analysis.

Population Group Minority Groups Racial Diversity Measure Quality of Life Measure

One Native Americans Dissimilarity Economic QOL
Two Asians Isolation Housing QOL
Three African Americans Relative Concentration Social QOL

Four Hispanics Absolute Centralization Total QOL (Aggregate)

Spatial Clustering

In order to test the hypotheses stated, this study uses the multiple regression

equation:

Y,- = a + bx,- + ch. (Equ. 4.1)

where:
Y =dependent variable - Quality of Life
X =independent variable — Racial Diversity
at =Y intercept - constant
b = b parameter — rate of change in Quality of Life
c = c parameter — degree of curvature of Quality of Life parabola

This formula implies that Y is dependent upon a variable X in a non-linear fashion. The

parameters of this non-linear relationship can be estimated with traditional least squares

36

regression. The Y intercept, a is the value of Y when both X1 and X2 are zero or when
|ij| - |ch| = 0.
A more Speciﬁc way to express the formula used for the analysis in this study is the

equation:

Wk = akij + bkiinj + CkijISij (Equ. 4.2)

where:
W = dependent variable for quality of life
I = independent variable for racial diversity
IS = independent variable for racial diversity squared (12)
a = W intercept
b = partial regression coefﬁcient
c = partial regression coefﬁcient _
i = minority group (Native American, Asians, African Americans, and Hispanics)
j = diversity index (dissimilarity, isolation, concentration, centralization, clustering)
k = quality of life index type (economic, housing, social, aggregate total)

These slight adjustments account for the parameters used in the multiple regression
analysis. Thus, this study examines 320 multiple regression equations. (4 quality of life
measures * 5 racial diversity index measures * 4 minority groups * 4 population groups =

320.) For example, examine the ﬁrst multiple regression model used for this study:

EQOL = a + b(NADIS) + c(NADIS*NADIS). (Equ. 4.3)

Here (EQOL), or economic quality of life, is the dependent variable. The independent
variable (NADIS), or the Native American dissimilarity index, is essentially used twice -
ﬁrst with the partial coefﬁcient b, and then its value is squared for the partial coefficient
c. This particular equation is executed for all four population groups. Since curvilinear

regression equations involve the terms X and X2, plotting the resulting curve will result in

37

a parabola. Generally as the power of X increases, the curve becomes more and more
complex and usually ﬁts a given set of data increasingly well.

This study hypothesizes that the relationship between racial diversity X and the
quality of life Y is curvilinear. In essence, this working model attempts to capture the
upward or downward turn in Y and the quality of life at the maximum or minimum value
of X.

The polynomial regressions for this study were all done within the multivariate
general linear hypothesis (MGLH) statistics section in SYSTAT©. The output from each
multiple regression included the coefﬁcient of the constant as well as the partial
regression coefﬁcients bX and cXZ. The output from SYSTAT© (Table 4.4) also records
the T-test value for the coefﬁcients, as well as the P values for the equations to check for
Signiﬁcance. The conﬁdence interval for these calculations was done at the 95% level for

a two-tailed test.

Table 4.3 - Sample SYSTAT© Printout of a Quadratic Regression with Economic
Quality of Life (EQOL) as Dependent Y, and Native American Racial Diversity
Dissimilarity Index (NADIS) as X and (NADISSQ) as x2.

 

Sample S YS T A T © Printout

 

DEP VAR: ECQOL N: 72 MULTIPLE R: 0163 SQUARED MULTIPLE R: 0.027
ADJUSTED SQUARED MULTIPLE R: .000 STANDARD ERROR 0F EST/MA TE: 3.55!

 

 

VARIABLE COEFFICIENT STD ERROR STD COEF TOLERANCE T P(2 TAIL)

 

CONSTANT 4.859 5.041 0.000 . -0.964 0.338
NADIS 34.768 25.369 0.987 0.027 1.371 0.175
NADISSQ -41.045 29.984 -0.986 0.027 - l .369 0.175

ANALYSIS OF VARIANCE

SOURCE SUM-OF-SOUARES DF MEAN-SQJARE F-RATIO P
REGRESSION 23.826 2 1 1.913 0.945 0.394
RESIDUAL 870.222 69 12.612

 

 

38

Examination of the signiﬁcance of the three coefﬁcients is important. This study
speciﬁcally predicts that all three coefﬁcients will be signiﬁcant at a two-tail 95 percent
conﬁdence interval. If only a and b are signiﬁcant, then the result is a straight line. If only
a and c are signiﬁcant, the result is a curved line. However, if a, b, and c are all
signiﬁcant then the study conﬁrms the presence of a parabolic relationship between
quality of life and racial diversity.

The shape and ‘direction’ of this parabola depends upon the value (positive or
negative) of the c coefﬁcient value. If e is positive as shown in Figure 4.2, the parabola

will be “convex downwards” or “U — shaped”(u). If the c parameter is negative in value

the resulting shape of the parabola will be “convex upwards” or “bell-shaped (0)”

 

 

 

 

 

 

 

 

 

 

 

(Figure 4.3).
Y=a+bX+cX“2 Y=a-bX-cX“2
.I 68 20
4“ -| 0 iPHWHHH
8 28 \ﬁ/ 8 '20 */ \
-40
NO ’\ )1 \ “Iv ‘0 Q) 2 77 «r v- N to co
Racial Diversity Racial Diversity

 

 

 

 

 

 

Figures 4.2 and 4.3 —- Inﬂuence of the (c — Parameter) on the Shape of the Resulting
Parabola.

To further examine the parameters and the resulting parabolas, the maximum or
minimum values of Y, must be determined. In a quadratic regression, if the coefﬁcient is

negative, then there will be a maximum value. If e is positive, there will be a minimum

39

value. The maximum or minimum value of a quadratic equation is at the following value

of the independent variable:

X0 = -b / 20. (Equ. 4.4)

By placing (X0) in the quadratic equation (Equ. 4.1) we can see that:

Y0 = a — (b2 / 4c) (Equ. 4.5)

Once the maximum or minimum value of Y, is determined, the corresponding value
of Xi can be found (Zar 1996). These values help identify the direction and shape of the
parabolas (See Appendix B).

The hypotheses for this study are set up in the following manner. If any of the three
coefﬁcients in the multiple regression equations are found to be insigniﬁcant then the null
hypothesis Ho (no relationship exists) is accepted, and that speciﬁc relationship is
declared non-conclusive. If all three coefﬁcients are found to be signiﬁcant, and the
minimum or maximum Y value indicates that either a “U” (U) or “bell-shaped” (0)
relationship exists, the research hypothesis H1 will be accepted and the relationship will
be deemed a reliable indicator. These assumptions provide a conservative yet
comprehensive set of decision rules regarding the relationship between quality of life and
racial diversity.

In addition, this study expects the shape of the signiﬁcant parabolas to be convex-
downwards (U). This is anticipated because the 5 indexes for racial diversity
(Dissimilarity, Isolation, Relative Concentration, Absolute Centralization, and Spatial

Clustering) all have index ranges that indicate a decrease in diversity as the scores

40

become higher. As mentioned earlier, this study expects to see an initial decline in quality
of life but as racial diversity levels among urban areas increase; the “U-shaped” parabola

will represent the improvement in quality of life or “upswing” in the curve.

CHAPTER 5
Analysis and Results

The analysis of the 320 multiple regression models indicated widely varying results
among the components (Racial diversity levels, Racial group, Quality of life scores and
MSA population Size) of the study. This section examines and summarizes the results of
the models for Race, Diversity Index Measure, and the four Quality of Life aspects by
MSA Population Group. Overall, the results do not illustrate a positive relationship
between racial diversity and quality of life. Evaluated together, the 320 regression
equations do not provide a decisive conclusion to the hypothesized relationship between
racial diversity and quality of life. This section summarizes the results of the models for
Race, Diversity Index Measure, and Quality of Life after separating the data by MSA
population group.

The quadratic equations describing the relationship between the racial diversity and
quality of life were not signiﬁcant. Less than 10 percent of the 320 multiple regressions
demonstrate a signiﬁcant relationship between diversity measures and quality of life
indicators (see Appendices A] — A. 16). Many of these insigniﬁcant results can be
attributed to the low population numbers of certain racial groups in various metropolitan
areas. For instance, of the 80 models describing the parameters of the Native American

population groups (Appendices Al, A5, A9, and A.l3), only one equation (Size group

41

42

4 — NAHDIS) is signiﬁcant. This single equation accounts for about one percent of all 80
equations tested for Native Americans in this study. Upon examination of the P-plot for
this equation (Figure 5.1), it is clear that among the Native American population, some

cities have more leverage, or act as outliers, than the majority.

 

 

HOTWO

-05 >-

 

 

 

00 01 02 03
NAUSSO

Figure 5.1 — Quadratic Plot for Native Americans Population Group Four (Housing
Quality of Life vs. Dissimilarity Diversity Index).

In ﬁgure 5.1, one can see that the majority of the smaller MSAS are plotted in the
middle-left portion of the axis. This is a clear indication that, among this group the
housing quality of life improves as the diversity increases. In this example, Laredo, TX
and Danville, VA have the most inﬂuence on the equation. Laredo has a very high
proportion of Native Americans in its population while Danville has a very small
percentage. High leverage values indicate that the data points associated with these towns

have disproportionate weight on the equation.

43

The entire data set, for all racial groups, was thoroughly examined for outliers and
high leverage values. While the majority of the residuals from the signiﬁcant equations
from all racial groups were homoscedastic, some outliers and leverage values were
identiﬁed. For this analysis, leverage values of 0.4 were considered to be inappropriate.
None of the signiﬁcant equations in this study have leverage values higher than 0.4; as a

result; none of the models were dropped due to high leverage.

Racial Groups

Despite the low overall signiﬁcance of the multiple regression models the research
hypothesis did hold true for many of the equations. In fact, the hypothesis is strongest (72
percent of all signiﬁcant equations have a U - shaped curve) at the smallest group size
level. As the urban area’s size diminishes the percentage of U— shaped curves increases as

well (Table 5.1).

