EPA-600/5-75-013
JULY 1975
Socioeconomic Environmental Studies Series
Secondary Impacts of
Transportation and Wastewater
Investments: Research Results
I
5
^
LU
CD
Office of Research and Developme
U.S. Environmental Protection Agency
Washington, D.C. 20460
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, Environmental
Protection Agency, have been grouped into five series. These five broad
categories were established to facilitate further development and application
of environmental technology. Elimination of traditional grouping was
consciously planned to foster technology transfer and a maximum interface
in related fields. The five series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
This report has been assigned to the SOCIOECONOMIC ENVIRONMENTAL STUDIES
series. This series includes research on environmental management, compre-
hensive planning and forecasting and analysis methodologies. Included are
tools for determining varying impacts of alternative policies, analyses of
environmental planning techniques at the regional, state and local levels,
and approaches to measuring environmental quality perceptions. Such topics
as urban form, industrial mix, growth policies, control and organizational
structure are discussed in terms of optimal environmental performance. These
interdisciplinary studies and systems analyses are presented in forms varying
from quantitative relational anayses to management and policy-oriented reports.
EPA REVIEW NOTICE
This report has been reviewed by the Office of Research and Development, EPA,
and approved for publication. Approval does not signify that the contents
necessarily reflect the views and policies of the Environmental Protection
Agency, nor does mention of trade names or commercial products constitute
endorsement or recommendation for use.
Document is available to the public through the National Technical Infor-
ration Service, Springfield, Virginia 22151.
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EPA-600/5-75-013
July 1975
SECONDARY IMPACTS OF TRANSPORTATION
AND WASTEWATER INVESTMENTS: RESEARCH RESULTS
By
S.E. Bascom
K.G. Cooper
M.P. Howell
A.C. Makrides
F.T. Rabe
EPA Program Element No. 1H1095, 21ART-11
HUD Program Element No. DCPD 48
CEQ Contract No. EQC 317
Project Officers
Edwin H. Clark, Council on Environmental Quality
Analytical Studies Staff
James Hoben, U.S. Department of Housing and Urban
Development, Office of Policy Development
and Research
D. Robert Scherer, U.S. Environmental Protection Agency
Ecological Impact Analysis Staff
Washington Environmental Research Center
Prepared for
Executive Office of the President
COUNCIL ON ENVIRONMENTAL QUALITY
Washington, D. C. 20006
Office of Policy Development and Research
U.S. DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT
Washington, D. C. 20413
Office of Research and Development
U.S. ENVIRONMENTAL PROTECTION AGENCY
Washington, D. C. 20460
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Foreword
The widespread use of environmental impact analysis as a means of
achieving Federal agency decision-making responsive to environmental
concerns was initiated by the passage of the National Environmental
Policy Act of 1969. The Act requires that Federal agencies prepare
statements assessing the environmental impact of their major actions
significantly affecting the quality of the human environment and indi-
cates a broad range of aspects of the environment to be surveyed. The
Council on Environmental Quality in guidelines for the preparation of
environmental impact statements, dated August 1, 1973, states that
many major Federal actions, in particular those that involve the con-
struction or licensing of infrastructure investments such as highways
and sewer systems "... stimulate or induce secondary effects in the
form of associated investments and changed patterns of social and
economic activities." Such secondary effects may in turn produce
secondary environmental impacts even more substantial than the primary
environmental impacts of the original action itself. The influence of
highways on development decisions has been extensively researched. It
appears that new sewer facilities are becoming increasingly more pre-
dominant in determining where development will occur, and this rela-
tionship has been much less investigated.
During the last eighteen months, the Council on Environmental
Quality, the U.S. Environmental Protection Agency, and the U.S. Depart-
ment of Housing and Urban Development have sponsored a study of the
secondary effects of these two important types of public investments
which stimulate land development - land transportation systems and
wastewater collection and treatment systems.
The first part of the study involved a comprehensive review of
previous research and literature related to secondary effects of waste-
water treatment and collection systems, highways and mass transit
systems on economic/urban development. This report (second part) pre-
sents the results of original research on the extent to which secondary
development can be attributed to such infrastructure investments and on
the conditions under which causal relations appear to exist.
The project was undertaken by the Environmental Impact Center, 55
Chapel Street, Newton, Massachusetts, 02158, under the directorship of
Dr. A. C. Makrides. The work was co-sponsored by the Ecological Impact
Analysis Staff, Washington Environmental Research Center, U.S. Environ-
mental Protection Agency, U.S. Department of Housing and Urban Develop-
ment, and the Council on Environmental Quality.
Edwin B. Royce, Director
Ecological Impact Analysis Staff
Washington Environmental Research Center
U.S. Environmental Protection Agency
ii
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PREFACE
Each year, Federal, State, and local governments invest over $11.5
billion on roads and over $2.4 billion on wastewater collection and
treatment facilities.1 Typically, such infrastructure facilities accom-
plish their primary purposes — speeding the flow of traffic or collect-
ing and treating sewage — efficiently and economically. However, there
is increasing concern that these investments may have impacts extending
beyond their primary accomplishments. Infrastructure facilities may
affect decisions on type and location of new development since they
change the relative accessibility and cost of development of land.
Impacts on land use and development are termed secondary effects of the
investment. Secondary effects may, in turn, be associated with a whole
series of environmental, economic, and social impacts on the immediate
area served by the investment and on the surrounding region.
The present study, sponsored by the Council on Environmental Quality,
the Environmental Protection Agency, and the Office of Policy Development
and Research, Department of Housing and Urban Development, was undertaken
to investigate secondary effects of investments in:
• Highways
• Public transit facilities
• Wastewater collection and treatment facilities
The study was in two parts. The first involved an extensive review of
previous research pertaining to secondary effects of infrastructure in-
vestments and of land use models which might be used to predict secondary
effects. The literature review and bibliography is published in a
s ep ar at e vo lume. *•
The second part of the study was directed at developing techniques to
assist project planners and reviewers in predicting type, magnitude,
and location of secondary effects associated with infrastructure invest-
ments. Case studies of recent development trends were made in four
metropolitan regions — Washington, D.C., Boston, Massachusetts, Denver,
Colorado, and Minneapolis-St. Paul, Minnesota. As used in this report,
the term "metropolitan region" refers to a group of urbanized and
urbanizing communities with strong economic interdependence. While this
corresponds roughly with the Bureau of Census' definition of a Standard
iii
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Metropolitan Statistical Area (SMSA), our discussion was not strictly
limited to SMSA's. Data for the four regions were analyzed using
econometric techniques and simulation modeling.
The present volume documents this work. The report consists of four
sections: an introduction and summary of principal findings; a technical
documentation of the case studies and econometric analyses; an evaluation
of the results and suggestions for further research; and an appendix
summarizing the dynamic model and its application.
The Authors
iv
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ABSTRACT
This report is the second of a two-part research study. The first report
involved an extensive review of previous research pertaining to secondary
effects of highways, mass transit, wastewater collection and treatment sys-
tems, and of land use models which might be utilized to project secondary
environmental effects. The report is published under the title: "Secondary
Impacts of Transportation and Wastewater Investments: Review and Bibliog-
raphy," (EPA No. 600/5-75-002, January, 1975).
The second report presents, in this publication, the results of original
research on the extent to which secondary development can be attributed to
highways and wastewater treatment and collection systems, and conditions
under which causal relations appear to exist. Case studies of recent devel-
opment trends were made in four metropolitan regions: Boston, Massachusetts,
Denver, Colorado, Washington, D.C., and Minneapolis-St. Paul, Minnesota. Data
for the four metropolitan regions were analyzed using econometric techniques
and simulation modeling. The data tape (TMP 243) is stored with Optimum
Systems Incorporated, Washington, D.C.
This report consists of four sections: an Introduction and Summary of
Findings; a technical documentation of case studies and econometric analysis;
an evaluation of the Findings and suggestions for Further Research; and
Appendices summarizing the dynamic model, its application, and documentation.
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TABLE OF CONTENTS
FOREWORD 11
PREFACE ill
ABSTRACT v
TABLE OF CONTENTS vi
LIST OF FIGURES vli
LIST OF TABLES ix
ACKNOWLEDGEMENTS xi
Sections
I. Conclusions 1
II. Introduction and Summary 3
A. Introduction 3
B. Approach 4
C. Summary of Findings 4
III. Empirical Estimation of Secondary Effects 7
A. Summary Review of Previous Relevant Research 7
B. A General Approach to Secondary Effects 9
C. Development of Quantitative Relations 15
D. Study Regions and Sample Characteristics 24
E. Regression Analyses 42
F. Regression Results 50
IV. Conclusions and Suggestions for Further Research 67
A. Implications of the Findings 67
B. Limitations 68
C. Areas for Further Research 68
V- References 76
VI. Appendices 79
I. The Land Use Simulation Model 80
II. Model Listing 120
III. Documentation of Data on Tape TMP 243 142
vi
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LIST OF FIGURES
No.
Appendix I - The Land Use Simulation Model
A. Structure of Zonal Industrial and Residential
Development 80(a)
A.I. Effect of Vacancy Rate on Regional Construction 84
A.2. Zonal Attractiveness as a Function of Land
Availability 85
A.3. Effect of Interzonal Travel Times on Accessibility 86
A.4. Form of Relationship between Employment Density
and Zonal Land Availability 88
A.5. Political Jurisdictions 90
A.6. Current Regional Development Pattern 92
A.7. Network of Major Highways 93
A.8. Water and Sewer Service Area 96
A.9. Dynamic Model Zones 98
A.10(a). Zonal Land Use Simulation, Zone B 101
(b). Zonal Land Use Simulation, Zone G 102
(c). Zonal Land Use Simulation, Zone M 103
A.11. Sensitivity to Travel Time 107
A.12(a). Contrasting Zonal Land Use Effects of Moratoria
(1970-1976), Zone D 112
(b). Contrasting Zonal Land Use Effects of Moratoria
(1970-1976), Zone E 113
A. 13(a). Contrasting Effects of Different Sewer Controls
on Zone E: Simultaneous Removal 115
(b). Contrasting Effects of Different Sewer Controls
on Zone E: Selective Removal 116
(c). Contrasting Effects of Different Sewer Controls
on Zone E: Early Removal and Investment
vii
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LIST OF FIGURES (Continued)
No.
Appendix III - Documentation of Data
B.I. Maximum DASTAK Input/Output 170
B.2. Illustration of Application c 171
B.3. EIC Analysis Zones for Boston 187
B.4. EIC Analysis Zones for Denver 192
B.5. EIC Analysis Zones for Minneapolis-St. Paul 199
B.6. EIC Analysis Zones for Washington, D.C. 206
viii
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LIST OF TABLES
No.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
Section III. Empirical Estimation of Secondary
Effects
Characteristics of Metropolitan Areas in 1960
Characteristics of 1970 SMSA's
Regional Characteristics - Boston
Regional Characteristics - Denver
Regional Characteristics - Minneapolis-St. Paul
Regional Characteristics - Washington, D.C.
Simple Correlation Coefficients - Boston
Simple Correlation Coefficients - Denver
Simple Correlation Coefficients - Minneapolis-
St. Paul
Simple Correlation Coefficients - Washington, D.C.
Simple Correlation Coefficients - Pooled Sample
Single-Family Housing Construction Normalized by
7
25
26
27
28
29
30
32
34
36
38
40
District Size 44
13. Single-Family Housing Construction, Second
Formulation 46
14. Single-Family Housing Construction, Third
Formulation 47
15. Single-Family Housing Construction, Unnormalized 49
16. Estimates of Single-Family Residential Construction 52
17. Estimates of New, Multi-Family Residential
Construction 54
18. Estimates of Land Conversion to Commercial Use 56
ix
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LIST OF TABLES (Continued)
No.
19.
20.
A.I.
A. 2.
A.3.
A. 4.
A. 5.
A. 6.
C.I
C.2
Estimates of Land Conversion to Industrial Use
Estimates of Forecast Year Stocks of Dependent
Variables
Appendix I - The Land Use Simulation Model
Population Change
Employment by Major Sectors
Single-Family Residential Units
Multi-Family Residential Units
Employment
Investment and Policy Impacts
Appendix III - Documentation of Data
Magnetic Tape Index TMP 243
Descriptor of Empiric Datasets
Page
59
65
91
94
104
105
106
111
184
185
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ACKNOWLEDGEMENTS
The authors wish to acknowledge the support and cooperation
received from the many Council on Environmental Quality, U. S.
Housing and Urban Development, U. S. Environmental Protection
Agency staffs, and those local, state public officals/staffs
who were so generous with their time and provided much of the data
and the information necessary for the performance of this research
study.
xi
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I. CONCLUSIONS
A basic conclusion of this study, supported by both the literature
review and the statistical analyses, is that public infrastructure in-
vestments can have an important impact on the location, type, and
magnitude of development, particularly for single-family homes. The
strong relationship with single-family homes should be interpreted as
meaning that the secondary effects are particularly strong at the
urban fringe since this is where most single-family home construction
has taken place over the past two decades. Other types of development
are also likely to be affected by infrastructure investment, although
the effect was less evident in the statistical analyses than in other
case studies summarized in the literature review.
A second conclusion of the study is that sewer investments seem to
have stronger and more direct secondary effects than new highways.
Unfortunately, there are very few case studies of sewer investments and
their associated developments to supplement the general statistical
analyses reported in this volume. Such studies would be valuable in
providing a better understanding of the various factors which influence
the generation of secondary effects by sewers. We can expect the rela-
tive importance of sewers to continue, or even become accentuated, as
water pollution controls become stricter, and as new highways continue
to have relatively less influence than earlier highways.
The work reported in this volume also showed that quantitative techni-
ques can be developed, for specific regions, which will allow project
planners and reviewers to estimate the magnitude and type of likely
secondary effects associated with proposed infrastructure investments.
Even the rather simple equations presented in this study allow these
predictions to be made with reasonable confidence, although any specific
projection should take careful account of the particular conditions —
topography, development pressure, land use ownership and controls —
existing in the area to be served by the investment.
The regression equations presented here are not general predictive tools
that can be used with reasonable confidence in all areas. In regions
not included in the case studies reported here, a useful approach would
be to develop a set of new equations, similar to the ones given in this
study, but reflecting the particular conditions and circumstances in
the specific regions. While this requires a rather substantial data
base, the alternative, application of the regression equations for the
pooled sample, may be pursued only with caution. No matter what
approach is taken, the application of statistically derived equations
should be supplemented with a careful review of local land use plans
and controls and the opinions and advice of local planners and officials.
This caution is particularly important in view of the fact that the
construction industry is currently in a state of substantial flux.
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Changing energy prices, demographic characteristics, personal values,
construction costs, and general economic conditions may result in new
developments quite different from what the United States has experienced
over the past two decades. An example of these changes is the increased
attention being given to mass transit investments both by localities
making such investments and by families looking for new residences.
Such investments, although they are too old or too new to have been
included in the statistical work reported in this volume, may well
provide a strong stimulus to high density residential and commercial
development along their routes. Since it is not yet clear what the new
trends will look like, and how much they will differ from the past,
predictions of future events from statistical analyses of past trends
must be viewed with great caution.
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II. INTRODUCTION AND SUMMARY
A. INTRODUCTION
According to the Council on Environmental Quality, "... many major
federal actions, in particular those that involve ... infrastructure
investments ... stimulate or induce secondary effects in the form of
associated investments and changed patterns of social and economic
activities. Such secondary effects, through their impact on existing
community facilities and activities, may be even more substantial than
the primary effects of the original action itself." 3
The National Environmental Policy Act (NEPA) of 1969^ and similar
acts in a number of states, require government agencies to prepare, in
advance, environmental impact statements for all major actions. The
CEQ guidelines call for an explicit analysis of secondary effects.3
Local governments, becoming more concerned about the implications of
rapid development, have also begun to focus on impacts of infrastruc-
ture investments in stimulating or at least supporting such develop-
ment.
In spite of these concerns, we lack analytical tools for predicting
secondary effects or for assessing the importance of various factors
which influence the magnitude, type and location of these effects.
A number of studies have been directed at assessing the economic and,
to a lesser extent, social impacts of highway construction; the impacts
of investments in mass transit and wastewater collection and treatment
have been virtually ignored.2
The present study was an attempt to fill this void. The focus was on
effects of highways and sewers. The central purpose was to develop
simple and accurate analytic techniques for forecasting secondary
effects. In particular, we wished to avoid reliance on sophisticated
computer models and extensive data bases. This necessarily entailed
compromises. In this sense, the study was a test of the feasibility
of analyzing a complex problem with a set of tools simple enough for
widespread application yet accurate enough to provide useful informa-
tion.
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B. APPROACH
Case studies in four U.S. metropolitan regions (Washington, B.C.,
Boston, Denver, and Minneapolis-Saint Paul) provided an empirical base
for the research. The case studies involved primarily collection of
cross-sectional data pertaining to highway and sewer investments and
land use changes during the period 1960 to 1970. The regions studied
were selected on the basis of data availability, social and economic
conditions, historical patterns of public investments, jurisdictional
arrangements, and natural features. In each metropolitan region,
data were collected for subregional districts ranging in size from
five to fifty square miles.
The data were analyzed with standard multi-variate statistical techni-
ques. The amount and location of (a) single-family home construction,
(b) multi-family dwelling unit construction, (c) commercial land
development, and (d) industrial land development were related to
several factors reflecting local land market conditions, highway
proximity and sewer service availability.
Multiple regression equations were estimated for each form of develop-
ment in each metropolitan region. In addition, the data were pooled
in order to estimate a set of equations representing average relation-
ships across all four regions.
The statistical analyses were supplemented by a dynamic model developed
to simulate land use changes as they relate to public investments.
The dynamic model was applied to the Washington, B.C. region for
empirical testing. The model helped to highlight factors which seemed
to have an important effect on development trends.
C. SUMMARY OF FINDINGS
The analyses identified a series of factors which seemed to explain
much of the variation in location and type of development in all four
metropolitan regions. These factors were availability of sewer service,
proximity of an area to major highways, amount of vacant land (parti-
cularly vacant land served by sewers), and residential vacancy rate.
However, the relative importance of each of these factors varied
substantially from one region to another, so that even though results
from pooled data were acceptable in terms of their aggregate statistical
significance, the set of regression equations developed from pooled
data cannot be expected to produce accurate predictions in all regions.
1. Sewer Service
The influence of sewer service was expressed in terms of amount of
vacant, sewered land available in each district during the 10-year
forecast interval. This variable was consistently a significant factor
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in the regressions. Generally, the results confirm that sewer invest-
ments cause moderate to large changes in land use of all types.
The greatest influence of sewer investment seems to be on the construc-
tion of single-family housing. This was true for all metropolitan
areas studied, regardless of variations in topographic and soil charac-
teristics . In some regions municipal water supply is probably equally
important, but the two services are usually provided together. Sewer
service was also consistently important as a stimulus for multi-family
housing construction and commercial and industrial land conversion,
but the magnitude of its influence was less for each of these develop-
ment types than for single-family housing construction.
These results seem to run counter to intuitive expectations. Single-
family housing, the lowest density form of development, has often
employed septic systems for wastewater disposal. On the other hand,
high intensity development generally presupposes availability of sewer
service. However, two considerations support the empirical findings.
Detached, urban homes are currently constructed on small lots (usually
a quarter acre or less) where septic systems are usually not satisfac-
tory. Further, sewer facility investments during the period studied
took place primarily in suburban "fringe" locations where demand for
land is strongly oriented toward detached homes. Hence the statistical
results accurately reflect the importance of public sewerage in the
location of single-family housing.
Multi-family housing, commercial, and industrial developments are,
of course, just as dependent on public wastewater facilities as new
detached homes. However, demand for such intensive development is
seldom high in outlying areas where new sewers are placed. Most high
density development takes place in areas close to the central city
where sewer service is already available and where there is relatively
little vacant land. This helps to explain the lower statistical
sensitivity of high density development to the amount of sewered,
vacant land.
It is important to recognize that these findings do not imply that
sewer investments have modest effects on intensive development in all
situations. In unsewered areas where demand for multi-family housing,
commercial, or industrial land is high, new sewers may stimulate
major increases in construction similar to those found for single-
family housing.
2. Proximity to Highways
The influence of major highway investments was measured by the proxi-
mity of a district to the nearest limited access, divided highway.
Two variables were used: the base year distance and the change (1960-
1970) in distance to highways.
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The statistical analyses did not provide a clear picture of the impact
of major highways on the location or magnitude of development. Al-
though analyses of pooled data indicated that new highways have^an
impact on single-family housing construction, analyses of individual
regions did not show any strong or consistent effect. In part, this
was probably because most of the regions analyzed had relatively good
highway accessibility even before 1960. Each new highway in a region
brings a successively smaller improvement in accessibility. These
diminishing marginal changes imply diminishing marginal effects on
location decisions and land use. Since all the regions studied had
well-developed highway networks in 1960, we may infer that the
secondary effects of later highway investments were modest.
It should also be noted that the two highway measures used in these
analyses tended to be collinear, and that they were not a particularly
sophisticated measure of a highway's impact. Changes in relative
accessibility are a more sophisticated measure, but require substantial
amounts of accurate data rarely available and difficult to employ.
Some earlier analyses did use changes in relative accessibility as a
variable; however, the results were no better than those using the less
sophisticated variables reported here.
The impact of highways on the other types of development was similarly
unclear, although previous studies have shown that highway interchanges
have a significant impact on particular types of development within
their immediate area.2
3. Vacant Land
The amount of vacant land in an area generally had a positive effect on
single-family housing construction and a negative influence on the more
intensive forms of development. The positive relationship for single-
family construction probably reflects two phenomena: (1) a diminishing,
but still present possibility, of private wastewater systems in the
absence of public service; and (2) a tendency of single-family housing
development to focus on areas with low land prices and ease of large
tract acquisition for sub-division. Intensive types of development
typically do not require large tracts of land and are more strongly
tied to the economic interactions and accessibility of inlying areas.
4. Residential Vacancy Rate
Not surprisingly, the amount of residential development (both single-
family and multi-family) was strongly and consistently related to
residential vacancy rates. Low vacancy rates indicate a strong
housing market which stimulates increased residential construction.
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III. EMPIRICAL ESTIMATION OF SECONDARY EFFECTS
The central component of our research was a series of case studies to
identify and quantify historical secondary effects in four metropolitan
areas. Econometric techniques were used to relate local land use
changes to land market conditions and public investments. Regression
equations were developed to estimate likely secondary effects in terms
of local urban development. These empirical analyses are documented
in this section.
The hypothe'ses and specifications we formulated for testing were derived
in large measure from a comprehensive review of the literature on
secondary impacts. The review and annotated bibliography are presented
in full in a separate volume. The following pages summarize findings of
previous relevant research.
A. SUMMARY REVIEW OF PREVIOUS RELEVANT RESEARCH
1. Highways
A general conclusion of previous studies is that highways have little
influence on single-family, low-density residential land use.5-7
Retrospective case studies typically have found no significant correla-
tions between single-family housing construction and distance to new
highways or changes in accessibility, although some exceptions are
evident.8 On the other hand, studies of residential preferences
(e.g., Reference 9) provide clear evidence that households are strongly
influenced in their residential location decisions by accessibility,
i.e., the length of the journey to work. However, such studies also
show that higher-income workers -- the principal consumers of single-
family housing — are less sensitive to access. In terms of housing
production, the response of professional developers to new highways
cannot be gauged by the preferences of consumers, since developers
need only satisfy some, but not all, consumer preferences.
Definite highway effects on multi-family residential construction
have been established, but their quantitative extent is unclear.
Several studies document apartment construction at urban highway inter-
changes ,10-12 particularly interchanges of circumferential highways.
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The actual probability that any specific interchange will be so
developed remains uncertain, as does the distance from the interchange
to which this influence extends.
There is general agreement that new highways stimulate commercial
development, particularly near interchanges.13,14 Several studies also
suggest that new urban highways have a negative impact on downtown
trade!5,16 by helping to shift trade to suburban locations.
Studies of highway effects on industrial land use are internally
inconsistent. Many attitude and preference surveys suggest that
industries desire sites in close proximity or with good access to
highways.17-19 Statistical analyses of actual industrial location in
relation to highways do not support the survey results.20 obviously,
this preference must be counter-balanced by other factors, for example,
land costs. It seems clear that a principal cause of industrial
suburbanization was availability of inexpensive land (relative to the
CBD) for new plants.21 However, the shift would have been impossible
without good access to suburban labor markets provided by highways.
To summarize, the available evidence suggests that households and
businesses prefer good access by highway, all other factors held
constant. In terms of actual location, single-family housing construc-
tion has a tenuous connection to new highways; multi-family residential
and commercial development appear to be influenced by highways; and
the relation of industrial development to highways is unclear.
2. Wastewater Facilities
Empirical evidence on the influence of wastewater investments on
development is limited and unclear. We may note, for example, that in
the various versions of EMPIRIC,22 the influence of sewer service is
inconsistent across household and employment categories. In the
original Boston EMPIRIC, sewered land weighted by vacant land was
positively correlated with most categories of employment change; no
similar correlations were found in Washington. However, the nature
of the dependent variables (i.e., district changes in shares of
households by income and employment by type) and the step-wise estima-
tion procedures used may have obscured actual correlations between
sewer service and land use.
Rogers23 found that availability of public sewer service was a
significant explanatory variable for conversion of vacant and agricul-
tural land to urban uses. It was also observed23 that while the
influence of other factors showed lags of from three to six years,
sewer service availability did not. The sewer variable, however, was
less influential than measures of accessibility to employment and
elementary schools.
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found a strong correlation between (vacant) land price
and public sewer service. Prices of land parcels within service areas
of trunk sewers were, on average, four times higher than for parcels
without sewer service. Multiple regression analyses showed that sewer
service, together with allowable developmental density (defined by
zoning), were the two most influential determinants of land price.
Kaiser^ incorporated sewer service in an index of public utilities
as one explanatory variable of residential subdivision development
within urban areas. However, the public utilities index was much less
important than a socioeconomic index reflecting various structural
and demographic characteristics of neighborhoods.
The weight of evidence suggests that public sewer service is a signifi-
cant factor in urban development. However, its precise importance is
unclear. Part of the difficulty is caused by the fact that there is no
clear cut way of defining levels of sewer service.
Most of the studies cited used a binary dummy variable for sewer
service, reflecting either its presence or absence from parcels of
vacant land. In evaluating the overall effects of wastewater invest-
ments rather than development of individual land parcels, it seems
preferable to examine the influence of a sewer facility by the size
of its service area, i.e., the amount of land in which service is
available.
Few studies have attempted to ascertain the influence of sewers on
different forms of land use. The EMPIRIC results for distribution of
households are inconclusive; other studies23-25 focused almost
exclusively on low density single-family residential land use. Intui-
tively, it seems that higher density land uses should be more sensi-
tive to the availability of public sewer service, since they require
some form of group collection and disposal system.
A reasonable inference from previous work is that extensions of public
sewer service stimulate residential development and that intensive
multi-family and commercial uses are particularly sensitive to sewer
service. A relation, if any, of industrial development to public
sewers has not been established.
B. A GENERAL APPROACH TO SECONDARY EFFECTS
1. Influence of Access and Sewer Service in Urban Development
A comprehensive economic analysis of urban growth requires extensive
formulation of utility functions, supply and demand curves, and market
clearing processes. While significant progress has been made in
specifying these relationships, the resulting mathematical models are
so complex as to limit their usefulness in practical application 26
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However, our interest is limited to the influence of public investments
on urban growth. Within this relatively narrow perspective, economic
analysis can be restricted to the actions of developers as producers
of new structures.
Such a simplification has several appealing features. Although urban
growth derives from the interdependent activities of households,
businesses, and government officials, developers make the original
decisions about where and what to build. A focus on their decisions
reduces the necessary analysis to one group rather than several.
While households and businesses may consider countless factors
subjective as well as objective — in making location decisions, a
developer considers the few most important objective factors, since he
has a limited amount of time and no motivation to evaluate individual,
subjective criteria. Finally, the developer has a clearly defined
motivation — profit — whereas households or businesses may have other
immediate interests in making locational choices.
Developers are concerned with satisfying the needs and preferences
of their customers while maximizing their own profits. The customers,
households and businesses, desire sites that are accessible, have
adequate public services, and have attractive socioeconomic features.
Their preference for structural characteristics is not included in
this analysis, since developers build a standardized mix rather than
tailor construction to individual desires.
Because consumers have overlapping preferences, they compete for sites,
driving up land prices in locations with a combination of attractive
features. Developers attempt to estimate the premium households or
businesses will pay for attractive sites and compare it with costs of
constructing various types of structures on each site. The result of
this comparison determines whether development will occur in a
particular location and the form it will take.
The influence of public investments is reflected in altered attractive-
ness and subsequent price adjustments. Increased accessibility and
higher potential density of development affect attractiveness and
hence price. Since price responses are imperfect, a new highway or
sewer may increase attractiveness while land costs remain unaffected
for some time. In fact, anticipation of investments may allow
developers to buy land at lower prices than warranted by the increased
attractiveness of the location after the investment is constructed.
A single unattractive feature of a location can effectively discourage
development in spite of several other attractive characteristics.
Depressed residential portions of central cities, for example, often
have high accessibility and relatively low land prices. Private
redevelopment seldom takes place because of the unattractive socio-
economic character of such locations.
10
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2. Factors Influencing Secondary Effects
a. Land Availability and Price -
Availability of land and raw land prices are central factors in developer
decisions about where and what to build. Price and availability are
usually inversely correlated. Higher prices generally require more
intensive development. Hence communities with large amounts of avail-
able land at low prices are most attractive to single-family housing
developers, while those with little available land, high land prices,
or — in the typical case — both, can only be developed at higher
densities.
Several combinations of conditions involving land availability and
price create a strong possibility for important secondary impacts
following a transportation investment. If large amounts of undeveloped
land are available at a relatively low price, any increase in accessi-
bility will have significant impacts. Modest increases in access
levels may stimulate single-family development. Large increases in
accessibility may encourage intensive development as well — multi-
family housing, industrial, and commercial. After a large increase in
access, condominiums, two-family houses, and apartment developments
are likely, with high rise apartments and business offices occurring
in "pockets" of high accessibility such as highway interchanges or
near transit stations. If, on the other hand, only small amounts of
land are available and existing prices are high, modest accessibility
increases will have no major impacts, while large increases may
stimulate high density construction.
Land availability and price play a similar role in determining the
impact of sewer investments. Here the important factor is how much
undeveloped land is in the service area of the new sewer, and the
related range of prices. Large amounts of vacant land at low prices
signify a potential for single-family housing construction. Higher
prices and/or lesser amounts of undeveloped land make multi-family
residential, industrial, and commercial development more likely.
Because highways traverse and serve many communities with different
combinations of land availability and price, they may cause a full
range of secondary impacts. Radial highways in metropolitan areas,
for example, may stimulate single-family housing construction in out-
lying areas with low land prices and extensive undeveloped land,
mixed single-family and higher density construction in partially
developed suburban communities, and high-intensity commercial, indus-
trial and residential construction in the fully-developed inlying
suburbs. The last impact, however, is atypical; central areas are
much more likely to lose population and business activities after
highways because of migration to the suburbs.
11
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b. Land Use Controls -
Zoning and other forms of local land use control are intended to pro-
tect existing residents from discordant forms of development. They
limit the use and intensity to which individual parcels of land may
be put. In theory, therefore, they influence the amount of development
of each kind that can occur in a community and potentially limit
secondary effects.
The simple fact is that local land use plans are seldom effective unless
they are made in conjunction with a long range master plan and are
rigorously enforced. This combination is the exception rather than
the rule. In most communities or counties, variances are so easy to
obtain that zoning provides almost no control of land use. Thus,
even where comprehensive land use plans exist, pressures to rezone
counter to planned uses often render them ineffectual.
In evaluating the likelihood of secondary impacts in a community,
the most significant features of its land use controls are the existing
amounts of undeveloped land zoned for each category of use and the
historical record of how thoroughly zoning has been enforced. If
variances have been difficult to obtain, then developmental impacts
probably will be restricted to levels near the amount of properly
zoned vacant land for each category of use. The most common implica-
tion of this situation is a limitation on the amounts of industrial,
commercial, and multi-family residential development that can occur,
with little or no limitation on single-family housing construction.
However, if variances are easy to gain, then zoning will have no
moderating or controlling influence on impacts; land availability and
price, access, and sewer service will determine the form and amount of
development that occurs.
c. Income Level of Existing Residents -
There is some evidence that, with all other factors held constant,
developers prefer to build single-family housing in areas where the
existing population have higher than average income levels. This
influence is caused by the preferences of families who desire detached
single-family homes for attractive socioeconomic features. They desire
the "right" kind of neighbors, as well as attractive structural charac-
teristics implied by upper income levels. This influence is not a
dominant one, but it suggests that where a new highway or sewer line
serves communities similar in most respects but varying in income
levels, the upper income communities will receive more single-family
construction than low or middle income areas. The relationship does not
hold for other forms of development.