Table 5.1 — Percentage of Parabolas for all Signiﬁcant Equations that Adhered to the
Research Hypothesis (by Group Size).

Total # # of (U) # of (n) %

2 4 33
50

2 2
3 2 60
5 2 72

 

Only 1 of all 80 equations (less than 2%) for Native Americans ﬁt the criteria for

acceptance of the hypothesis that racial diversity affects quality of life. Signiﬁcant

44

relationships between diversity and quality of life appear more often in the other racial
groups. The Hispanic, African American, and Asian groups all had close to a 10%
signiﬁcance rate. Since the Native American population group did not match the other
percentages, determining overall trends among all groups became problematic. For this
reason, the Native American results were not included in the majority of the trend
analyses.

Signiﬁcant equations appear more frequently in the three remaining racial minority
groups than in the Native American population (African Americans 8%, Hispanics 9%,
and Asians 10%). While these rates are Similar, the type of index, group size, and quality
of life measure they represent are drastically different. In addition to these differences,
the size and shape of the parabolas of the signiﬁcant equations also indicates a large
amount of variance between racial diversity and quality of life.

The African American group (Appendices A3, A7, Al 1, and A.15) has an 8%
signiﬁcance rate, and many of these equations adhered to the second part of the research
hypothesis. Speciﬁcally, only four of six (66%) of these equations had a “U—shaped” (u)
parabola (Figures 5.2, 5.3). In these ﬁgures the differences in parabola shape is quite
extreme. Figure 5.2 is very well deﬁned and indicates an increase in social quality of life
as the diversity improves. The parabola depicted in Figure 5.3 is also quite deﬁned, and
supports the hypothesized relationship as well.

When examining the b and c parameters for the two equations (Table 5.2), we see
that the c parameter for (B2) shows a slightly larger increase in quality of life (social and

total quality of life respectively, in these cases). These two equations are particularly

45

 

SOTWO

 

 

 

 

EEPCSO

Figure 5.2 — Quadratic Plot for African Americans Population Group Four (Social
Quality of Life vs. Spatial Clustering Diversity Index).

 

TOTWO

 

 

 

 

10 15 20 25 30
EEPCSO

Figure 5.3 — Quadratic Plot for African Americans Population Group Four (Total Quality
of Life vs. Spatial Clustering Diversity Index).

46

interesting because they both agree with the research hypotheses, and also Show a similar
relationship for social and the aggregate quality of life. While these equations come from
the same size population group (4), we see that both are partially explained by the Spatial

Proximity index.

Table 5.2 — Constant and Parameter Values for Two Signiﬁcant Multiple Regression
Equations.

 

 

 

Equation (a) Constant (b) Parameter ((9 Parameter Shape
AA4SSPC (B1) 167.834 -268.862 +100.775 (U)
AA4TSPC (B2) 227.767 -358.908 +131.415 (U)

 

 

 

 

 

 

 

This result provides some evidence that an increase in African American diversity helps
improve the social quality in some areas, and also indicates that in some cases the
improvements take place at more than one MSA population group Size.
The other signiﬁcant equations did not support this type of conclusion however,
especially among African Americans in larger MSAS. In fact, half of the signiﬁcant
equations portray a downward or (n) shaped parabola. For instance, in examining the
two equations (Figures 5.4, 5.5), the curves of the parabolas are clearly “bell” shaped.
The above two equations are revealing because they Show that centralization is
particularly important within the African American populations, especially at the medium
MSA size level (2 & 3). Even though the overall hypotheses are not supported by these
models, a closer analysis of the two ﬁgures clearly indicates that the housing quality of
life conditions generally diminish as centralization diversity decreases or as segregation

levels increase. This trend helps to further substantiate claims of previous segregation

47

 

00 ~

HOTWO

-05 .—

 

 

 

 

-lO
-O.5

BACESO

Figure 5.4 — Quadratic Plot for African Americans Population Group Two (Housing
Quality of Life vs. Absolute Centralization Diversity Index.)

 

 

HOTWO
a

 

 

 

_2 I I I 1
OO 02 04 06 08 1O

BACESO

Figure 5.5 — Quadratic Plot for African Americans Population Group Three (Housing
Quality of Life vs. Absolute Centralization Diversity Index).

48

studies (Massey and Denton 1988, Ginsburg 1990, Bickford and Massey 1991, Murdie
1994, Fong 1994).

Similar results can be seen within the Hispanic and Asian populations as well. As
previously mentioned, these two groups had slightly higher signiﬁcance rates (Hispanics
9%, Asians 10%) and also reveal other aspects of the relationship between diversity and
quality of life. Within the Asian population group’s Signiﬁcant equations (Appendices
A2, A6, A.lO, and AM), half (4 of 8) support the research hypothesis. All four of these
equations are partially explained by the dissimilarity or Spatial proximity measures; in
addition, we see improvements in quality of life with increased diversity at all four
population size groups. Two of these equations (SPC) indicate social improvements,
while the (DIS) equations account for improvements in economic and housing quality of
life. Like some of the equations for the African American population group, the equations
that adhere to the hypothesis indicate that many quality of life aspects improve with an
increase in diversity levels (Figures 5.6, 5.7). In these graphs, one can see that as the level
of dissimilarity increases (more diverse MSAS), both the housing and economic quality
of life show enhancements. In these cases the quality of life types occur frequently where
diversity levels are higher and generally decrease as more dissimilarity is seen among
MSAS.

The signiﬁcant equations that are bell-Shaped (0) indicate that quality of life
conditions are enhanced with a more diverse population. The four equations that did not
meet the hypothesis are partially explained by the isolation measure, This which assisted

in helping to explain the social and overall quality of life in 3 of the 4 population size

groups.

49

 

HOTWO

 

 

 

 

OO 01 02 03 04
A0880

Figure 5.6 — Quadratic Plot for Asians Population Group Two (Housing Quality of Life
vs. Dissimilarity Diversity Index).

 

EOTWO

 

 

 

 

OO OI O2 03 04
A0680

Figure 5.7 — Quadratic Plot for Asians Population Group Three (Economic Quality of
Life vs. Dissimilarity Diversity Index).

50

Several interesting aspects of the relationship between diversity and quality of life
were seen in the Hispanic population group (Appendices A4, A8, A.12, and A.16) as
well. The analysis shows that of the 7 Signiﬁcant equations, only 2 (28%) of the resulting
parabolas had U-shaped (U) curves,
supporting the proposed hypothesis. The equations for the Hispanic population explain
that in most cases the resulting parabolas are bell-Shaped (0). Here all three types of
quality of life slightly diminish with an increase in Hispanic diversity. The largest
decreases can be seen in conjuncture with the social quality of life aspects (Figures 5.8,
5.9) In these graphs the increases in the b parameter and decreases in the c parameter
occur as the MSAS become more diverse.

Each graph has a few outliers that had some leverage on the equations. For example
in Figure 5.8, the points for the cities of Los Angeles, New York, and Miami can all be
seen on the right end of the diagram just above the “quadratic ﬁt” curve. High leverage
values indicate that the associated data points have disproportionate weight on the
equation. In other cases, the MSAS with very large Hispanic populations such as Chicago,
Hartford, and Fresno (Figure 5.9), the leverage values have little inﬂuence on the
diversity and quality of life variables. None of these cases have a leverage value over 3.5
and the results were still deemed reliable and their inclusion in the data set was

appropriate.

51

 

SOTWO

 

 

 

_05 I I I I 1
OO 01 02 03 O4 05 06

HBOSO

 

Figure 5.8 - Quadratic Plot for Hispanics Population Group One (Social Quality of Life
vs. Isolation Diversity Index).

 

SOTWO

 

 

 

 

10 15 20 25
HSPCSO

Figure 5.9 — Quadratic Plot for Hispanics Population Group One (Social Quality of Life
vs. Spatial Clustering Diversity Index).

52

Quality of Life Types

All three aspects of quality of life, as well as the aggregate quality of life index,
provide varying accounts of the positive and negative inﬂuences of racial diversity.
Patterns within the quality of life categories can be detected across all signiﬁcant
equations (Figure 5.10).

In the signiﬁcant equations for this study, the social and housing quality of life
aspects are affected the most by racial diversity. The social quality of life accounts for
35% of the signiﬁcant equations and occurs most frequently in the largest metropolitan
areas. While the changes in social quality of life are not always increases, the results
show that diversity levels have more of an effect on the variables within this category.

One interesting aspect of social quality of life can be seen in Figure 5.10. In both

large and small metropolitan areas, social quality of life is inﬂuenced more by racial

 

 

 

 

8

c .

g ISocral

8 Housing
9 ‘ I Economic
o

2 IAggregale
E

 

Population Group Size

 

 

 

Figure 5.10 — Quality of Life Types and their frequencies among the Signiﬁcant
Equations for Population Size Groups.

53

diversity than is economic and housing quality of life. The large amount of federal and
state ﬁnancial assistance allocated for municipal buildings and cultural centers in large
cities may help to explain part of this trend. By simultaneously examining size and levels
of diversity, one can see other explanations for the relationship. Within a few large cities,
the high levels of isolation or spatial clustering between ethnic groups inhibits the
exposure or access of some minority groups to the social amenities available in these
areas. This pattern, which essentially says that ethnic isolation inhibits social quality of
life, is shared across all of the multiple regression equations, not just the signiﬁcant ones.
In some cases, however, even large increases in diversity do not improve the social
quality of life. Additional, more detailed investigations are necessary to further quantify
these possibilities.

Among the signiﬁcant equations (32%), the housing quality of life is also heavily
inﬂuenced by the diversity patterns. Although less than half of the equations have
corresponding parabolas with U-shaped (U) curves the housing quality of life indicator is
the only quality of life aspect that was affected at all four population group sizes. In fact,
for this study, the housing quality of life indicated a positive and/or negative change from
the inﬂuence of diversity the most reliably.