Communities with very high average income, on the other hand, are
likely to be exclusive with regard to multi-family housing, or large
12
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commercial and industrial development. The exclusion may also extend
to relatively dense single-family housing — two or more units per acre.
Such exclusive practices, usually reflected in land use controls or
policies, serves to constrain new development and, therefore, secondary
effects. Very high income ranges, therefore, can signify that important
secondary effects are unlikely in spite of land availability, substan-
tial increases in accessibility, or new sewer investments.
d. Existing Levels of Access andSewer Service -
The availability of transportation and public sewer facilities and
existing levels of service in a community or area strongly affect the
probability for major secondary impacts following a new investment.
Increases in accessibility beyond a certain point, or extension of
sewer service in locations where substantial amounts of sewered land
are already available, have only a marginal effect on the attractive-
ness of the area for development. It is large and dramatic shifts in
accessibility or sewered vacant land that create the potential for
significant secondary effects. In metropolitan areas with an existing
and extensive network of highways, further investments will, on the
whole, have a modest influence on development. However, this does not
imply that no important secondary effects will occur; almost always,
a few portions of the region will experience major increases in access.
But the extent of significant impacts will be highly localized, rather
than widespread.
The importance of existing accessibility levels is more complex than
that of sewered available land. Since access changes are ultimately
reflected in land prices, developers of low density structures, e.g.,
single-family housing, must build where accessibility is relatively
low, while the higher intensity developers can afford locations with
high access. Therefore, in some intermediate range, higher accessi-
bility causes an area to become decreasingly attractive to low density
developers and, at the same time, increasingly attractive to higher
intensity developers. If a predominantly single-family community
with moderate accessibility experiences a large increase in access,
the ultimate effect, as land prices adjust, will be to discourage
single-family housing construction. Such an impact, of course, would
depend on the other conditions in the community — land availability
and existing price, zoning, etc. On the other hand, extensions of
sewer service area or increased sewer or treatment plant capacities
do not discourage any form of development; their positive influence
is, however, smaller where already existing levels of service are
adequate.
e. Vacancy Rates -
Residential, commercial, and industrial vacancy rates are indices of
local market conditions to developers. High vacancy rates serve as
13
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AC = g(AP, K, L, Zn, X, W) (2)
K = h(r, X, W, S-^
L = k(H, W, X, t)
where for each structure type in each local market area:
AP = change in expected selling price
H = highway service measure
AH = change in highway service
Zc = socioeconomic characteristics
R = regional growth rates
V = vacancy rate
W = wastewater service
AW = change in wastewater service
AC = construction (number of units)
K = costs of construction per unit structure
L = land costs
X = number of acres of undeveloped land
Zn = zoning index
S-^ = soil characteristics
t = local tax rate
r = interest rate (cost of capital)
Each variable carries implicit time and location subscripts. The
change variables, indicated by A's, occur over some pre-specified
time interval; all other variables represent conditions in the base
year of that interval.
Equations 1, 3, and 4 can be substituted into 2 leaving a single
vector equation (reduced form) to be estimated:
AC = f(AH, H, Zc, R, V, W, AW, t, r, X, S1 , Zn) (5)
Several important simplifications have been made to derive this struc-
ture and reduced form. The most drastic is for equation (1). Rather
than modeling proper demand and supply functions for the entire stock
of structures and positing particular mechanisms for market clearing
and price adjustment, it is assumed that changes in variables that
increase demand have an upward pressure on prices. Developers take
cognizance of changes and projections of factors affecting demand and
form expectations of price movements in the absence of substantial
change in the stock of structures. Highway service, neighborhood
(zone) characteristics, wastewater service, and tax rates are all
assumed to affect the price that users would pay for structures of
certain sorts in particular zones. Regional growth rates of particular
user classes (population and business) are assumed to affect the ex-
pected increase in demand pressures, while vacancy rates suggest how
16
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much additional demand can be accommodated without eliciting price
increases. Highway service and changes in highway service are
reflections of actual transport systems, actual and expected conges-
tion on various segments, and expected additions to the system.
The only other equation in which highway service enters, (4), indicates
that improved accessibility will increase land prices, and this in turn,
through (2) will affect the rate of construction adversely. In fact,
the role of land prices and demand factors in (2) is purposefully
ambiguous. If markets for land were perfectly competitive and all
actors had equal access to information, the price of land should
capture all excess profits that would be associated with developing
it for its most profitable use. That is, if developers were to
acquire land at competitively determined prices, the profitability of
developing any parcel of land in the metropolitan area would be equal,
reflecting the cost of capital, for the most profitable use of the
land. In such a world, equation (4) would be redundant. In fact, we
know that the world does not provide equal information to all parties.
The possibilities of "sharp" developers being spurred to develop
properties that they are able to buy cheaply from naive owners are
real. If firms are not perfect profit maximizers, high land prices
will force them to consider high density developments that they might
otherwise not consider.
The actual quantities of construction that take place are determined
by equation (2). The assumption is that construction levels will vary
directly with levels of profitability. Expected price increases will
stimulate construction, while high construction and land costs will
dampen the supply response to increased demand. Zoning and wastewater
services can facilitate or retard implementation of otherwise profit-
able development. While there is room to argue that zoning and sewerage
decisions are accommodative to developmental pressures, we consider
them to be exogenously-determined variables that impinge on the
developer's decision. Finally, availability of large tracts of
undeveloped land makes the problem of land assembly simpler and should
be an important variable explaining the quantity of development that
takes place.
Equation (3) merely states that construction costs are affected by
interest rates, soil conditions, availability of large tracts of land,
and presence or absence of wastewater facilities.
Land prices as determined by equation (4) are assumed to be affected
by highway service, wastewater facilities, tax rates, and the quantity
of undeveloped land in the zone.
When the substitutions are made into (5), it is obvious that the
coefficients of each of the independent variables in (l)-(4) are not
recoverable. Rather, the coefficients estimated for (5) will be
17
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combinations of the various coefficients from the basic equations.
For instance, the coefficient associated with highway service will
embody both the demand factors of (1), the land-price effects from (4),
and indirectly, the effect of land price on construction from (2).
However, as argued earlier, the principal effects of transportation
investments on development come through the highway access-induced
premium that will be paid by demanders of structures.
In contrast, the wastewater service variable enters each of the equa-
tions. It is to be anticipated, however, that the effect on demand
will be minimal, while the influence of sewer service on construction
costs will be significant.
The reduced form (equation 5) served as the basic model in subsequent
statistical analyses. Further simplifications were made because of
problems with lack of data on tax rates, soil conditions, zoning, and
regional growth vectors. Interest rates were omitted since they are
generally uniform within a metropolitan area. In later stages of the
research, additional variables were introduced to try and represent
explicitly competition for land among consuming groups. These changes
are documented in subsequent portions of this section.
Because of the simplified nature of the regression specifications and
the combination of several parameters from the structural system of
equations into single coefficients for estimation, it was difficult to
make inferences from statistical results concerning the adequacy or
validity of the original equations. Since we were interested in
single-equation models rather than a recursive simultaneous equation
system, no attempt at such inferences was made. The structural form
was used principally as a guide for early specifications.
2. Definition of Variables
Multiple regression analyses were used to estimate versions of the above
reduced form. Data were acquired for four U.S. metropolitan areas:
Boston, Massachusetts; Denver, Colorado; Minneapolis-St. Paul, Minnesota;
and Washington, D.C. For each of the regions, EMPIRIC model data
sets2? constituted the principal source of information on land use.
Supplemental data on housing and vacancy rates were acquired from the
U.S. Census of Housing. Precise definitions of variables and related
data sources are provided later in this section.
All of the data were cross-sectional. Regions are subdivided into
districts numbering from 85 in Minneapolis-St. Paul to 182 in Denver.
Each district represents a unit of observation for our variables;
cross-sectional data from 1960 represent the base-year in each case,
while the dependent and investment (policy) variables reflect 1960-
1970 changes.
18
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Four dependent variables were used in most regressions: single-family
and multi-family housing construction, and commercial and industrial
land conversion. The basic set of explanatory variables included five
measures of public investments and two measures of local market condi-
tions. Two forms of highway service variable were used: accessibility
and proximity to highways. The full set of variables is defined in
detail below.
a. Dependent Variables -
1. Number of single-family dwelling units in the district - The
definition of a housing unit is that of the 1970 Housing Census, User's
Guide, Part I, p. 113, U.S. Department of Commerce, October 1970. Data
were obtained from Table H-l, "Occupancy and Structural Characteristics
of Housing Units." Single-family homes comprise structures with one
unit.
2. Number of multi-family dwelling units in the district - The same
Census tables were used and all structures with more than one housing
unit were included.
3. Number of acres of commercial land in the district - Primary data
(EMPIRIC) came from aerial photographs in each city, with the following
kinds of land use classified as commercial:
• Hotels, motels, tourist homes or tourist camps
• Retail establishments, including: food, supermarket,
drug store, hardware
• Mixed retail, services, and residential (either 1-story
mixed retail and services, or 2-story building with 2nd
floor residential)
• Eating and drinking places
• General retail and dry goods (clothing, apparel, accessories,
department store, furniture, appliances)
• Lumber, building materials, feed (retail)
• Gasoline service stations
• Automotive dealer, farm and heavy equipment, marine
equipment, trailer sales (retail)
• Personal services: barber-beauty shops, cleaning and
dyeing, collection, shoe-shine
• Office buildings — business services, dental services,
electronics (research and development), legal and professional
services, medical services, offices and office buildings not
classified elsewhere (does not include transportation,
communication, and utilities), repair services (except
automotive repair), wholesale services (without stocks)
• Finance, insurance, real estate services — banking services,
savings and loan offices, finance and insurance corporation
services, insurance and real estate brokers services
• Vacant office buildings
19
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• Hospital — including clinic, institutional home, nursing
home, old people's home, rest home, orphanage
• Indoor recreation, entertainment — including athletic club,
gymnasium, bowling alley, clubs, lodges, fraternities,
sororities, indoor swimming pool, skating rink, indoor
theater, movie house, night club, YMCA, YWCA
• Cultural, religious — including art gallery, museum,
assembly hall
4. Number of acres of industrial land in the district - The following
categories of land were included in the industrial category:
Durable Manufacturing:
• Furniture, lumber, other durable goods — manufacturing
• Metals and allied fabricating — manufacturing
• Machinery, transportation equipment — manufacturing
(except electrical machinery)
• Scientific and professional instruments, electrical
machinery — manufacturing
Non-Durable Manufacturing:
Food, allied products — manufacturing
Textiles, apparel, allied products — manufacturing
Chemicals, petroleum, plastics, rubber, allied
products — manufacturing
Paper, allied products — manufacturing
Printing, allied industries
Leather, leather products — manufacturing
Vacant manufacturing building — all types of manufacturing
Non-Manufacturing:
• Bus, taxi — motor passengers terminal, depot, garage
• Truck transportation
• Dock, port facilities
• Vacant transportation, communications, public utility
building
• Intensive wholesale, storage (enclosed) — allied products,
appliance, automotive, dry goods, electrical, food, hardware
• Extensive wholesale, storage (open yards) — auto salvage,
building materials, chemicals, lumber, petroleum (gas-oil),
wrecking yard
• Vacant wholesale, warehouse, storage building
Extensive Industrial:
• Railroad facilities — depot, repair shop, yards
• Airport facilities (non-military)
20
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• Mine, quarry, sand and gravel pit
• Utilities, communications — electric, gas, sanitary
services, plant sub-station, valve station, power line,
gas line, row, radio, tv antenna, telegraph-telephone
facilities
b. Explanatory Variables -
1. Base year distance (in miles) to the nearest major highway -
Measurements were made on maps of each region. All limited access,
divided highways were included. Some ambiguities were encountered
where highways changed from limited to free access or from divided
to undivided within the region. In such cases we did not include
stretches that were undivided or free access. For definition of high-
way types, see: Highway Research Board, "Highway Capacity Manual,"
Special Report 87, Division of Engineering and Industrial Research,
Washington, National Academy of Sciences-National Research Council,
1965.
2. Change in distance (1960-1970) (in miles) to the nearest major
highway - The same definitions and procedures were used.
3. Base year highway accessibility - Accessibility is defined as
follows:
a
where: Ac.£p = accessibility of district i to activity p
= activity value for district j and activity p
= number of districts
t-jj = travel time for district i to district j
a = shape parameter of the gamma distribution
3 = scale parameter of the gamma distribution
T(a) = the Gamma function
The variables defining accessibility are travel times between zones and
amount of activities (in this case, employment and households). The
other parameters, a and $, are functions of observed trip distributions
in each region.
4. Change in highway accessibility (1960-1970), defined as above -
To avoid simultaneity of forecast quantities, 1960 activity distribu-
tions are used with projected 1970 travel times to compute 1970 accessi-
bilities. Use of 1970 activity distributions would, of course, require
as an input the quantities to be forecast.
5. Sewered vacant land - Public sewer service was measured by vacant,
sewered land (in acres) in each district. Vacant land is defined in
21
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(6) below. Originally, base year sewered vacant land and change
(1960-1970) in sewered vacant land were included as separate variables.
There was, however, a high degree of correlation between the two;
accordingly, they were combined into a single sewer service variable.
6. Vacant, developable land (in acres) in the district - Privately
owned agricultural land, vacant lots, forest land and woodlands are
considered vacant developable land. Undevelopable land is land that
is swamp or has excessive slope (greater than 15%) or has some other
clear impediment to development.
7. The total residential vacancy rate in the district - The vacancy
rate was defined as the ratio of total housing units less total house-
holds divided by total housing units.
3. Problems with Estimation of Variables
a. Dependent Variables -
The definition of commercial land given above was not consistently
applied in EMPIRIC data for different regions. A major discrepancy
apparently arose from the inclusion of additional categories —
principally government and institutional — in commercial land for
Boston and Washington. Access to the original raw data proved impos-
sible; accordingly, such inconsistencies could not be corrected.
However, in view of the highly aggregate nature of the commercial
(and industrial) variables, resulting errors in the regressions affect
primarily values of the constant. Errors in the estimated coefficients
of the independent variables arising from this inconsistency in the
data are probably small.
b. Explanatory Variables -
Sewered vacant land - Definitions of base year, sewered vacant land
and of change (1960-1970) in sewered vacant land in a district are easy
to formulate but difficult to apply with data usually available. For
each district, the definitions are:
(base year sewered vacant land)
= (vacant land in sewer service area)
= (total land in sewer service area) - (developed land in
sewer service area)
(change in sewered vacant land)
(1970 sewered vacant land) - (base year sewered vacant land)
The first difficulty in using these definitions is the precise delinea-
tion of sewer service area. Usually "legal" service areas are
22
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proscribed for sewers within which new hookups may be authorized.
Unfortunately, these boundaries tend to expand as developmental pressure
increases. An alternative measure of the service area of interceptor
sewers is the area physically bounded by drainage patterns and topo-
graphy. An interceptor sewer typically serves a definite catchment
area. However, this measure tends to overstate effective service area,
since portions of the drainage basin are usually too distant to allow
hookups without substantial further investments in collector sewers.
Furthermore, the drainage-defined sewer area may be expanded at any time
by construction of pumping stations and force mains to transport waste-
water between watersheds.
The service area used in the regressions was the legal service area,
wherever possible. Where legal service areas were not available, we
assumed that service areas extended one mile on both sides of inter-
ceptor and trunk sewers. This is admittedly a crude measure, but is
more likely to reflect effective service areas than topographically
defined boundaries.
With service area thus defined, the total land in the service area of
a sewer within a given district is easily obtained. However, developed
land within the service area is not known. What is available for each
district is total developed land. The assumption was made that all
developed land in the base year was within the service areas of existing
sewers. With this assumption, base year sewered vacant land is given
by total land in sewer service area less total developed land in the
district. This procedure doubtless introduced some error, since some
fraction of developed land within a district was probably unsewered.
However, as we point out below, this error is counterbalanced to some
extent by the approximations made in estimating changes in sewered,
vacant land.
Change in sewered, vacant land is estimated by calculating newly
sewered land using, as above, the legal service area for the new sewers
(or a one-mile band on both sewer sides) and subtracting base year,
sewered vacant land. Since in the final specification, a variable
consisting of base year plus change in sewered, vacant land was used,
this combined variable can be obtained by calculating total land in
the service area for all sewers in the district and subtracting total
developed land within the district. An appropriate correction is made
for land that is not developable (parks, other public lands, and land
that is swamp or has a slope greater than 15%).
In applying the regression expressions to a proposed new sewer, the
simplest way of calculating the sewer related variable is to obtain
total, developable land within the legal service area of sewers in the
district, including the proposed sewer, and subtract total developed
land within the district.
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D. STUDY REGIONS AND SAMPLE CHARACTERISTICS
The four metropolitan areas used in the case studies were selected
according to several criteria. Of overriding concern was the availa-
bility of a unified, comprehensive data base for each region to
minimize field data collection. Beyond this requirement, however,
our principal objectives were to select regions representative of
U.S. metropolitan areas in general, and to obtain a mix of regional
conditions that might influence the extent or magnitude of secondary
effects. Areas studied were Boston, Massachusetts; Denver, Colorado;
Minneapolis-St. Paul, Minnesota; and Washington, D.C.
These regions meet the stated criteria. Two Eastern Seaboard cities
represent the dense population centers of the country. Boston, of
course, is characteristic of the old, traditional urban center, with
a slow rate of growth and somewhat stagnant economy. Washington, on
the other hand, is of the new order, growing explosively with no sign
of a slowdown. Minneapolis-St. Paul and Denver are typical of Midwes-
tern and Western cities, with moderate to strong growth around estab-
lished urban core areas. Economically, the regions range from service-
oriented (Boston) and manufacturing (Twin Cities) to government-oriented
(Washington, D.C.) and an even mix of businesses (Denver). Jurisdic-
tionally, the Boston region is based on municipalities, the Washington
region on counties, while Denver and Minneapolis-St. Paul reflect a
blend of authority among these two levels of government. The regions
also vary broadly in physical characteristics such as size, climate,
topography, and soils. Hence the influence of these factors on
secondary effects are represented at least roughly in the case studies.
For statistical work, each metropolitan area (the Census SMSA with
minor changes) was subdivided into a number of districts, ranging from
85 for Minneapolis-St. Paul to 182 for Denver. Metropolitan population
and land use characteristics and district averages are given in
Tables 1 through 6.
The characteristics summarized in Table 2 are generally relevant to the
topics considered in this study and help define the metropolitan areas
selected for study. In terms of 1970 population, these SMSA's ranked
7th (Washington), 8th (Boston), 15th (Minneapolis-St. Paul) and 27th
(Denver) among the approximately 250 SMSA's in the country. In terms
of population growth rates, Boston was among the slowest growing
regions; Denver and Washington among the fastest; and Minneapolis-St.
Paul near the average value of 17.0% for the 150 SMSA's with population
over 200,000 in 1970.
Tables 7 through 11 present simple correlation coefficients for the
principal variables in each region and in the pooled sample for all
regions.
24
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Table 1. CHARACTERISTICS OF METROPOLITAN AREAS
IN 1960a*b
Total Land
(thousands acres)
Population
(thousands)
Change
Single-Family Housing
(thousands units)
Change
Multi-Family Housing
(thousands units)
Change
Commercial Land
(thousands acres)
Change
Industrial Land
(thousands acres)
Change
Boston
1,021
3,108
8.6%
449.2
4.1%
446.0
16.4%
29.0
69.4%
38.2
8.0%
Denver
643.1
915.8
31.1%
221.9
24.3%
77.7
60.7%
3.09
76.5%
20.2
37-0%
Minneapolis-
St. Paul
1,045
1,483
22.4%
318.2
15.9%
146.0
43.4%
7.56
53.4%
27.3
23.4% '
Washington
718.1
2,077
34.0%
350.0
26.1%
251.0
55.1%
16.20
65.7%
7.49
66.2%
a Study area in each metropolitan region was slightly different from
SMS A.
13 Changes are for 1960-1970, except for Washington commercial and
industrial land use data which are for 1960-1968.
25
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Table 2. CHARACTERISTICS OF 1970 SMSA'S'
Population Distribution
Central Cities
Percent Change
Outside Central Cities
Percent Change
Employment Distribution
Manufacturing
Wholesale & Retail Trade
Services"
Government
Unemployment
Single-Family Housing
Distribution
Percent of Total Units
in SMSA
Percent of Units in
Central Cities
Percent of Units Outside
Central Cities
Automobile Ownership & Use
Percent Households with:
No Automobile
One Automobile
Two or More Automobiles
Percent Making Work Trip
by Automobile
Boston
23.3%
-8.1
76.7%
11.3
21.5%
22.7
32.2
13.7
4.3
43.7%
15.0
53.9
24.8%
49.5
25.6
67.4
Denver
41.9%
4.2
58.1%
63.7
17.8%
24.6
24.5
18.6
3.3
68.0%
58.0
76.7
12.0%
41.1
46.9
86.4
Minneapolis-
St. Paul
41.0%
-6.1
59.0%
55.9
26.4%
24.1
24.0
13.6
3.4
63.3%
48.7
76.6
14.7%
47.1
38.2
81.9
Washington
26.4%
-1.0
73.6%
61.9
3.8%
19.6
27.6
37.7
2.6
54.0%
36.9
61.3
20.1%
45.5
34.3
73.2
Data are for 1970; changes for 1960-1970.
b Includes F.I.R.E. (Finance, Insurance, and Real Estate).
26
-------
Table 3. REGIONAL CHARACTERISTICS - BOSTON
(Number of Districts (N) = 125; Mean District Size = 8,200 acres)
Single-Family Dwelling Units
Multi-Family Dwelling Units
Commercial Land
Industrial Land
(acres)
(acres)
Development per District
Mean
1960
3600
3570
230
125
Mean
1970
3740
4155
390
160
Mean
Change
140
585
160
35
7
/o
Change
3.9
16.4
69.6
28.0
Base Year
Change in
Distance to Highway (miles)
Distance to Highway (miles)
Sewered Vacant Land (Base + Change) (acres)
Base Year
Base Year
Total Vacant Land (acres)
Residential Vacancy Ratea (%)
Local Conditions
(per District)
Mean per
District
3.0
1.1
650.
4500.
8.4
Standard
Deviation
3.07
2.63
1266.
4017.
10.
a . , . . c c t. A' 4- •
Average values for the area as a whole were 6.0% in 1960 and
3.9% in 1970.
27
-------
Table 4. REGIONAL CHARACTERISTICS - DENVER
(Number of Districts (N) = 182; Mean District Size = 3,530 acres)
Single-Family
Multi-Family
Dwelling Units
Dwelling Units
Commercial Land (acres)
Industrial Land (acres)
Development per District
Mean
1960
1220
430
17
100
Mean
1970
1515
685
30
133
Mean
Change
295
255
13
33
%
Change
24.2
59.3
76.5
33.0
Base Year
Change in
Distance to Highway (miles)
Distance to Highway (miles)
Sewered Vacant Land (Base + Change) (acres)
Base Year
Base Year
Total Vacant Land (acres)
Residential Vacancy Ratea (%)
Local Conditions
(per District)
Mean per
District
2.7
1.2
794.
2151.
6.7
Standard
Deviation
2.3
1.8
1110.
4169.
3.6
o
Average values for the area as a whole were 5.8% in 1960 and
4.2% in 1970.
28
-------
Table 5. REGIONAL CHARACTERISTICS - MINNEAPOLIS-ST. PAUL
(Number of Districts (N) = 85; Mean District Size = 12,200 acres)
Single-Family
Multi-Family
Dwelling Units
Dwelling Units
Commercial Land (acres)
Industrial Land (acres)
Development j>er District
Mean
1960
3740
1720
58
297
Mean
1970
4340
2460
89
369
Mean
Change
600
740
31
72
7
/o
Change
16.0
43.0
53.4
24.2
Base Year
Change in
Distance to Highway (miles)
Distance to Highway (miles)
Sewered Vacant Land (Base + Change) (acres)
Base Year
Base Year
Total Vacant Land (acres)
Residential Vacancy Rate^ (%)
Local Conditions
(per District)
Mean per
District
2.5
.99
1984.
9016.
7.7
Standard
Deviation
2.5
1.8
2364.
10843.
9.0
a This value is the mean of vacancy rates for each district.
Average values for the area as a whole were 5.4% in 1960 and
3.5% in 1970.
29
-------
Table 6. REGIONAL CHARACTERISTICS - WASHINGTON, B.C.
(Number of Districts (N) = 103; Mean District Size = 6,970 acres)
Single-Family
Multi-Family
Dwelling Units
Dwelling Units
Commercial Land (acres).
Industrial Land (acres)
Development per District
Mean
1960
3400
2440
99
73
Mean
1970
4290
3785
164
121
Mean
Change
890
1345
65
48
%
Change
26.2
55.1
65.7
65.8
Base Year Distance to Highway (miles)
Change in Distance to Highway (miles)
Sewered Vacant Land (Base + Change) (acres)
Base Year Total Vacant Land (acres)
Base Year Residential Vacancy Rate3 (%)
Mean per
District
2.8
1.3
2950.
4665.
7.7
Standard
Deviation
2.3
1.9
4682.
7357.
8.5
Local Conditions
(per District)
This value is the mean of vacancy rates for each district.
Average values for the area as a whole were 5.5% in 1960 and
4.2% in 1970.
30
-------
Notes for Tables 7-11
VARIABLE NAMES FOR SIMPLE CORRELATIONS
SFUNIT = 1960 single-family housing (dwelling units)
SFUNIT70 = 1970 single-family housing (dwelling units)
SFCON = 1960-70 single-family housing construction (dwelling units)
MFUNIT = 1960 multi-family housing (dwelling units)
MFDNIT70 = 1970 multi-family housing (dwelling units)
MFCON = 1960-70 multi-family housing construction (dwelling units)
CLU = 1960 commercial land use (acres)
CLU70 = 1970 commercial land use (acres)
COMCON = 1960-70 increase in commercial land (acres)
ILU = 1960 industrial land use (acres)
ILU70 = 1970 industrial land use (acres)
INDCON = 1960-70 increase in industrial land (acres)
D60 = 1960 distance to highway (miles)
DELTAD = 1960-70 change in distance to highway (miles)
VLU = 1960 vacant land (acres)
SSERVICE = 1960 + 1960-70 change in sewered vacant land (acres)
TVACRATE = total residential vacancy rate (percent)
TOTLU = total land (acres)
31
-------
Table 7. SIMPLE CORRELATION COEFFICIENTS - BOSTON
CO
tsj
SECTION
SFUNIT
SFUNIT8
SFCON
MFUNIT
MFUNIT8
MFCON
CLU
CLU8
COMCON
ILU
ILU70
D60
OEL.TAD
VLU
SSERVICE
TVACRATE
TOTLU
1
SFUNIT
1.0000
0,9516
-0.2801
0.2426
0.3369
0.4805
0.2530
0,2288
0.1340
0.3967
0.2034
-0,3035
-0.2042
-0.3058
0.5126
-0,2134
-0.0208
SFUNIT8
1.0000
0.0266
0.0785
0.1762
0.5034
0.2405
0.2blfl
0,2043
0.3538
0.2491
-0.2866
-0.1824
-0.1571
0,5941
-0.2S96
0.1216
SFCON
1.0000
-0.5439
-0,5517
0.0094
-0.0719
0.042?
0.2022
-0,1852
0.0230
0.0920
0,0944
0.5038
0.1883
-0.1165
0.4472
MFUNIT
1.0000
0.9822
-0.1856
0,2247
0.0970
-0,1191
0.4441
0,1048
-0,2512
-0.0955
-0,4467
-0,0677
-0.1634
-0,4026
MFUNIT8
1.0000
0.0026
0.2531
0,1162
-0,1186
0,4869
0,1296
-0,2690
-0,1029
-0.4705
0,0068
-0.2082
-0,3995
-------
Table 7 (continued). SIMPLE CORRELATION COEFFICIENTS - BOSTON
u>
u>
SECTION
MFCON
CLU
CLU8
COMCON
ILU
ILU70
060
DELTAD
VLU
SSERVICE
TVACRATE
TOTLU
SECTION
2
MFCON
1.0000
0,1277
0,0913
0
0
0
-0
-0
-0
0
-0
0
•
,
,
,
,
,
.
*
«
3
0135
1823
1200
0700
0298
0829
3957
2189
0538
1
0
0
0
0
-0
-0
-0
0
-0
0
ILU70
ILU70
060
DELTAD
VLU
SSERVICE
TVACRATE
TOTLU
SECTION
1
-0
0
0
0
-0
0
,
•
,
.
*
,
•
4
0000
0124
0607
1142
1136
1040
1797
1
0
0
-0
0
0
TVACRATE
TVACRATE
TOTLU
1
0
•
.
0000
3047
1
CLU
.0000
.9335
,5949
.2480
.1171
,0891
,0968
.0141
.3337
.0509
.2149
060
.0000
,8370
.4037
• 1666
.307*
.2884
TOTLU
.0000
i
0
0
0
-0
-0
0
0
-0
0
1
0
-0
0
0
CLU8
.0000
,8436
.2261
,1552
,0886
,0892
,0795
,3220
,0734
,3038
DELTAD
.0000
,4042
.0953
.1524
.2808
1
0
0
-0
-0
0
0
-0
0
1
-0
0
0
COMCON
.0000
,1354
,1726
.0651
.0549
,1992
.2220
.0883
,3592
VLU
.0000
,0225
.3339
.9172
1
0
-*0
0
-0
0
-0
-0
1
-0
0
ILU
,0000
.6273
.1482
,0098
,1528
,?278
.1955
.0122
SSERVICE
.0000
.1827
.1311
-------
Table 8. SIMPLE CORRELATION COEFFICIENTS - DENVER
SECTION
SFUNIT
SFUNIT70
SFCON
MFUNIT70
MFCON
CLU
CLU70
COMCON
ILU
ILU70
INOCON
060
OELTAD
VLU
SSERVICE
TVACRATE
TOTLU
I
SFUNIT
1.0000
.0.7809
-0.2843
0.2469
0.2911
0.4682
0.3117
0.0328
-0.0597
-0.0721
-0.0817
-0.2340
-0.1057
-0.3414
-0.2791
-0.4791
-0.2987
SFUNIT70
1.0000
0.3725
0.0484
0.2307
0,2953
0.3464
0.2384
-o.ioea
-0.1019
-0.0118
-0.1437
-0.0462
-0,2310
0.0279
-0,2918
-0.1989
SFCON
1.0000
-0.2932
-0.0791
-0.2547
0.0655
0.3237
-0,0819
-0.0525
0.1037
0,1097
0.0632
0.1371
0,4534
0.2638
0,1245
MFUNIT70
1.0000
0,5999
0.2805
0.1437
-0.0428
-0.0702
-0,0954
-0,1457
-0.1448
-0,1606
-0.2?21
-0,2287
-0,0579
-0.2198
MFCON
1,0000
0,1858
0,1517
0.0538
-0.0609
-0.0623
-0,0310
-0,0351
-0,0489
-0,1450
-0,0767
-0,2430
-0,1362
-------
Table 8 (continued). SIMPLE CORRELATION COEFFICIENTS - DENVER
CO
Ul
SECTION
CLU
CLU70
COMCON
ILU
ILU70
INDCON
060
OELTAD
VLU
SSERVICE
TVACRATE
TOTLU
SECTION
INDCON
060
OELTAD
VLU
SSERVICE
TVACRATE
TOTLU
SECTION
1
0
0
0
0
0
-0
-0
-0
-0
-0
-0
1
0
0
0
0
0
0
2
CLU
,0000
.7350
,
,
,
•
.
.
.
.
.
.
3
I
.
*
•
.
,
,
,
4
1709
0710
0711
0288
1422
0592
1601
0824
0624
1297
NDCON
0000
1029
0849
3152
3905
1594
3393
1
0
0
0
0
-0
-0
-0
0
0
-0
1
0
0
0
0
0
TVACRATE
TVACRATE
TOTLU
1
0
.
•
0000
3160
1
CLU70
.0000
.7937
.0089
.0208
.0592
,1909
.1390
,1424
.0373
,03Jfl
.1230
060
.0000
,8002
.3757
.2003
.1554
,3954
TOTLU
.0000
1
-0
-0
0
-0
-0
-0
0
0
-0
1
0
0
0
0
COMCON
.0000
.0508
.0335
.0602
.1498
,1489
.0633
.1281
.1052
.0623
OELTAO
.0000
.1740
.0792
,0667
.1517
1
0
0
0
0
0
0
-0
0
1
0
0
0
ILU
.0000
.9788
.3034
.2815
,0658
.0813
.1098
.0511
.1994
VLU
,0000
.5357
,3552
,9639
1
0
0
0
0
0
-0
0
1
0
0
ILU70
.0000
,4923
,2793
,0783
,1421
,1843
.0124
.2552
SSERVICE
.0000
.3510
.5399
-------
Table 9. SIMPLE CORRELATION COEFFICIENTS - MINNEAPOLIS-ST. PAUL
U)
SECTION
SFUNIT60
SFUNIT70
SFCON
MFUNIT60
MFUNIT70
MFCON
CLU
CLU7
COMCON
ILU
INDLU7
INOCON
060
DELTAD
VLU
SSEHVICE
TVACRATE
TOTLU
I
SFUN1T60
I. 0000
0.9308
-0.2205
0.3235
0.4599
0.3932
0,5899
0.4542
0.2268
0.1315
0.2074
0.1632
-0.2722
-0.1014
-0.5279
0.0209
-0,3547
-0,4565
SFUNIT70
1.0000
0.1512
0.1691
0.3339
0.5U94
0.5341
U.5700
0.4437
0,1591
0.3026
0.3167
-0,2697
-0.0739
-0.4287
0,2652
-0,4037
-0.3514
SFCON
1.0000
-0,4237
-0.3529
0.2987
-0,1700
U.2S28
0,5/08
0.0691
0,2467
0,4037
0,0165
0.0771
0.2838
0.6512
-0,1180
0,2969
MFUNIT60
1.0000
0.9562
-0,3033
0.6270
0.2916
-0.1749
0.0881
0.0120
-0,0437
-0.1657
-0,0449
-0,3233
-0.2735
-0.0788
-0,3147
MFUNIT70
1.0000
-0,0111
0,7204
0,3460
-0,0503
0,0867
0.0352
0.0458
-0.?249
-0.0902
-0,4087
-0.1761
-0.1634
-0,3884
-------
Table 9 (continued). SIMPLE CORRELATION COEFFICIENTS - MINNEAPOLIS-ST. PAUL
LO
SECTION
MFCON
CLU
CLU7
COMCON
ILU
INDLU7
INDCON
D60
DELTAD
VLU
SSERVICE
TVACRATE
TOTLU
SECTION
INDLU7
INDCON
D60
DELTAD
VLU
SSERVICE
TVACRATE
TOTLU
SECTION
SSERVICE
TVACRATE
TOTLU
2
MFCON
1,0000
0,2034
0
0
-0
0
0
-0
-0
-0
0
-0
-0
1
0
-0
-0
-0
0
-0
-0
1
-0
.1303
,4339
,0187
,0735
.2985
,1663
.1403
.2261
.3610
.2627
.1893
3
INDLU7
,0000
.6254
,1888
,0754
.0754
.1378
,1489
.0332
4
SSERVICE
.0000
.1239
0.1097
CLU
1.0000
0
0
0
0
0
-0
-0
-0
0
-0
-0
1
-0
-0
-0
0
-0
-0
1
0
.6262
.2556
.2369
.1965
.2131
• 2to35
• I23fl
,4306
.0438
.2071
.3660
INDCON
.0000
.2112
.0423
.2116
.3070
.3587
.1849
TVACRATE
• 0000
.4942
1
0
0
0
0
-0
-0
-0
0
-0
-0
CLU7
.
.
»
.
.
.
.
,
.
.
.
0000
5475
1166
2800
3329
3069
1048
2798
3656
2230
2227
1
0
0
0
-0
0
-0
0
-0
0
D60
1
0
0
-0
0
0
.
.
.
.
.
.
0000
7200
6377
0845
4144
6302
1
0
-0
0
0
COMCON
.0000
.0358
,1267
,3675
,0998
.0122
,0162
,6565
.1801
.0262
DELTAD
.0000
.2140
.0312
.1180
.2009
1
0
0
-0
-0
0
0
0
0
1
0
0
0
ItJJ
.0000
.8115
.2575
.1243
.0828
.0254
.0461
,0329
,0684
VLU
.0000
.0647
.4922
.9910
TOTLU
i
.
0000
-------
Table 10. SIMPLE CORRELATION COEFFICIENTS - WASHINGTON, D.C.
oo
SECTION
SFtHl
SFD«J70
SFCON
MFDU
MF-DU70
MFC ON
CLU60
CLU?0
COMCON
ILU60
ILU70
INDCON
060
DtLTAD
VLU
SSt^VICE
TVACHATE
TOTLU
1
SFOU
1.0000
0.9078
0.0293
0.3467
0.4724
0.3225
0,1932
0.2508
0.26^5
0.099b
0.138«
0.1534
-0.0784
-0.0870
-n.2()8f>
0.00t>2
-0.0475
-0.0971
SFUU70
1 .OUOO
U . 4 4 b 8
0.1791
0.3668
0.3865
0.2134
0.3090
U.349Q
0.1489
0 , 2 1 1 ft
0,>2d«3
-0.0u93
-O.OblB
-0,005? "
0.1973
-0.0o43
0.1113
SFCON
1.0000
-0.3131
-0.1339
0*2328
0.0961
^.^Oil
0.2Y37
0.1425
U.20B7
Q.2406
0.1 4b?
-0.0093
Q.<+328
0.4570
-0,0519
0.4^27
MFOU
1.0000
0.8296
-0.0238
0,0221
-0,0ft92
-0.1906
-0.0366
-^, 11 57
-0.1923
-0,1454
-0.1308
-0.3589
-0.3065
-0,0106
-0.332?
MFOU 70
1.0000
0.5385
0,0929
0.0138
-0.0739
0.0200
-0,0095
-0.0478
-0.1554
-0.1278
-0.3014
-0.1583
-0,1301
-0.2315
-------
Table 10 (continued). SIMPLE CORRELATION COEFFICIENTS - WASHINGTON, D.C.
SECTION
MFCON
Cl U60
CLU70
COMCON
ILU60
ILU70
INDCON
DbO
DEI TAD
VLU
SSEHVICE
TVACRATE
TOTLU
SECTION
ILU70
INDCON
D60
DElTAD
VLU
SSEHVICE
TVACRATF
TOTLU
SECTION
SSERVICF
TVACRATE
TOTLU
?
MFCON
1.0000
0.1331
0.1593
0.1555
0.0911
0.1575
0.2045
-0.058B
-0.0315
0.0020
0.1791
-0.2169
o.o86a
3
ILU70
1.0000
0,8399
0.0712
-0.1748
0.470B
0.4976
0.0579
0.5347
4
SSERVICE
1.0000
-0.0223
0. 7377
CLU6Q
i.oooo
0.910S
0.6323
0.570?
0.5647
0.39bO
-0.0047
-U. IH J8
o,??2q
U.33BO
-0.0138
0.2Q43
INDCON
1.0000
Q.Q318
-0.07Hf
0.4435
0»4280
O.C97?
0.4963
TVACPATE
1 . 0 0 0 0
0.0300
CLU70
1.0000
0,8961
0.4879
0.5951
U.5730
-0,0 119
-0.1585
0.3201
0.4104
0.0294
0.3903
060
1 . 0 0 0 0
0.79 IB
0.2837
0,2340
0.0215
0.3120 t
TOTLU
1 .0000,
COMCON
1.0000
0.3021
0.5087
0.6495
-0.0172
-0.1426
0.3604
0.4059
0.0698
0.4152
DELTAD
1.0000
-0.1000
-0.0857
0.0680
-0.0926
ILU60
1.0000
0,9158
0.5359
0.0849
-0,2079
0.3931
0.4449
0.0177
0,4518
VLU
1.0000
0,6992
0,0697
0,9808
-------
Table 11. SIMPLE CORRELATION COEFFICIENTS - POOLED SAMPLE
SECTION
SFUNIT
SFUNIT70
SFCON
MFUNIT
MFUNIT70
MFCON
CLU60
CLU70
COMCON
ILU60
ILU70
INDCON
060
DELTAO
vtu
SSERVICE
TVACRATE
TOTLU
I
SFUNIT
1.0000
0.9270
-0.0731
0.3273
0.4434
0.4026
0.3189
0.3211
0,2618
0,1476
0.1366
0,0598
-0.1873
-0,1300
-0.1748
0.1217
-0,1440
-0.0350
SFUNIT70
1 .UUOO
0,3064
0.1879
0.3265
0.4624
0,2837
0,3190
0.3061
0.1372
0,1624
0,1328
-0.1493
-0.1663
-0,0370
0.3163
-0.1389
0.0953
SFCON
1.0000
"0,3311
-0,2573
0,2077
-0.0552
0,0331
0,1493
-0.0098
0,0849
0,2013
0.0783
0.0473
0,3453
0,5321
-0,0038
0.3421
MFUNIT
1.0000
0,9506
-0,0694
0.3027
0.1972
0.0123
0.1138
0.0210
-0,1347
-0,1673
-0,0922
-0.2175
-0.1298
-0.0764
-0,1743
MFUNIT70
1,0000
0,2436
0,3324
0.2314
0,0461
0,1181
0.0403
-0,0954
-0,1822
-0.1025
-0.2202
-0.0390
-0,1300
-0.1574
-------
Table 11 (continued). SIMPLE CORRELATION COEFFICIENTS - POOLED SAMPLE
SECTION
MFCON
CLU60
CLU70
COMCQN
ILU60
ILU70
INDCON
060
DELTAD
VLU
SSEHVICE
TVACRATE
TOTLU
SECTION
ILU70
INDCON
060
DELTAO
VLU
SSERVICE
TVACRATE
TOTLU
SECTION
SSERVICE
TVACRATE
TOTLU
2
MFCON
1.0000
0.1227
0.1277
0.1098
0.0237
0,0641
0.1142
-0.0628
-0.0412
-0.0281
0,2802
-0,1791
0.0389
3
ILU70
1.0000
0,5737
0.0815
0.0059
0.2276
0.2211
-0.0321
0.2955
4
SSERVICE
1.0000
-0.0192
0,4268
CLU60
i.OOUO
0,9424
0.6793
0,1126
0.1032
0,0461
-0,0364
-0,0883
0,0310
0.1315
-0,0013
0.1524
INDCON
1*0000
0.0230
0.0325
0.1054
0.203]
-0.0473
0.1345
TVACRATE
1 .0000
0.2767
CLU70
1.0000
0.8855
0.0915
0,1188
0.1182
-0.0322
-0,0876
0.0836
0,1720
0.0136
0,2056
060
1*0000
0,7976
0,3645
0.0607
0.2356
0.3511
TOTLU
1.0000
COMCON
1,0000
0,0443
0.1173
0,1955
-0,0202
-0,0697
0,1405
0,1947
0,0316
0,2393
DEI.TAD
1,0000
0,1279
-0.0322
0.0949
0,0968
ILU60
1,0000
0.8977
0,1892
0,0631
-0,0208
0,1600
0,1441
-0,0426
0,2339
VLU
1.0000
0,3898
0.2926
0,9713
-------
E. REGRESSION ANALYSES
A series of multiple regression analyses was performed for each depen-
dent variable. Several issues that could not be resolved a. priori were
addressed empirically. Most important were normalization of the equa-
tions to account for variations in district size, selection of highway
service variables, and changes in the specification to reflect competi-
tion for land among alternative user groups. These issues, of course,
have a major bearing on whether a simple reduced expression can
adequately model secondary impacts.
The issue of normalization presents both theoretical and practical
problems. Theoretically, there is a choice between extensive specifica-
tions involving absolute amounts or levels of stocks and activities in
each district and intensive specifications involving proportions or
rates. The first specification implies that amounts of construction
(e.g., number of units) are related to amounts of characteristics in a
district (e.g., number of acres vacant). The second implies that rates
or proportions of construction (e.g., units/acre) are related to pro-
portions of district characteristics (e.g., percent vacant). The
practical issue is whether variation in district size leads to statis-
tical bias because of lumpy data. The statistical problem that results
is heteroscedasticity — residual errors with changing variance
correlated with the size of the districts.
As a preliminary resolution of this issue, we chose a normalized
specification in which land-related variables (e.g., construction,
total vacant and sewered vacant land) were divided by total acres in
each district. This specification was used to establish whether amounts
of construction are partially determined by district size and to correct
for the possibility that larger districts have larger random variations
in construction than smaller districts. This preliminary specification
was subsequently modified.
Selection of highway service variables — accessibility levels or
proximity to highways — was principally a matter of testing alternative
combinations for statistical significance and explanatory power.
Accessibility seems preferable because it measures highway influence
in a more detailed way. However, calculations of accessibilities
require substantial amounts of data and thus would be difficult to use
in practice. Distance to highway, a gross measure of highway influence,
has the advantage of being easy to determine.
The lack of representation of competing or interacting land uses is
perhaps the greatest weakness of the specification. We hoped to
alleviate this problem in the course of the statistical analyses by
including new explanatory variables as simple indicators of competition
and its effects. However, competition proved far too complex to be
addressed by the simple structural forms adopted here.
42
-------
A series of regressions with each dependent variable was carried out
in order to resolve these issues. In some cases, several iterations
were made using data for alternative groups of regions. The general
procedure followed is illustrated by the analyses for Single-Eamily
Housing given below. It is interesting to note that the simpler
specifications and variables were generally more successful than more
complex variables or expressions.
1. Initial Normalized Equation -
The initial specification for single-family housing construction
(units/acre) included base year and change in access to employment,
percent sewered vacant land, and percent total vacant land as explana-
tory variables. Separate regressions were run on data for Boston,
Denver, and Washington.
Results for these tests are shown in Table 12. Surprisingly, base year
accessibility had negative and significant coefficients in two of the
three regions. These negative coefficients, which occurred rather con-
sistently for single-family construction, seem counter-intuitive since
access is a desirable characteristic of locations. However, a reasonable
interpretation is that high levels of base year accessibility imply land
prices too high to permit low density housing construction.
Change in access to employment had the theoretically expected positive
effect for Denver and Washington. Low levels of significance, indicated
by the t statistics, may have been caused by collinearity between the
two access variables (r = .94). Since no data were available on change
in accessibility for Boston, a change in distance to highway variable
was used.
Sewered vacant land and total vacant land had positive effects on
single-family housing construction. The fact that parameters for
sewered vacant land are larger than those for vacant land in two of
the three regions provided the first indication that public sewer
service is an important determinant of single-family housing construc-
tion. In spite of multi-collinearity, most of the t statistics are
significant at the 10% or 5% level.
2. Normalized with Vacancy Rate and Competing Land Uses -
A second specification was made to test change in distance to a major
highway as an indicator of highway construction in all regions, while
retaining base year access to employment. In addition, three new
independent variables were included to represent factors influencing
the construction of single-family units. The first variable was total
residential vacancy rate, a measure of housing market tightness within
each district. To take into account competition for land with other
43
-------
Table 12. SINGLE-FAMILY HOUSING CONSTRUCTION NORMALIZED BY DISTRICT SIZE'
Specification- /Single-Family Housing
~" x Total Land
+ b2 (Change in Access to
+ Constant
Explanatory Variable:
Base Year Access to Employment:
Change in Access to Employment:
Change in Distance to Highway:
/Sewered Vacant Land\ .
Total Land
/Vacant Land-. .
Total Land
Constant;
Coefficient of Determination (R2) :
F-value :
Construction-, _ , /B y
EnmlovmcnO + b, (Sewered Vacant
Employment; + b3(. ^^ ^^
Denver
bi = -13.2
t-L = (-1.930)
b2 = .58
t2 = (.950)
-
b3 = .715
t3 = (5.741)
b4 = .417
t4 = (3.172)
a;L = -.32
0.39
28.0
ir Access to
LandN j. K /•
• / > D^ ^
Washington
-2.12
(-1.201)
1.899
(.654)
-
.286
(1.290)
.036
(.137)
.053
0.11
2.10
Employment)
Vacant Landx
Total Land
Boston
.076
(.399)
-
.143
(1.542)
.207
(1.430)
.546
(5.159)
-.34
0.25
10.0
Approximate values for t «,. and F Q, are 1.66 and 3.17.
-------
types of development, both the observed multi-family units constructed
and industrial land conversion over the 1960-1970 interval were included
in the single-family equation. It was expected that large amounts of
competing land uses would reduce single-family construction.
Regression results for this single-family formulation are shown in
Table 13. For Denver and Washington, the sewer service, vacant land
and access variables show relative stability compared to the previous
results. Vacancy rates and the competing land use variables perform
as expected for Denver, in which all the independent variables except
industrial construction are significant at the .025 level. In Washing-
ton, however, the vacancy and competing land variables show positive
but generally insignificant relationships. Further examination of the
calculated vacancy rates for Washington revealed that the inclusion of
military housing in the total stock coupled with demolition of housing
for highway construction created errors in the variable subsequently
corrected.
The inclusion of the new variables in the Boston equation caused a
reversal in signs of the highway variables, suggesting multi-collinearity.
While the vacancy rate parameter was negative and significant as hypo-
thesized, competing land use variables were positive and insignificant.
The fact that all of the land in Massachusetts is incorporated at the
municipal level rather than the county level may explain in part
apparent co-location of single-family, multi-family, and industrial
development, since our districts conform to municipal boundaries.
While the competing land variables seem appropriate for Denver, their
unexpected parameters in Washington and Boston suggest that small
analysis districts are necessary to model competition effects. In
addition, questions of land assembly, demolition of existing stock or
conversion from one type of use to another are problems which should be
addressed in modeling interactions between intensive development types
as they bid for land resources. These questions are essentially
dynamic in character, while our approach is a static representation.
Therefore, competing land use variables were omitted from subsequent
regressions.
3. Normalized with Proximity to Highways -
A third specification with distance to highway replacing access measures
was estimated for Minneapolis, Washington, and Boston. In this case,
base year distance and forecast year distance to highway were included,
with negative parameters expected for both. Competing land use variables
were excluded, while residential vacancy rate was retained. Results
are shown in Table 14.
Parameters for the highway variables remained inconsistent. Coeffi-
cients for sewer service, vacant land, and vacancy rates are relatively
stable. For Boston, a dummy variable was added to reflect the orienta-
45
-------
Table 13. SINGLE-FAMILY HOUSING CONSTRUCTION, SECOND FORMULATION*
o>
Soecification- /Single-Family
Housing Construction _ ^ n*OQ£> Vf>,
-* Total Land
+ b2 (Change in Distance to Highwj
+ b5 (Residential
+ i /Industrial
7 Total
Explanatory Variable
Base Year Access to Employment:
Change in Distance to Highway:
(Sewered Vacant Land/Total Land
(Vacant Land/Total Land) :
Residential Vacancy Rate:
(Multi-Family Construction/
Total Land) :
(Industrial Land Conversion/
Total Land) :
Constant
Vacancy Rate) +
Land Conversions
Land
bi =
^1 ~
b2 -
to "
: b3 =
b4 . -
DC =s
tc =
D/- —
o
b7 :
ai •
2
Coefficient of Determination (R ) :
F-value :
' I
ly) + IK,, (Sewered Vacant I
3 Total Land
, /Multi-Family Housing
6 Total Land
+ r*on ci t"^ti t"
OUHO L-CLLl L-
Denver
-6.38
(-2 . 305)
.163
(2.070)
.755
(6.829)
.490
(3.909)
-.539
(-3.475)
-.047
(-2.076)
-1.333
(-1.372)
-.338
.46
21.1
ir Access to Employment)
l,andN + ^ (Vacant Land^
4 Total
Construction-^
/
Washington
-1.04
(-1.198)
.075
(.435)
.293
(1.365)
.002
(.009)
.048
(.172)
.051
(1.418)
3.53
(.902)
.065
.12
1.87
Land
Boston
-.057
(-.326)
-.089
(-1.402)
.181
(1.171)
.496
(6.516)
-.471
(-2.436)
.078
(.366)
.445
(.577)
-.22
.43
12.4
Approximate values for t Q5 and F Q1 are 1.66 and 3.17.
-------
Table 14. SINGLE-FAMILY HOUSING CONSTRUCTION, THIRD FORMULATION*
Specification- (Single-Family Housing Construction
* • • • • • • ' Total
+ \>2 (Forecast Year
j_ u /-Vacant Land\ .
~**vTotal Land '
Explanatory Variable:
Base Year Distance to Highway:
Forecast Year Distance to Highway:
(Sewered Vacant Land/Total Land) :
(Vacant Land /Total Land) :
Residential Vacancy Rate:
Dummy Variable for second Homes:
(Boston only)
Constant :
Land
Distance to Highway)
= b-^(Base Year
. , f Sewered Vacant
Distance to
Land^
Highway)
J Total Land '
• bij (Residential Vacancy Rate) 4- Constant
bl =
b2 =
t2 =
t3 =
A.
A
b5 =
t5 =
al =
Coefficient of Determination (R^) :
F-value :
Minneapolis
-.012
(-1.007)
-.011
(-.638)
.106
.481
(5.868)
-.430
(-1.503)
-.173
.31
7.15
Washington
+.012
(+.418)
-.042
(-.884)
.304
(1.290)
.175
(.736)
.031
(.089)
-.05
.07
1.42
Boston
-.006
(-.730)
.004
(.295)
.299
(1.826)
.545
(6.884)
-.710
(-2.855)
.104
(1.274)
-.25
.31
8.97
Approximate values for t C and F - are 1.6- and 3.17.
-------
tion in a few coastal towns toward second homes and seasonal rental of
dwelling units, causing very high vacancy rates. The coefficient is
positive as expected, and borders on significance.
The performance of these three specifications across metropolitan area
was quite varied. R2's ranged from .07 to .46, while the F statistics
were significant at the 5% level or better with a single exception.
Among individual variables, sewer service was consistent in terms of
sign and significance, although changes in magnitude of the coefficients
from region to region are apparent. Vacant land and vacancy rates were
also generally significant. Results for the highway variables were
disappointing in the sense that no consistent relationships were estab-
lished. In view of the multi-collinearity problems in the data, it was
not possible to conclude with confidence that no such relationships
existed.
Examination of residuals for this series of regressions revealed strong
correlations between error terms and district size. However, this
correlation was in the opposite direction than anticipated, i.e.,
decreasing district size increased variance in errors. Since virtually
all the small districts were in central cities, this finding may reflect
different conditions in or near the urban core, as well as urban renewal
activities unrelated to the private market factors considered here.
As a statistical heteroscedasticity test, absolute values of error
terms were regressed with total land in each district. The sample was
selected from the specification which showed the least heteroscedasti- ,
city. Results showed district size to be significant at the one percent
level in explaining residual values.
4. Unnormalized and Pooled -
Normalization by district size clearly increased the heteroscedasticity
of the residuals since, in effect, it weighted the small districts more
heavily than the large districts. Therefore, a second series of
regressions was run using unweighted linear specifications. These tests
were run on all four regions and with a pooled sample from all regions.
Explanatory variables included base year and change in distance to high-
way, sewer service, vacant land, and residential vacancy rate. For the
pooled sample, dummy variables were added to distinguish between
regions.
Results are shown in Table 15. Sewer service is the only completely
stable explanatory variable in terms of sign and significance. Vacant
land and vacancy rate fulfill a_ priori expectations with single
exceptions. Only the two highway variables are inconclusive in their
results for individual cities, although the pooled result seems correct.
It should be noted, however, that the base year distance to highway and
change in distance were strongly collinear in all of the samples, which
may explain the instability.
48
-------
Table 15. SINGLE-FAMILY HOUSING CONSTRUCTION, UNNORMALIZEDa
VO
Specification; Single-Family Housing Construction = bi(Base Year Distance to Highway)
+ b£ (Change in Distance to Highway) + b$ (Sewered Vacant Land)
+ b4 (Vacant Land) + b$ (Residential Vacancy Rate) + Constant)
Explanatory Variable:
Base Year Distance to Highway: b^ =
Change in Distance to Highway: b2 =
Sewered Vacant Land: b3 =
Vacant Land: b4 =
Residential Vacancy Rate: b5 =
Constant: ai =
Dummy Variables : Minneapolis
Boston
Washington
Coefficient of Determination (R2) :
F-value :
Minneapolis
-195.2
(-2.726)
189.9
(2.532)
.270
(7.361)
.056
(4.718)
-2128
(-1.926)
21.1
.56
20.2
Denver
21.0
(.576)
-1.92
(-.045)
.310
(6.289)
-.035
(-2.462)
2944
(2.134)
-126
.24
11.4
Washington
-65.3
(-.564)
99.0
(.736)
.092
(2.422)
.052
(1.954)
-659
(-.784)
473
.24
6.2
Boston
36.9
(.948)
-70.0
(-1.577)
.099
(1.984)
.136
(7.776)
-2654
(-3.850)
-340
.38
14.7
Pooled
-52.3
(-1.775)
65.8
(1.975)
.165
(9.683)
.036
(4.762)
-584
(-1.072)
188
-134
(-1.082)
200
(1.787)
-172
(-1.688)
.33
29.8
a Approximate values for t 05 and F 01 are 1.66 and 3.17.
-------
In every region except Boston, the coefficient of the sewer service
variable is substantially larger than that of the vacant land variable.
This suggests that unsewered vacant land is less influential in a
single-family residential development than sewered vacant land under
ordinary circumstances. In the Boston region, however, a significant
portion of single-family housing construction in the 1960's was
suburban large-lot homes, for which septic tanks could be used. In
Boston, unsewered vacant land had a larger influence than sewered
vacant land.
The dummy variables reflect differences between regions as a whole and
variations in district sizes between regions. However, there is no
clear correspondence between either aggregate growth rates or district
growth rates and the values of the dummy variables. Hence unincluded
exogenous factors are being represented.
F. REGRESSION RESULTS
The most promising form of equation for all the dependent variables was
a specification with the following set of explanatory variables:
base year distance to highway, change in distance to highway, sewered
vacant land, total vacant land, and — for housing construction —
residential vacancy rate. Attempts to include additional variables to
account for more complex relations, such as competition for land and
co-location or agglomeration, yielded ambiguous results. The simpler
specifications therefore were chosen for final regressions.
Heteroscedastic errors remained in all of the equations. This problem
clearly was caused by the large proportion of small districts within
each regional sample. In Washington, for example, more than half of
the districts fell within the 100 square miles composing the urban core.
While there is no theoretical reason why the equations should not apply
to these small geographical units, the actual data for land use changes
in these areas are very uneven. Statistically, these lumpy data reduce
the efficiency of the regressions.
For this reason, we used weighted least squares (WLS) to correct the
bias. WLS weights unreliable observations less than the more reliable
ones, and therefore allows the regression to estimate parameters more
accurately. All variables were weighted by multiplying by district
size. Thus, the larger districts were emphasized more than the small
districts. The absolute value of the residuals from this test were
then regressed on district size to determine whether any correlation
remained. The coefficient for district size was highly significant and
positive, indicating that this procedure over-corrected, leaving large
districts with large residuals. A second WLS regression was performed
using the square root of district size as a weight. This time the
residuals and district size were uncorrelated. Accordingly, this weight
was selected as appropriate for single-family housing. A similar test
50
-------
with multi-family construction showed that the same weight was appro-
priate. The weight for industrial land use was total land in the
district, while no weight was necessary for commercial land use.
An additional (unshown) set of regressions were performed using these
same variables defined as district shares of regional totals rather
than as actual district values. The dependent variables thus were
district percentages of total regional land use changes. The tests
were made to establish empirically whether such a specification would
improve the explanatory power or significance of the regressions.
However, the results almost exactly duplicated those of the regressions
presented below in terms of both t statistics and R^'s.
1. Single-Family Housing Construction
The final equation, in a difference form, for single-family units
constructed is shown in Table 16. The equation with the pooled data
shows significant coefficients for the independent variables with the
direction of impact conforming to expected behavior. The further an
area is from a highway in the base year the less development occurs;
a decrease in distance to a highway during the forecast period increases
development of single-family units. Availability of vacant land and
sewer service increase single-family construction, with sewer service
having a larger impact. A slow housing market in the area in the base
year, as measured by a high vacancy rate, discourages development of new
single-family units.
2. Multi-Family Construction
The final equation, in difference form, for multi-family construction
is given in Table 17. In the pooled sample, all variables are signifi-
cant with appropriate signs. The coefficient of the vacant land term
has a negative value for multi-family units. The negative sign on the
vacant land supply variable can be interpreted as indicating lack of
demand in areas with larger amounts of vacant land, that is, in areas
with little access and many acres of vacant land.
3. Commercial Land Conversion
Results for commercial land development are shown in Table 18. The
same pattern emerges as for the residential equations. The large con-
stant for the Boston equation, and Boston dummy variable in the pooled
equation, reflect the inclusion of additional land uses in the Boston
data for commercial land. However, the coefficients of various para-
meters for Boston fell within an acceptable range compared with other
regions, so that no major bias (aside from the value of the constant)
was apparent.
In the pooled equation, change in distance to highway is not signifi-
cant but has the appropriate positive sign.
51
-------
Table 16. ESTIMATES OF SINGLE-FAMILY RESIDENTIAL CONSTRUCTION3
Change in Units within District =
b2(Base Year Distance to Highway) + b$(Change in Highway Distance)
+ b4(Base Year Vacant Land) + b5(Base Year + Change in Sewered Vacant Land)
+ bg(Residential Vacancy Rate) + bf(I/Square Root of Total Land) + Constant
+ bs(Dummy Variable for Metropolitan Areas)^
, b8
Coefficient b2 b3 b4 b5 b6 b7 Constant Boston Minneapolis Washington
Ui
M Average Value -0.74 75.2 0.024 0.074 -19.3 -3.38x10* 951 -73 218 830
T-statisticb -4.39 3.06 2.82 5.37 -3.57 4.40 -0.52 1.51 5.19
Coefficient of Determination (R2) = 0.49
F-valuec =51.87
aPooled data for Boston, Denver, Minneapolis, and Washington. Total number of districts, N=495.
bT-statistics at the 1%, 5%, and 10% levels are 2.326, 1.645, and 1.282, respectively.
cThe F-value at the 1% confidence level is 2.37.
regional adjustment for Denver is implicit in the value of the constant.
-------
Ul
UJ
Table 16 (continued). PARTIAL CORRELATION COEFFICIENTS FOR SINGLE-FAMILY CONSTRUCTION
BY INDIVIDUAL METROPOLITAN AREA
(Equations Weighted by the Square Root of Total Land)
Boston
Denver
Minneapolis-
St. Paul
Washington,
B.C.
Pooled
Base Year
Distance
To Highway
(miles)
-0.15
0.18
0.15
0.42
0.25
Change in
Distance
To Highway
(miles)
0.11
0.04
0.20
0.07
0.09
Base Year
Vacant
Land
(acres)
0.49
0.31
0.33
0.56
0.44
Base Year
Plus Change
in Sewered
Vacant Land
(acres)
0.23
0.52
0.64
0.49
0.58
Base Year
Total
Residential
Vacancy Rate Constant
-0.02 0.43
0.39 0.40
-0.02 0.49
0.03 0.66
0,13 0.50
Dummy Variables
Pooled
Boston
-0.06
Minneapolis
0.22
Washington
0.50
-------
Table 17. ESTIMATES OF NEW, MULTI-FAMILY RESIDENTIAL CONSTRUCTION3
Change in Units within District =
b2(Base Year Distance to Highway) + b$(Change in Distance to Highway)
+ b4(Base Year Vacant Land) + b5(Base Year + Change in Sewered Vacant Land)
+ bg(Residential Vacancy Rate) + by(I/Square Root of Total Land) + Constant
+ b8(Dummy Variable for Metropolitan area)**
bo bo b/. bc bx b-
Coefficient "2 "3 "4 P5 P6 P7 Constant Boston Minneapolis Washington
Average Value -0.52 52.9 -0.11 0.050 -17.0 -6.96x10^ 2130 200 688 1380
T-statisticb -2.24 1.59 -7.98 3.05 -2.63 6.95 1.17 3.95 7.21
Coefficient of Determination (R2) = 0.34
F-valuec « 24.95
aPooled data for Boston, Denver, Minneapolis, and Washington. Total number of districts, N=495
bT-statistics at the 1%, 5%, and 10% levels are 2.326, 1.645, and 1.282, respectively.
°The F-value at the 1% confidence level is 2.37.
regional adjustment for Denver is implicit in the value of the constant.
-------
tn
Ui
Table 17 (continued). PARTIAL CORRELATION COEFFICIENTS FOR MULTI-FAMILY CONSTRUCTION
BY INDIVIDUAL METROPOLITAN AREA
(Equations Weighted by the Square Root of Total Land)
Boston
Denver
Minneapolis-
St . Paul
Washington,
B.C.