When considering the housing quality of life, this study shows that a decrease in
racial diversity is often concurrent with an improvement in housing quality. The results
also indicate that higher African American and Hispanic diversity can negatively affect
some aspects of housing quality of life (Table 5.3). This effect varies spatially as well as
by city size. For instance, among the largest metropolitan areas, an increase in the relative

concentration of Hispanics indicates a substantial decline in housing quality of life. When

54

examining the second and third population group sizes, the declinations coincide with

increases in African American levels of absolute centralization.

The opposite seems to be true of the Asian population. The only signiﬁcant equation

that suggests a change in housing quality in relation to Asian diversity (absolute

centralization) occurs at the population group Size 2. This model resulted in a “U” shaped

(U) parabola, that clearly indicates an increase in housing quality (Table 5.3).

The economic quality of life scores are the least inﬂuenced by the diversity

measures. Only 3 of 22 (14%) of the Signiﬁcant equations revealed a change of

Table 5.3 - Constants and Parameters for all Signiﬁcant Multiple Regression Equations
Involving Housing Quality of Life. (Racial Group, MSA Group Size, and Diversity Index
denote the Equations.) '

 

 

 

 

 

 

 

 

 

 

 

 

 

Equation (a) Constant (b) Parameter (c) Parameter Shape
H1(RCO) .343 -.366 -1.037 (n)
H2(RCO) .261 -.561 -.328 (n)
A2(DIS) 2.727 -l3. 164 14.922 (U)
AA2(ACE) -.215 .995 -.819 (n)
H3(ACE) -.245 .883 -.508 (n)
AA3(ACE) -.175 1.091 -.927 (n)
NA4(DIS) 1.481 -8.493 1 1.172 (U)

 

economic quality of life, and two of the three equations revealed “U” shaped (U)

parabolas (Figure 5.10). Among the results of the largest urban populations, racial

diversity levels do not affect economic quality of life very often. In addition, these results

Show that even as city size diminishes the relationship between diversity and economic

quality of life does not change much; there is neither major improvement nor degradation

of economic conditions as size decreases. These ﬁndings further support the claims of

other quality of life studies (Johnston 1988; Dasgupta and Weale 1992; Bumell and

 

55
Galster 1992; Stover and Leven 1992) which report that quality of life is not determined

solely by economic factors. In this case, the social and housing quality of life measures
are affected by diversity, while the economic quality of life type fails to account for the
relationship.

The aggregate quality of life measure only had four signiﬁcant equations (19% of
the total). Of these, only one (25%) supports the research hypothesis and indicates a U-
shaped (U) curve (Table 5. 4). The one positive relationship seen for this model type was
the spatial clustering index for African Americans. Here the increase in the c parameter
coincides with the improvement in the aggregate quality of life. These results are seen at
the smallest metropolitan level where often the economic, housing, and social conditions
are poor for all people - especially minorities. The two equations that Show the most

improvement in the aggregate quality of life measure are found in the Asian

Table 5.4 — Constants and Parameters for all Signiﬁcant Multiple Regression Equations
Involving the Aggregate Quality of Life Measure. (Racial Group, MSA Group Size, and
Diversity Index denote Equations.)

 

 

 

 

 

 

 

 

 

 

Equation (a) Constant (b) Parameter (c) Parameter Shape
H1(ISO) .545 3.059 -6.089 (n)
A3(ISO) -.505 17.152 -66.062 (n)
A4(ISO) -1.029 20.383 -60.384 (0)

AA4(SPC) 24.475 -38.394 13.991 (U)

 

 

population group sizes three and four. At these levels we can see a solid decline in overall
quality of life as the diversity levels of isolation among Asians increases. Both of these
examples show a similar, very large decrease in the c parameter despite their varied

population sizes. This trend is intriguing because it suggests that isolated areas such as

56

stereotypical “China Towns” can be detrimental to an urban area’s quality of life. In fact,
it appears that the less isolated the Asian community is among medium sized cities, the
better the overall quality of life is likely to be.

The evidence from this study shows that the criteria chosen to represent the quality
of life of a city’s inhabitants are very important. As seen here, the various types of quality
of life are affected by cultural diversity in various manners. Another important
determinant to this relationship is the population size of the metropolitan area. These
examples all indicate that results differ among the four population Sizes selected for this
study. Both of these measures shed a different type of insight into the relationship
between racial diversity and quality of life; it is crucial that more attention be given to

them in future studies.

Index Type

All ﬁve racial diversity indexes help to understand the relationship between
diversity and quality Of life. The ﬁve indexes varied according to the minority groups as
well as by the size of the metropolitan statistical area. No single index captures all the
aspects of the relationship between diversity and quality of life. However, some indices
provide more insight into this relationship than others do. For example the isolation index
(ISO) was found to affect 32% of all the Signiﬁcant equations while the relative
concentration index (RCO) affected less than 9%. This discrepancy attests that the
isolation index was more beneﬁcial to this study in helping to understand the relationship

between racial diversity and quality of life.

57

Of the ﬁve diversity indices, the isolation index (ISO) and the spatial clustering
index (SPC) performed in the most dependable manner. These indexes accounted for 7 of
22 (over 60%) of the 22 signiﬁcant equations (Figure 5.11). The dissimilarity index (DIS)
and absolute centralization index did not perform as well as the previous two indices, but
did an adequate job helping with the examination of the relationship. These indexes
accounted for 3 of 22 (14%) of the signiﬁcant equations. The one index that did not
provide much insight into the relationship between racial diversity and quality of life was
the relative concentration index (RCO). This index only accounted for 2 of the 22

signiﬁcant equations (less than 10%).

 

 

 

 

 

 

 

 

   

 

 

8 5

5 4

S 3 _ é IDIS

8 2 3 I180
‘ /

3 1 - g IRCO
Groupl Group2 Group3 Group4 .SPC

 

Population Size Group

 

 

 

Figure 5.11 — Racial Diversity Indices and their Frequencies among the Signiﬁcant
Equations for Population Size Groups.

The frequencies in which the indices appear in the signiﬁcant equations are not the
only criteria for these assessments. The other aspects that were examined are the resulting
parameters and parabolas from the signiﬁcant equations each diversity index was part of.

For instance, of the three signiﬁcant equations in which the dissimilarity index played a

58

role, all three resulted in U-shaped (U) parabolas. This indicates that the index does a
reasonable job examining positive changes in quality Of life. The resulting curves Show
that it is a good tool for measuring a positive inﬂuence of diversity on quality of life.
The two indexes that most helped explain change in the signiﬁcant equations the
have drastically different parabola shape results. In fact, even though the isolation index
and the Spatial clustering index help to explain seven signiﬁcant equations, the shape of
the fourteen parabola curves are opposite. The parabola shape results for the isolation

index equations all depict bell-Shaped (n) curves (Table 5.5).

Table 5.5 - Constants and Parameters for all Signiﬁcant Multiple Regression Equations
Involving the Isolation Index. (Racial Group, MSA Group Size, Quality of Life Type, .
and Diversity Index denote Equations.)

 

 

Equation (a) Constant (b) Parameter (c) Parameter Shape
HISISO .306 1.112 -1.520 (n)
H 1 TISO .545 3.059 -6.089 (n)
Al SISO .247 1.994 -2.335 (n)
H2EISO —.265 4.440 -8.468 (n)
A3TISO -.505 17.157 -66.062 (n)
A4SISO -.51 1 8.727 -27.026 (0)
A4TISO -1.029 20.383 -60.3 84 (n)

 

 

From this table it is easy to pinpoint the useﬁrlness of the isolation index. Even
though all seven equations have drastically different parameter changes, the shapes of the
parabolas all indicate that an increase in isolation diversity can have an adverse affect on
the quality of life in certain areas. These results show that the isolation index is the most

useful for examining other aspects of diversity.

59
As mentioned, the spatial proximity index did the best job describing the

relationship hypothesized by this study. In fact, when the signiﬁcant equations for the
spatial clustering index are examined, we see that the resulting parabola curves are all U-
shaped (Table 5.6) - opposite of those of the isolation index. Despite a wide variety of
parameter changes, all the equation results indicate an improvement in quality of life with
varying diversity levels. However, unlike the isolation index that represent equations

from all four population size groups, the

Table 5.6 - Constants and Parameters for all Signiﬁcant Multiple Regression Equations
Involving the Spatial Clustering Index. (Racial Group, MSA Group Size, Quality of Life
Type, and Diversity Index denote Equations.)

 

 

Equation (a) Constant (b) Parameter (c) Parameter Shape
H1 SSPC -7. 120 12.751 -5.305 (U)
A1 SSPC -44.679 82.231 -37.282 (U)
AA3SSPC 11.414 -18.658 7.359 (U)
A4SSPC -420.689 808.401 -3 88.224 (U)
AA4ESPC 16.290 -25.825 9.636 (U)
AA4SSPC 12.910 -20.682 7.752 (U)
AA4TSPC 24.475 -38.394 13.991 (U)

 

 

Spatial clustering index inﬂuences the fourth population group Size the most. Upon
further examination, the spatial proximity index scores are in fact very high (limited
diversity) among the smaller metropolitan areas. This indicates that the spatial clustering
index is particularly useful at the smaller city level, but can also be relied upon as a
diversity measure for all four population size groups.

The remaining three diversity indexes do not help as much with the relationship

between diversity and quality of life as the ﬁrst two. Both the dissimilarity index and the

60

absolute centralization indices only account for six of the twenty-two (13% each)
signiﬁcant equations. The signiﬁcant equations that the dissimilarity index helped explain
suggest that certain aspects of quality of life improve as diversity levels increase. In fact,
the resulting parabolas for all three equations have U-shaped (U) curves (Table 5.7). The

opposite is true for the absolute centralization index. All three of these

Table 5.7 - Constants and Parameters for all Signiﬁcant Multiple Regression Equations
Involving the Dissimilarity and Absolute Centralization Indexes. (Racial Group, MSA
Group Size, Quality of Life Type, and Diversity Index denote Equations.)