Pooled
Base Year
Distance
To Highway
(miles)
-0.11
-0.07
-0.14
0.06
-0.0007
Change in
Distance
To Highway
(miles)
-0.06
-0.03
-0.11
0.01
-0.02
Base Year
Vacant
Land
(acres)
0.04
-0.19
-0.30
0.05
0.05
Base Year
Plus Change
in Sewered
Vacant Land
(acres)
0.64
-0.03
0.44
0.22
0.35
Base Year
Total
Residential
Vacancy Rate
-0.15
-0.09
-0.18
-0.23
-0.04
Constant
0.20
-0.05
-0.003
0.34
0.24
Dummy Variables
Pooled
Boston
0.03
Minneapolis
0.05
Washington
0.38
-------
Table 18. ESTIMATES OF LAND CONVERSION TO COMMERCIAL USE3
Change in Acres within District =
b2(Base Year Distance to Highway) 4- b$(Change in Distance to Highway)
+ b4(Base Year Vacant Land) + bs(Base Year + Change in Sewered Vacant Land)
•f Constant + bg(Dummy Variable for Metropolitan Area)
~~"b6 ~
Coefficient b2 b3 b4 b5 Constant Boston Minneapolis Washington
Average Value -6.20 2.07 0.002 0.011 15.13 147.6 -8.18 26.25
<" T-statisticb -1.713 0.505 1.906 5.100 11.749 -0.539 1.904
Coefficient of Determination (R2) = 0.29
F-valuec = 24.98
aPooled data for Boston, Denver, Minneapolis, and Washington. Total number of districts, N=495.
bT-statistics at the 1%, 5%, and 10% levels are 2,326, 1.645, and 1.282, respectively.
cThe F-value at the 1% confidence level is 2.37.
regional adjustment for Denver is implicit in the value of the constant.
-------
Table 18 (continued). PARTIAL CORRELATION COEFFICIENTS FOR COMMERCIAL DEVELOPMENT
BY INDIVIDUAL METROPOLITAN AREA
Ul
Boston
Denver
Minneapolis-
St. Paul
Washington,
D.C.
Pooled
Base Year
Distance
To Highway
(miles)
0.13
-0.07
-0.006
0.25
-0.02
Boston
Change in
Distance
To Highway
(miles)
0.01
-0.13
-0.07
-0.008
-0.07
Dummy Variables
Base Year
Vacant
Land
(acres)
0.32
0.06
0.12
0.44
0.14
Base Year
plus Change
in Sewered
Vacant Land
(acres)
0.17
0.19
0.64
0.40
0.19
Constant
(0.50)
(0.17)
(0.30)
(0.52)
0.03
Minneapolis Washington
Pooled
-------
4. Industrial Land Conversion
The final equation for industrial conversion with the pooled sample has
expected signs for the coefficients of the highway variables (Table 19).
Vacant land has a negative sign, as it does in multi-family housing, but
is of lower significance. Sewer service is positive and significant in
the pooled sample. It should be noted that the linear equation for
industrial land was weighted by total land rather than by the square
root of total land, as the residuals were more properly distributed
in the former weighting. To the extent that the industrial base within
each city consists of differing industries, one would expect that the
slopes of the independent variables would differ among cities, making
estimation of a generalizable industrial equation difficult. The
relatively poor fit of the pooled equation (R2 = .17) is indicative of
this problem.
It should be noted, however, that both commercial and industrial land
use data probably suffer from substantial measurement errors. If these
errors are random, they will reduce the quality of the statistical fits,
but will not bias parameters. If this is the case, the coefficients
for the highway and sewer variables will reflect marginal influences
of these factors, even if the overall statistical fit is poor.
5- General Discussion oftheRegression Equations
When the final equations are compared with the initial set of hypo-
theses concerning the influence of public investments and other local
factors on land use, some revision of the original hypotheses is
necessary. Most surprising, perhaps, is the sensitivity of single-
family housing construction to both highway and wastewater facilities.
The concensus of previous studies is that highways do not effect strongly
single-family residential land use. However, most of these studies
investigated effects in relatively small areas within a small (1 to
2 miles) distance from new highways. The regression equation for
single-family housing implies that new highways affect construction
in areas much further away, and that this influence is consistently
measurable for larger study districts. An area need not be bisected
by a highway to be affected; a change in distance to highway from 10 to
5 miles (implying that the new highway is still 5 miles away) may have
a major impact on single-family housing construction.
The substantial influence of wastewater facilities on single-family
housing is not surprising in retrospect. Most single-family construc-
tion during the 1960's took place on small lots, making public sewers
a substantial advantage, if not a necessity. The correlation between
sewer service and construction may be a result of coordination between
public officials and private developers rather than a simple cause-
effect relationship.
58
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Table 19. ESTIMATES OF LAND CONVERSION TO INDUSTRIAL USEa
Change in Acres within District =
b2(Base Year Distance to Highway) + b3(Change in Highway Distance)
+ b4(Base Year Vacant Land) + b5(Base Year + Change in Sewered Vacant Land)
+ by(I/Total Land) + Constant + bg(Dummy Variable for Metropolitan Area)
bo b/ b,. b-
Coefficient "2 U3 "4 P5 1 Constant Boston Minneapolis Washington
Average Value -17.2 19.4 -0.002 0.005 -25.0xl04 175 -29.1 -93.8 8.46
U1
*° T-statisticb -3.68 3.61 -1.25 2.14 4.77 -0.89 -3.05 0.24
Coefficient of Determination (R2) = 0.17
F-valuec = 12.63
Q
Pooled data for Boston, Denver, Minneapolis, and Washington. Total number of districts, N=495.
bT-statistics at the 1%, 5%, and 10% levels are 2.326, 1.645, and 1.282, respectively.
cThe F-value at the 1% confidence level is 2.37.
"The regional adjustment for Denver is implicit in the value of the constant.
-------
Table 19 (continued). PARTIAL CORRELATION COEFFOCIENTS FOR INDUSTRIAL DEVELOPMENT
BY INDIVIDUAL METROPOLITAN AREA
(Equations Weighted by Total Land)
Boston
Denver
Minneapolis -
St. Paul
Washington,
D.C.
Pooled
Pooled
Base Year
Distance
To Highway
(miles)
0.16
-0.42
-0.34
0.53
0.03
Boston
0.08
Change in
Distance
To Highway
(miles)
0.11
0.08
0.02
0.27
0.08
Dummy Variables
Base Year
Vacant
Land
(acres)
0.24
0.66
-0.32
0.63
0.07
Base Year
Plus Change
in Sewered
Vacant Land
(acres)
-0.04
0.76
0.11
0.54
0.22
Constant
0.22
0.62
-0.22
0.69
0.19
Minneapolis Washington
-0.05
0.18
-------
The hypothesis that intensive development — multi-family, commercial,
and industrial — is more sensitive to public facilities than single-
family housing is not supported by the statistical results. Effects of
highways and sewers on single-family and multi-family dwelling units
are roughly equivalent in magnitude. While commercial and industrial
development are measured in different units (acres), their relative
sensitivity to investments (see below) is of the same order as for
housing. This similarity may be caused in part by the use of distance
to highway variables in the regressions. The literature suggests that
intensive land uses are strongly affected by "pockets" of accessibil-
ity — interchanges and virtual contiguity to highways. "Distance to
highway" measures do not properly reflect detailed variations in dis-
tances to highways at the lower range (e.g., one mile or less) of
highway access. It seems possible that intensive construction
activities implied by the coefficients for a 10 square mile district
would occur in fact in small subareas having very high accessibility.
Thus, for example, a new highway might cause increases of 500 dwelling
units for both single- and multi-family housing. But while single-
family units might be constructed throughout the district, the multi-
family units may be concentrated in complexes near interchanges or be
contiguous to the highway. Projected impacts for commercial and indus-
trial land use changes can be given the same interpretation.
It is evident that the equations account for residential construction
more fully than for commercial and industrial land conversion. This
is no doubt caused in part by the fact that more factors enter in
commercial and industrial location decisions. It is also true, however,
that land use by businesses is inherently difficult to measure with
precision. It is likely that the data contained substantial noise
caused by measurement errors which reduced the statistical fits for
commercial and industrial development.
There is no clear explanation for the values and signs of the dummy
variables in the pooled regressions. They do not correspond to
regional growth vectors, mean district growth vectors, or any other
intuitive regional differences. It seems likely that the dummy
variables are representing a complex combination of these factors as
well as correcting for parametric differences among explanatory
variables across regions.
Coefficients of determination for the regressions are modest, ranging
from 0.17 to 0.49. This is largely a reflection of the extreme
simplicity of the specification and, by itself, should not be viewed
as a drawback. However, when coupled with the parametric instabilities
of the equations — particularly for the highway and wastewater policy
variables — it does raise questions about the interpretation of
findings. These issues are discussed more fully in the following
section.
61
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6. Chow Tests onthe Pooled Data
The pooling of data across metropolitan areas raises a question con-
cerning the interpretation of the final parameters. If relationships
between explanatory and dependent variables are the same for each
region, then pooling allows estimation of generally appropriate para-
meters , with the regional dummy variables helping to explain region-
specific differences external to the equations. If, on the other hand,
parameters vary from region to region, then pooling allows estimation
of average rather than general relationships.
Regression of individual regions show that coefficients do in fact
vary from region to region, in some cases substantially. To test
formally how much of this variation was caused by different parameters
rather than random noise in the data, Chow* tests were performed on the
pooled and unpooled regression residuals. The Chow test involves con-
struction of an F statistic to test the (null) hypothesis that two
sets of coefficients are equal. The F statistic takes the following
form:
F =
((Pooled Residuals)^ - (Unpooled Residuals)^)(1/d.f. pooled-d.f. unpooled)
((Unpooled Residuals)2)(1/d.f. unpooled)
It may be noted that the Chow test does not allow confirmation of the
null hypothesis that coefficients from pooled and unpooled regressions
are equal. Rather, it provides a probability that the two sets of
coefficients are not equal, i.e., that the null hypothesis is incorrect.
Chow tests were run with both unweighted and weighted specifications.
According to these results, we may reject the hypothesis that all
coefficients are equal across the four metropolitan regions at the five
percent confidence level for all expressions (except the unweighted,
multi-family housing equation). In view of these results, some
additional discussion of the pooled coefficients and their meaning is
necessary.
In reviewing the regression equations, by far the greatest variations
in parameters across regions occur for the two highway variables and
vacant land. It seems that in some regions and for some types of
development, highway investments did not play a particularly important
role. Yet in the pooled regressions, base year distance and change in
distance to highways have the correct signs and are significant. It
See Chow, Gregory C., "Tests of Equality between Subsets of Coeffi-
cients in Two Linear Regressions," Econometrica, 28, 1960, pp. 591-
605.
62
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is possible that many of the t statistics and some coefficients for
individual regions were biased by intercorrelation. However, in the
pooled sample, the coefficients are quite stable in spite of continuing
collinearity.
Vacant land poses a more serious problem, since in some instances
coefficients in different regions had opposite signs and were statisti-
cally significant. Clearly in this instance the pooled regressions
averaged these opposing effects. The fact that the multi-family
equation, in which vacant land had a consistently negative effect, was
the only one to pass the Chow test suggests that the vacant land
coefficient is the principal difference among pooled and unpooled para-
meters for the other equations. The ambiguity of vacant land as an
indicator of lack of demand (development pressure) is disappointing.
In addition to the above problems, the variability in the sewered
vacant land parameters deserves mention. While this parameter was
positive and highly significant in almost all regressions, it ranged
an order of magnitude in size from region to region. Such instability
reflects the influence of factors not represented in the specification
and suggests that the pooled regression parameters do not necessarily
reflect the "true" population parameter. Only more complete specifica-
tions attempting to represent exogenous forces can determine the
generality of the pooled regression parameters.
Overall, the failure of the pooled regression coefficients to pass the
Chow test is not surprising. It is unlikely that public investments
would have precisely the same marginal influence in all areas. The
results must be considered as average rather than general relations.
The confidence intervals for the highway and sewer service variables
are averages which include variations from region to region in the
sample.
7. Equations withForecast Year Stocks as Dependent Variables
As a final adjustment, the equations were recast with forecast year
stocks of dwelling units or acres of land use as dependent variables
and base year stocks as additional explanatory variables. This format
is more appropriate for applying the equations, since it helps to
insure that any projected land use changes and secondary effects will be
evaluated in the context of base year conditions. Clearly the impor-
tance of development is a function of existing conditions. Construction
of 500 apartment units, for example, has different significance in an
area that is largely undeveloped than in an area with substantial stock
of apartments.
Quite obviously, the new format with base year stock as an explanatory
variable improves the stability and performance of the equations.
While this is desirable from a pragmatic viewpoint, it should be
63
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recognized that there is no effect on the accuracy of the equation? in
predicting land use changes. The new format merely changes the base on
which the R2 an(j p statistics are calculated. All of the additional
variation that is explained is attributable to the base year stock
variables.
The recast equations are shown in Table 20. There are very modest
changes in the previous coefficients, aside from those expected from
the change of the numerical magnitude of the dependent variable.
Coefficients for the base year stocks are all highly significant. Of
these, only the single-family housing variable has a coefficient
smaller than unity. A value of less than unity reflects a tendency
for the number of single-family homes in a district to decrease in the
absence of other stimuli. This is in accordance with events during the
period from 1960-1970, when multi-family housing gained in popularity
while many older, single-family homes were demolished or renovated as
apartment buildings.
All of the equations except that for commercial land were estimated
using Weighted Least Squares, and contain correction terms (i.e.,
I/total land or I/square root of total land) to account for differences
in land use caused by district size. For commercial land, WLS was not
justified on the basis of heteroscedastic errors. However, we felt
that district size might still be an important influence on commercial
land, so a similar correction term was added to the specification in
the final regression. As the Table shows, this term is consistent with
the other equations and is highly significant.
64
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V/i
Table 20. ESTIMATES OF FORECAST YEAR STOCKS OF DEPENDENT VARIABLES3
Units or Acres*5 in Forecast Year =
b^(Base Year Units or Acres) 4- b2(Base Year Distance to Highway)
+ b3(Change in Distance to Highway) + b^Base Year Vacant Land)
+ b5(Base Year + Change in Sewered Vacant Land) + bg(Residential Vacancy Rate)
+ by(I/Square Root Total Land or I/Total Land)c + Constant
Value of ^ v -U v v. i. u Constant
on o^ b, b_ b,. b.
Coefficient 1 2 3 4 5 -,JL- _ 7 Boston Denver Minneapolis Washington
Single-Family 0.944 -0.72 66.3 0.015 0.079 -20.0 -3.59x10* 1170 1100 1490 2060
T-statistic 44.05 -4.26 2.69 1.63 5.75 -3.72
Multi-Family 1.012 -0.52 52.7 -0.11 0.050 -16.7 -7.01x10* 2300 2120 2790 3480
T-statistic 54.67 -2.21 1.58 -7.74 3.06 -2.58
Commercial 1.402 -6.49 3.98 0.0021 0.0044 - -1.17x10* 73.0 21.8 -6.1 27.0
T-statistic 52.98 -2.21 1.19 2.80 2.48
Industrial 1.154 -7.25 10.7 - 0.0033 - -13.8x10* 81.9 75.7 45.4 97.3
T-statistic 36.27 -2.10 2.46 1.52
Single-Family Multi^Family_ Commercial Industrial
Coefficient of Determination (R2): 0.92 0.89 0.90 0.84
F-value: 533.4 352.5 492.4 309.9
aSee footnotes a-d of Table 16.
bUnits for residential categories; acres for commercial or industrial.
°Square root of total land for residential categories; total land for commercial or industrial.
-------
Table 20 (continued). PARTIAL CORRELATION COEFFICIENTS FOR 1970 STOCK EQUATIONS
^^Independent
\Variable
\^^ Base Year
Dependent^x 1960 Distance
Variable N. Stock To Highway
Change in
Distance
To Highway
Base Year
Base Year Plus Change Residential
Vacant in Sewered Vacancy
Land Vacant Land Rate
Constant
\
Forecast Year 0.92 0.13
Single-Family
Housing
Forecast Year 0.91 -0.10
Multi-Family
Housing
Forecast Year 0.94 -0.03
Commercial Land
Forecast Year 0.91 0.48
Industrial Land
Forecast Year
Single-Family Housing
Forecast Year
Mult i -Family Housing
Forecast Year, Commercial Land
Forecast Year, Industrial Land
0.02
-0.07
-0.09
0.22
Boston
0.25
0.15
0.49
0.04
0.25 0.49 0.13
-0.09 0.10 -0.10
0.08 0.17
0.54 0.39
Dummy Variables
Minneapolis Washington
0.24 0.36
-0.03 0.16
-0.12 0.008
0.46 0.23
0.52
0.05
-
0.60
-------
IV. CONCLUSIONS AND SUGGESTIONS FOR FURTHER RESEARCH
A. IMPLICATIONS OF THE FINDINGS
In broad terms, the econometric results have three important implica-
tions. The first is public investments in transportation and waste-
water facilities have identifiable and measurable effects on urban
growth. As an hypothesis, this statement is accepted without issue
by most planners and analysts. The present study supports this hypoth-
esis with empirical evidence and partial quantification of effects.
Another implication is that highways and sewer facilities, on average,
are associated with relatively modest changes in urban development
patterns. The results suggest that if impacts are measured as a propor-
tion of land use change attributable to public investments, sewer lines
are responsible for some 5 to 15 percent of new development over a 10
year period, and highways for approximately the same amount. The
maximum elasticity of construction to sewers was for single-family
housing. The value, calculated at the mean, is about 0.5. The maximum
elasticity for change in distance to highways was about 0.2. Neither
of these elasticities is particularly large.
It is crucial to recognize, however, that while average impacts are
small, a significant portion of impacts are more substantial. Thus,
for example, while the average sewer investment may lead to an increase
of about 15 percent in single-family housing construction, the largest
10 percent of sewer investments (measured in terms of sewered vacant
land) may be associated with increases of 40 percent and more.
The results support what seems intuitively correct in this instance.
The majority of public investments have modest (but still significant)
impacts on urban growth. However, when major investments are made in
areas with appropriate market conditions, the impacts may be substan-
tial. For example, a new highway traversing many portions of a region
will cause minor changes in most parts of the region. Only districts
which were previously inaccessible to highways are likely to experience
substantial secondary effects. Hence, a given highway is likely to
cause a variety of small, moderate, and large impacts in different
portions of a single metropolitan area.
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B. LIMITATIONS
In practical terms, perhaps the most severe limitation of the regression
equations is the variability of parameters and of the residual varia-
tion from region to region. These problems, discussed in Section II,
create difficulties in application of the regression equations without
situation-specific re-estimation of the parameters. Of course, histor-
ically derived regression equations, no matter how accurate in a
statistical sense, cannot be trusted as a sole source of information
for decision-making. Nevertheless, the instability of policy variables
from region to region, and the low coefficiencts of determination, imply
that the equations should be applied even with greater caution than is
normally the case.
From another point of view, the results are quite encouraging. In view
of the simplicity of the underlying model, the stability of parameters
and the values of R2's are acceptable and, in some instances, impressive.
The results suggest that basic forecasting techniques of this structural
type are possible, given a more complete specification. To reach a
better specification, some problems only briefly addressed in this work
require a more thorough examination. In this context, the limitations
of the study may be summarized as follows:
• the issues of timing and feedback between the capital
planning process and development were not investigated;
• the various mechanisms available to policy-makers for
controlling urban growth and secondary effects received
only a small amount of study;
• the analytic techniques derived from the research were
not integrated into the planning process as a whole; and
• several promising extensions and refinements of the
regression equations were beyond the scope of the
project.
These limitations are discussed more fully in the following section.
C. AREAS FOR FURTHER RESEARCH
1. Timing and Feedback in Planning and Development
The principal issues of timing and feedback are planner-developer
interactions and lags (or leads) in the relationships between develop-
ment and planning. Developers not only respond to the availability of
existing and planned public service facilities, but influence planning
decisions about where and how much to extend public facilities. This
interaction makes it difficult to establish whether development is an
68
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"effect" caused by public investments. What is needed is a more de-
tailed understanding of these interactions and their timing.
This area of research may be subdivided into several specific ques-
tions :
• How do developers respond to capital planning?
• How (or to what extent) are capital plans formulated
in response to pressures for development?
• What are the response times for these interactions?
a. Developer Response -
Statistical analyses have established a correlation between develop-
ment and the construction of new public facilities. This relationship,
however, might be more appropriately represented as a response to
investment plans rather than actual construction of facilities. In
evaluating the lag between investment decisions and development, it
may be more accurate to use the date of planning approval or public
announcement of plans rather than dates of construction activities.
Additionally, the length of time over which developer response occurs
remains uncertain.
b. Developmental Pressure for New Facilities -
The phrase "developmental pressure" is frequently used to describe a
situation in which high levels of demand and/or attractive locational
characteristics cause developers to request or petition government
officials for the necessary facilities and permission to construct
new structures. Developmental pressure may cause spot zoning variances,
altered master plans, and extensions of public services and facilities
into previously unserved areas. However, little is currently documented
about the frequency or extent to which capital plans are influenced
by such pressure.
c" Response Times -
Several timing scenarios are possible for planning-development inter-
actions. Developers may purchase land without access to public
facilities, subsequently attempt to convince authorities to provide
facilities, and if successful, proceed with development. Alternatively,
developers may purchase land after plans for extending facilities have
been approved but prior to their construction. Finally, developers may
purchase land and initiate construction after new facilities are in
place. The relative frequency with which these alternatives occur has
important implications for the planning process, occurrence of windfall
profits, and equitable financing of capital facilities.
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2, Mechanisms for Controlling Development
The traditional and most common mechanism for controlling development
and land use in the United States is zoning. A large percentage of
cities have prepared master plans containing long range guidelines for
land use, with zoning specified as the means of insuring that growth
conforms to plans. However, in a growing number of metropolitan areas
it has become obvious that zoning is not an adequate mechanism for
control of land development, at least not as it is usually administered.
Variances are readily obtainable. Local jurisdictions often rush to
permit commercial or industrial development to increase tax bases in
spite of the fact that such developments violate approved master plans.
Recently, new control mechanisms such as moratoria on sewer hookups or
building permits have been introduced. These controls have had a
variety of consequences on housing prices, local economies, and develop-
ment, most of which were not fully anticipated. While public controls
on land use appear in theory to offer the best means of environmental
protection, a more complete understanding of the relationships between
controls, development, and local socioeconomic conditions is essential
for designing appropriate policies.
The central research questions are:
• To what extent has zoning served historically to control
development?
• What are the implications of alternative control measures?
The issue of enforcement is an important one, because any form of direct
land use control, such as zoning, could be effective if it were rigor-
ously enforced. But this implies a lack of change over time, which may
be more questionable than freely changing land use controls, since land
market conditions and requirements for space are constantly changing in
urban areas. Research should be aimed at determining the frequency of
zoning variances, criteria by which they are made, and consequences of
rigid zoning. In addition, the consequences of alternatives to zoning
such as phased growth strategies should be explored to the extent that
data permit.
3. Extensions and Refinements of Current Techniques
The regression equations developed as forecasting techniques for
secondary effects contain several limitations which might be removed
through further research. The limitations are a result of the approach
used in estimating equations and of weaknesses in our understanding of
the processes leading to urban development.
One of the principal criteria for the equations was simplicity of form
to insure ease of use. This criterion should also apply in future
70
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research. While results of other efforts involving complex structural
forms and multiple equation systems should, of course, be utilized, the
emphasis in this project is to be on reduced forms and simple recursive
systems.
4. Variations in Relationships Across Regions
As noted previously, the substantial variation in relationships across
metropolitan regions constitutes a major stumbling block in the develop-
ment of general forecasting techniques. Increased understanding of the
causes of this variation is an important goal of further research.
While some interregional variation occurred for all parameters, the
most significant instabilities involved the highway variables, sewer
service, and vacant land. Problems with the highway variables no
doubt were principally a result of the extreme simplicity of the
measure. Future efforts should be directed toward improving the vari-
ables employed. For sewer service, on the other hand, improved results
should follow from a more complete specification of exogenous factors,
such as soil characteristics, topography, and local regulations, which
influence the importance of public sewer service to developers. In
addition, further investigation of combined capacity-service area
measures and treatment availability measures is justified.
Problems with the vacant land variable appear most severe. The vari-
able is ambiguous in representing both supply and demand (or lack of
demand). Perhaps the most promising approach is to include a more
thorough specification of demand factors, so that vacant land avail-
ability will be limited to a representation of supply.
5. The Influence of Accessibility
Substantial ambiguities were encountered in determining how accessibil-
ity and new transportation investments influence development. Coeffi-
cients for accessibility and proxy variables showed little stability,
changing signs and levels of significance across metropolitan areas
tested. There are several possible explanations, both theoretical and
practical.
The response of developers, particularly in the residential sector, to
accessibility may be strongly nonlinear. While workers require access
to their place of employment, they may satisfy this preference rather
than optimizing it. If this is true, then we should expect that for
values of accessibility below or above some intermediate range, little
correlation would be found between access and development. The question
of nonlinearities was not fully investigated in our statistical analyses.
It is also to be expected that different socioeconomic groups will
respond differently to changing accessibility. According to Kain, an
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important variable is the value attached to (travel) time, in addition
to transport costs. Several studies, however, have found positive
correlations between income levels and travel time to work. While this
does not contradict Rain's theory, it does suggest that other con-
straints prevent high income workers from reducing journey-to-work
travel times. In general, little systematic research has been directed
toward the transportation preferences of various socioeconomic groups.
Observed trip distributions, a typical proxy for travel demand, do not
necessarily conform to actual preferences.
The question of preferences points to a related practical problem in
defining accessibility. The most common approach is to measure access
by using a gamma function or friction factor representing observed trip
distributions. In essence, the gamma function expresses a probability
that workers will travel some given time. This distribution function,
however, reflects observed trips rather than preferences, and therefore
usually shows a higher probability that people will travel 15 to 20
minutes than 5 minutes. This approach could be improved if the gamma
function reflected instead the probability of "willingness" to travel a
specified time for a specified group. The influence of access would
then be isolated from other factors.
Clearly, "willingness to travel" is not a fixed characteristic, but
rather varies over time and in different areas. An investigation of
these variations would represent a very substantial long-term research
program in itself. In the short term, however, important insights might
be gained by investigating the response of different household catego-
ries and of developers to access, using existing measures. In particular,
disaggregation of development into more detailed categories — residen-
tial by density, industrial and commercial by type of business — should
allow refinements in the existing equations. Ongoing studies by the
National Bureau of Economic Research provide tentative evidence that
such disaggregation is very helpful for residential development.
Breaking down development into more detailed categories would also
provide the means for estimating public costs of development in terms
of new facilities and services. This would allow planners to design
and evaluate policies for encouraging efficient growth patterns in a
financial sense.
6. Finer Geographic Scale
The second major potential refinement of the equations is reduction in
the size of the geographic areas to which they may be applied. Greater
detail would provide more useful information for municipal and environ-
mental planners.
It must be recognized, however, that at some point, detail becomes less
useful because of lower accuracy. The point of diminishing returns is
72
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uncertain, but NBER housing studies clearly indicate that trade-off
between detail and accuracy. Their regressions for areas smaller than
census tracts show low (i.e., less than .25) percentages of explained
variation in spite of significant independent variables. The census
tract could be a suitable compromise, but some normalization may be
necessary to overcome wide variations in the size of tracts.
If the geographic scale is reduced to a census tract level, several new
explanatory variables will probably be necessary to maintain acceptable
statistical fits. In particular, neighborhood socioeconomic character-
istics, structural qualities, and land use policies may be important.
In our tests of such variables for areas 10 to 40 square miles in size,
they were not consistent in their effect or level of significance, a
result which we interpreted as indicating substantial variations for
these variables within districts. At the census tract level, however,
there should be little internal variation.
To incorporate more detail, the dependent development variables should
be disaggregated to represent several densities of residential construc-
tion and categories of industrial and commercial development. However,
the number of dependent variables may ultimately be minimized by
reaggregating residential, industrial, and commercial categories that
have similar responses to the explanatory variables.
7. Social Impacts
An important area not explored in our research was analysis of social
impacts — changes in demographic and social features of local popula-
tions as a result of public investments. Social impacts may be regarded
as arising from land use and housing market changes and therefore as
derivative to them. However, in view of their importance for social
planning and public policy, the techniques should be extended to address
social impacts directly.
It is theoretically feasible to estimate equations for social and demo-
graphic conditions in much the same fashion as for land use, with land
use changes used as an explanatory rather than dependent variables.
Such equations would allow a two-stage analysis in which land use impacts
of public investments would be first projected and then used to project
social impacts of investments.
Dependent variables for social impact equations should be descriptive
of demographic conditions most important to local planners. Among the
possible variables are measures of family income or income distribution,
age of household heads, family size, and racial mix.
Costs of land and housing are probably important influences of the above
variables. Since the original equations provide estimates of changes
in stocks of structures and land uses, but not changes in prices or
73
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rents, the explanatory variables for the social impact equations should
include measures of housing market and social conditions in the base
year. In addition, social policy variables concerning housing might be
included. Assuming a lagged cross-sectional formulation, such as used
for land use, the social impact equations would project demographic
changes over a 10 year interval by base year housing market and social
conditions, social policies during the interval, and development and
land use changes over the interval.
8. Analysis of Developmental Effects in the Planning Process
Research and related studies sponsored by the Council on Environmental
Quality in association with other Federal agencies have led to new
techniques for evaluating secondary effects and the costs of urban
development. There remains, however, the important task of integrating
these techniques in the local and regional planning processes. The
investigation of secondary effects has established the central role that
planning plays in the generation of impacts. Often a simple lack of
coordination or cooperation between planning groups is the origin of
problems arising from development. Therefore, an attempt should be made
to disseminate information concerning the implications of uncoordinated
local policies and plans as well as the available means of projecting
effects of alternative policies and plans.
The most fruitful short-term product of such an effort would be a
manual which discusses secondary effects of public investments and
their significance from the viewpoint of the local planner. The urban
planning process should provide the context of the manual, and all
elements, including transportation, sewerage, water, land use, other
services, and financial planning should be addressed. New techniques
should be compared with and integrated with more traditional planning
methods. The interactions between these elements of the planning
process should be discussed in view of their combined influence on
urban growth patterns and rates, and the subsequent implications of
growth for each planning element.
Since stringent growth controls and no-growth policies are under serious
consideration in many parts of the country, these issues should be
addressed in the manual. An analysis of ways in which local governments
fail to control or guide growth, the impact of sprawl on public services
and finances, available mechanisms for controlling or restricting growth,
and possible effects of growth control policies should be discussed.
In the Washington, D.C. metropolitan area, for example, moratoria have
been accompanied by rapidly escalating prices and rents, a lack of
moderate- and low-income housing, and growing concern about the
continued economic vitality of portions of the region.
It is essential to recognize that as local jurisdictions become aware
of the dangers of uncontrolled growth, the central issue in policy
74
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design shifts from how to control growth to defining the optimum rate
of local growth. In areas that have undergone massive urban develop-
ment and its consequences, the new generation of planners and policy-
makers is increasingly confident that future growth can be guided
effectively. Their concern now is where, what kind, and how much
development would be best for their jurisdictions. Three criteria
are evident: (1) environmental, (2) public finances, and (3) housing
availability and cost. This project, therefore, should address not
only controls and their effects, but the issue of designing optimum
growth strategies and managing development to reach objectives.
While these discussions should provide a clear overview of major issues,
the central orientation of the manual should be analysis. It should
present guidelines for projecting the location, form, and amount of
development likely to result from alternative local government actions
and socioeconomic conditions. Additionally, methods of estimating the
financial consequences of such development in terms of public services
and tax structures should be included. Where possible, the manual
should also present techniques for projecting impacts of new development
on the physical environment, particularly air and water quality.
The guidelines should identify and discuss factors that must be consid-
ered in analyses and sources of data. Where feasible, alternative
data sources, methods, and techniques should be presented to provide a
range of alternative approaches. Rules of thumb and rough approxima-
tions that sacrifice some accuracy for simplicity may be very useful
to planners and other officials with limited resources or time for such
evaluations.
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V. REFERENCES
1. U.S. Bureau of the Census, Government Finances in 1972-73,
Series GF 73, No. 5, Washington, D.C., U.S. Government Printing
Office, October 1974 (Table 9).
2. This report, entitled Secondary Impacts of Transportation and
Wastewater Investments; Review and Bibliography, is being pub-
lished by the Office of Research and Development, Environmental
Protection Agency, in their Socioeconomic Environmental Studies
Series, report no. EPA-600/5-75-002. It will also be available
from the National Technical Information Service, U.S. Department
of Commerce.
3. Council on Environmental Quality, "Preparation of Environmental
Impact Statements: Proposed Guidelines," Federal Register, 38:84,
(May 2, 1973).
4. Public Law 91-190 (January 1, 1970).
5. McKain, W. C., The Connecticut Turnpike - A Ribbon of Hope,
University of Connecticut, Storrs Agricultural Experiment Station,
1965.
6. Adkins, W. G., "Land Value Impacts of Expressways in Dallas, Hous-
ton, and San Antonio, Texas," Highway Research Board, Bulletin 227,
pp. 50-65, 1959.
7. Carroll, D. D., et al., The Economic Impact of Highway Development
upon Land Use and Value, University of Minnesota, September 1958.
8. Philbrick, Allen, Analyses of the Geographical Pattern of Gross
Land Usesand Changesin Numbers of Structuresin Relation to
Major Highways in the Lower Half of the Lower Peninsula of
Michigan, Michigan State University, 1961.
9. Mayo, Stephen K., "An Econometric Model of Residential Location,"
in The NBER Urban Simulation Model: Volume II, Supporting Empirical
Studies by John F. Kain, National Bureau of Economic Research,
New York, 1971.
76
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10. Connally, Julia A., The Socid-Economic Impact of the Capital Belt-
way on Northern Virginia. Bureau of Population and Rmnr^-tn
Research, University of Virginia, Chariottesville, Virginia, 1968.
11. Cribbins, P. D., W. T. Hill, and H. 0. Seagraves, "Economic Impact
of Selected Sections of Interstate Routes on Land Value and Use,"
Highway Research Record, No. 75, pp. 1-31, 1965.
12. Neuzil, D. R., The Highway Interchange Problem; Land Use Develop-
ment and Control, University of California. Berkeley, California
1963.
13. Adkins, W. G., op. cit.
14. Cribbins, P. D., et_ al_._, ibid.
15. Kanwit, E. L. and A. F. Eckartt, "Transportation Implications of
Employment Trends in Central Cities and Suburbs," Highway Research
Record, No. 187, pp. 1-14, 1967.
16. Real Estate Research Corporation, Highway Networks as a Factor in
the Selection of Commercial and Industrial Locations, prepared for
the U.S. Bureau of Public Roads, U.S. Department of Commerce, 1958.
17. Kiley, E. Y. , "Highways as a Factor in Industrial Location,"
Highway Research Record, No. 75, pp. 48-52, 1965.
18. Real Estate Research Corporation, op. cit.
19. Kinnard, W. N. and Z. S. Malinowski, Highways as a Factor in Small
Manufacturing Plant Location Decisions, University of Connecticut,
1961.
20. Bleile, G. W. and L. Moses, "Transportation and the Spatial Distri-
bution of Economic Activity," Highway Research Board, Bulletin 311,
pp. 27-30, 1962.
21. See, for example, A. J. Bone and M. Wohl, "Massachusetts Route 128
Impact Study," Highway Research Board, Bulletin 227, Washington,
1959.
22. The various EMPIRIC models are documented in separate volumes. The
basic reference is: Traffic Research Corporation, Reliability Test
Report; EMPIRIC Land Use Forecasting Model, prepared for the Bos-
ton Regional Planning Project, New York, 1964.
23. Rogers, Andrei, The Time Lag of Factors Influencing Land Develop-
ment, Institute for Research in Social Science, University of North
Carolina, Chapel Hill, N.C., October 1963.
77
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24. Milgram, Grace, The City Expands, Institute for Environmental
Studies, University of Pennsylvania, Philadelphia, prepared for
the U.S. Department of Housing and Urban Development, March 1967.
25. Kaiser, E. J., A Producer Model for Residential Growth, Center for
Urban and Regional Studies, University of North Carolina, Chapel
Hill, N.C., November 1968.
26. See, for example, John F. Kain, The NBER Urban Simulation Model.;
Volume I, National Bureau of Economic Research, New York, 1971.
27. The four EMPIRIC data bases used were acquired separately.
Documentation for these data may be found in: (a) Traffic Research
Corporation, Reliability Test Report; EMPIRIC Land Use Forecasting
Model, prepared for the Boston Regional Planning Project, New York,
1964. (b) Peat, Marwick, Mitchell, and Company, EMPIRIC Activity
Allocation Model; Application to the Denver Region, prepared for
the Denver Regional Council of Governments, December 1972.
(c) Peat, Marwick, Mitchell, and Company, EMPIRIC Activity Alloca-
tion Model: Application to the Washington Metropolitan Area, pre-
pared for the Metropolitan Washington Council of Governments,
December 1972. Similar documentation of the Minneapolis-St. Paul
data is not available.
78
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VI APPENDICES
Page
APPENDIX I. THE LAND USE SIMULATION MODEL 80
APPENDIX II. MODEL LISTING 121
APPENDIX III. DOCUMENTATION OF DATA ON TAPE TMP 234 143
APPENDIX I. Contents: Page
A. Introduction 80
B. The Land Use Simulation Model 80
C. Application of the Model to the
Washington, D.C. Metropolitan Area 89
D. References 118
79
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APPENDIX I
I. THE LAND USE SIMULATION MODEL
A. INTRODUCTION
In addition to econometric analyses, a dynamic model was constructed
for simulating metropolitan growth, land use changes, and the influence
of public investments on these changes. The effort was intended to
supplement our statistical work by allowing a more thorough study of
the dynamic aspects of secondary effects, including interactions between
different forms of urban development and between different portions of
a metropolitan area. The dynamic model was also used to evaluate
investment-related policies such as sewer moratoria and their impacts,
subjects for which inadequate data were available to perform statistical
analyses.
The model was tested in an application to the Washington, D.C. metro-
politan area. Historical simulations were compared to actual changes
within the region to provide an indication of the model's accuracy.
Sensitivity analyses were performed to evaluate the importance of
parameters in determining system behavior. Subsequently, the secondary
effects of major highway and wastewater investments in the Washington,
D.C. region during the period 1960-1968 were estimated. The effects
and implications of the controversial sewer moratoria imposed between
1969 and 1973 were also evaluated.
The results of these efforts are documented in this Appendix. Section B
describes the structure of the model and its computational sequence.
Section C presents the results of the model application to Washington,
including accuracy evaluations and estimated secondary effects of his-
torical investments. Appendix II provides a complete listing of the
model. See Figure A, page 80Ca) for a FLOWCHART for the model.
B. THE LAND USE SIMULATION MODEL
The land use model simulates changes in population, employment, and land
use throughout a metropolitan area, at six-month intervals over a
twenty-five year period. The model is programmed in the DYNAMO III
simulation language. It was based on previous work on modeling regional
growth and land use=
80
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Figure A
STRUCTURE OF ZONAL INDUSTRIAL AND RESIDENTIAL DEVELOPMENT
Regional
Growth
Regional
*" Activity
(Population or
Employment)
^Regional
Space
Requirements
Regional
Vacancy
Regional
Construction
Total
Available
Space
i ,
Other
Zones -
Relative
^ Zonal —
Zonal
•Construction -
Attractiveness
Attractiveness
Zonal
Zonal
Developable
Land
Attractiveness^-
4
Land Price
_ Effects-
Available
~~ Sewer
Service
Zonal
-Occupied-
— Land
Zonal —
•Activity-
Vacant
"Sewered"
Land
Available.
— Sewer
Sewered
Land
Sewer
Investments
and Control
Policies
d
Se
— •«•»!
Capacity
Capacity
Zonal
Accessibility-
Interzonal
Travel.Times
Other Zones
Activity
jTransportationInvestments.
80 (a)
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1. Model Structure — An Overview
The central component of the model, the land use sector, accepts fore-
casts of regional growth and simulates the distribution of growth among
subregional zones. Each zone — a community, a planning district, or
a watershed — is represented individually in terms of population,
employment, and land use. The number of zones is variable; for the
Washington test application, a relatively small number, 15, was used.
a. The Land Use Sector -
Land in each of fifteen zones in the Washington area is classified in
five major categories: single-family housing, multi-family housing,
industrial (including commercial and institutional), vacant (i.e., unused
developable land), and undevelopable or recreational land. The sector
models interactions between demand and supply for various types of
structures, and accounts for changes in stocks, densities, and conversion
of vacant land to urban uses.
1. Developers - The most influential private decision-makers in the
urban land use market are professional developers. The land use sector
reflects this fact; developer decisions about when, where, and what to
build are the principal underlying causes of changing land use. While
developers necessarily take into account the preferences of their cus-
tomers concerning locational and structural characteristics, their
principal motivation is economic. Hence, developers are modeled as
selecting forms of development and locations that maximize profits.
The demand for new structures to which developers respond is determined
in part by regional population and employment levels. Developers in
the region respond with construction in three categories (business
space, multi-family housing, single-family housing) on the basis of
the vacancy rate for each type of structure.
2. Relative Attractiveness - Each zone shares in the total regional
amount of residential and industrial construction in proportion to its
attractiveness to developers relative to all other zones in the region.
Five factors influence developer decisions in each zone: accessibili-
ties, levels of wastewater service, availability of land for develop-
ment, land prices, and local control policies. The importance of each
factor varies for different types of development. As construction
occurs over time, accessibility, densities, vacant land, sewered land,
and land prices continuously change, altering relative attractiveness,
and affecting subsequent development.
3. Investments - Highway investments affect development by altering
travel times between zones and hence accessibilities to employment and
to households. New sewer investments, on the other hand, may affect
development by changes in the area served or in the level (capacity) of
services, or both. These changes affect zonal attractiveness through
81
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their effect on the availability of land for uses of different
intensities.
**• Policies - Local policies affect development in various ways. For
example, zoning ordinances can be represented in the model as a density
limit or as a change in the availability of vacant land for a particu-
lar use. Wastewater policies, such as moratoria, place restrictions
on available capacity and sewer service area. Developmental policies,
such as building permit restrictions, place limitations on the timing,
type, and number of structures that can be started, regardless of the
zone's economic attractiveness to developers. Other policies can be
specified and evaluated through their impact on local land markets
and public service availability.
b. Regional Growth -
Overall regional growth may be entered in the model exogenously.
However, the model also includes demographic and industrial sectors for
simulating population and employment growth.
1. Population - Three factors directly affect population — births,
deaths, and net migration. A characteristic common to all three is
their variation in magnitude among different age groups of the popula-
tion. This variation makes the age structure of the population an
important dynamic element in demographic analysis. Because of the
long-term importance of age structure, the population was disaggregated
into six age classes, with each class relatively homogeneous with
respect to birth, death, and migration rates.
The modeling mechanisms describing birth, death, and migration rates
are similar. In each case, regional data are averaged to provide a
basic rate, with local or regional influences left implicit. Varia-
tions in migration rates are caused by changes in the availability of
jobs in the region. Birth and death rates are trended to show shifts
caused by forces exogenous to the model.
The principal causal link between the demographic and industrial
sectors is through the labor force. Labor force, computed for each
age class, forms the supply of workers necessary for industrial expan-
sion. Labor force availability plays an important role in economic
growth.
2. Employment - Industry is modeled in accordance with export-base
theory. Industries were divided into export and local-serving
categories, with the economic base of the region formed around the
export businesses. Local-serving industry responds to population and
export industry growth. Economic activity is specified in terms of
employment.
82
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The growth of export industry in a region is assumed to depend on the
relative attractiveness of that region with respect to wage levels and
access to raw materials and markets compared to the national average.
Increases in government employment were specified exogenously in the
Washington model.
Local-serving industries are divided into two groups: household-
serving and business-serving. The household-serving businesses supply
the needs of ultimate consumers and include subgroups such as retailers,
doctors, teachers, local governments, etc. Employment in the household-
serving group is proportional to population, with the requirements
trended over time. The business-serving industries supply goods and
services needed by other businesses and grow in proportion to their
growth.
The industrial sector affects population change through employment
opportunities. Specifically, changes in unemployment rates were assumed
to influence net population migration.
2« Computational Sequence of the Land Use Sector
The focus of the simulation modeling effort in this project was the
development of the land use sector. The computational sequence for the
land use sector is discussed in this section.
a- Determining Total Construction -
The first step in each computation interval is to transform regional
growth into a total amount of construction for the region. To do this,
vacancy rates for each of three structure types (business space, multi-
family housing, single-family housing) are computed for the metropolitan
area as a whole. For business structures, the ratio of existing employee
space to total regional employment is computed. Residential vacancy is
computed as the ratio of the sum of zonal housing units to housing units
required for the current total population.
The vacancy rate computed for each structure type determines additional
construction for each type for the region as a whole. This is accom-
plished by use of DYNAMO's TABLE function, which allows the user to
specify any linear or nonlinear relationship between two variables. In
this case, the computed "vacancy" ratios determine total new construc-
tion starts in the region in each time period as a percentage of existing
stocks. New construction is then allocated among zones. The TABLE
transformation is illustrated in the following figure.
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CONSTRUCTION STARTS (
(as a percentage of existing stock)
.9 1 1.1
VACANCY RATIO
(Existing Units/Required Units)
Figure A.I. Effect of vacancy rate on regional construction
b. Geographic Allocation of Total New Construction -
Central equations in the model are those that compute the attractiveness
of zones for development. Zonal characteristics which determine attrac-
tiveness are updated in every computation interval. The new values
reflect development that has resulted from previously perceived
attractiveness.
Total regional construction starts of each type are allocated among
zones in proportion to each zone's attractiveness relative to all other
zones:
N
ijt
A
z=l
izt
)(Cit)
where:
N
= construction starts of type i in zone j at time t
= attractiveness for construction type i in zone j at time t
= total regional construction starts of type i at time t
= number of zones
The attractiveness equation is based on an adaptation of land rent-
84
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transportation cost tradeoff theory:
Attractiveness = f-FLD +
where FLD = Fraction of Land Developed (proxy for land price), and
AC = Accessibility.
These two effects work in opposite directions, with higher accessibil-
ity (higher attractiveness) generally corresponding to a higher fraction
of land already developed (lower attractiveness).
The proxy used for land price is the fraction of developed land to total
developable land within the zone. A DYNAMO TABLE function specifies a
nonlinear relationship between the fraction developed and the attrac-
tiveness of the zone to each development type. As can be seen in the
Table, attractiveness for low-density, single-family development
diminishes more rapidly than multi-family and industrial development.
1.0
ATTRACTIVENESS
FOR DEVELOPMENT
(dimensionless)
Figure A.2.
.4 1
FRACTION OF ZONED LAND DEVELOPED
(acres developed/developable acres)
Zonal attractiveness as a function
of land availability
Industrial/
Commercial
Multi-Family
Single-Family
The accessibility to employment or to households afforded by a zone
is traded off against "land price." Accessibility differs for indus-
trial-commercial and residential development. For the latter,
accessibility means accessibility to employment, representing the sizes
of "market areas" from which developers can draw renters and homebuyers.
In business development, accessibility means access to households that
represent potential customers or labor supply. Accessibilities are
computed by calculating travel times between every pairing of zones in
the region for a series of points in time. Travel time changes reflect
transportation investments (no modal differentiation is currently
included in the model). A TABLE function is specified for each type
of development to define a nonlinear relationship between interzonal
travel times and a variable multiplier that is used in calculating
accessibility to employment or residents.
85
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1.0
ACCESSIBILITY
MULTIPLIER
(dimensionless)
-Family
0
Industrial/
Commercial
Figure A.3.
INTERZONAL TRAVEL TIME
(Minutes)
Effect of interzonal travel times
on accessibility
In computing zone A's accessibility to jobs or residents in zone X,
the appropriate multiplier, as derived above from the zone A-to-zone X
travel time, is multiplied by the number of jobs/residents in zone X.
To obtain zone A's total accessibility, the products of these zone A-
zone X multiplications are summed across all zones 1...X. . .N to obtain
zone A's accessibility. The same procedure is repeated for every zone
and every development type.
N
Access
A
(TTMD,A-Z * Actlvitiesz^
where:
A = Zone A
D = Development type
N = Number of zones
Activities = Jobs or households, as appropriate
TTM = Travel time multiplier
In order to standardize the "access effect" in the attractiveness
equation to the same scale as the fraction of land developed (0-1) ,
each zone's computed accessibility to jobs /households is divided by the
total number of jobs/households in the metropolitan area.
Since zone sizes vary, two zones could have the same "attractiveness"
based on fraction developed and access , and yet have quite different
development potential by virtue of one having a greater absolute
quantity of developable land. To adjust for this variation, and to
reflect the greater ease of tract assembly in larger zones with more
vacant land, the computation of zonal attractiveness is multiplied by
the amount of vacant developable land in the zone:
86
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Attractiveness = (FLD 4- AC) * (Vacant Developable Land)
The key word is "developable," as the topography of vacant land can
eliminate some or all types of development. Topographically undevelop-
able land and land designated for open space in a zone is subtracted
from the zone's vacant land. Developable land also can be constrained
by policy, such as zoning restrictions, location of sewer lines, and
availability of sewer hookups. Most of these development constraints
are represented in the model.
Policies may be imposed on zones within the model structure to limit
land availability. Wastewater service plays the most significant role
among these policies. Sewer service within each zone is represented
in two terms, the capacity afforded the area and the service area of
sewer lines. Both are specified exogenously over time, thereby
representing wastewater investments in the model. Service areas are
represented in acres, and capacities in gallons per day. The specified
capacity is that of sewer lines or of treatment plants, whichever is
smaller. Land uses and activities that require sewer service reduce
the availability of sewered land and treatment/line capacity for
subsequent development. The "Sewered Vacant Land" term in the equation
below represents the minimum of the vacant service area and the amount
of land that could be developed within available capacity constraints.
Since not all activities require sewer service, both sewered vacant
land and unsewered vacant land are included in the attractiveness for-
mulation, weighted (0 to 1) for each development type:
Attractiveness = (FLD + AC) * [(Weight) * (SVL)
+ (1-Weight) * (UVL)]
where:
SVL = sewered vacant land
UVL = unsewered vacant land
Restrictions such as moratoria may be exogenously imposed and removed
in any zone at any time to deny or restore availability of sewer
service. A moratorium is represented by a multiplier of zero applied
for a specified period to any available sewer capacity. This zero
multiplier is lagged, however, in order to allow interim construction
permitted by any backlog of previously approved sewer hookups or
building permits.
Following the initially computed allocation of development among zones,
new construction may be subjected to further policy constraints, repre-
senting local ceilings on the amount of each type of development, or
on the rate of permit issuance. This maximum allowable rate of construc-
tion is specified exogenously over time. The amount of each type of
87
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construction in a zone at a particular point in time is either the
allocated share of regional construction or the imposed ceiling, which-
ever is less. The ceiling can be specified so as to replicate the
enforcement of an adequate public facilities ordinance.
c. Computing Land Use and Densities -
As construction occurs within a zone, accounts of land uses are updated.
Total land and recreational and undevelopable land are exogenously
specified. Running totals are kept for industrial-commercial and
residential land uses. Since the units allocated by the attractiveness
function are employment and housing units, a conversion must be made
to obtain the additional land (and sewer service area) corresponding
to new construction.
-i
Since the categories of residential development in the model are rather
broad, they could correspond to different densities for the same type
of development in different zones. Therefore, the densities of new
single- and multi-family development are specified exogenously over
time. This assumes that developers build housing at as high a density
as is permitted by local zoning.
Employment densities can be specified exogenously as well. As an
alternative, a formulation was tested, with moderate success, that
allows employment densities to vary according to zonal land price.
the zonal land price proxy increases, employment density increases
(acres per employee decreases) according to the TABLE transformation
shown below.
EMPLOYMENT DENSITY
(acres/employee)
As
0
DEVELOPED LAND/DEVELOPABLE LAND
(dimensionless)
Figure A.4.
Form of relationship between employment
density and zonal land availability
One such curve was specified for each zone. The shape of the curve
remained consistent, but the curve itself was shifted up or down so that
the base year employment density in each zone corresponded to the zone's
base year fraction of developed land.
Following these computations of new land use, "other development"
88
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(local roads, parking facilities) is accounted for in each zone by the
multiplication of zonal industrial and residential land use bv a
fraction (typically .1-.2).
Vacant land, available sewer service, land prices, and accessibilities
are subsequently calculated, based upon the newly updated conditions.
These form the basis of local attractiveness for further development
and the sequence of land use calculations is repeated in the next
computation interval. This continues until the specified forecast
year of the simulation is reached.
The values used for parameters in the Washington model are provided in
the listing in Appendix II. The following section describes results of
the model application.
C. APPLICATION OF THE MODEL TO THE WASHINGTON, B.C. METROPOLITAN AREA
Empirical tests of the land use model were carried out by programming
the model with historical data from the Washington regions. Simulated
changes in regional growth and land use patterns were then compared
with actual changes.
The study period for these tests was 1960-1968. Conditions in 1960
were used to supply input parameters for the model, while 1968 condi-
tions provided a checkpoint for model forecasts. The only exogenously
specified changes for that period were highway and wastewater invest-
ments and policies. All other phenomena were simulated endogenously.
The following section summarizes the actual changes that took place in
the Washington region during the period in question. Subsequently, we
describe the geographic representation of the region in the model and
evaluate the model's accuracy. Finally, estimated secondary effects of
historical investments and policies are presented and assessed.
1. An Overview of Urban Growth in the Washington, D. C.
Metropolitan Area (1960-1968)
a. Standard Metropolitan Statistical Area (SMSA) -
The 1960 Washington, D.C. SMSA included the Maryland counties of
Montgomery and Prince George's, and the Virginia counties of Fairfax
and Arlington. Charles (Maryland), Loudoun (Virginia), and Prince
William (Virginia) Counties have been added to the SMSA since 1960.
b. Population -
Between 1960 and 1968, the rate of population growth (37%) in the
metropolitan Washington area was among the highest for the nation's
largest metropolitan areas.3 In absolute terms, the population
89
-------
MONTGOMERY
COUNTY
WASHINGTO
^ D.C.
ARL.
CO.
PRINCE GEORGE'S
COUNTY
Figure A. 5. Political jurisdictions
90
-------
increased from about 2 million to about 2.8 million in the Sixties.
Net migration into the Washington area was the greatest of any SMSA.
Table A.I. POPULATION CHANGED
Metropolitan Section3 Pop. 1960 Pop. 1970 % Change
Urban Core
Mature Developed Area
Developing Area
Suburban Area
Low Density Area
169,839
1,073,225
632,814
74,368
82,189
148,000
1,126,000
1,101,000
203,000
125,000
-12.9
4.9
74.0
173.0
52.1
a See Figure A.6 for geographic definitions.
c. Land Use -
Trends in land use in the last twenty years have been: increasing
specialization of the downtown area; residential expansion and filling
in of in-lying areas, followed more recently by "leapfrogging" develop-
ment; new commercial and employment concentrations; and extension of
the Federal establishment into the suburbs. Commercial and industrial
growth has extended outward from previously developed areas along major
highways such as Route 1 north, Baltimore-Washington Parkway, and
Interstate 70. The Capital Beltway has created development nodes at
major radial intersections and has facilitated industrial development
and growth within its perimeter.5
Employment densities continue to be highest in the urban core and have
increased in all regions during the period 1960-1968.6 The results of
the Council of Governments 1968 Home Interview Study indicate that the
most important single land use is that of "office," which accounts for
39% of all employees. Shopping and consumer services account for
15.5% of employees while industrial land use has 7.8% of all workers.
Residential development has occurred at low densities because of local
zoning policies. Prior to 1955, little high density residential develop-
ment occurred outside of Arlington, the District of Columbia, and
sectors of Alexandria. From 1956 to 1959, the bulk of high density
residential development was confined to the in-lying suburbs. Very
little high density development occurred beyond the present location of
the Capital Beltway (see Figure A.7). From 1959 to 1967, high density
development increased significantly in both the District of Columbia
(due to redevelopment) and suburban areas (garden apartments and town
houses.8 However, net residential density has decreased in the urban
core as a consequence of household relocation. Densities in the region
91
-------
Urban Core
Mature Developed
Developing
Suburban
Special Purpose
Low Density
Figure A.6.
Current regional development
pattern
(From: Areawide Land Use Elements 1972,
Metropolitan Washington Council of
Governments, July 1972, p. 20.)
92
-------
MONTGOMERY
COUNTY
PRINCE GEORGE'S
COUNTY
Figure A.7. Network of major highways
(Adapted from: Areawide Land Use Elements 1972, Metropolitan Washington
Council of Governments, July 1972 p.73)
93
-------
as a whole have increased.
Eighty
9
Regional open space grew by 24,450 acres between 1960 and 1968.
percent of this increase was in the developing and suburban areas
Open space acquisitions supported regional growth objectives only to a
limited extent. 1"
Over 58,000 acres were converted to urban use (residential, commercial,
industrial, institutional) in the period 1960-1968. In the core area,
an additional 97 acres were converted to urban use; in the mature
developed areas, 3,500 acres; in the developing areas, 40,000 acres;
and in outlying suburban areas, 15,000 acres. Overall, the increase
in urban land use from 1960-1968 was 45%.H
d. Economic Characteristics -
The Washington SMSA has an economy based largely on three types of
employment: government, services, and retail trade. Other sources
are relatively minor, although manufacturing is growing. The economy
is relatively stable and incomes are higher than the national average.
A sizeable portion of the economy is based on tourism.12
The Federal government is the largest single employer in the Washington
area: 32.8% in 1964. Employment by major sectors is shown in Table A.2.
Table A. 2. EMPLOYMENT BY MAJOR SECTORS
1964
Est.13
Annual Rate of
1968 Growth, 1960-
Est.14 196915
Agricultural
Contract Construction
Manufacturing
Trans. & Utilities
Wholesale Trade
Retail Trade
F.I.R.E.
Services
Self-employed
Household Workers
Federal Government
State 5s Local Government
0.2
6.1 "
5.0
4.0
3.2 "
13.2
5.2
15.1 .
4.9
3.0
32.8
6.8
14.0
17.0
27.0
.39.0
3.2%
3.3
4.1
5.4
5.8
6.0
7.9
4.0
10.8
94
-------
e. Transportation -
The Washington metropolitan area contains 9,000 miles of streets and
highways which accommodate about 33 million miles of travel each workday.
Mass transit bus operators supply 121,000 bus-miles of service each
weekday, 11% of which occur in rush hours. Eighty percent of peak hour
service is in the CBD. While mass transit ridership has leveled off at
a constant annual ridership of between 160-170 million passengers,
ridership has decreased constantly on a per capita basis.-^
The opening of the Capital Beltway, 1-495, in 1964 was a significant
event. The 66 mile long circumferential highway increased accessibility
between most parts of the Washington area. For example, the number of
workers commuting between Virginia and Maryland increased 133% between
1960 and 1968. During the same period, total jobs in the region in-
creased a little more than a third.17
A rail rapid transit system, METRO, is currently under construction.
f. Sewer and Water -
The Potomac is the principal source of water supply for the Washington
SMSA. The river is heavily polluted due to silting from the watershed
area and runoff from new development areas. Water requirements have
increased due to increases in population, per capita consumption and
(potential) industrial demand.
The Washington Aqueduct, operated by the Army Corps of Engineers and
the District of Columbia Government, provides water to the District
of Columbia, Arlington, Falls Church, and military installations in
Virginia. It has emergency connections to the Washington Suburban
Sanitary Commission.
The Washington Council of Governments (COG), in its 1969 report on
Washington,!8 recommended the use of planned sewer extensions and
zoning to maintain the integrity of the wedges and corridors concept
for regional development.
As of 1970, 93% of housing units in the Washington SMSA had public
sewer service. Newly sewered areas in the period 1960-1968 consisted
of 419 square miles and were distributed as follows:
Urban Core 0
Mature Developed Area 0
Developing Area 40%
Suburban Area 52%
Low Density Area 6%
Special Purpose Areas 2%
95
-------
Existing Service Area
Future Service Area Expansion
Figure A.8. Water and sewer
service area
(From: Areawide Land Use Elements 1972, Metropolitan Washington
Council of Governments, July 1972, p. 66.)
96
-------
Expansion and improvement of waste treatment facilities did not keep
pace with extension of sewer service; this imbalance led to sewer
moratoria in Montgomery, Prince George's, and Fairfax Counties. The
moratoria have resulted in short-term building booms in controlled
areas, and intensification of development in outlying areas not subject
to moratoria.
2. Boundaries in the Washington Model
The regional geographic boundaries were those of the 1960 SMSA.
Fifteen zones were delineated in the Washington area, based on juris-
dictional boundaries, data availability, and homogeneity of past
development patterns. The zones are shown in Figure A.9. A brief
description of each follows.
a. Montgomery County, Maryland: Zones A,B,C,D -
Zone A encompasses the northern portion of Montgomery County. 1-70
bisects the zone. The area includes predominantly farm land and low
density development. There has been recent commercial development
along 1-70. Federal employment facilities at Germantown and Gaithers-
burg are in the zone. Land area is approximately 200 square miles.
Zone B includes the City of Rockville and a section of 1-70. Rapid
residential development has occurred around the Rockville area.
Commercial growth has occurred in the City. Many research and profes-
sional firms have located along 1-70. Land area, 55 square miles.
Zone C surrounds the northern tip of the District of Columbia. Included
are the areas of Bethesda, Chevy Chase, Four Corners, Silver Spring,
and Wheaton. The zone includes the I-70/I-495 (Beltway) interchange.
Large increases in population and employment occurred between 1960 and
1968, and new commercial activity has developed along the Beltway
section. Federal establishments at Bethesda include the U.S. Navy
Hospital and the National Institutes of Health. Land area, 60 square
miles.
Zone D is located north of the growth areas of Zones B and C. There
has been recent single-family development and a scattering of commercial
development along Route 29. Land area, 75 square miles.
b. Prince George's County, Maryland: Zones E,F,G,H -
Zone E encompasses northeast Prince George's County. The National
Agriculture Research Center occupies a northern section of the zone.
Substantial single-family development occurred from 1960 to 1968, with
concentrated development in the towns of Bowie and Belair in the north-
east. Land area, including the Agriculture Center, 110 square miles.
Zone F lies in northwest Prince George's County. The zone contains
a section of the Beltway, and the interchange with Route 50. The growth
97
-------
Washington Metropolitan Area
Montgomery County - A,B,C,D
Prince George's County - E,F,G,H
Arlington-Alexandria - I
Fairfax County - J,K,L,M,N
District of Columbia - 0
Figure A.9. Dynamic model zones
98
-------
pattern is similar to Zone C — rapid development of all types in outer
areas near the Beltway. There has been a marked trend toward industrial
and commercial land uses along radial highways and the two railroads
(B&O and Penn). Land area, 75 square miles.
Zone G borders the southeastern boundary of the District of Columbia.
The area has seen rapid residential development. Commercial develop-
ment has occurred on Route 4 and near the Suitland Parkway (between
Andrews Air Force Base and B.C.). Land area, 80 square miles.
Zone H includes the entire southern portion of Prince George's County.
Development in the zone has been sparse. Land area, 200 square miles.
c. Arlington County and Alexandria, Virginia; Zone I -
Zone I is the location of National Airport, the Pentagon, and the
Arlington National Cemetery. Approaching the zone are several major
radial highways including 1-95 south, U.S. 66, and the George Washington
Parkway. Recently, there have been large commercial and multi-family
residential developments. Land area, 40 square miles.
d. Fairfax County, Virginia: Zones J,K,L,M,N -
Zone J is located south of Arlington. It includes the Capital Beltway
and a section of 1-95. The area has growth rates lagging slightly
behind those of other zones which encompass portions of the Beltway.
Land area, 50 square miles.
Zone K lies in southern Fairfax County. Located in the zone are Fort
Belvoir, the District of Columbia Department of Corrections, and the
U.S. Coast Guard Reservation. There has been substantial low-density
residential growth, and moderate commercial and industrial development
along 1-95 and Routes 1 and 123. Land area, 100 square miles.
Zone L includes all of western Fairfax County. It contains sections of
the Dulles Access Road and 1-66. Located in the zone is the new town
of Reston. Residential units and employment more than doubled from
1960 to 1968. Land area, 185 square miles.
Zone M lies west of Arlington County. It includes parts of the Capital
Beltway and Route 66. There have been substantial multi-family residen-
tial and commercial developments along Routes 66 and 50 and in the City
of Fairfax. Land area, 45 square miles.
Zone N lies northeast of Arlington County. It includes parts of the
Capital Beltway and the Dulles Access Road. There has been little
residential development. Commercial and industrial development has
occurred at the intersection of the Dulles Access Road and the Capital
Beltway. Land area, 15 square miles.
99
-------
e. Washington, D.C.; Zone 0 -
While land use has remained relatively unchanged, a large increase in
employment has occurred. Residential development has been minimal.
Land area, 63 square miles.
3- Simulation of Metropolitan Development — Results of
Washington Model Tests
Simulation results with the Washington model are presented, and land
use impacts of infrastructure investments made in the 1960fs are dis-
cussed. Effects of sewer moratoria were also examined by simulation.
a. Model Accuracy -
In evaluating the "accuracy" of the land use model projections, two
criteria were employed: (1) correspondence of projected rates of zonal
development (periods of slow/steady/accelerated growth) to observed
local growth phases; and (2) comparison of model projections to obser-
ved levels in an intermediate year for which consistent data were
available.