 

 

Equations (a) Constant (b) Parameter (c) Parameter Shape
A2HDIS 2.727 -l3.164 14.922 (U)
A3EDIS 3.423 -15.354 15.393 (U)

NA4HDIS 1.481 -8.493 1 1.172 (U)

AA2HACE -.215 .995 -.819 (n)

H3HACE -.245 .883 -.508 (n)

AA3HACE -. 175 1.091 -.927 (n)

 

 

parabolas indicate a slight decrease in housing quality of life when diversity levels
improve (Table 5.7).

The results of the equations Show that both indexes can be useful for examining
various population group levels. For instance, signiﬁcant equations are seen for the
dissimilarity index at the second, third and fourth population groups. The absolute
centralization index equations have Similar results that indicate the index is useful at the
second and third population group level.

As stated earlier, the relative concentration index is the least helpful in the

examination of the relationship between racial diversity and quality of life. This index

61

account for less than ten percent of the signiﬁcant equations (two equations), and both of
these indicate limited increases in quality of life. Although the results of this study
indicate that the relative concentration index is not inﬂuential in the examination of this
relationship, further research incorporating all ﬁve indices should continue to be

undertaken.

CHAPTER 6

Conclusions and Future Considerations

Although the results from the analysis did not indicate a strong a relationship
between racial diversity and quality of life, some aspects of the results were quite
promising. There were also a few negative outcomes from the analysis that helped to
conﬁrm the results of previous studies involving segregation as well as housing quality.
Having identiﬁed a gap in the racial diversity and quality of life literature, this study
should provide a substantial platform for similar, more detailed investigations.

The interpretation of the statistical data would be more readily discemable if the
results had shown a stronger relationship between racial diversity and quality of life. One
area where a strong relationship was seen was among the smaller metropolitan areas. In
these cases, over 70% of the signiﬁcant equations adhered to the research hypothesis.
Overall, the smaller metropolitan areas had more diversity with relatively larger ratios of
African American, Hispanic and Asian communities than the larger cities. In most cases,
as the metropolitan area increased in population size the relationship between racial
diversity and quality of life became weaker. Part of this can be attributed to the lower
overall populations of minorities among urban areas of this size. As explained earlier,
minority ratios vary amid cities of different size as well as regions. In these situations,

smaller minority populations can assimilate with the majority population easier, thereby

62

63

lowering the diversity index scores. The resulting higher levels of diversity help to
improve the social quality of life conditions in such cities as Sioux Falls, South Dakota or
Burlington, Vermont. In addition, smaller cities with universities such as Charlottesville,
Virginia or Bloomington-Norrnal, Illinois have even better diversity and quality of life
scores.

Another reason the relationship is stronger at this level might be due to the fact that
many of these smaller metropolitan areas do not have established or deﬁned central
business districts. This factor plays a large role in determining the diversity scores for the
centralization and concentration measures. In many cases among minority groups, the
diversity scores for the smaller cities are much better (lower scores) than those of the
large metropolitan areas. In cities such as New York, and Chicago, which have very
established central business districts, the results indicate that the scores for the
centralization and concentration indices are much higher (less diversity).

The measure of Spatial proximity also plays a major role at this level. As seen in
Figure 5.11, this index provided the most insight into metropolitan areas with smaller
populations. The results indicated that many smaller urban areas have a more racially
diverse community and that these improvements in diversity have a positive impact on
the social quality of life. In contrast, many large cities had very high scores for their
spatial proximity measure, which indicate that the massive populations of the larger
urban areas inﬂuence the ratios between the majority and minority groups.

Denton (1994) addresses this dimension and refers to it as the “checkerboard
problem” in her article “Are African Americans Still Hypersegregated?”. In this study

she examined 44 MSAS (almost all would ﬁt into the group 1 population size category in

64

this study) and reported that many of these cities experience high levels of segregation
along all ﬁve diversity indices. In addition the diversity conditions of many of these areas
worsened from 1980 - 1990.

Although some of the results from my study support her conclusions, I feel that her
outlook is rather bleak. In addition to working from a small sample Size (n=44), she chose
to use variables that only reﬂect the economic well being of the minority groups. Without
question this is an important criteria. Previous literature (Suﬁan 1993; Bumell and
Galster 1992; Dasgupta and Weale 1992; Stover and Leven 1992; Glatzer and Mohr
1991; Smith 1982) shows that the economic well being Of a population is not always a
direct reﬂection of the quality of life of that same group. Denton’s study identiﬁes the
nature of segregation and income distribution disparity in Metropolitan Areas, but it does
not make an attempt to ﬁnd a solution as to how we can improve the living conditions of
minority populations.

By including separate housing and social quality of life components, my study was
able to uncover several positives that could have been overlooked, as they were in
Denton’s study. These results indicate that in some cases the economic, housing, and/or
social quality of life improves with an increase in racial diversity. Although most of these
improvements were seen at the smaller and medium MSA levels, several were also seen
among the largest population size group.

For example, among the largest metropolitan areas, the signiﬁcant equations for
Asians and Hispanics indicated that the social quality of life improves as the clustering of
this minority group diminishes. While the overall results were not as signiﬁcant as

anticipated, equations such as these provide valuable insight into a very complex

65

relationship. By “unearthing” some positives amidst the normal negatives associated with
previous studies, perhaps some insight has been Shed into addressing some of society’s
inherent difﬁculties.

Another area in which a strong relationship could be seen was among the
dissimilarity and spatial proximity indexes. As mentioned in Chapter 5, these two
dimensions of racial diversity accounted for almost half (10 of 22) of the signiﬁcant
equations in this study. In addition to this high percentage, the resulting parabolas were
all U-shaped (U). These results suggest that the quality of life in the various cities
improved as their populations became more diverse. Among these ten equations, we see
that all of the minority groups have an impact on at least one of the population size
groups. For example, at the largest metropolitan area level, there was an equation for both
the Hispanic and Asian populations. The equation for the Hispanic minority group
indicates that the social quality of life for large cities improved signiﬁcantly, as the
dimension of spatial clustering became more diverse. Likewise, as the spatial proximity
index values showed improvement in diversity for the Asian inhabitants, the quality of
life scores increased. Although the results indicated only a slight enhancement in the
social quality of life in these cases, the impressive aspect of these results stems from the
fact that diversity levels of two different minority groups had an impact on metropolitan
areas of such large size.

These results shed some insight into a previous study. In Bumell and Galster’s
article (1992) “Quality-of—life Measurements and Urban Size: An Empirical Note,” the

authors set up their study to determine if an optimal population size exists where quality

66

of life is highest. The authors point out that large cities have a tremendous “down side” to
them.

Certain amenities decline as population increases. There are lower levels of
enviromnental quality, higher crime rates, and increased congestion of some publicly
provided goods such as highways. Amenities such as beaches and scenic areas become
congested as population size grows (Bumell and Galster 1992, p.727).

According to these types of assessments, it would be unlikely to ﬁnd any type of
positive relationship between diversity and social quality of life. However, the Hispanic
and Asian equations clearly indicate that signiﬁcant positive relationships can occur
among the largest urban areas. In these cases the spatial proximity index scores for the
largest metropolitan areas revealed that as the minority groups were more dispersed
among the entire population, the social quality of life improved.

As mentioned in Chapter ﬁve, all seven of the signiﬁcant equations that the
isolation measure helped to explain resulted in bell shaped (n) parabolas. Four of these
helped represent the largest metropolitan statistical area group. Although these results
imply that there is no positive relationship between diversity and quality of life at this
population level, all four strongly indicated that all quality of life categories decrease
with less diversity. In these cases the isolation index scores revealed numerous pockets of
extremely isolated populations of all minority backgrounds.

The resulting parabolas for both the isolation and spatial clustering indexes,
although opposite in shape, unveil some intriguing insight into some of the residential
problems that plague our cities today. These two indices seem to assemble two important
pieces to the complex puzzle between diversity and quality of life. In particular, the

equations for the isolation index Show that the worst quality of life conditions exist in big

67

cities with high incidents of isolation among the minority groups. Amid these same urban
areas, the only index that identiﬁes some improvements or provides a positive “slant” on
the relationship is the spatial proximity index. This ﬁnding is logical because it reveals
that the worst diversity conditions exist within areas of high minority isolation. It
becomes intuitively obvious that an increase in the spatial proximity of this same
minority group to the majority population should result in an improvement of various
quality of life measures. In these situations, the social quality of life improved in several
of the spatial proximity equations. In this regard, the spatial proximity index is an
important tool in examining the relationship between racial diversity and quality of life.
Finding new methods to utilize the spatial proximity measure, or reﬁning the index,
might result in more detailed ﬁndings in future studies.

The results of the spatial proximity equations were also beneﬁcial because they help
to substantiate the claims of previous authors on the subject of racial diversity and
assimilation (Massey and Mullan 1984; Massey 1985; Portes and Mullan 1985). In
Massey and Mullan’s article, the authors suggest that as minority groups assimilate into
the host society, the physical proximity between the minority group and the members of
the majority becomes closer, indicating that minority groups and majority members will
be more likely to share neighborhoods. In fact, research has shown that recently arrived
immigrants often remain in their ethnic enclaves (Massey 1985; Portes and Mullan 1985).
They are often reluctant to assimilate into the host society because of poor language
ability. This often results in a lower residential proximity with the majority group (Fong

1994)

68
The 1990 US Census Bureau data used for this study support these claims,

especially among the largest metropolitan statistical areas. Among these cities most of the
spatial proximity index scores were high, indicating that the assimilation or checkerboard
effect was minimal. The African American group had the highest spatial proximity index
scores overall and the results indicated no signiﬁcant equations for this group. However,
as mentioned, the social quality of life for the Asian and Hispanic populations improved
as diversity levels increased. Not surprisingly, the spatial proximity index scores for these
two groups were lower than the Aﬁican American group, indicating a more diverse
distribution and assimilation with the majority population.