On the basis of observations by local planners and reference to local
publications, simulated growth in most zones followed the correct
timing of construction booms and lags. For example, simulation results
for Zones B, G, and M are shown in Figures A.10(a), (b), and (c) ,
respectively. Zonal land uses are shown against time. Three variables
are plotted for each zone:
« Multi-family residential land (acres)
* Single-family residential land (acres)
• Industrial-commercial land (acres).
Projected values beyond 1974 are based upon a hypothetical program of
wastewater investments and simultaneous removal in 1976 of the existing
widespread moratoria. No specific land use or sewer controls have been
postulated. The projections from 1974 to 1985, therefore, do not
necessarily represent a probable course of events, but rather the
development potential of zones totally free of development controls
and under programs of moderate investment in wastewater facilities.
A more quantitative assessment of model accuracy can be made by com-
paring projections of the model with available data for the year 1968.
Comparisons of simulation projections to observed 1968 values* are
presented in Tables A.3 to A.5.
* The 1960 and 1968 "Actual" Values are derived from data collected by
the Council of Governments in its EMPIRIC modeling effort.
100
-------
3000
H
CO
p
1500
H
H
1960
19*65
1970
I 15000
.7500
1975
1980
1985
CO
H
Pi
H
I
w
M
to
Legend;
Single-Family
Multi-Family
Industrial/Commercial
Figure A.10(a). Zonal land use simulation, Zone B
-------
o
to
CO
3
M
H
co
fa
M
H
3000
1500 —
1960
1965
1970
15000
1980
7500
1985
CO
I
w
M
CO
Legend:
Single-Family
Multi-Family
Industrial/Commercial
Figure A. 10(b). Zonal land use simulation, Zone G
-------
3000
H
CO
En
I
1500
"15000
—7500
1960
1965
1970
1975
1980
1985
w
hJ
g
HI
cn
Legend:
Single-Family
Multi-Family
Industrial/Commercial— —- — —
Figure A.10(c). Zonal land use simulation, Zone M
-------
Table A.3. SINGLE-FAMILY RESIDENTIAL UNITS
Zone
A
B
C
D
E
F
G
H
I
J
K
L
M
N
0
1960
Actual
17,390
7,256
44,870
2,894
6,255
37,550
20,940
3,079
46,320
21,260
5,073
5,047
23,560
6,741
101,700
1968
Model
19,380
13,658
56,727
5,252
12,029
49,824
33,012
3,882
50,060
28,946
6,810
6,478
29,800
8,098
99,110
1968
Actual
18,660
13,610
56,220
5,663
17,950
47,410
31,780
6,292
45,800
27,280
14,320
9,963
28,510
8,849
97,550
1968
Model/Actual
1.03
1.00
1.00
.93
.67
1.05
1.04
.62
1.09
1.06
.48
.65
1.05
.92
1.01
104
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Table A. 4 MULTI-FAMILY RESIDENTIAL UNITS
Zone
A
B
C
D
E
F
G
H
I
J
K
L
M
N
0
1960
Actual
11,450
938
4,138
118
1,291
18,300
5,723
136
36,750
2,148
781
324
3,078
36
150,400
1968
Model
12,625
9,295
26,487
4,283
7,983
42,431
20,923
220
44,675
11,135
2,558
1,706
11,027
3,661
155,460
1968
Actual
19,080
3,952
17,800
182
8.195
41,890
27,820
366
63,320
9,686
3,268
1,530
14,850
878
155,980
1968
Model/Actual
.66
2.35
1.49
23.5
.97
1.01
.75
.60
.71
1.15
.78
1.12
.74
4.17
.99
105
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Table A.5. EMPLOYMENT
Zone
A
B
C
D
E
F
G
H
I
J
K
L
M
N
0
1960
Actual
6,130
11,298
60,840
3,198
8,462
41,234
23,043
2,438
124,099
11,198
16,884
3,300
18,940
4,733
404,180
1968
Model
22,164
36,573
103,180
4,824
17,671
85,511
60,238
2,555
124,960
41,215
20,601
21,595
35,886
8,373
427,770
1968
Actual
11,735
24,150
106,445
5,983
20,090
70,250
42,530
4,828
146,400
20,820
27,540
9,517
37,920
12,640
528,400
1968
Model/Actual
1.89
1.51
.97
.81
.88
1.22
1.42
.53
.85
1.98
.75
2.27
.95
.66
.81
106
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l- Residential Construction - Projections of single-family housing
units are generally accurate, except for Zones E, H, K, and L. In
considering general growth patterns, the Zone E discrepancy, though
quantitatively large, follows the actual pattern: the model projects
a doubling, versus the actual tripling, of units. However, there is
in general a notable under-allocation of growth to outlying zones.
This indicates that the accessibility function* — the sensitivity to
travel times — used to evaluate the attractiveness of a zone for
single-family development, may have been too restrictive. Alternative
shapes for the estimated curves are illustrated in Figure A.11
1.0
SENSITIVITY
TO TRAVEL
TIMES
(dimensionless)
0 -
Alternative
Original
INTERZONAL TRAVEL TIME
(minutes)
Figure A.11. Sensitivity to travel time
Multi-family housing projections are not as accurate as those for
single-family units, though most high-growth areas were successfully
simulated. Zones B, C, and D have had large development (D only since
1968), but the model allocated too much growth too soon to these
zones. We believe that this was caused by incomplete data on sewer
service extensions, a major factor in the attractiveness formulation.
Data were obtained for 1960 and 1968 sewer service in the zones. No
consistent information on the interim timing of sewer investments was
available, so sewer service increases were linearly interpolated over
the 1960-1968 period. Examination of the Montgomery County Capital
Improvement Program later showed that major investments in Zones B, C,
and D were made just prior to 1968, setting the stage for subsequent
development. The model simulated development earlier on the assumption
that sewer facilities were actually available in the period 1960-1968.
The ratio of projected to actual for Zone G is 0.75 — the model pro-
jecting a tripling of the 6,000 base year units, versus an actual
quadrupling.
The model under-allocated development to Zone I. The model formulation
* See page 85.
107
-------
of development attractiveness is based upon vacant land and does not
include conversion of low density to high density residential use.
However, in Zone I (Arlington), substantial conversion of single-family
residential land to multi-family use occurred.
The large over-allocation by the model to Zone N versus actual was, at
least in part, attributable to unusually restrictive zoning in force
within parts of the zone.
2. EmploymentDistribution - The development attractiveness formulation
is not generally adequate for the projection of zone employment. Large
under-allocations of employment to Zones I and 0, Arlington and the
District of Columbia, amounted to 120,000 employees. This suggests
that the model does not account for growth of employment in central
areas with little vacant land. While it is difficult to quantitatively
estimate errors arising from central cities, it is a reasonable hypo-
thesis that under-allocation to the two central zones (Arlington and
the District of Columbia) resulted in substantial over-allocation to
several other zones. A remedial alternative is to specify exogenously
employment growth in central zones (without trying to account explicitly
for redevelopment and the influence of the Federal government in the
central Washington zones) and distribute the rest of development to the
rest of the zones where variations in land availability, prices, and
public facilities play a larger part in the location of industrial and
commercial establishments.
The general performance of the relative attractiveness formulation was
encouraging for two reasons: (1) the factors in the formulation were
simple and straightforward, designed to represent only major economic
forces and major policy interventions; and (2) no attempt was made to
calibrate the model with statistically-derived weights or to use
Washington-specific factors (e.g., Federal facility location) to insure
a good "fit" with observed changes.
In order to provide more succinct and generally recognizable measures
of accuracy, 1968 model projections for the fifteen zones were correlat-
ed with observed 1968 values. The coefficients of determination (R2)
are shown below.
R2
Single-family units .98
Multi-family units .98
Emp loyment . 9 8
The statistically-measured accuracy of the 1968 simulation projections
are aided considerably just by initialization of base year (1960)
conditions. The results of correlating projected zonal changes with
observed changes are presented below.
108
-------
R2
Single-family units .59
Multi-family units .50
Employment .12
Considering the simplicity of the attractiveness formulation relative to
statistically-based models fitted to observed historical patterns, the
results are good. They indicate that for residential development'the
formulation has, as hoped, captured the major economic forces behind
the location of new construction.
Comparison of 1960-1968 employment projections (R2 = .12) and 1968
employment level projections (R2 = .98) demonstrated that, relative to
base year conditions, the distribution of employment changed very
little. The factors and weights used in the attractiveness function
for employment location were obviously inadequate to describe quantita-
tively patterns of employment changes within the region.
b. Investment and Policy Tests -
The land use impacts of specific infrastructure investments or invest-
ment combinations were identified by simulating development patterns
under two conditions: with the investment (or policy), and without it.
The differences in development patterns were attributed to the specified
investment or policy.
To test this approach, impacts of a series of investments made in the
1960's and recent sewer control policies were simulated. Tests
included:
• Interstate 66 - A major radial artery in an east-west
direction, connecting with the Capital Beltway. Major
travel time changes in 1964 were from Zone L in Fairfax
County to all zones directly served by the Beltway and
to Arlington-Alexandria and the District.
• The Capital Beltway (Interstate 495) - A 66-mile circumferen-
tial highway completed in 1964. The Be1 ray encircles the
District of Columbia, running through inlying portions of
Montgomery, Prince George's, and Fairfax Counties.
• Potomac Interceptor - A substantial increase in sewer
service for Zone B.
• Moratoria - Restrictions reduced the availability of new
sewer service in Prince George's County zones in 1970, and
in Montgomery and Fairfax County zones in 1972.
109
-------
• Postulated Staggered Removal of Moratoria - The same restric-
tions described above, with one exception: Prince George's
County moratoria are postulated to be removed two years
ahead of others, in 1974.
Impacts were estimated by using 1975 as the forecast year. The results
of the tests are summarized in Table A.6. A striking feature of the
Table is the limited geographic extent of impacts from major investments
as contrasted to the widespread effects of control measures (moratoria).
Moratoria on construction were imposed beginning in 1970 throughout the
counties of Prince George's (Maryland), Montgomery (Maryland), and
Fairfax (Virginia). Most of these are still in effect and their dates
of release are uncertain.
Comparing simulated growth in 1975 with and without moratoria shows that
zones in Prince George's County had less growth, and zones in Montgomery
and Fairfax more, as a result of imposition of moratoria. This high-
lights the importance of timing of controls that may be imposed exten-
sively throughout a metropolitan area. Prince George's County was the
first subject to moratoria, followed over the next two years by Mont-
gomery and Fairfax. This sequence of events yielded the following
impacts in 1975:
• Initial imposition of moratoria within Prince George's
County discouraged new development there.
• Developers turned instead to suburban Montgomery and
Fairfax Counties.
• Additional development in these two counties exacerbated
already large growth rates and heightened sewer problems.
• Subsequently imposed moratoria in Montgomery and Fairfax
Counties were not nearly as effective as in Prince George's.
Developers had established legal commitments for construc-
tion in the form of a backlog of development authorizations.
• The resulting building boom, especially in Montgomery,
has only recently begun to slow — after two years of
moratorium controls.
The impacts of moratoria were examined further by extended simulations
to 1985 and by making the assumption that moratoria would be lifted in
1976. The simulation of land use in Zone D (Figure A.12(a)) illustrates
effects of the moratoria. Residential construction continues through
the first years of the moratorium, slowing in 1974, only to resume
growth upon the 1976 release of the moratorium. This should be con-
trasted with the Zone E (in Prince George's County) simulation in
Figure A.12(b). Zone E development is halted by the 1970 moratorium,
110
-------
Table A.6. INVESTMENT AND POLICY IMPACTS
Zone B Sewer Staged
1-495 (Potomac Release
1-66 (Capt'l. Bltwv.) Interceptor) Moratoria Moratoria
N^ Inve s tmen t
\Policy
Zone \
A
B
C
D
E
F
G
H
I
J
K
L
M
N
0
*
cfl o
•H O
4-1 QJ 1 CO
C co -to
0) P 4J P
T3 CO
•H tJ S T3
CO S t3 C
0) CO {3 Cd
fXS iJ H jJ
-H-
•
H i
CO O
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JJ CO Id)
SCO • CO
P *J S3
•t) CO
•H T3 3 T3
CO t3 T3 C3
0) cO C cd
(*! i-4 M tJ
+
-
-
-
*
rH 1
CO O
•H 0
4-1 CU 1 OJ
SCO • CO
CD w SD
T3 CO
•H t> 3 t3
CO C T3 C
co cd C cd
tA iJ M ,J
+f
rH B
CO O
•H 0
4J CO 10)
CM -CO
CO p 4J |3
T3 CO
•H T3 3 13
CO C T3 £
CO cd C cd
OS iJ H ,-J
+
-H-
-
—
— _
| -H- -H-
++
-H- +
-H-
++
H i
cd o
•H 0
4J CD 1 CU
a co -co
CO p 4J P
T3 CD
•H T) 3 *O
CO ti *& &
co cd C cd
pj ^ M iJ
__ _
—
— _
++
-H- -H"
-H- -H-
— _
-
— —
— _
—
Impacts as of 1975: Blank = little or no (0-5%) deviation from base run values; +,- =
moderate (5-15%) deviation from base run values; ++,— = large (greater than 15%) deviation
from base run values.
-------
1000
5
H
H
1965
1970
1975
1980
1985
ho
Legend;
Single-Family-
Multi-Family
Industrial/Commercial-
Figure A.12(a)
Contrasting zonal land use effects
of moratoria (1970-1976),
Zone D
-------
1000
-J.OOOO
5
M
H
I960
1965
1970
SgOW»W«W!*&sa»
1975
1980
1985
U>
Legend:
Single-Family—
Multi-Family • -
Industrial/Commercial —
Figure A.12(b).
Contrasting zonal land use effects
of moratoria (1970-1976),
Zone E
-------
but it is important to note that growth following the moratorium is
at a more rapid rate than before. By 1985, the constraining effect of
Zone E moratoria are virtually nullified by more rapid growth occurring
without further controls.
Further evidence of the importance of timing is demonstrated in the
impacts of removing moratoria in one county (Prince George's in this
example) earlier than in other suburban counties. As shown in
Table A.6, significant changes are observable for all three counties
involved, but in the opposite direction of all impacts of the simul-
taneously released moratoria.
Figure A.12 presents the results of simulating Zone E land use under
three different conditions. Figure A.13(a) illustrates Zone E land use
from 1960 to 1985 when moratoria are simultaneously released throughout
the metropolitan area. Figure A.13(b) illustrates Zone E land use
when the sewer moratorium in Zone E is removed two years earlier
(in 1974) than other moratoria. Figure A.l3(c) illustrates Zone E land
use when an additional investment in Zone E sewer service is made to
corre'spond with a 1974 removal of the Zone E moratorium. The contrasting
rates of post-1974 growth in Zone E emphasize the importance of the
relative timing of sewer service restrictions and investments.
In sum, the simulation results suggest that relatively isolated but
significant impacts result from major highway and sewer investments.
Shorter-term, though metropolitan-wide and significant, land use
impacts result from the imposition of moratoria in suburban counties.
114
-------
1000
CO
M
—15000
3
o
B
to
1970
1975
Ul
1980
1985
Legend:
Single-Family
Multi-Family
Industrial/Commercial
Figure A.13(a).
Contrasting effects of different sewer controls
on Zone E: simultaneous removal
-------
co
H
en
I
H
H
1000—,
15000
1960
1965
1970
1975
1980
1985
5
I
H
CO
Legend:
Single-Family
Multi-Family-
Industrial/Commercial— — — —
Figure A.13(b). Contrasting effects of different sewer controls
on Zone E: selective removal
-------
»-*
1000 — I
H
—15000 OT
1960
1965
1970
1975
1980
1985
a
i
Legend:
Single-Family
Multi-Family-
Industrial/Commercial
Figure A.13(c). Contrasting effects of different sewer controls
on Zone E: early removal and investment
-------
V. REFERENCES
1. A. L. Pugh, III, DYNAMO III Users' Manual (Draft), available from
Pugh-Roberts Associates, Inc., 5 Lee Street, Cambridge, Mass.
2. Environmental Impact Center, InCn. , "A Methodology for Assessing
Environmental Impact of Water Resources Development," Cambridge,
Mass., November 1973. NTIS Publication No. PB 226-545.
3. U.S. Bureau of the Census, Current Population Reports Series,
p. 25, No. 432.
4. Metropolitan Washington Council of Governments (hereafter 'COG'),
"The State of the Region," Vol. 2, pp. 20-21 (Composite Table).
5. COG, "The Changing Region: Policies in Perspective - A Comparison
of Plans and Policies with Development Trends," 1969, p. 16.
6. COG, "The State of the Region," Vol. 2, p. 26.
7. National Capital Region Transportation Planning Board: Information
Report No. 42, "Regional Employment Characteristics," September
1971, pp. 2-15.
8. COG, "The Changing Region: Policies in Perspective - A Comparison
of Plans and Policies with Development Trends," p. 31.
9. COG, "The State of the Region," Vol. 2, p. 27.
10. Ibid., p. 28.
11. Ibid., p. 23.
12. Wilbur Smith & Associates, "Maryland Capital Beltway Impact Study,
Final Report, Washington SMSA and Maryland Counties," June 1968,
pp. 11-18.
13. Ibid.
14. National Capital Region Transportation Planning Board: Information
Report No. 29, May 1970, Figure 1.
15. COG, "The State of the Region," Vol. 3, p. 10, Table 2.
16. COG, "The State of the Region," Vol. 1, p. 7.
17. COG, "The State of the Region," Vol. 2, p. 33.
118
-------
18. COG, "The Changing Region: Policies in Perspective - A Comparison
of Plans and Policies with Development Trends," pp. 23-25.
19. COG, "The State of the Region," Vol. 2, p. 31, Table 13.
119
-------
APPENDIX II. MODEL LISTING
Appendix II. Contents
A. Population Sector
B. Employment Sector
C. Business-Serving Industry Employment
D. Industrial Sector
E. Land Use Accounts Sector
F. Sewer Service Sector
G. Attractiveness For Residential
Development 140
H. Travel Times Sector 141
120
-------
APPENDIX II
A. POPULATION SECTOR
ACS=6
ACS - AGE CLASSES
POP.K(1)=POP.J< 1 ) +DT*(N6RTH.JK-AGOUT.JK( 1
DTH.JM 1) +INMI6. JK ( 1 > )
POP - INITIAL POP (MFN)
NBRTH - NET BIWTH HATE (MEN/YK)
AGOUT - LOSS DUF TO AGING (MfcN/YH)
DTH - DEATHS BY AGE CLASS (MEN/YR)
- INMIG OF AGE CLASS 1
POP.K(AC2)=POP.J(AC^)+OT*MAGOUT.JK(AC2-1)- 3» L
AGOUT { AC? )-DTH.JK(ACH) * INMIG. JK ( AC2) )
POP(AC)=IPOP(AC) 3,2* M
IPOP-588166/160864/135059/599715/333233/122390 3,3, T
HOP - INITIAL POH (MEN)
AGOUT - LOSS DUF TO AGING (MEN/YR)
DTH - DEATHS BY AGE CLASS ( MEN/YR)
INMIG - INMIG OF AGE CLASS 1
TTPOP.K=SUMV(POP.Kt 1 »ACS) 4. A
TTPOP - TOTAL POPULATION (MEN)
POP - INITIAL POP (MFN)
ACS - AGE CLASSES
AGOUT. KL(AC)=POP, K (AO/LACUC) 5f R
LAC=14/6/5/20/20/lE30 5.1. T
AGOUT - LOSS DUE TO AGING (MEN/YR)
POP - INITIAL POH (MFN)
LAC - LENGTH OF AGE CLASSES (YHS)
OTH.KL(AC)=DTHR(AC)*POP.K(AC) 6» R
DTHR=1.9E-3/.3E-3/.7E-3/l,5F-3/6.6E-3/64.8E-3 6.1. T
DTH - DEATHS BY AGE CLASS (MEN/YR)
DTHR - TABLEf DEATH RATES (1/YR)
POP - INITIAL POP (MFN)
NBRTH. KL=TTRTH.K*(i-DTINF.K) 7» R
NBRTH - NET BIRTH HATE (MEN/YR)
TTBTH - TOTAL BIRTHS (MFN/YR)
DTINF - DEATH RATE OF INFANTS (1/YR)
TT8TH.K=SUMVV(POP.K»?f ACS»BRTH.K»2»ACS) 8» A
TTBTH - TOTAL 8lRTHS'
121
-------
BRTH,K(AC2)=TBR.K*IBR(AC2>
BRTH - 8IRTH RaTE PY AGE CLASS(IXYR)
TPR - TREND IN BIRTH RATES (0)
IBR - INITIAL BIRTH RATES Cl/YR)
TBR.K=TARHL(TTBRf TIME .K t I960 » 1985 »5) 10» A
TTBR=l/.8915/i6067/.B306/;8471/.856t> 10. 1» T
IBR=Q/30E-3/130E-3/SOE-3/,lE-3/0 10.3* T
TPR - TREND IN BIRTH RATES (D)
TTBR - TABLE* RlRTM RATE TREND (D)
jIMf _ EMPLOYMENT SECTOR RELATIVE WAGES
IBR - INITIAL BIRTH RATES (1/YR)
DTINF - DEATH RflTE Of- JNEANTS (1/YR)
1NFMRT . INFANT MORTALITY (1/YR)
TDDTIH - TREND IN INFANT DEATHS (D)
TDDTIN.K=TABHL
-------
1NMIG.KL(AC2)=POP.KUC2)*(EFA(AC2)+EF6(AC2>«
UEFEM.K)
EFA=0/-.0091/.0037/,0017/-,0061/-.0062
EFB=0/-,499/-1.31/-,30B/-,l34/-.0«
INMIG - INMIC. OF AGE CLASS 1
POP - INITIAL POP (MEN)
EFA - MIG FACTOR A (0)
EFB - MIG FACTOR B to)
UEFEM - UNEMPLOYMENT EFFECT ON MIG (D)
B. EMPLOYMENT SECTOR
WKPP.KsTTPOP.K-POP.KU>
WKPP - WORKING POPULATION (MEN)
TTPOP - TOTAL POPULATION (MEN)
POP - INITIAL POP (MEN)
16,It
16.3f
T
T
17t A
REWKPP.KsTTFPWK.K/WKPP.K
REKKPP - RATIO OF EMPLOYED TO WORKERS
TTEPWK - TOTAL EMPLOYED WORKERS (MEN)
WKPP - WORKING POPULATION (MEN)
TTEPWK. K=EXINWK,K+BSSVwK.K+HHSVWK.K+GEMWK.K
TTEPWK - TOTAL EMPLOYED WORKERS (MEN)
EXINWK - EXP IND WORKERS (MEN)
8SSVWK - INIT BS SERV WKRS (MEN)
HHSVWK - INITIAL HSHOLO SERVG wlKRS (MEN)
6FMWK - GOVTf EDUCt MILIT WORKERS (MEN)
AGLFPR.K=TTLRFR.K/TTPOP.K
AGLFPR - AGGREGATE LABOR FORCE PARTICIPATION
TOTAL LABOK FORCE FORECAST
TTLBFR - TOTAL LABOR FORCE (MEN)
TTPOP - TOTAL POPULATION (MEN)
TTl BFP.K=SUMVV(L8FRPR.K»?»ACS*POP.K»2»ACS)
TTL8FR - TOTAL LABOR FORCE (MEN)
ACS - A6E CLASSES
POP - INITIAL POP
19» A
20t A
(D)
LBFRPR.K(AC2)slNLBFR(AC2)*(LKA
-------
UNEPWK.K=TTLBFH.K-TTEP*K.K 23f *
UNEPWK - UNEMPLOYED WORKERS (MEN)
TTLBFR - TOTAL LABOR FORCE (MEN)
TTEPWK - TOTAL EMPLOYED WORKERS (MEN)
LCUNEP,K=UNEPWK.K/TTLBFR,K 24. A
LCUNEP - LOCAL UNEMPLOYMENT (D)
UNEPWK - UNEMPLOYED WORKERS (MEN)
TTLBFR - TOTAL LABOR FORCE (MEN)
SLUERT.K=SMOOTH(LCUNEP.K,1) 25» A
SLUEPT - SMOOTHED LOCAL UNEMPLOYMENT (D)
LCUNEP - LOCAL UNEMPLOYMENT (D)
UEFEM.K=MIN(DUER.K,DDUER.K) 26» A
UEFEM - UNEMPLOYMENT EFFECT ON MI6 (D)
DUER - DIFFERENCE IN UNEMP RATES (D)
DDUER - DELAYED D1FF IN UNEMP RATES (D)
DDUER.K=SMOOTH(OUER.K»2) 27» A
DDUER - DELAYED UIFF IN UNEMP RATES (D)
DUER - DIFFERENCE IN UNtMP RATES (D)
28f A
DUER - DIFFERENCE IN UNEMP RATES (D)
LCUNEP - LOCAL UNEMPLOYMENT (0)
NATUER - NATIONAL UNEMPLOYMENT (Q)
NATUER=.05 28.lt C
TIME=196Q 28.2» N
NATUFR - NATIONAL UNEMPLOYMENT (Q)
TIME - EMPLOYMENT SECTOR RELATIVE WAGES
R6RLWG.K=R6RLWG.J*DT*(1/10)(TGRLWG.J-RGRLWG.J) 29» L
RGRLWGsINRRWG 29.2» N
INRRWG=1.1 29.3« C
RGRLWG - REGIONAL RELATIVE WAGE (D)
TGRLWG - TARGET RELATIVE "AGE (D)
TGPLWG.K=.4*,3*(AGAVWG.K) 30t A
TGRLWG - TARGET RELATIVE WAGE (D)
AGAVWG - AGGREGATE AV. WAGES ($/MAN)
124
-------
AGAVWG,K=(EXINWG.K+BSSVWG.K+HHSVWG.K+GEMWG.K)/ 31» A
(TTEPWK.K)
AGAVWG - AGGREGATE AV, WAGES (4/MAM
EXINWG - EXP IND WAGES ($)
BSSVWG - BUS SERV IND WAGES ($)
HHSVWG - HSHD SERV WAGES ($)
GEMWG - GOV»FO»MlL WAGES ($)
TTEPWK - TOTAL EMPLOYED WORKERS (MEN)
EXINWG,K=(RGRLWG,K) (INEXW6) (EXINWK.K) 3?» A
INEXWG=?,49 32.1» C
EXINWG - EXP IND WAGES (*)
RGRLWG - REGIONAL RELATIVE WAGE (D)
INEXWG - INITIAL EXP WAGES (S/MAN)
EXINWK - EXP INO WORKERS (MEN)
BSSVWG.K=(RGRLWG.K5 (INBSWG)(BSSVWK.K) 33» A
INBSWG=2,65 33»1» C
BSSVWG - PUS SERV IND WAGES ($)
RGRLWG - REGIONAL RELATIVE WAGE (D)
INBSWG - INITIAL BUS SERV WAGES (S/MAN)
BSSVWK - INIT BS SERV WKRS (MEN)
HHSVWG.Ks(RRLMDWG.K)(INHHWG)(HHSVWK.K) 34. A
34.1, C
HHSVWG - HSHD SEPV WAGES ($)
INHHWG - INIT HSHD SEHV WAGES ($/MAN)
HHSVWK - INITIAL HSHOLO SF1RV6 WKRS (MEN)
GEMN6.K=(RGRLWG,K)(INGEMW)(GEMWK.K) 35t A
INGEMW=2.51 35.If
GEMWG - GOV»ED»MIL WAGES ($>
RGRLWG - REGIONAL RELATIVE WAGE (D)
INGFMW - INIT GOV»ED»M1L WAGES ($/MAN)/
GEMWK - GOVT, EOUC* MILIT WORKERS (MtN)
RRLMDta.K=RRl.MD«,J + DT*(l/lO) ( TRLMDW. J-RRLMUV. J ) ^'0L
RRLMOWsIRLMl
IRLMDW=1.08
RRLMOWsIRLMDW O^'T' r
36,j• C
«ptur..ii. - DEPENDENT WAGE (D)
TPLMDW - TARGET RELATIVE LABOR MARKET DEPENDENT WAGE
(D)EXPORT INDUSTRY EMPLOYMENT
IRLMDW - INITIAL RELATIVE LMD WAGE (D)
TRLMDW.Ks.5?B*(,3) (AGAVWG.K)*(-2.5) (SLUERT.K) 37» A
TRLMOS - TAR6LT RELATIVF LABOR MARKET DEPENDENT WAGE
(D)EXPORT INDUSTRY EMPLOYMENT
AGAVWG - AGGREGATE AV, WAGES (S/MAN)
SLUERT - SMOOTHED LOCAL UNEMPLOYMENT (D)
125
-------
KXINWK.KsEXlMWK.J*(OT)(EPCH.JK)
EXINwK=lNFXWK
INEXWK=116234
EXINWK - EXP IND WORKERS (MEN)
EPCH - EMPLOYMENT CHANGE. (MEN/YR)
INEXWK - INITIAL EXP WORKERS (MEN)
EPCH.Ks(PEPCH.K)(EXINWK.K)
EPCH - EMPLOYMENT CHANGE (MEN/YR)
PEPCH - FRACTION EMPLYMNT CHG (l/YR)
EXINWK - EXP INO WORKERS (MEN)
PEPCH.K=GRLBDM-(CSLES)(RLCST.K)
PEPCH - FRACTION EMPLYMNT CHG
GRLBDM - GRWTH RT IN L8 DEMAND
CSLES - COST ELASTICITY(D)
RLCST - RELATIVE COST (D)
RLCST.K=TTCSIX.K-1
RLCST - RELATIVE COST (0)
TTCSIX - TOTAL COST INDEX (D)
(l/YR)
(MEN/YR)
38» L
38,1» N
38.?, C
39, A
40* A
41, A
TTCSIX.K=CNCSFC*MKACFC*MTACFC+(LBCSWTJ (RGRLWG.K)
GRLBDMs.Ol
CSLES=.4
CNCSFC=.7519
MKACFC=.027
MTACFC=,018
LBCSWT=,2?
TTCSIX - TOTAL COST INDEX (D)
CNCSFC - WEIGHTED REG CNST CST FCTR (D)
MKACFC - WGHTD REG MKT ACCESS FCTR (D)
MTACFC - WGHTD REG MTL ACCESS FCTR (D)
L8CSWT - WEIGHT FOR REG LB CST FCTR (D)
REGIONAL RELATIVE WAGE (D)
GRWTH RT IN LB DEMAND (MEN/YR)
COST ELASTICITY(O)
42i A
42.2, C
43.3, C
4?,4, C
42.5, C
42.6, C
42.7, C
GRLBDM -
CSLES -
GtMWK.KsTABHL(TGtM,TIME.Kli960,1990»10)
TGEM=316836/463430/620000/770000
GEMWK - GOVT, EDUCt MILIT WORKERS (MEN)
TGEM - TABLE, GOV»ED»MIL EMPL (M£N)
TIME - EMPLOYMENT SECTOR RELATIVE WAGES
431 A
43,2, T
126
-------
C. BUSINESS-SERVING INDUSTRY EMPLOYMENT
BSSVWK=INBSWK
BSSVWK - INK BS SERV WKRS (MEN)
HSIGR - RUS SERVG INO GRWTH HT (MEN/YR)
44> L
44.1, N
44.2» C
RSIG» - RUS SERVG iNU GRwTH RT (MEN/YR)
LBAVML - LABOR AVAILABILITY MULTlP (D)
OF3SIGR - DESD OS SEN GRwTH RT (M£N/YR)
DBSIGP.K=TABHL(TDBSG,DAHSWK.Kt-lE5f 1E5»1E5)
TDBSG=-1E5/0/3E4 (MEN/rH)
DHSIGP - DESU «S SER GR^TH RT (MEN/YR)
TDBSG - TABLttDKSD HS SERV GRWTH HT (MEN/YR)
DAHSWK - OESRD ADD HUS SEPVG WKRS (MEN!
DABSWK.KsDBSWK.K-HSSVWK.K
DABSWK - DESRD AnU BUb SERVG «KRS (MEN)
DBSWK - DESHD BUS SfRVG WKRS (MEN)
BSSVWK - INIT BS SERV WKRS (MEN)
46»
46.