The big picture depicted by the spatial proximity index and the African American
population is by no means bleak. There is no question that serious, more effective
economic and housing strategies are sorely needed for the African American
communities in large urban areas. However, on a more positive note, the spatial
proximity scores for the African American groups Show overall improvement, as the
city’s population size becomes smaller.

Some of the brightest examples of this phenomenon are the seven signiﬁcant
equations that indicated an improvement in quality of life as racial diversity increased.
Four of these seven equations clearly Show that as the spatial proximity of African
Americans became more dispersed (improved), most quality of life types improved. All
four of these equations occurred at the two smaller population size groups (3 and 4). In
separate equations- the economic, social, and aggregate total quality of life showed

improvements, as the cities became more diverse.

69

As well as some of the racial diversity indexes performed, it is imperative that new
and improved indices are continually brought forth in the literature. Massey and Denton
(1988) have made great strides by identifying the ﬁve most effective indexes from the
plethora that exist (twenty). However, my study in particular would have beneﬁted from
the use of a model that could incorporate all the minority ratios in the same equation. The
ﬁve diversity indexes used here all measure a speciﬁc minority group by their
population’s relation to a city’s white majority. While these measures provide valuable
information on the diversity of a speciﬁc minority group within an urban area’s
population, they are unable to provide information on the diversity between minority
groups. This would be extremely helpful in examining the complex relationships found in
studies of this type. For instance, the isolation index score for a minority group within a
given city depicts how isolated the group is from the majority population. What these
indices do not reﬂect is whether or not the four minority groups are isolated from one
another. Urban geographers and sociologists should continue to advance and improve
these types of indices. As the index measures become more comprehensive, the research
can continue to move from a theoretical framework to an “applied science.”

Another very interesting aspect of my study was the quality of life criteria. As
mentioned in Chapter 3, constructing a quality of life measure by combining various
elements is a difﬁcult and complex challenge. Previous studies (Giannias 1997; Evans
1994; Suﬁan 1993; Bumell and Galester 1992; Dasgupta and Weale 1992; Lawrence
1992; Glatzer and Mohr 1991; Blomquist et al. 1988; Johnston 1988; Berger et a1. 1987;
Myers 1987; Dahmann 1985) offer numerous approaches and methods for creating an

accurate and comprehensive quality of life index. In setting out to construct quality of life

70

indexes for the economic, housing, and social attributes of the metropolitan areas, my
study incorporated a variety of these components.

As past studies have shown, there is no deﬁnitive way to establish a perfect quality
of life index. The researcher has to utilize the best data available and make an effort to
avoid including variables that might have too much collinearity (Glatzer 1991). As in
most quality of life studies, some variables are more inﬂuential in helping to explain each
component. In his article, “Toward a Comprehensive Quality-of-Life Index,” Johnston
(1988) tried to emphasize this point by noting that an index does not provide a single
measure of the overall quality of life of the population of the United States. However, it
does offer a reasonably comprehensive assessment about the general conditions in the
society and whether those circumstances are improving, remaining static, or
deteriorating.

Future considerations towards this type of research should continue to improve upon
the variable selection and criteria for the quality of life components. Various economic,
housing, and social data are becoming more readily available every day. In addition,
more detailed data at a smaller (neighborhood) scale are beginning to be collected on a
regular basis by the US Census Bureau. This study would have beneﬁted greatly from
this type of data. Although the metropolitan statistical area level has the most data
available, the large areas and populations tend to blur the details between the diversity
and quality of life indices. Recent research (Mehretu and Sommers 1994, Mehretu et al.
1995) suggests that using a microgeographic scale (county, ward, or tract) for analysis
can help provide more accurate and detailed results than a city level can. The type of

research in my study would be ideal at a neighborhood or census tract level.

71

Unfortunately, these types of data were not available at the time my project was
undertaken. It should be a priority to urban geographers, sociologists, and urban planners
to collect and compile more information databases at this smaller, more precise level.

This microgeographic scale becomes very evident when comparing the results of the
different population size groups. As the MSA populations became smaller, it was easier
to determine some of the inﬂuences the ethnic diversity levels had on quality of life
types. This ‘diseconomies of scale’ example would be even more prevalent had my study
been able to examine the relationship between diversity and quality of life at the census
tract level. For instance, if we were to examine a census tract with 1,500 people, we
would have a much clearer picture of not only the diversity index values but also how
much the quality of life was being affected. This is not to say that subjects as complex as
diversity and quality of life or their relationship, would be any easier to interpret. Rather,
that utilizing a microgeographic scale would be a good starting point when examining
this type of subject material.

Most urban geographers and sociologists agree that these combined measurements
need to be continually improved. Even though the process for choosing the quality of life
variables was highly selective and thorough, some of the variables reﬂected the various
quality of life types better than others. An example of this can be seen within the social
quality of life component. The aggregate scores from this component are basically
comprised of two different types of variables. As mentioned in Chapter 3, the ﬁrst ﬁve
variables are descriptors of quality of life aspects for individuals within various urban
areas. The other eight variables all characterize the social living conditions of the

metropolitan areas themselves.

72

In hindsight, a more effective component structure for social quality of life might
have been to utilize these two types of variables separately. This method might have been
able to Shed insight into two different aspects of social quality of life. However, the two
variable types selected were truncated because a lack of social quality of life data exists
for the 288 metropolitan statistical areas this study examined. In addition, these 13
variables used together formed a comprehensive and inclusive social quality of life
measure. An ideal situation for future studies in this area would be to have more data on
the populations within the 288 cities (perhaps 15 social quality of life variables). The
study could then compare these results with the results of the social quality of life
variables (an additional 15) that describe the conditions of the cities themselves. This
would ensure a very thorough and encompassing examination, as well as insight into two
dimensions of social quality of life.

Another interesting aspect of this study was the lack of signiﬁcant results for the
total or aggregate quality of life component. Only four of the twenty-two (18%)
signiﬁcant equations showed an improvement, or decrease, in the combined quality of
life as diversity increased. This helps conﬁrm previous studies that suggest breaking up
an overall quality of life measure. Stover and Leven (1992) recommended using more
than one type of measure for quality of life; ”There may also exist more than one relevant
variable set. Separate ratings based on separate indicators of physical environment,
cultural environment, recreational environment, and so on, may prove useful” (p.746).

Lawrence, another advocate of using separate quality of life components,
emphasizes the importance of housing quality. In his article, “Housing Quality: An

Agenda for Research,” he advises, “a range of values, costs and beneﬁts ought to be

73

borne in mind if interpretations of the qualitative aspects of housing are to be undertaken
in a comprehensive manner (p. 1663).” Lawrence iterates that housing quality of life is an
essential barometer of the health of an urban populace. As stated in Chapter 3, although
such variables as Median Gross Rent could be utilized in an economic quality of life
index, it was to my advantage to have a separate housing component.

These types of suggestions were indeed valuable as they helped to provide for 18
additional signiﬁcant equations in this study. In addition, it enabled me to identify how
various qualities within an urban area compare. For instance, each resulting signiﬁcant
equation in this study revealed important insight into one of four categories. From a
solution standpoint, this meant 18 additional relationships could be examined. Had the
study only relied on one aggregate quality of life measure, only four relationships could
have been investigated.

As mentioned earlier, the ideas, concepts, and indices utilized in this research
project are continually being updated and improved. Journals such as Ueran Studies and
Social Indicators Resear_c_11 continually offer the latest concepts and methodology
available on these subjects. Studies of this type (Denton 1994; Fong 1994; Chakravorty
1992; Hanison and Weinberg 1990; Denton and Massey 1988b; Darden 1987; Allison
1978) seem to be moving away from the measuring of segregation and towards
investigating the relationships between and among various racial and cultural populations
(Houghton and Swati 1995; Jordan 1995; Gildwald and Habich 1991; Harvey et a1. 1990;
Dahmann 1985; Douthitt et al. 1992). This switch, combined with a shift from a
theoretical to an applied basis, could help provide a number of solutions to some of

societies most complex relationships.

74

Contemporary studies should embrace this movement and focus on how to improve
the diversity and quality of life issues within our metropolitan areas. Urban geographers
and sociologists have brought the unequal economic, housing and social conditions
within US cities to society’s attention. There is no inherent gain in continuing to point out
our deﬁciencies and weaknesses. Studies such as Denton’s (1994) “Are Aﬁ'ican
Americans Still Hypersegregated?” help to see if conditions are worsening or improving,
but provide little insight on how to devise a solution for these ongoing problems.

In contrast, more modern studies like Dahmann’s (1985) “Assessments of
Neighborhood Quality in Metropolitan America” not only examine the relationships of
various racial groups within metropolitan areas, but offer some possible solutions to help
improve conditions within these areas. Authors of some studies (Sullivan et a1. 1997),
“Where Does Community Grow? The Social Context Created by Nature in Urban Public
Housing” take this notion one step further as they actually apply their hypothesis in hopes
of improving the social welfare conditions among public housing. These types of studies
should become the benchmark pieces of the racial diversity and quality of life genres.

The more information we can learn about the elaborate relationship between diversity and
quality of life, the sooner we can begin to implement strategic solutions to improve

conditions in both areas.