WBSSs.165
DBSWK
WBSS
WKISNV
DESRD BUS SERVG fcKRS (MEN)
DESD BS StRV WKRS PE& WKR (D)
WKRS IN INDUSTRIES SERVED (M&N)
SERVING INDUSTRY EMPLOYMENT
WKISNV.K=TTEPWK,K-BSSVWK.K
WKISNV - WKRS IN INDUSTRIES SERVED (MEN)
SERVING INDUSTRY EMPLOYMENT
TTEPWK - TOTAL EMPLOYED WORKERS (MEN)
BSSVWK - INIT BS SEHV WKRS (MEN)
47» A
48» A
48.1. C
HUUSEHOLD-
49«
HOUSEHOLD-
HHSVWK.KsHHSVWK.J+DT»(HSl6R.J)
HHSVWK=INHSWK
IMMSWK=3Q1646
HHSVWK - INITIAL MSHOLD SERVG WKRS
HSIGR - HSHOLD SERVG IND tiKWTH RT
(MEN)
(MEN/YR)
50. L
50.1« N
50.2» C
HSIGR,K=(LBAVML,K) (DHSIG.K) CIIJVr^
HSIGR - HSHOLD SERVG INO GRWTH RT (MEN/YR)
LBAVML - LABOR AVAILABILITY MULflP (D)
OHSIG - DESRD HSHOLD SERV IND GRWTH RT (MkN/YR)
51» A
127
-------
DHSlG.K=TABHL(TDHS6»nAHSw.K,-lE5»3E5,iE5)
-lF:5/0/3E4/6E4/9F-:4 (MFN/YR)
DHSIG - DESHD HSHULO SERV IND GRWTH RT (MEN/YR)
TDHSG - TABLE, DESO HSHO SER GRw/TH pT
DAHSW - DESHD A[)0 HSHO|,.U SERV6 WKRS (MEN)
52» A
52.3,
DAHSW.K-OHSWK.K-HHSVK'K.K 53» A
DAHSW - DESRD ADD HSHOl0 SEHV6 WKRS (MEN)
DHSWK - DESRB HSHLD SERVG WKRS
HHSVWK - INITIAL HSHOLD SERVG ^KRS (MEN)
DHSWK,KsWHSS*TTPOP,K*TRWHSS.K 54» A
WHSS=,1956? (D) 54.I, C
DHSWK - DESRB HSHLD SERVO WKRS
WHSS - DESD HSHD S£KV WKR Pt« CAPITA
TTPOP - TUTAL POPULATION (MEN)
TRWHSS»K*TA8HL
-------
CHlNLN.KL(Z)=DELAYPi INCO(M.K(Z) »CINDEL»ICIPZ.K(Z) ) 62* 3
I.E. 62.2* C
CMINLN - COMPLETED I NO CONST (ACRES/YR)
INCON - IND CONST 1H PROCESS (ACRES)
CINDEL - CONST DELAY FOR INE) (YRS)
INCON.K(7)=MAX (0* (ZINDM.K(Z)-(ICIPZ.K(Z)) (OPZIC)))» 63* A
ZA1LML.KCZ)
INCON(Z)=IICIP£(Z) 63.2, N
IIClPZ = 218/162/«?80/«9/133/S4/117/20/68/10S/B7/106/ 63.3t T
10B/0/29
DPZIC=1.0 63.5, C
INCON - IND CONST IN PROCESS (ACRES)
ZINDM - ZONAL IND LAND DEMAND (ACRES)
DPZIC - OEVEL'S PERCEPT OF IND CONST IN PROCESS (D)
ZAIL^L - AVAIL I^D LAND MULTIP (0)RESIDENTIAL SECTOR
IICIP7 - INITIAL CONST ON INO LAND (ACRES)
ZINDM.K(Z)=(ATIND.K(Z)/TTATjN.K)*INLDDM.K* 64* A
INCNF.K(Z)
ZINDM - ZONAL IND LAND DEMAND IACRFS)
ATIND - ATTRACTIVENESS FOR INDUSTRY (D)
TTATIN - TOTAL ATTRACTIVENESS FOR IND <0)
INLDDM - REG'L IND LAND DtMMAND (MEN)
INCNF - INITIAL IND DENS (ACRES/MAN)
INCNF.K (Z)=CNFM(Z)«IICNF.M7> 65f
65 .
65.2. T
INCNF(Z)=IINCN(Z) 65.1, N
1.6/.71/1.H7/1.2
INCNF - INITIAL INI' DENS (ACRES/MAN)
CNFM - IND DEC'S ADJ FACTOR A
UNDEVR - UNDEVELOPED RATIO (0).
UNDFV - UNDEVELOPED LAND <*C«tS)
ZDL ' - ZONAL DEVELOPABLE tANU (ACRES)
129
-------
UNDEV.K(Z)=ZDL.K(Z)-(INDLND.K(Z>+TRL,K(Z))
68* A
ODEV(Z))
UNDFV
ZDL
INDLND
TRL
ODEV
UNDEVELOPED LAND (ACHES)
ZONAL DEVELOPABLE LAND (ACRES)
INITIAL IND LAND (ACRES)
TUTAL ZONAL HESID LAND (ACRES)
TABLE* OTHER DEVEL FRACTION
-------
HSU.MD»7>*HSU,J<0»Z)+DT*(CMHSU.JK(D,Z)-CLHSU€JK
HSU(0*Z)=IHU(D»Z) 76,2, N
lHU<»tl)sU450/l4390/3000 76.3, T
IHU(*»2)s938/6256/1000 76.4, T
lHU(*»3)s4138/40370/
-------
PPHSU.K(D)=TABHL(1PPHU(«,D),TIME.K»1960»1968»8) 80» A
TPPHU(*,l)=2.60/2.76 80.2, T
TPPHU(*,2)s3.82/3.52 80.3, T
TPpHU(«,3)=3.82/3.52 80,4, T
RCF(*»1)=.Q2/.08/.0« 80.6, T
RCF(**2)=.015/.OH/.08 80.7, T
NCF(*»3)S.015/.08/.08 80.8, T
RCF(*»4)=,02/.08/.08 80.9, T
RCF(*»5)=.015/.08/.08 81.1, T
PCF{*»6)=.015/.08/,08 81.2, T
RCF<*»7)=.015/.08/108 81.3, T
RCF(*»8)s.Oa/.08/.08 81.4, T
RCF(*»9)=.015/.08/,08 81.5, T
RCF(*,10)=.015/.08/.08 81.6, T
RCF(*»ll)=,015/.Oe/.08 . 81.7, T
RCF(*»12)=,015/.U6/.00 81.8, T
RCF<*»13)=.015/.08/.08 81.9, T
RCF<*,14)=.02/.08/.Q8 82.1, T
RCF<*»15)=.0085/.043/.043 82.2, T
PPHSU - MEN PER UNIT (MEN/UNIT)
TPPHU - TABLEi MEN PER UNIT (MEN/UNIT)
TIME - FMPLOYMFNf SECTOR RELATIVE WAGES
RCF - RESID CONST FACTORS UC«£S/MAN)
CMHSU.K|_
RC - RESID CONST (UNITS)
DRC - DESIRED RESlO CONST (UNITS)
ATTH - ATTRACTIVENESS TO HOUSING (D)
TTATTH - TOTAL ATTRACTIVENESS FOR HSIN6
TARLML - TOTAL AVL RES LAND MULTIP (o)
DRC.K(D)=TTHSU.K (D)*RVCML..K(D) 85, A
DRC - DESIRED RESID CONST (UNITS)
TTHSLI - TOTAL HOUSING UNITS (UNITS)
RVCML - RESID VACANCY MULTIP (D)
132
-------
RVCML.K(D)=TABHL(TRVCM(*»D) .RVC.K(D) ,-.2, .3, ,05) 86, A
TRVCM(*tl)=.06/.055/,05/,05/.03/.008/,002/lE-15/ 86.2, T
1E-15/1E-15/1E-15
TRVCM(*»2)=.06/.055/.05/.05/,Q3/.OG8/,002/1E-15/ 86.4, T
1E-15/1E-15/1E-15
TRVCM{*»3)=.OQ8/.006/.OOW,OOH6/.OOl2/.0008/iE-l5/ 86.6, T
1E-15/1E-15/1E-15/1E-15 ,
RVCML - RESID VACANCY MULTIP (0)
TRVCM - RESID VAC MULTlP (D)
RVC - RESID VACANCY (D)
RVC.K(D)sl-{THSUD.K(D)/TTHSU,K(0) ) 87, A
RVC - RESID VACANCY (0)
THSUD - TOTAL HOUSING DEMANDED (UNITS)
TTHSU - TOTAL HOUSING UNITS (UNITS)
THSUD. K(0)=TTRP.MD)/PPHSU.K(D) ««» A
THSUD - TOTAL HOUSING DEMANDED (UNITS)
TTRP _ TOTAL POP BY RESID PREF (MEN)
PPHSU - MEN PER UNIT (MEN/UNIT)
TTRP,K(D)sSUMV(RPF.K(*»0) ,2, ACS) 89, A
TTRP - TOTAL POP 6Y RFSIO PREF (MEN)
RPF - RtSID POP FRACTION PREFERRING LOW DENS
(MEN)
ACS - AGE CLASSES
RPF.K
-------
TARLML.K(0)=MTN(1»TAVLNR,K(D)/(DRC.K(D)*PPHSU.K(0)» 93* A
ARCF(D)))
ARCF=.02/.OH/.Od 93.2, T
TARL.ML - TOTAL AVL RES LAND MUL^IR
RECLN - RECREATIONAL LAND (ACHES)
RECLN.K(Z)=TABHL(TRECLN(*fZ),TIME.K»1960»1968,8) 96, A
TRECLN(*,1)=3000/12000 96.2, T
TRECLN{»,?)=4000/10000 96.3, T
TRECLN(^»,3)=10000/13000 96.4, T
TRECLN<«,4)=3000/5000 96.5, T
TRECLN(«,55=2000/3000 96.6, T
TRECLN(*,6)=4000/4000 96.7, T
TRECLN(*,7)=2000/3000 96.8, T
TRECLN(»,8)=2000/5000 96.9, T
TRECLN(»,9)=2000/2000 97.1, T
TRECLN(»,10)=700/1000 97.2, T
TRECLN(*,11)=700/1500 97.3, T
TRECLNt*,125=200/9000 97.4, T
TRECLN(»,13)=700/1500 97,5, T
TRECLN(*,14)=700/1000 97.6, T
TRECLN(<*,lh)=9000/9000 97.7, T
RECLN - RECREATIONAL LAND (ACHES)
TRECLN - RECREATIONAL LAND (ACRES)
TIME - EMPLOYMENT SECTOR RELATIVE WAGES
134
-------
ZUL.K(Z)=ZOL.K(Z)/(l*CDtV(/» 98* A
OOEVs,25/,25/.2h/.2b/.2b/.25/.2D/.2b/.25/.25/.25/ 9fi.l»
.?5/.25/.H5/.=SUMVENS NON-SEwERED
AVAL - ZONAL AVAIL LAND (ACRES)
RCF - RESID CONST FACTORS (ACRES/MAN)
PPHSU - MEN PER UNIT (MEN/UNIT)
Cl.HSU,KL(1*Z)=0 103. R
CLHSU - CLEARED LOW DENS NON-SEwERED
CLHSU,KL(3»Z)=0 10*» R
CLHSU - CLEARED LOW DEMS NON-SEWEREO
SAT,K(Z)s(TRL.K(Z)*lNDLND,K(Z))/ZUL.K(Z) 105* A
SAT - ZONAL DEVEL SATURATION
TRL - TOTAL ZONAL HESID LAND (ACRES)
INDLNO - INITIAL IND LAND (ACRES)
ZUL - ZONAL USABLE LAND (ACRES)
135
-------
F. SEWER SERVICE SECTOR
TTCAP
FTCAP
TTCAP
TTCAP
TTCAP
SEWCAP.K(Z) =TABHL(TTCAP(«'»Z) ,riME.K, 1960,1968,8) + 106, A
STEP(ITCAP(Z),1TCI(Z))
TTCAP(»,1)=2919000/5728000 106,3, T
TTCAP(tt»?)=9447000/l4796000 106,4, T
TTCAP(»,3)=30384000/47309000 106,5, T
4) s7R5600()/2?71 7000 106,6, T
5)=65R4000/12923000 106,7, T
6)=31762000/37113000 106,8, T
7)al8013000/28480000 106,9, T
B)=141000/1565000 107.1, T
9)=20786UOO/23743000 107.2, T
10)=10744000/16958000 107,3, T
11)=2140000/4717000 107.4, T
12)=315000/4897000 107.5, T
13)=9296()00/I52460QO . 107,6, T
14)=3633000/46B5000 107,7, T
15)=r>9280()00/6252lOOO 107.fi, T
ITCAP=630000/1500000/BOOOOOO/2500000/2000000/ 108,1, T
4400000/3400000/22000U/3400000/190QOO0/520OOO/
540000/1600000/510000/0
ITCI=1976/1976/1976/1976/1976/1976/1976/1976/1976/ 106.4, T
1976/1976/1976/1976/1976/1976
SEWCAP - SEWER TREATMENT CAPACITY (GAL)
• TREATMENT CAPACITY (GALS)
• EMPLOYMENT SECTOR RELATIVE WAGES
• INVESTMENTS IN TRTMT CAP (GALS)
> TIMF OF 1RTNT CAP INVSTMl (YR)
TTCAP(*<
TFCAP(«>
TTCAP(*,
T fCAP<*<
TTCAP(«>
TTCAP
TIME
ITCAP
nci
SWUSE.K(Z)=ZEPWK.K(Z)»INSWF+ZRP.K(1»Z)*RSWF(1)* 109, A
ZRP.K(?,Z)*RSWF(2)
HSWF=40/70 109.2, T
INSWF=30 109.3, C
SWUSE - SEWER USE (GAL)
ZEPWK - ZONAL EMPLOYMENT (MEN)
INSWF - IND SEWER USE FACTOR (6ALS)
ZRP - ZONAL RESIU POP (MEN)
RSWF - RESIO SEWER USE FACTOR (GALS)
ZRP.K(D,Z)=HSU.K(D»Z)»PPHSU.K(l» 110, A
ZRP - ZONAL P-FSIO POP (MEN)
HSU - INITIAL HOUSING UNITS (UNITS)
PPHSU - MEN PER UNIT (MEN/UNIT)
EXCAP.K(Z)=MAX(1,SEWCAP.K(Z)-SWUSE.K(Z)) 111* A
EXCAP - EXCESS CAPACITY (GAL)
SEWCAP - SEWF:R TPEATMENT CAPACITY (GAL)
SWUSF - SEWEH USF (GAL)
136
-------
IAVLNI .K > =1/1/1/1/1/0/0/0/0/1/1
LPOLT (*»6) =1/1/1/1/1/0/0/0/0/1/1
LPOLT (*»?)=!/ I/ I/ 1/1/0/0/0/0/1/1
LPOLT (»tH) =1/1/1/1/1/0/0/0/0/1/1
LPOLT (»,Q)=l/l/l/l/l/l/l/l/l/l/l
I PQLT<«, 10) =1/1/1/1/1/1/0/0/0/1/1
IPOLT<*,] i)sl/l/i/l/l/
LPOLT (». 13) si/ l/l/ I/ IV 1/0/0/0/1/1
LPOLT < * « 1 4 ) = 1 / 1 / 1 / 1 / 1 / 1 /O/ O/O/ 1 / 1
i POI
LPOLT
TIME
- i. OCAI st'wea HOOK-UP POLICY ,
ASVAR.K(Z))
SEWSW=0
AVLNI - ACTUAL AVAIL IND LAND (ACRES)
SEWSW - SEWER SWITCH (D)
SALI - SANCTIONED AVAIL LAND (ACRES)
ASVAR - AVAIL SERVICE AREA
117, A
117.2* C
137
-------
ASVAR.K(Z)sMAXU» (SVAREA.K(Z)-(lNDLND.K(Z)+RL.Ka, 11«« A
Z)+RL.K(2,Z) ) (I+OOFVUM ) )
ASVAW - AVAIL SERVICE. AREA CACHES)
SVAREA - TOTAL SERVICEAREA (ACRtS)
INDLNH - INITIAL INI) LAND (ACRES)
RL - RESIDENTIAL LAND (ACRtS)
ODEV - TABLE* OTHER OEVEL FRACTION (0
SVAREA,K(Z)=TABHL(TSAR(*,Z),TIME.K»i960,1976,16)+ 119» A
STEP(ISA(7)tlSAT(Z))
TSAR(*»1)=:U06/22941 119.2* T
TSAR(»»2)=8851/237U 119,3« T
TSAR(»»3)=?4096/43tt66 119.4, T
TSAR(*t4)=546/123*9 J19.5* T
TSAR<*.»5)s6088/lB610 119.6, T
TSAR(*»6)=2l2Q4/35633 119,7, T
TSAR<»»7)=19486/43480 119,«, T
TSAK(*»8)=0/18396 119,9, T
TSAR(*»9)slBS50/H4870 I20.lt T
T5AR(*tlO)al781«>/30831 120,2, T
TSAR(*»115=4835/27472 1?0,3, T
TSAR(*»12)*1735/5S701 120.4, T
TSAR(*»13)=10666/?3927 120,5, T
TSAR(*»14)=2763/5832 120.6, T
TSAR(**15)=?356ft/37649 120,7, T
ISA=200/4000/6000/10()0/2000/5000/4000/1000/4000/* 120.9, T
2000/2000/1000/2000/1000/0
ISAT=1976/l976/1976/197fa/ly76/1976/l976/19/6/1976/ 121.3, T
1976/1976/1976/197h/1976/1976
SVAREA - TOTAL SFRVlCfcAREA (ACRES)
TSAR - TABLE, SERVICE AREA
TIME - EMPLOYMENT SECTOR RELATIVE WAGES
ISA - TABLE,SERVICE AREA INVSTMTS (ACRES)
lAVLNR.K(DS,Z)=MAX(lE-lb,(EXCAP.K(Z)/(HSWF(DS)* 122, A
RCF.K(nS,/)*LPOL.K(/))))
IAVLNR - INDICATED AVPlt RESID LAND (ACRES)
EXCAP - EXCESS CAPACITY (6AL>
RSWF - RESID SEWER usp. FACTOR
-------
SALR.K(DS,Z)sMAX(IAVLNR.K»J»ZNS) 130, A
ACLAB - ACCESS TO LABOR (0)
L«F - IABOR FORCE
IAF - INDUSTRIAL ACCESS FACTOR (D)
139
-------
LRF.K(7)=OELAY1(LAB.KU) *L6FDEL) I31' A
LBFOEL=1 131.2* C
LBF - LABOR FORCE
LAB - LABOR POTENTIAL (MEN)
LBFDEL - LABOR FORCE DELAY (YRS)
LAB.K(Z)=SUMV(ZRP.K(**2) , 1 *DENS) *AGLFPR ,K 132* A
WAC=1 132»2« C
LAB - tABOR POTENTIAL (MEN)
ZRP - ZONAL RES ID POP (MEN)
AGLFPR - AGGREGATE LABOR FORCE PARTICIPATION (D)
TOTAL LABOR FORCE FORECAST
WAC - WEIGHT GIVEN TO LABOR ACCESSI-BIL I TY FACTOR
(D)
FAVLNI.K(Z)=AVLNI.K(Z)/TAVLNI,K 133, A
FAVLNI - FRACTION AVAILABLE IND LAND IN EACH ZONE
* 136, A
(WL*FAVLNR.K (D*Z) )
ATTH - ATTRACTIVENESS TO HOUSING (D)
WS - WEIGHT GIVEN TO SATURATION IN
ATTRACTIVENESS FCT (0)
DSATML - SATURATION MULTIPLIER BY DENSITY (D)
WAC - WEIGHT GIVEN To LABOR ACCESSI-BIL ITY FACTOR
(D)
RACEMP - RtSID ACCESS FACTOR BY DENSITY (0)
WL - WEIGHT GlVtN To Ay/All, LAND FACTOR (D)
FAVLNR - FRACTION OF AVAIL RESID LAND IN EACH ZONE
(D)
TTATTH.K(D)=SUMV(ATTw.K(D»*) »1*ZNS) 137, A
TTATTH - TOTAL ATTRACTIVENESS FOR HSING
ATTH - ATTRACTIVENESS TO HOUSING (D)
140
-------
DSATMLtK(DtZ)=TABHL(TDML(*tO) tSAT.Kt ,5»1.0»,05) ]38t A
TDMU»tl)*l/l/l/l/l/l/l/l/.7/.2/lE-lS 138.?, T
TDML(«f2)al/.95/,85/.70/.55/,3b/.lb/.Ob/lE-lb/ 138.4. T
1E-1S/1E-15
TDML(*t3)=l/,95/,85/.70/.55/.3b/,15/.05/lE-15/ 138.5, T
1E-15/1E-15
DSATML - SATURATION MULTIPLIER BY DENSITY (D)
TOML - TABLEtRESID DENSITY MULTIP (0)
SAT - ZONAL DFVEL SATURATION (D)
RACEMP.K 140t A
INPDEL*! 140.2, C
DIND - DELAYED INDUSTRY (MEN)
ZEPWK - ZONAL EMPLOYMENT (MEN)
INPDFL - INDUSTRIAL PERCEPTION DELAY
RAF. K(ZT»ZF»0) =TA8HL(TRA(*,n) ,TT.K(ZT»ZF> ,0,60*10) 141t A
TRA(*»l)=l/,9/.7/.5/.3/. 1/0. 0001 141.?, T
TRA(*t2)sl/l/.9/.9/,R/.7/.6 141.3, T
TRA(*,3)=l/l/.9/.9/.B/, 7/.b 141.4, T
RAF - PES1D ACCESS FACTORS BY DENS (D)
TRA - TABLEt WILLINGNESS TO TRAVEL (0)
FAVLNR.K(DtZ)aAVLNR.K(OtZ»/TAVLNR.K)
AVLNR - ZONAL, ATTRACTIVENESS FOR INDUSTRIAL
Of- VELOPMEMT
H. TRAVEL TIMES SEC T OH
TT.K(ZTtZn=TABHL(TTT(^/T,7F),riME.K,1960,1969,l) 145, A
TIME - EMPLOYMENT SECTOR RELATIVE WAGES
141
-------
APPENDIX III
DOCUMENTATION OF DATA ON
TAPE: TMP 234
APPENDIX III. Contents
NOTE: TAPE NO. TMP 234 is available through OPTIMUM SYSTEMS INCORPORATED,
5272 River Road, Washington, D.C. 20016, (301) 652-2181 x 252
OSI Tape Librarian
Page
Section 1. Source of Data and Retrieval Procedure 143
I. Empiric Programs 144
A. Program Compositions 144
B. Core Requirements 146
C. Empiric Data Sets 148
D. Space 150
E. System Completion Codes 153
F. Program Error Stops 155
G. Error Stops 156
II. General Operating Instructions 157
A. Print Data Sets 159
B. Example Empiric Output Formats 162
III. Data Staking Block - "DASTAK" 163
A. Function 163
B. Application 163
C. Input 164
D. Output 165
E. Execution Cards 166
F. Error Checks . 167
G. Core 168
IV. Program Setup-Data Stacking Block DASTAK 169
A. Order of Cards 169
B. Program Cards 172
V. Sample DASTAK Setups 180
142
-------
Appendix III. Contents (Continued)
Page
Section 2. Tape Index and Data File Information 183
Section 3. Contents of Datasets 186
A. IMP 243 Tape File Numbers 3 to 5: Boston 188
B. TMP 243 Tape File Numbers 6 to 8: Denver 193
C. TMP 243 Tape File Numbers 9 to 11: St. Paul
Minneapolis 200
D. TMP 243 Tape File Number 12: Wash. D.C. 207
142a
-------
APPENDIX III
Section 1
Sources of Data and Retrieval Procedure
The datasets on tape EICCEQ were compiled by the Environmental
Impact Center, Inc. (EIC) from sources in four U.S. cities (Boston, Denver,
Minneapolis-St. Paul, and Washington, B.C.). The data from each city are
part of databases constructed for use in EMPIRIC land use model studies
conducted by the Traffic Research Corporation, and later Peat, Marwick,
Mitchell & Company. While the Environmental Impact Center has attempted
to verify and supplement some data, it cannot vouch for the accuracy of
the EMPIRIC databases in entirety. Any user of this tape who has questions
or experiences any problems with the data is advised to contact EIC for
direction to the appropriate persons in each city studied.
The first two files on the tape contain respectively the load
modules and source programs needed to read and manipulate the data. While
the entire EMPIRIC software package is on the tape, the user will only need
one of the programs to read the data. The directions for use of this pro-
#.
gram have been taken from the EMPIRIC Users' Manual and are presented
below. The user is directed to Peat, Marwick, Mitchell & Company for
specific questions about the EMPIRIC package.
* EMPIRIC Activity Allocation Model Users' Manual. IBM OS/360 Version,
Peat, Marwick, Mitchell & Company, 1025 Connecticut Avenue, N.W.,
Washington, B.C.
143
-------
I. EMPIRIC Programs
A. PROGRAM COMPOSITIONS
All EMPIRIC programs follow the same basic format in
program composition: A main program, routine(s) that pro-
cesses control cards and perform the program functions, and
routines to allocate core storage and distribute the core
among the required arrays.
MAIN
1
\
1
1
-•* M*
S ru
\
2
\
'I.MAI.N
S - ,TMfl
iRK
4
/
JN
Other Routines
The main program first calls the 'I1 main program to
process control cards and determine the core to be allocated.
Control is returned to the main program (2) which in turn
calls MARK (3), an assembly language subroutine which allo-
cates core with a GETMAIN MACRO. If the core is successfully
allocated MARK calls the 'J1 main program (4) which distributes
the core among the required arrays. The 'J1 main program then
calls the 'I1 main program again (5) to perform specified
functions which may involve calling other routines.
The main program is called the same name as the execu-
table load module (e.g., DASTAK,COMVAR). The 'I1 main and
11 j" main program usually are called the same names as the
executable load module prefixed by an 'I1 and 'J1 respec-
tively (e.g., IACES,JACES,IAGTWN,JAGTWN).
Several routines are common to all EMPIRIC programs:
1. The FORTRAN I/O routines and NAMELIST processor are
so utilized.
2. MARK (including subroutine SHFT02) is used to
allocate core.
3. IN processes the user labels and header record for
EMPIRIC input data sets.
144
-------
4. OUT processes the user labels and header record
for EMPIRIC output data sets.
5. PRNOUT prints the EMPIRIC data set in the three
standard print formats.1
The following list of routines comprise all of the
EMPIRIC routines except for the FORTRAN supplied I/O and
related processing routines. They are listed in alphabetic
order rather than grouped by usage:
ACES
AGTWN
ARITH1
ARITH2
ARITH3
COMERR
COMVAR
DACOR
DAMOD
DASTAK
DIFF
ERROR
FACTOR
FORCST
FORERR
GMMMA
GRAPH
GUARD
IACES
IAGTWN
ICOMVA
IDACOR
IDAMOD
IDASTK
IDIFF
IFAC
IFACT
IFRCST
IGRAPH
IKPNCH
IMONIT
IN
INVERT
IOLS
IPNORM
IRELIA
IREPRT
ISSTK
ISTRT
ITSLS
JACES
JAGTMN
JCOMVA
JDACOR
JDIFF
JFAC
JFRCST
JGRAPH
OMONIT
JPNORM
JRELIA
JREPRT
JSSTK
KPNCH
LU
MARK
MATXIN
MONITO
OLS
OUT
PNORM
POSIT!
PRNOUT
PRSFNC
REGRES
RELERR
RELIAB
REPORT
RGFC
SHFT02
START
STEP
STPRG
STREQ
SUPP
SUSTAK
TLA
TLB
TREAD
TSLS
The Twin Cities version of PRNOUT contains three additional
print formats in addition to those described in Section
IV.i. They are:
BCD = 8 Fll.O format with line numbers for each
zone but no "Subregion Number xx" iden-
tifier as printed by options 5, 6 and 7.
= 9 F11.8 format, parallel to BCD = 8
= 10 Gil.4 format parallel to BCD = 8
145
-------
B. CORE REQUIREMENTS
In an MFT or MVT environment, the amount of core storage
required to run a step must be determined to specify the
partition size required in MFT or the REGION size in MVT.
In PCP, the program uses the entire core excluding the system
area regardless of what the program actually needs. To
determine the amount of core required the following value
must be computed:
CORE=PGM+ARRAY+BUFF+SYS+MISC
. CORE - minimum core required to execute program
. PGM - program size as determined by linkage editor
. ARRAY - table and matrices required by program. Since
most of the arrays are variable length, they are
allocated at execution time based on an algorithm
for each program. The values for the algorithm are
obtained from the control card parameters. Each
program description contains its respective ARRAY <
algorithm.
. BUFF - I/O buffers. Each data set a program uses
must have allocated core for its buffers. This
core is allocated when the data set is first used.
The size of the buffer area for a given data set
may be expressed:
BUFF=BLKSIZE*BUFNO
where BLKSIZE is the blocksize specified in the
DCB of the DD statement in the data set label, or
system default. BUFNO is the number of buffers to
be allocated to this data set. The system defaults
to a value of two <2). Each program requires a variety
of data sets which are described in the INPUT and
OUTPUT sections of each program description. These
include a system input data set (FT05F001), a systems
output data set (FT06F001), and one or more input
and output data sets. Soin.e programs requie scratch
data sets and program ACES requires a standard skim
tree data set.
. SYS - System routines. During execution, various system
routines are required to accomplish several functions
such as I/O processing. These routines are linked or
146
-------
brought into core when they are needed. For EMPIRIC
they require only a small amount of core (approximately
5K).
. MISC - A small amount of core should be allowed as a
hedge against underestimating any of the above values
and for rounding. A value of 5K is generally sufficient,
though may be reduced if storage requirements are
critical.
Each program description supplies the required informa-
tion to determine CORE.
147
-------
EMPIRIC DATA SETS
All EMPIRIC data sets share the same basic format. This
standardization results in a highly flexible data set that
can be input into any of the EMPIRIC programs. The data
set consists of three parts: the optional user label
records, an identification or header record, and one homo-
geneous data matrix.
User Label Records
An EMPIRIC data set label is a label at the beginning
of a user's EMPIRIC data set used to visually identify the
data set. The labels may contain any valid alphameric
character and may be of any length. The user can give a
data set a label by placing label cards in the appropriate
place in the input data stream which is creating that data
set. Each label card consists of 80 character records
with an asterisk (*) in column 1. Any number of label
cards may be used to create a single label. Each label
card produces an 80 byte record with an asterisk in the
first byte. Since user labels are optional, an EMPIRIC
data set may not necessarily have label records in the
beginning.
Identification Record
Following the label records (if any) is the EMPIRIC
data set header. This 80 byte record contains information
identifying the data set and is created by the program
creating the data set from information supplied by the pro-
gram. The header contains the following data:
Bytes 0-3
"PAR" - The 3 letter word "PAR" indicating parameter
data follows.
Bytes 4-7
"IDENT#" - Identification number. This number is
checked by the computer against an identical
number supplied on a control card to the
program block which will use this data set
as input. A second number supplied on the
control card is written by the program block
on the Header of the output data set.
148
-------
Bytes 8-11
"NSUB" - Number of subregions. Specifies the number of
rows in the homogeneous matrix of this EMPIRIC
data set. It is checked against specified
on the control card for a program block. If the
program block changes the number of rows (i.e.,
subregions) the control card for the program
block also specifies a new value of "NSUB"
which is written on the Header of the output
data set.
Bytes 12-15
"NVAR" - Number of Variables. Specifies the number of
data categories in each row of the homogeneous
matrix of the data set. It is checked against
input as specified on the control card for a
program block. If the program block changes
the number of data categories, i.e., columns,
the control card for the program block also
specifies a new value of "NVAR" which is written
on the Header of the output data set.
Bytes 16-19
"YEAR" - The 4 digit number is obtained from the DASTAK
control card and remains on the Header. It is
used only for descriptive purposes.
The remainder of the record is blank. The above variables
are all binary (integer) numbers.
Data Matrix
Following the Header record is the homogeneous data
matrix. The data matrix consists of as many records as
rows in the matrix (e.g., NSUB). Each record contains
the row identification (subregion numbers) and as many
variables as specified by.NVAR in the Header record. The
row identification is a 4 byte binary (integer) number.
The variables are all 4 byte floating point numbers.
DP Statement
When creating an EMPIRIC data set on the IBM 360, the
following DCB and SPACE parameter guidelines should be observed:
149
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DCB (Data Control Block):
RECFM=VBS or VS - All EMPIRIC data sets must have a
record format of VBS (Variable (Blocked) Spanned). The
data sets are read and written with FORTRAN unformatted
I/O statements which require the record to be in V[B]S
format.
f84
LRECL=max | (NVAR+2)*4 (logical data length)
Each record of the data matrix contains NVAR variables
plus the subregion number resulting in (NVAR+1)*4 bytes.