APPENDICES

 

APPENDIX A

APPENDIX A

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table A.I. Equations for Population Group 1. (Native Americans)
Constant X x2 Hyp. m.)
Group 1. (Native
Americans)
Economic QOL
Dissimilarity -.810 +5 .795 ~6.841 Reject
Isolation 1.954 -.546 -5 .381 Reject
Relative Concentration .329 +.156 .006 Reject
Absolute Centralization .702 -l .602 +1 .431 Reject
Spatial Clustering .942 -.730 .l 12 Reject
Housing®L
Dissimilarity 1.797 -7.649 6.292 Reject
Isolation .005 -3.222 8.213 Reject
Relative Concentration -.080 -.169 -.006 Reject
Absolute Centralization -.351 .910 -.633 Reject
Spatial Clusteriﬁ -2.164 2.452 -3.375 Reject
Social QOL
Dissimilarity -2 .925 +37.94l -34. 160 Reject
Isolation 5.189 +3 1.374 -70.686 Reject
Relative Concentration 5.887 -.0487 -.024 Reject
Absolute Centralization 6.747 -11.146 +14.553 Reject
Spatial Clustering -7.829 +16.368 -2.744 Reject
Total QOL @gg_r_egate)
Dissirrrilarity .666 1.183 -2.810 Reject
Isolation .705 -.085 .375 Reject
Relative Concentration .695 -.079 -.003 Reject
Absolute Centralization .873 -l.703 2.130 Reject
Spatial Clustering -1 .737 2.869 -.453 Reject

 

 

 

 

 

 

76

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table A.2. Quadratic Equations for Population Group 1. (Asians)
Constant x x2 Hyp. (H.)
Group 1. (Asians)
Economic QOL
Dissimilarity -17.359 +1 16.989 -168.769 Reject
Isolation 1.265 +6.489 -1.447 Reject
Relative Concentration 1.407 +2794 -3. 105 Reject
Absolute Centralization 4.437 -5.923 2.913 Reject
Spatial Clustering -398. 161 +740.206 -341.1 18 Reject
Housing QOL
Dissimilarity 2.509 -1 1.23 8 1 1.441 Reject
Isolation 1.043 -20.272 +28.535 Reject
Relative Concentration 2.651 -l6.494 +18.161 Reject
Absolute Centralization -2.730 -.933 +5.979 Reject
Spatial Clustei'ng 410.949 -756.899 +346.786 Reject
Social QOL

Dissimilarity 14.518 49.912 +69.224 Reject
Isolation .274 +1.994 -2.335 Accept
Relative Concentration 5.735 -1 .723 +3.237 Reject
Absolute Centralization 6.816 -8.013 +8.805 Reject
Spatial Clusteriﬂ 44.679 82.231 -37.282 Accept
Total QOL (aggggate)

Dissimilarity 1.120 2.1 14 -8.1 16 Reject
Isolation .543 1.597 -. 101 Reject
Relative Concentration .938 -1.489 1.731 Reject
Absolute Centralization .923 -1.663 1.779 Reject
Spatial Clustering -59.462 109.102 -49. 150 Reject

 

 

 

 

 

 

77

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table A.3. Quad. Equations for Population Group 1. (African Americans)
Constant X X2 Hyp. (H1)
Group 1. (African -
Americans)
Economic QOL
Dissimilarity -.049 +12.225 -l3.728 Reject
Isolation 2.363 +5.095 -10.254 Reject
Relative Concentration 2.210 +2.898 -4.291 Reject
Absolute Centralization 5.076 -6.997 +3.400 Reject
Spatial Clustering 12.760 -14. 166 +4.38l Reject
Housing QOL
Dissimilarity -1 . 191 5.609 -5 .756 Reject
Isolation -1.21 1 +13.543 -20.037 Reject
Relative Concentration 3.270 -1.074 -5.447 Reject
Absolute Centralization -2.869 +8.983 -6.755 Reject
Spatial Clusterng -3. 126 5.462 -2.325 Reject
Social QOL
Dissimilarity 7.489 -3 .041 +881 Reject
Isolation 8.852 -10.236 +7.6l4 Reject
Relative Concentration 6.417 -0.641 -0.205 Reject
Absolute Centralization 6.802 -2.973 +2.138 Reject
Spatial Clusteang 35.653 -43.806 +15.717 Reject
Total QOL (amate)
Dissimilarity .082 5.052 -6.082 Reject
Isolation .892 1.622 -3 .390 Reject
Relative Concentration 1.190 .318 -l .335 Reject
Absolute Centralization 1.013 -.380 -.O31 Reject
Spatial Clustering 2.498 -1 .492 .093 Reject

 

 

 

 

 

 

78

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table A.4. Quadratic Equationgfor Po ulation Group 1. lHispanics)
Constant X X2 Hyp. (H l)
Group 1. (Hispanics)
Economic QOL
Dissimilarity 1.364 +1.364 -1.036 Reject
Isolation .606 +19.522 -33.746 Reject
Relative Concentration 1.124 +2.537 -.801 Reject
Absolute Centralization 3.881 -5.l93 +2.962 Reject
Spatial Clustering -67.665 +121.957 -52.810 Reject
Housing QOL
Dissimilarity -.377 +3.986 -7.235 Reject
Isolation .591 -9.146 +12.878 Reject
Relative Concentration .343 -.366 -1.037 Accept
Absolute Centralization -2.090 +4089 -1 .838 Reject
Spatial Clustﬂg 21.647 -31.749 +10.615 Reject
Social QOL
Dissimilarity -.436 +22.234 -14.565 Reject
Isolation .306 1.112 -1.520 Accept
Relative Concentration 4.929 +.370 +3.223 Reject
Absolute Centralization 6.220 -5.890 +7.269 Reject
Spatial Clustering -7.120 12.751 -5.305 Accept
Total QOL (agggggte)
Dissimilarity -4.385 +64.516 -78.856 Reject
Isolation .545 3.059 -6.089 Accept
Relative Concentration 8.781 -.312 -4.616 Reject
Absolute Centralization 8.011 -6.994 +8.393 Reject
Spatial Clustering -176.533 +315.855 -l33.593 Reject

 

 

 

 

 

 

79

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table A.5. @adratic Equations for Population Group 2. (Native Americans)
Constant x x2 Hyp. (III)
Group 2. (Native
American)
Economic QOL
Dissimilarity 4.637 -38.718 +65.279 Reject
Isolation .869 - 129.846 +560.983 Reject
Relative Concentration -1 . 157 +4.799 +.920 Reject
Absolute Centralization .071 -9.000 +10.999 Reject
Spatial Clustering 481.668 -863.560 +381.433 Reject
Horﬂg QOL
Dissimilarity 1.047 -4.331 2.650 Reject
Isolation -.574 +32.36O -79.475 Reject
Relative Concentration .003 -1.910 -.228 Reject
Absolute Centralization -.303 .936 -.506 Reject
Spatial Clustering -73.001 +12 1 .802 49.036 Reject
Social QOL
Dissimilarity 10.877 -83.453 +144.500 Reject
Isolation -.366 -13.967 +127.597 Reject
Relative Concentration -.650 +2.29] +.507 Reject
Absolute Centralization .047 -.757 +900 Reject
Spatial Clustermg 5.188 -15.702 + 10.01 1 Reject
Total QOL (aggregate)
Dissimilarity 23.891 -156.817 230.979 Reject
Isolation -.071 -1 1 1.453 +609.105 Reject
Relative Concentration -.242 +737 +.164 Reject
Absolute Centralization -l .735 -1 1.360 +18.645 Reject
Spatial Clustering 413.855 -757.460 +342.409 Reject

 

 

 

 

 

 

80

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table A.6. Qu_adratic Equations for Population Group 2. (Asians)
Constant X X2 Hyp. (11,)
Group 2. (Asians)
Economic QOL
Dissimilarity -6.095 +24. 5 19 -26.266 Reject
Isolation -2.027 +36.926 -1 15.257 Reject
Relative Concentration .200 -9.408 +12.935 Reject
Absolute Centralization .024 -2.335 2.683 Reject
Spatial Clustering -580.602 +1083.399 -504.461 Reject
Housing QOL
Dissimilarity 2.727 -l3.164 +14.922 Accept
Isolation .520 -16.657 +50.995 Reject
Relative Concentration -.610 +3.722 -4.549 Reject
Absolute Centralization -.297 .894 -.535 Reject
Spatial Cluster‘iﬂ 492.268 -924.730 +433 .068 Reject
Social QOL

Dissimilarity 7.479 -38.65 1 +44.665 Reject
Isolation -1.983 +43 .455 -127.297 Reject
Relative Concentration .526 ~5.648 +6.550 Reject
Absolute Centralization .032 -.950 1.088 Reject
Spatial Clusteﬂ -808.389 +151 1.706 -704.937 Reject
Total QOL @gglegate)

Dissimilarity 1.943 - 10.376 12.077 Reject
Isolation -3.489 +63.724 -191.558 Reject
Relative Concentration .117 -11.335 +14.936 Reject
Absolute Centralization -l .817 -19.204 +25.961 Reject
Spatial Clustering -99.803 185.779 -86.303 Reject

 

 

 

 

 

 

81

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table A. 7. Quad. Equattons for Population Group 2. (African Americans)
Constant X2 Hyp. (Hi)
Group 2. (African -
Americans)
Economic QOL
Dissimilarity -20.538 +70.292 -59.558 Reject
Isolation -2.505 +16.720 -24.720 Reject
Relative Concentration -3.571 +3.715 +1.271 Reject
Absolute Centralization -1.189 -9.935 +12.772 Reject
Spatial Clustering -78.434 +131.490 -55.054 Reject
HousﬂQOL
Dissimilarity 5.1 19 - 12.036 +5.075 Reject
Isolation -.618 +5.951 -9.1 12 Reject
Relative Concentration 1.917 -1.860 -1.849 Reject
Absolute Centralization -.215 .995 -.819 Accept
Spatial Clustcﬂg -18.351 +3 1.993 - 1 3.839 Reject
Social QOL
Dissimilarity -5. 125 +24.125 -26.392 Reject
Isolation . 1 17 -.058 -.774 Reject
Relative Concentration -1.615 -.881 +3.633 Reject
Absolute Centralization .019 -9.138 + 10.232 Reject
Spatial Clusteriﬁ -28.916 +54.645 -25 .678 Reject
Total QOL (agﬂgate)
Dissimilarity -20.544 +82.382 -80.876 Reject
Isolation -l .600 +21.923 -43.894 Reject
Relative Concentration -3.269 +.974 +3.054 Reject
Absolute Centralization -2.887 -11.113 +16.455 Reject
Spatial Clustering - 1 25 .700 +218.129 -94.571 Reject

 

 

 

 

 

 

82

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table A.8. Quadratic Equations for Population Group 2. (Hispanics)
Constant X X2 Hyp. (11,)
Group 2. (Hispanic)
Economic QOL
Dissimilarity 6.951 -43.623 +53.671 Reject
Isolation -.265 +4.440 -8.468 Accept
Relative Concentration -2.320 +3.158 +1.992 Reject
Absolute Centralization -.548 -9.719 +12.856 Reject
Spatial Clustering 76.487 - 1 32.979 +56.402 Reject
Housing QOL
Dissimilarity -.125 +1.980 -3.823 Reject
Isolation 1.066 -1 1.750 +14.042 Reject
Relative Concentration .261 -.561 -.328 Accepn
Absolute Centralization -.21 1 .844 -6.52 Reject
Spatial Clustﬂg 31.912 -54.727 +23 .065 Reject
Social QOL

Dissimilarity 4.025 -25.952 +32.822 Reject
Isolation -1.255 +12.964 -l9.920 Reject
Relative Concentration -1.096 +.176 +2.552 Reject
Absolute Centralization .023 -.764 +916 Reject
Spatial Clustering 3.070 -10.438 +6.66l Reject
Total QOL (aggregate)

Dissimilarity 9.975 -53.736 +5591 1 Reject
Isolation -1 .228 +3.968 -8.245 Reject
Relative Concentration -1.325 -l.153 +1.918 Reject
Absolute Centralization -l .937 -12.894 +19.545 Reject
Spatial Clustering 1 1 1.469 - 198. 144 +86.128 Reject

 

 

 

 

 

 

83

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table A. 9. Qﬂtdratic E nations for Population Group 3. (Native Americans)
Constant X X2 Hyp. (III)
Group 3. (Native
Americans)
Economic QOL
Dissimilarity -4.436 +35.235 -58.028 Reject
Isolation .094 +39.729 -218.752 Reject
Relative Concentration .077 +4.004 +.803 Reject
Absolute Centralization .727 -10.705 +14.139 Reﬁtct
S atial Clustering -47.761 +90.941 -42.700 Reject
Housiﬂ QOL
Dissimilarity 10.899 -59.426 +68.007 Reject
Isolation -.185 +18.832 -42.370 Reject
Relative Concentration .096 -.667 -.075 Reject
Absolute Centralization -1.967 +7.216 -3.978 Reject
Spatial Clustﬂg 48.499 -86.381 +37.978 Reject
Social QOL
Dissimilarity -8.063 +41.010 -58.729 Reject
Isolation -1.943 +36.750 -l96.316 Reject
Relative Concentration -l.854 +1.671 +.419 Reﬁct
Absolute Centralization -2.269 -4.336 +7.997 Reject
Spatial Clustering -86.165 +149.671 -65.204 Reject
Total QOL (aggggte)
Dissimilarity -1.600 +16.819 -48.751 Reject
Isolation -2.033 +95 .3 12 -457.438 Reject
Relative Concentration -l .681 +5.009 +1.147 Reject
Absolute Centralization -3.509 -7.825 +18.159 Reject
Spatial Clustering -85 .428 +154.231 -69.925 Reject

 

 

 

 

 

84

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table A. 10. Quadratic Equations for Population Grout) 3. (Asians)
Constant X X2 Hyp. (H1)

Grotm 3. (Asians)

Economic QOL
Dissimilarity 3.423 -15.354 15.393 Accept
Isolation -.693 +44.905 -204.092 Reject
Relative Concentration .394 +3.145 -5.231 Reject
Absolute Centralization .227 -l .669 1.713 Reject
Smitial Clustering 402.755 +808.29O 405.034 Reject

Housing QOL
Dissinrilarity -3.742 +14.871 -10.381 Reject
Isolation -1.438 +51 .000 -143.854 Reject
Relative Concentration -.195 -.019 .748 Reject
Absolute Centralization -.338 .630 -.038 Reject
Spatial Clustering -.421.98 +813.478 -391.985 Reject

Social QOL '

Dissimilarity .930 -6.610 -2.364 Reject
Isolation -2.729 +42.806 -182.842 Reject
Relative Concentration -3.374 +3.900 -.488 Reject
Absolute Centralization -2.451 -5.480 +8.426 Reject
Spatial Clustering 102 1 .1 71 -2016.696 +993 .930 Reject
Total QOL (aggggate)
Dissimilarity 17.723 -83.866 +79.615 Reject
Isolation -.505 +17.152 -66.062 Accept
Relative Concentration 4.540 +6.894 +.261 Reject
Absolute Centralization -3.794 -10.454 +18.400 Reject
Spatial ClusterinL -2757.427 +5301.578 -2546.988 Reject

 

 

 

 

 

 

85

 

Appendix Table A.11. Quad. Equations for Population Group 3. (African Americans)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Constant X X2 Hyp. (H1)
Group 3. (African -
Americans)
Economic QOL
Dissimilarity 14.503 48.538 +37.919 Reject
Isolation 2.303 -8.263 +1.774 Reject
Relative Concentration -. 103 +387 +.630 Reject
Absolute Centralization .254 -l .751 +1.690 Reject
Spatial Clustering 55 .605 -86.9 1 3 +33.064 Reject
Housing QOL
Dissimilarity .400 +7.719 -] 5.877 Reject
Isolation -.743 +5.60] -5.819 Reject
Relative Concentration 1.770 -1.711 -1.703 Reject
Absolute Centralization -0.175 +1.09] -.927 Accept
Spatial Clustering -61 .372 +105.435 44.714 Reject
Social QOL
Dissimilarity .41 1 -l .212 +228 Reject
Isolation .170 -1.750 +1.443 Reject
Relative Concentration -2.439 +1.744 -.457 Reject
Absolute Centralization -1.775 -5.831 +7.002 Reject
Spatial Clustering 11.414 -18.658 7.359 Accept
Total QOL (aggregate)
Dissimilarigl 2.878 -8.337 4.563 Reject
Isolation .461 -2.427 1.01 l Reject
Relative Concentration -.773 +.420 —1.530 Reject
Absolute Centralization -1.652 -7.614 +9.724 Reject
Spatial Clustering 13.010 -19.964 7.280 Reject

 

 

 

 

 

 

86

 

 

Appendix Table A.12. Quadratic Equations for Population Group 3. (Hispanics)
2

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Constant X X Hyp. (11,)
Group 3. (Hispanics)
Economic QOL
Dissimilarity -2.058 +12.262 -]2. 184 Reject
Isolation -.787 +27.879 -65.075 Reject
Relative Concentration -1.164 +3.53] +2.400 Reject
Absolute Centralization .116 -1.721 2.113 Reject
Spatial Clustering - 143.653 +2628 1 6 -1 19.106 Reject
Housing QOL
Dissimilarity .472 -1.872 1.037 Reject
Isolation .176 -.921 - l .33 Reject
Relative Concentration .184 -.295 -.037 Reject
Absolute Centralization -.245 +.883 -.508 Accept
Spatial Clustering -50.74O +93. 162 42.433 Reject
Social QOL

Dissimilarity -.224 -5.523 +2.323 Reject
Isolation -.865 -16.496 +33 .445 Reject
Relative Concentration -l .970 +1.664 -.764 Reject
Absolute Centralization -2.148 -5.06] +7.82] Reject
Spatial Clustering 223.339 403.017 +178.767 Reject
Total QOL (aggggate)

Dissimilarity 1 .496 -8.234 -1 .565 Reject
Isolation -1.475 + 10.463 -31.763 Reject
Relative Concentration -1.725 +1.848 +.012 Reject
Absolute Centralization -3.412 -8.326 +16.436 Reject
Spatial Clustering 28.946 47.039 +17.228 Reject

 

 

87

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table A.13. Quad. Equations for Population Group 4. (Native Americans)
Constant X X2 Hyp. (H1)
Group 4. (Native -
Americans)
Economic QOL
Dissimilarity -l3.248 +8291 1 -134.673 Reject
Isolation -.846 -53.855 +285.801 Reject
Relative Concentration -1.544 +2.608 -2. 136 Reject
Absolute Centralization -2.37l +1 .065 +596 Reject
Spatial Clustering 593.280 -] 141.246 +546.631 Reject
Housing QOL
Dissimilarity 1.481 -8.493 +11 .172 Accept
Isolation -.224 +35.945 -150.332 Reject
Relative Concentration .180 -.256 +.778 Reject
Absolute Centralization -.368 +735 -.122 Reject
Spatial Clustenlg -] 14.293 +205.295 -90.874 Reject
Social QOL
Dissimilarity ~8.391 +28.624 40.474 Reject
Isolation -3. 160 -70.184 +510.610 Reject
Relative Concentration 4.247 +3.972 -.516 Reject
Absolute Centralization 4.838 -3.025 +6.896 Reject
Spatial Clusteriﬂ -278.67] +518.862 -244.072 Reject
Total QOL (agglegate)
Dissimilarity -9.789 +43.589 -85.773 Reject
Isolation 4.231 -88.094 +646.079 Reject
Relative Concentration -5.611 +6.325 -1.873 Reject
Absolute Centralization -10.156 +3.922 +6.512 Reject
Spatial Clustering 200.316 417.089 +21 1.685 Reject

 

 

 

 

 

88

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table A.14. Qpadratic Equations for Population Group 4. (A sians)
Constant X X2 Hyp. (H1)
Group 4. (Asians)
Economic QOL
Dissimilarity 5.61 1 40.954 +54.802 Reject
Isolation -2.762 +53 .029 -l79.176 Reject
Relative Concentration -.316 -1.619 2.933 Reject
Absolute Centralization -.948 -8.025 +9.143 Reject
Spatial Clustering -3 165.878 +6104.948 -2942.0 1 O Reject
Housing QOL
Dissimilarity ] .079 -1 .079 -1.489 Reject
Isolation -.458 +2254] -27.962 Reject
Relative Concentration -.357 +065 +1.864 Reject
Absolute Centralization -.46] +555 +. 135 Reject
Spatial Clustering 457.272 +844.469 -387.545 Reject
Social QOL

Dissimilarity -.734 -26.575 +46.728 Reject
Isolation -.51 l +8.727 -27 .026 Accept
Relative Concentration -.238 -l .444 +2.240 Reject
Absolute Centralization -3. 147 -16.525 +19.577 Reject
Spatial Clustering 420.689 +808.401 -388.224 Accept
Total QOL (aggregate)

Dissimilarity 5.955 -69.261 +100.040 Reject
Isolation -l.029 +20383 -60.384 Accept
Relative Concentration -5.98 -3.055 5.405 Reject
Absolute Centralization -7.780 -20.107 +29.798 Reject
Spatial Clustering -1005.494 +193 1 .45 1 -927.002 Reject

 

 

 

 

89

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table A.15. Quad. Equations for Population Group 4. (A frican Americans)
Constant X X2 Hyp. (H1)
Group 4. (African —
Americans)
Economic QOL
Dissimilarity 1 1.741 46.393 +36.715 Reject
Isolation .09] -1 .407 -.422 Reject
Relative Concentration 4.519 +2.316 +3.465 Reject
Absolute Centralization -.306 -6.216 +5.666 Reject
Spatial Clustering 16.290 -25.825 +9.636 Accept
Housing QOL
Dissimilarity -1.358 +1 1.957 -16.834 Reject
Isolation .193 +005 +.615 Reject
Relative Concentration -.377 +1 .455 -.410 Reject
Absolute Centralization -.461 +555 +. 135 Reject
Spatial Clustﬂg -37.810 64.902 -27.173 Reject
Social QOL
Dissimilarity 1 1.914 -50.277 +34. 181 Reject
Isolation .018 -] .837 +949 Reject
Relative Concentration -5.627 +.743 +2.874 Reject
Absolute Centralization -2.160 -7.957 +7.054 Reject
Spatial Clustering 12.910 -20.682 +7.752 Accept
Total QOL (aggregate)
Dissimilarity 2.704 -]0.105 6.644 Reject
Isolation . 133 -3 .244 .604 Reject
Relative Concentration ~10.522 +4.514 +5.930 Reject
Absolute Centralization -5.901 —11.246 +15. 145 Reject
Spatial Clustering 24.475 -38.394 +13.991 Accept

 

 

 

 

9O

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table A.16. Quadratic Equations for Population Grou 4. (Hispanics)
Constant x x2 Hyp. (H1)
Group 4. (Hispanics)
Economic QOL
Dissimilarity 5.777 40.962 +50. 161 Reject
Isolation -. 124 -. 120 -2.756 Reject
Relative Concentration -.613 .747 .676 Reject
Absolute Centralization -2.068 -3.771 +6.292 Reject
Spatial Clustering 357.104 -646.056 288.393 Reject
HousinLQOL
Dissimilarity -1.647 +18.141 -36.869 Reject
Isolation -.320 +8.229 -6.352 Reject
Relative Concentration -.025 +.454 +.678 Reject
Absolute Centralization -.369 +589 +.009 Reject
Spatial Clustering -321 .939 +591.037 -269.412 Reject
Social @L

Dissimilarity 2.212 -26.690 +18.628 Reject
Isolation -2.697 -l6.679 +14.951 Reject
Relative Concentration -6.036 +4.393 +4.554 Reject
Absolute Centralization 4.491 -6.353 +10.018 Reject
Spatial Clustenﬂ 301 .494 -546.367 +241 .966 Reject
Total QOL (aggggate)

Dissimilarity 6.342 49.51 1 +31.921 Reject
Isolation -3 .759 -9. 170 -7.935 Reject
Relative Concentration -l .080 1.142 1.111 Reject
Absolute Centralization -9.514 -5.414 +16.382 Reject
Spatial Clustering 336.659 -601 .387 +260.947 Reject

 

 

 

 

 

 

 

APPENDIX B

APPENDIX B

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table B. 1. Min / Max Values for Significant Equations
Constant (a) X (b1) X2 (b2) Point Y Point X
Equations 1.39 17.769 -7.74286 1 1.58447 1.147444
Group 1
HHRCO 0.343 -0.366 -1.037 0.375294 -0. 17647
HSISO 0.306 1.112 -1.52 0.509379 0.365789
HSSPC -7.12 12.751 -5.305 0.542017 1.201791
HTISO 0.545 3.059 -6.089 0.929196 0.251191
ASISO 0.274 1.994 -2.335 0.6997 0.426981
ASSPC 44.679 82.231 -37.282 0.664177 1.102824
Group 2
HEISO -0.265 4.44 -8.468 0.317003 0.262163
HHRCO 0.261 -0.561 -0.328 0.500879 -0.85518
AAHACE -0.215 0.995 -0.819 0.087205 0.607448
AHDIS 2.727 —l3. 164 14.922 -0. 17628 0.441094
Group 3
HHACE -0.245 0.883 «0.508 0.138705 0.869094
AAHACE -0.175 1.091 -0.927 0.146004 0.588457
AASSPC 11.414 -18.658 7.359 -0.41237 1.267699
AEDIS 3.423 -15.354 15.393 -0.40577 0.498733
ATISO -0.505 17.152 -66.062 0.608314 0.129817
Group 4
AAESPC 16.29 -25.825 9.636 -1.0131 1.340027
AASSPC 12.91 ~20.682 7.752 -0.88467 1.333978
AATSPC 24.475 -38.394 13.991 -l.86513 1.372096
ASISO -0.511 8.727 -27.026 0.193512 0.161456
ASSPC 420.689 808.401 -388.224 0.145478 1.041153
ATISO - l .029 20.3 83 -60.384 0.691 103 0.168778
NAHDIS 1.481 -8.439 11.172 -0.] 1264 0.377685

 

 

 

 

 

 

91

 

 

APPENDIX C

APPENDIX C

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table C. 1. Trend Results for Population Group 1.
P Y X Value Trend
Hispanics
(Housing @L)
Relative Concentration .00] .375 -.176 Maximum Downward
(Social QOL)
Isolation .002 .509 .365 Maximum Downward
Spatial Clustering .001 .542 1 .202 Minimum Upward
(Total QOL)
Isolation .001 .930 .25 1 Maximum Downward
Asians
(Social QOL)
Isolation .001 .670 .426 Maximum Downward
Spatial Clustering .001 .664 1 . 10 Minimum Upward

 

 

 

 

 

 

92

 

 

93

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table C. 2. Trend Results for P0 ulation Group 2.
P Y X Value Trend
Hispanics
(Economic QOL)

Isolation .00] .317 .262 Maximum Downward

(Housing QOL)
Relative Concentration .00] .501 -.855 Maximum Downward

African Americans

(Housing QOL)

Absolute Centralization .001 .087 .607 Minimum Upward
Asians

(Housing QOL)

Dissimilarity .001 -.176 .441 Minimum Upward

 

 

 

 

 

 

 

94

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table C. 3. Trend Results for Population Group 3.
P Y X Value Trend
Hispanics
(Housing QOL)
Absolute Centralization .001 .139 .869 Minimum Upward
African Americans
Q—Iousing QOL)
Absolute Centralization .001 1.091 -.927 Maximum Downward
(Social QOL)
Spatial Clusterig .001 -.412 1 .268 Minimum Upward
Asians
(Economic QOL)
Dissimilarity .001 -.406 .500 Minimum Upward
(Total QOL)
Isolation .001 .608 . l 30 Maximum Downward

 

 

 

 

 

 

 

95

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Table C. 4. Trend Results for P0 ulation Group 4.
P Y X Value Trend
African Americans
(Economic QOL)
Spatial Clustering .001 -l .013 1 .340 Minimum Upward
(Social QOL)
Spatial Clusteriﬂ .001 -.885 ] .334 Minimum Upward
(Total QOL)
Spatial Clustermg .001 -1 .865 1.372 Minimum Upward
Asians
(Social QOL)
Isolation .001 .193 .161 Maximum Downward
Spatial Clustering .001 .145 l .04 Minimum Upward
(Total QOL)
Isolation .001 .691 . 1 69 Maximum Downward
Native Americans
(Housirg QOL)
Dissimilarity .00] -. 1 13 .378 Minimum Upward

 

 

 

 

 

 

 

 

BIBLIOGRAPHY

BIBLIOGRAPHY

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Baldassare, M. and G.Wilson. 1995. Urbanization and the decline in suburban quality-of-
life ratings. Urban Affairs Review 30: 708-72].

Bickford, A. and D. Massey. 1991. Segregation in the second ghetto: racial and ethnic
segregation in American public housing, 1977. Social Forces 69(4): 1011 - 1036.

Blomquist, G., M. Berger and J. Hoehn. 1988. New estimates of the quality of life in
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Bumell, JD. and G. Galester. 1992. Quality of life measurements and urban size: an
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Chakravorty, S. 1992. A measurement of spatial disparity: the case of income inequality.
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Coulton, C., J. Chow, B. Wang and M. Su. 1996. Geographic concentration of afﬂuence
and poverty in 100 metropolitan areas, 1990. Urban Affairs Review 32(2): 186-
216.

Darden, J. 1987. Choosing neighbors and neighborhoods: the role of race in housing
preference. Divided Neighborhoods: Changing Pattem_s of Racial Segzegation
Urban Affairs Annual Reviews 32: 15-42.

Dahmann, D. 1985. Assessments of neighborhood quality in metropolitan America.
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Denton, N. 1994. Are Aﬁican Americans still hypersegregated? In Residential Apartheid
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F ong, E. 1994. Residential proximity among racial groups in US. and Canadian
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Ginsburg, N. 1990. Institutional racism and local authority housing. Foundation: Race
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Harrison, R.J. and DH. Weinberg. 1994. Racial and ethnic residential segregation: 1990.
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Herrnstein, R.J. and C. Murray. 1994. The Bell Curve. New York, NY: The Free Press.

Houghton, M., A. Dominique and M. Swati. 1995. The economic measurement and
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