The label records and Header record contain 80 bytes. Since
the records are all variable length (RECFM=V[B]S, an addi-
tional 4 bytes is added for the word containing the record
length.
LRECL+4) < blocksize <. maximum.
Since the record format is spanned (V[B]S), the BLKSIZE
may be any value up to the maximum capacity of the device.
It is recommended, however, that BLKSIZE be at least 4
greater than the LRECL and that some attempt be made to
optimize the BLKSIZE with respect to the output device.
For example, a tape has a maximum blocksize of 32,757 bytes,
a 2314 disk pack track has a capacity for 7294 bytes, and
a 2311 disk pack track has a capacity of 3625 bytes.
Excessive values of BLKSIZE may cause core allocation
problems when executing subsequent programs as BLKSIZE con-
trols the size of I/O buffers. If full track blocking on a
2314 is utilized, each EMPIRIC data set will require approxi-
mately 15K of core storage (2x7294) with BUFNO=2. The user
faced with core storage limitations should carefully structure
his data assembly procedure such that a large number of highly
blocked data sets are not required in a single run. (See
section on DCB information, for further detail on
BLKSIZE and BUFNO.)
D. SPACE - Direct Access Space
When creating data sets on a direct access device such
as a disk pack, SPACE must be specified for allocation (see
SPACE parameter in the DD statement discussion in the JCL
• section). The user can calculate the amount of space he
needs with the following techniques:
150
-------
1. Space Allocated in Blocks
If the user allocates SPACE in blocks where the
block is the BLKSIZE of his data set, the number
of blocks, n, is approximately:
n = [(NL+1)*84 + (NSUB*(NOVA+l))*4]/(BLKSIZE-4)
where:
NL = number of user supplied LABEL records
NSUB = number of subregions in the' data matrix
NOVA = number of variables in the data matrix
BLKSIZE = blocksize of data set
2- Space Allocation in Tracks
If the user allocates space in direct access tracks
(TRK) the number of tracks, t, is:
t = n/NB
where:
n = number of blocks as calculated as if space
were allocated in blocks
NB = number of blocks per track which is approxi-
mately the capacity of a track in bytes
divided by BLKSIZE, truncated to the nearest
whole number.
For optimum I/O processing, the BLKSIZE
should be the same as the facility maximum
(e.g., 7294 on a 2314). Care should be taken
when a block is a fraction of a track since
allowance should be made for the inter-
record gaps (IRG). See the IBM Reference
card for the devices of the installation
for the capacity formulas (X20-1700 series
and C20-1649).
Allocating space in tracks is more efficient than
allocating in blocks.
151
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3. Space Allocation in Cylinders
If the user allocates space in direct access
cylinders (CYL) which is the best method/ the
number of cylinders, c, is:
c = t/NTK
where:
t = number of tracks as calculated.above
NTK = number of tracks per cylinder for the
specified device. For example, a 2314
facility has 20 tracks/cylinders, a
2311 pack has 10 tracks/cylinders.
In addition to the above, the user should keep in mind
the following: The entire data set is best allocated if it
is completely contained in the initial or primary allocation.
The RLSE parameter should be used to release unused tracks
from a newly created data set.
152
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System Completion Codes
Code Meaning
213
322
80A
System cannot find a
data set
Time limit exceeded
Insufficient core
806
B37
System cannot find
load module
Insufficient space
on data set
Response
Check all data set names
on data definition cards
Increase time limit on
program card
If using a multi-programming
computer, increase the re-
quested core. If running
in fixed core, adjust problem
size downward by buffer re-
duction if possible
Check specification of pro-
gram reference on EXEC and
STEPLIB cards
Check space allocation on
all output data sets, in-
cluding print data set and
increase allocation if
necessary
FORTRAN Object Messages:
IHC207I
IHC208I
IHC209I
Computational overflow Check that all required
Computational underflow parameters have been set.
Divide check Check that all variables
on the right hand side of
COMVAR function cards exist
or have been previously
calculated
IHC211I
IHC215I
IHC217I
Invalid character in
a format statement
Invalid character in
data being read
End of data set
reached during read
Check all user-supplied
format statements
Bad record will be printed;
identify and correct (may
be caused by having control
cards in wrong order)
Check to see if proper num-
ber of zones, purposes, etc.,
has been specified on NAME-
LIST control card
153
-------
Code
Meaning
IHC219I Missing data defini-
tion card
ICH222I NAMELIST name not in-
cluded in program
IHC251I Negative square root
Response
Missing unit number will
be printed; check to see
if data definition cards
have been supplied for all
units specified on the NAME-
LIST control card and for
the system card reader and
printer
Check spell-ing of all names
on NAMELIST control card,
for commas between all
entries, and for "SEND"
terminator
As for IHC207I; if occurs
in REGRES, indicates that
specified equation is too
poor for computational
adequacy - respecify equation
154
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F. Program Error Stops
Each of the EMPIRIC programs have several different error
messages. These messages are explained in detail in the
sections dealing with the individual program write-ups.
Basically, these errors are of two types, the first a series
of numerically coded messages which usually refer to improper
specification of parameters on the program control card.
The most common errors of this type concern improper speci-
fication of data set control parameters, such as the number
of variables, number of subregions, or the identification
number. These messages are self-explanatory and the errors
are readily corrected.
The second set of messages are special purpose types
generated because of errors in computation... In most cases,
a special error message is printed giving the cause of the
error. In most cases these errors are fatal and requires
restructuring of the program inputs. An exception to this
is the COMVAR function card arithmetical exception checks
for which a pre-specified "fix-up" is taken. The error in
this case does not cause termination of the run but an indi-
cative message is printed.
Fatal program errors will return a completion code of
16, identical to that from FORTRAN messages. The COMVAR
warning messages will return a completion code of 15. Thus,
if warning messages are anticipated (e.g., if divisions by
zero are unavoidable in some subregions), later job steps
can be run by setting the condition parameter to COND=(16,LT)
(see section on completion codes ) •
155
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G. ERROR STOPS
Error stops occur whenever the computer cannot resolve
some inconsistency encountered during execution. These
stops can be of two types/ those produced by the system and
those produced by the program. The latter are generally
incorporated into the program to avoid the occurrence of a
potentially costly system stop, or to provide the user with
more specific information on a particular condition or mal-
function than can be provided by a general purpose system
message.
System Error Stops
Although any one of the several hundred IBM 360 system
stops could theoretically occur, the v.ast majority should
not be encountered when executing a fully "debugged" program.
However, a few common stops, caused primarily by user mis-
takes in coding basic data handling or system operation
control cards, frequently do occur. A few of the more common
are listed below together with suggested user action to be
taken to correct them. More complete explanations of these
codes and a full listing of all other codes can be found in
the IBM publication "IBM Systems/360 Operating System Mes-
sages and Codes," publication number GC28-6631-7. ( 7 )
Common system codes can be divided into three broad
categories. The first are those produced by the operating
system itself. These error stops, generally referred to as
"completion codes", are usually associated with job control
language (JCL) problems and are always "fatal" in that they
terminate execution of the job when they are encountered.
The second group of error stops are produced by the FORTRAN
object program during execution. These errors may or may
not be fatal, but nearly always indicate an invalid run.
The third group include compiler and linkage editor errors
which may be encountered when creating COMVAR ARITH sub-
routines. These errors are not documented here but may be
found in the Completion Code Manual ( 7).
156
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II, General Operating Instructions
The following Sections give the necessary details for
running each of the programs in the EMPIRIC package. The
user should first familiarize himself with the background
material presented in the previous sections and with the
relevant details of the operating procedures of the parti-
cular installation he is utilizing.
The EMPIRIC programs may be broadly divided into two
categories, those concerned primarily with data assembly,
manipulation and display; and those concerned with the
calibration, validation, and forecasting with an Activity
Allocation Model. The first group of programs have broad
application for a variety of general data processing
applications, while only some of the second group have any
substantial application outside the development of an
Activity Allocation Model. A capsule summary of the
major purposes of each of the programs is included below:
Data Assembly, Manipulation^ and Pisp1ay
DASTAK
SUSTAK
COMVAR
DAMOD
AGTWN
PNORM
DIFF
ACES
Raw data assembly; merge data sets of equal
vertical dimension; dumping contents of
data set.
Merge data sets of equal horizontal di-
mension; reduce vertical dimension of data
set.
Delete data categories; rearrange data
categories; create new data categories;
selectively adjust data categories.
Revise individual data items within a
matrix; revise numbering scheme of obser-
vations.
Aggregate observations.
Compute fractional!zed or normalized
variables.
Subtract or add data sets of equal size.
Compute generalized accessibilities to
various activities by mode.
157
-------
GRAPH
Prepare visual display of cross-stratified
data.
REPORT Prepare summaries of data for inclusion
in report.
Activity Allocation Calibration, Testing and Forecasting
DACOR
FACTOR
REGRES
FORCST
Compute bivariate correlation coefficients
for a data set.
Perform principal components factor analysis
on a data set.
Compute least squares regression coefficients
for single equations; compute step-wise
regression coefficients for single equations;
and compute simultaneous regression coeffi-
cients for systems of equations.
Prepare Activity Allocation forecasts for
small areas.
MONITO Adjust forecast activities for exogenously
specified controls.
RELIAB Test reliability of calibrated activity
Allocation Model.
158
-------
Print Data Sets
All of the data assembly programs and most of the
other programs produce EMPIRIC (binary) output data sets
and an optional (BCD) printed tabulation of the data. This
print data set is, of course, invaluable for checking the
data and for maintaining a visual summary of the information.
However, the creation of the data sets may add substantially
to the total cost of the computer run. In many EMPIRIC
applications, data sets are linked together in many compound
fashions and thus the data may appear in several places.
Unless required for a specific purpose, it is suggested that
these intermediate print data sets be suppressed for
maximum project efficiency.
The suppression of the print data set is accomplished
in most of the programs by setting the NAMELIST control
card parameter BCD equal to one. If the print data set
is required, it may be produced in most of the programs by
setting BCD to one of three other values, dependent upon
the nature of the data. If all data is expressed as whole
numbers (i.e., population and employment counts), BCD should
be set to two which produces printed output of whole numbers
in a 10F.O format. If all data is expressed as fractions
(i.e., shares and changes in shares), BCD should be set to
three which produces printed output of decimal numbers in
a 10F11.8 format. If the data consists of mixtures of whole
numbers and fractions (i.e., demographic data and densities
or ratios), BCD should be set to four which produces printed
output of values in scientific notation in a 10G11.4 format.
With the latter format, very large or very small numbers
will appear as +nnnnE+mm, whereas "medium" sized numbers
will appear as decimals.
For extremely large data sets, an additional printing
option is. provided to place index numbers for the rows
of the data set (i.e., 10, 20, 30, 40, ...). The same three
print formats discussed above can be invoked by specifying
BCD=5,6, or 7, respectively, for Fll.O, F11.8, and Gil.4
output formats. This option, however, requires the utili-
zation of a less efficient output procedure and will
increase the cost of running the program.
The standard print data sets produced by most of the
EMPIRIC programs consist of all the data for each subregion
159
-------
grouped together, with 10 values per row. For some pur-
poses , it is more useful to have all of the data for a single
variable in direct vertical sequence. Therefore, a special
print option is available in program DASTAK to produce
"strips" of 10 variables from a large data matrix. This
option adds to the running time of the program, but
can be useful in specific applications.
The following pages give illustrations of each of the
print formats and the special print options.
160
-------
B. Example Empiric Output Format
UNIT 9 LABEL * ILLUSTRATES BCD OPTION 2, Fll.O FORMAT
1 2 3 l» 5
100 1000. 250. 500. 167. 671*.
200
300
493.
695.
3849.
3303.
if 79.
614.
38568.
835.
83.
832.
UNIT 9 LABEL * ILLUSTRATES BCD OPTION 3, F10.8 FORMAT
1 2 3 ' 4 5
100 1.00000000 0.25000000 0.50000000 0.16700000 0.67399997
200 0.1+9299997 3.48999977 0.47899997 3.5679998** 0.82999998
300 0.69499999 3.02999973 0.61399996 0.83499998 0.83199996
UNIT 9 LABEL * ILLUSTRATES BCD OPTION 4, Gil.4 FORMAT
12345
100 1.111 0.4000E-06 2.456 102.3 0.1232E 07
200 5.235 0.1000E-06 3.287 99.10 0.4939E 06
UNIT 9 LABEL * ILLUSTRATES BCD OPTION 5, Fll.O FORMAT
SUBREGION
0
SUBREGION
0
SUBREGION
0
1
NUMBER
01
1000.
NUMBER
01
493.
NUMBER
01
695.
2
100
02
250.
200
02
3849.
300
02
3303.
3
03
500.
03
479.
03
614.
4
04
167.
04
38568.
04
835.
5
05
674.
05
83.
05
832.
161
-------
B. Example Empiric Output Format (Continued)
UNIT 9 LABEL * ILLUSTRATES BCD OPTION B, F10.fi FORMAT
SUBREGION NUMBER 100
01 02 03 Ok 05
0 1.00000000 0.25000000 0.50000000 0.16700000 0.67399997
SUBREGION NUMBER 200
01 02 03 Ok 05
0 0.49299997 3.^8999977 0.^7899997 3.5679998^ 0.82999998
SUBREGION NUMBER 300
01 02 03 0** 05
0 0.69^99999 3.02999973 0.61399996 0.83U99998 0.83199996
UNIT 9 LABEL * ILLUSTRATES BCD OPTION 7, Gil.I* FORMAT
SUBREGION NUMBER 100
01 02 03 Ok 05
0 1.111 O.lfOOOE-06 2.1*56 102.3 0.1232E 07
10 5.235 0.1000E-06 3.287 99.10 0.**939E 06
162
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HI. DATA STACKING BLOCK - "DASTAK"
A. Function
DASTAK's functions are:
1.) Transcribe a homogeneous data matrix from a BCD
data set (e.g. cards) to an EMPIRIC data set. The
program checks that the cards are in the proper
order and performs additional consistancy checks.
2.) Increase the horizontal dimension of a data matrix
by merging matrices with identical vertical dimen-
sions from:
a) two or more (up to twenty) EMPIRIC data sets; or
b) a BCD data set and one or more (up to twenty)
EMPIRIC data sets.
B. Ap p 1 i c a t i on s
In general, DASTAK is used to perform the functions men-
tioned above. The most common specific applications are the
following:
a) Read cards or card images through the unit specified
by control card parameter READER and convert them
into an EMPIRIC data set.
b) Merge data sets Tn - T ; (2< n< 20) each data set
1 n — —
containing one matrix.
c) Merge data sets T - T ; (!<_ n< 20) with new data
cards (one deck only).
d) Read one data set and write it.
The output is a single matrix on an EMPIRIC output data
set and an optional printed listing. All input matrixes must
have identical vertical dimensions (number of subregions),
but the subregion numbers themselves on T^ through Tn do not
have to be the same. That is, the computer checks that each
data set has the same number of subregions, but it does
not check that subregion numbers match. The output will
contain the subregion numbers from the BCD input data set
or from the last EMPIRIC input data set (Tn) if there is
no BCD input.
163
-------
In addition to DASTAK's specified uses of converting a
BCD data set to an EMPIRIC data set and merging data sets, the
program can also be used to do the following:
1. Check the contents of a data set. (application
d with printed output)
2. Change the location (data set name) of a data set.
(application d)
3. Change the IDENT or YEAR in the header of a data set.
4. Change a data set user label. (cards' following
control card)
5. Convert an EMPIRIC data set to a BCD data set.
DASTAK may be used to print out a data set to check its
content or for display purposes. In the latter case, an
alternative output format is included, which is not available
with the other EMPIRIC programs. This format produces "strips"
of the total data matrix, each strip containing 10 variables
across the sheet of printed output, and extending as many
sheets as required to print all the subreg'ions. Succeeding
strips may then be separated and placed side by side for
photographic reproduction of the total data matrix. When
using this option, a DUMMY output data set cannot be used, as
the program must rewind the output data set for each strip.
A temporary data set on a system scratch device can be
specified, however.
If application a) or c) is selected, the user has the
option of supplying the raw data from a source other than
the systems input (card reader) and in a format other than
the standard format. To specify another input data set
for raw data, "READER" should be specified on the control
card. If a user-supplied format is desired, control card
parameter FMT must be set to 1 or 2 and a standard FORTRAN
format must follow the end label card. The number of variables
obtained from raw data input is determined by program by
subtracting the total number of input variables from all
EMPIRIC data sets from the number of output variables
specified on the control card. See Section VI-1-1 for the
description and use of variable formats.
C. Input
The input data sets can consist of:
164
-------
1. 0 to 20 EMPIRIC data sets and/or
2. a BCD data set
D. Output
The output of DASTAK is a single homogenous data matrix
on a EMPIRIC output data set optionally printed on unit
member 6 and optionally as a BCD data set on a user-specified
device and in user-specified format. This single matrix
may be a transliteration of a single input matrix or the
result of a merge.
165
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E. Execution Cards
See Program Setup below for the execution cards required
for a DASTAK run.
Note the following specific requirements on the control
card parameters for each of the applications described above
(See Control Card Description):
output data set header identification number (IDOUT),
output data set FORTRAN unit number (TOUT),
number of variables on output data set' (NOVOUT),
number of subregions on output data set (NSUB),
the year specified on the input data set(s) (YEAR),
and number of variables on the output data set (NOVOUT).
Each application has the following additional control
card requirements:
a. No additional requirements
b. Header-identification number(s) on input data set(s)
IDENT (i) , i = 1,...,n
FORTRAN unit number(s) for input data set(s),
T(i), i = 1,.. . ,n
Number of variables on input data set(s), NOV(i),
i = 1,...,n
NOVOUT = n NQV(i)
i=l
c. IDENT(i), T(i), NOV(i) (i = l,...,n)
NOVOUT .= n
£ NOV(i) + number of variables on cards.
d. IDENT (1), T(l), NOV(l)
NOVOUT = NOV(l)
NOTE: n = number of input data sets. Parameter BCD must be
set to 2, 3, or 4, if printed output is desired. IDENT
(i), T(i), NOV(i), i = 1,...,20 will default to 0 for
all values not specified.
Note that a label on the output data set is optional,
but the end of label card must be included in the execution
cards.
166
-------
F. Error Checks
If the years as indicated on the control card and headers
of input data sets do not match, a warning is printed and the
program continues. If the card number on an input data
card is not sequential with surrounding cards, a warning
is printed and the program continues. The following are
printout codes for errors which cause a halt of the DASTAK
run:
101 IDENT (i) does not match header of T(i).
102 NSUB or NOV(i) on T(i) header does not agree with
NSUB or NOV(i) on control card.
104 Number of variables per subregion on T(i) on in-
put data cards does not agree with NOV(i) on
control card.
105 Subregion code numbers on input data cards are
inconsistent.
106 Year on raw data cards is inconsistent.
107 Deck number on raw data cards is inconsistent.
108 Subregion identification card does not have a "1"
in column 72.
167
-------
G. Core
CORE = PGM + ARRAY + BUFF + SYS + MISC
= 32K + ARRAY + BUFF + 5K + 5K
ARRAY = (NOVOUT+10) * 4 bytes
BUFF: DASTAK may use the following data sets:
1. FT05F001 Systems input (card reader)
2. FT06F001 Systems output (printer)
3. FT1READER'FO01 Alternate (and optional) unit
for raw data.
4. FT'T'(i) 'F001 Input EMPIRIC data set "i".
Up to 20 EMPIRIC data sets
may be provided with an
appropriate buffer area for
each.
5. FT'TOUT'FOOl Output EMPIRIC data set.
6. FT'BCDOUT'FOOl Optional BCD output data set.
168
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IV. Program Setup-Data Stacking Block DASTAK
A. Order of Cards
Control Card(s)
Label Card(s) (Optional)
End of Label Card
(If FMT = 0): Subregion Identification Card
Raw Input Data Card(s) for Subregion 1
Subregion Identification Card
Raw Input Data Card(s) for Subregion 2
Subregion Identification Card
Raw Input Data Card(s) for Subregion n
(If FMT = 1 or 2): BCD Input Format Card
Data Cards (following user-specified
format), one set for each subregion.
(If BCDOUT>0) : BCD Output Format Card
169
-------
0 > n > 20
Control
Card
flJnit 5\
Printer
(Unit 6)
Reader
Maximum DASTAK Input/Output
Figure B.I.
170
-------
NSUB <
NOV(l)
o < n < 20
DATA MATRIX
NOVOUT
Illustration of Application c
Figure B.2.
171
-------
NOTE: FOR PRINTING AN EMPIRIC DATASET
B. Program Cards
PROGRAM:
NOV(i) = NOVOUT IS NECESSARY
Data Stacking Block DASTAK
CARD: Control Card
NUMBER OF CARDS: Any Number
DESCRIPTION:
&PARAM
IDENT(i)
NOV(i)
IDOUT
TOUT
NOVOUT
NSUB
YEAR
BCD
READER
These cards contain the necessary parameters
to guide the operations of DASTAK
Parameter identification; "&" must be in Column 2,
=n identification number to be found on header
of T(i); must be coded if EMPIRIC data set
'i1 is to be input; assumed 0.
=n FORTRAN unit number for EMPIRIC input data set,
must be specified if data set 'i1 is to be
input; assumed 0.
=n number of data categories or variables per
subregion on EMPIRIC data set 'i', must be
specified if data set 'i1 is to be input;
assumed 0-
=n identification number of EMPIRIC output;
must be specified.
=n FORTRAN unit number for EMPIRIC output data
set; must be specified.
=n number of data categories or variables to
be output; must be specified.
=n number of subregions for EMPIRIC input data
sets, raw data input data set and EMPIRIC
output data set; must be specified.
=n year in which data was collected; must be
specified.
=n BCD printout indicator
1-no printout,
2-F11.0 format,
3-F10.8 format,
4-G11.4 format; (See note (5) below)
assumed 1 (no printout)
=n FORTRAN unit number for raw data input;
assumed 5 (card reader)
172
-------
FMT
-n FORMAT indicator for raw data input:
CONST
PUTALT
BCDOUT
SEND
NOTES:
0
1
standard DASTAK raw data
user supplied FORMAT for raw data with
SUBREGION number, YEAR, and EXPANSION
factor in the first three fields fol-
lowed by data categories; assumed 0;
need not be coded if no BCD input data
is supplied.
2 - user supplied FORMAT for raw data with
SUBREGION number in the first field
followed by data categories; assumed 0;
need not be coded if BCD input data is
to be supplied.
=n.f constant expansion to be used when expan-
sion factor is.not supplied with raw data
i.e., FMT = 2) , assumed 1.0.
=n special printed output alternative
0 - normal printed output with all data
for subregion in a block
1 - special output with data in strips of
10 variables each; assumed = 0
NOTE: Option I may be used when it is
desirable to construct a date matrix for
display purposes. It should not be used
normally, as it adds considerably to the
running time of the program.
=n special BCD output option
0 - no BCD output is desired
Any other integer FORTRAN unit number of
the BCD output device; assumed 0
end of control card(s)
CU 1
-------
(a) T = 8, 9, 12, 16, 20
IDENT = 60, 60, 68, 0, 8
NOV = 1, 2, 5, 2, 3
(b) T(l) = 8, T(2) = 9, T(3) = 12
T(4) = 16, T(5) = 20, IDENT(2) = 60,
IDENT(3) = 68, IDENT(4) = 0, IDENT(5) = 8,
NOV(l) = 1, NOV(2) = 2, NOV(3) = 5,
NOV(4) = 2, NOV(5) = 3, IDENT (1) = 60
(5) Large data sets may be optionally printed with
row sequencing by setting BCD=5,6, or 7. See
"General Operating Instructions" for further
discussions and examples.
(6) See "EMPIRIC Execution Cards" for control card
rules.
174
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PROGRAM:
CARDS:
NUMBER OF CARDS
DESCRIPTION:
Label Cards:
End of Label
Card:
Data Stacking Block - DASTAK
Label Card, End of Label Card
Any number
These cards supply information to be written
on the output EMPIRIC data set label.
* in column 1; any characters in columns 2-80
giving information to be written on the
EMPIRIC output data set label
Non-asterisk (any other alphanumeric character
including blank) in column 1.
EXAMPLE:
* THIS IS AN EXAMPLE
* OF AN OUTPUT
* BINARY DATA SET LABEL
THIS IS AN END OF LABEL CARD
col. 1
175
-------
PROGRAM:
CARD:
NUMBER OF CARDS
DESCRIPTION:
Data Stacking Block DASTAK
BCD Input Format Card
0 (if FMT on control card is 0)
1 (if FMT on control card is 1 or 2)
FORTRAN format for creating raw data that is
not in the standard format. The statement
must be enclosed in parentheses. The for-
mat may be either of. two forms depending
on the value of FMT:
a) If FMT = 1
Field 1: (subregion number)
integer, "I","G", or "A" type
format.
Field 2: (year) , integer,
"I", "G", or"A" type format.
Field 3: (expansion factor), real number,
"F", "E", "G", or "A" type format.
Field 3 + i: (data categories) where
i = number of data categories,
real number, "F", "E" , "G", or
"A" type format.
EXAMPLE: 18 data categories must be input
(14, 14, F6.2,3(/6F8.3))
b) If FMT = 2
Field 1: (subregion number)
integer, "I", "G", or "A" type
format.
Field 1 + i: (data categories) where
i = number of data categories,
real number, "F", "E", "G",or
"A" type format.
EXAMPLE: 6 data categories to be input
(14, 6G12.6)
NOTE: See section VI-1-1 for the rules for use of variable
formats.
176
-------
PROGRAM:
CARD:
NUMBER OP CARDS:
DESCRIPTION:
SUBR:
D
TW
EXP
NOTES:
Data Stacking Block DASTAK
Subregion Identification Card (standard format)
1 for each subregion (in front of each sub-
region's data cards); required only for raw
data input with FMT . 0 on control card.
Labelling card for raw input data on cards.
Subregion code f , 5 digits; checked by com-
puter against data cards which follow, columns
40 - 44; integer format (15).
, °f Year in Which data was collected;
checked by computer against data cards which
follow, columns 45 - 46; integer format (12).
= 2 digits indicating deck #; checked by com-
puter against data cards which follow, columns
47 - 48; integer format (12).
= 3 digits indicating to which town a given
subregion belongs; ignored by computer, columns
49 - 51; integer format (13).
= Expansion factor, 4 digits, by which all raw
data for this subregion is to be multiplied-
columns 52 - 55; integer format (14).
= A "1" punched in column 72 identifies this card
as a subregion identification card to the
computer.
If EXPN is not specified, it is assumed to be 1. All
other columns may contain any visual information the
user deems useful. This card is necessary only when
FMT a 0 on the control card. If FMT = 1 or 2 , the
user specifies his own format for this card and the
raw data input cards.
10 15 20 25 30 35 40 45 50 55 &0 65 70 75 80
ANY VISUAL IDENTIFICATION
Jill IllllllllllltillLLiJ-l t I I I I I I I I I I II
SUBR
i 11
TW
EXP
111
177
-------
PROGRAM:
CARD:
NUMBER OF CARDS
DESCRIPTIONS:
SUB
CD
V1-V8
D
T
Data Stacking Block DASTAK
Raw Input Data Card (Standard Format)
For each subregion: Number of data
categories/8 or next highest integer;
required only for raw data input
FMT=0 on control card.
Supplies raw input data for updating
EMPIRIC data set or creating a new file.
Subregion code f, 5 digits, columns 1-
5; integer format (15).
3 digits specifying card sequence number,
columns 6-8; integer format (13).
Value of 8 data categories for each sub-
region up to 7 digits with implied
decimal point r,ight adjusted, (decimal
may be punched anywhere in field),
columns 9-15, 16-22, 23-29, 30-36,
37-43, 44-50, 51-57, 58-64; real format
(F7.0).
Last 2 digits of year in which data was
collected, columns 65-66; integer format
(12).
2 digits indicating deck #, columns
67-68; integer
3 digits indicating to which town a
given subregion belongs; ignored by com-
puter, columns 69-71; integer format (13).
1 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80
1 1 1 V
SUB
nil
CD
11
VI
1 1 II 1 1
V2
null
V3
it 1 in
V4
III 1
V5
Mill!
V6
1 1 1411
V7
1 1 II
1
V8
1 1 1 1 II
Y
1
D
1
TW
it
E
/////
y\/ xy^
NOTE:
The format on this card is used only if FMT=0 on
the control card. The user has the option of
specifying any format he wishes for this card
(FMT=1 or 2)
178
-------
PROGRAM:
CARD:
NUMBER OP CARDS
DESCRIPTION:
NOTE:
Data Stacking Block DASTAK
BCD Output Format Card
(if BCDOUT > 0 on control card)
i
FORTRAN format for creating optional
BCD output data set on unit BCDOUT.
The format must provide a single inte-
ger (I-type) field for the subregion number
and sufficient real fields (F-type) to
provide for all variables associated
with each subregion.
EXAMPLES:
(a) 15 variables, whole numbers, card
image output:
(I10,7F10.0/8F10.0)
(b) 16 variables, decimal fractions, tape or
disk output:
(I10,16F10.7)
(c) 35 variables, whole numbers, card image
output utilizing repeat feature of for-
mat:
(I5/UOF8.0))
See Section VI-1-1 for the rules for use of
variable formats.'
179
-------
SAMPLE DASTAX SETUPS
1.) EMPIRICData Set Input Only
&PARAM IDENT = 68,68,60,60,1=8,10,11,12,NOV=8,2,8,2,1DOUT = 60 ;L Control
TOUT=9,NOVOUT=20,MSUB=160,YEAR=1960,BCD=4 &END JCards
^COMPLETE ASSEMBLED CALIBRATION INPUT Label
9 End of Label
NOTE: These cards will cause DASTAK to read from four
EiMPIRIC data sets and assemble the data in the
order in which these data sets are read (i.e.,
data sets from unit numbers 8, 10, 11, and 12.)
Printed output as fractions is requested.
2.) EMPIRIC Data Set and Card Input (User Specified Format)
&PARAM IDENTO) = 60 ,IDENT(2)=60,T(1)=8,T(2)=10,NOV(1)=5,NOV(2) = 2*1 Control
IDOUT=60,TOUT=9,NOVOUT=10,NSUB=10,YEAR=1960,BCD=2,FMT=2
*RAW DATA INPUT FROM:
Label
End of Label
Format Card
&END /Cards
HOME INTERVIEW SURVEY (TAPE)
*
*
9
(13
1
2
3
4
5
6
7
8
9
10
HOME IN
AND CEN
,8X,
60
60
60
60
60
60
60
60
60
60
3F7.3)
1000.
1000.
1000.
1000.
1000.
1000.
1000.
1000.
1000.
1000.
80.
220.
115.
165
96.
76.
92.
63.
64.
76.
32.
85.
68.
112.
82.
52.
56.
32.
28.
48,
Raw Input
Data Cards
NOTE: This setup will cause DASTAK to read five variables
and two variables for each of 10 subregions from
data sets on unit numbers 8 and 10, respectively,
then three variables for the 10 subregions from
cards. The auxiliary information on these cards
will be ignored. Printed output as whole numbers
is requested.
180
-------
EMPIRIC Data Set and Card Input (Standard Format]
&PARAM
IDENT=1 ,
YEAR=1960
&END
* NEW 1
*
*
*
T = 8,
960 DATA
VARIABLE
VARIABLE
VARIABLE
FOR
1
2
3
NOV=12
IDOUT=2,TOUT=9,NOVOUT=15,NSUB=4
EXTERNAL ZONES
POPULATION
WHITE COLLAR EMPLOYMENT
BLUE COLLAR EMPLOYMENT
THIS IS AN END LABEL CARD
HOWARD COUNTY, MARYLAND 0016260 1
00162 1 22. 8. 5.
ANNE ARUNDEL COUNTY, MARYLAND 0016360
00163 2 18. 6. 3.
CHARLES COUNTY, MARYLAND
4. 2.
VIRGINIA 0016560 1 41000
6. 2.
11000
1 21000
0016460 1 31000
00164 3 13.
FAUQUIER COUNTY,
00165 4 15.
60 1
60 1
60 1
60 1
1
11
1
21
1
31
1
41
NOTE: With this control card, DASTAK will read twelve
variables from the data set on unit number 8 and
three variables from the data cards included in the
setup deck. There is no format card when standard
format is used.
4.) BCD Output Data Set
&PARAM
IDENT=10,12,T=8,10,NOV=15,3,IDOUT=23,TOUT=9,
NOVOUT=18,NSUB=130,BCD=2,BCDOUT=11
SEND
*FULL DATA SET
*OUTPUT EMPIRIC DATA SET IS DUMMY
QUIT
(I8,8X,8F8.0/10F8.0)
NOTE: With these control cards, DASTAK will merge 15
and 3 variables respectively from units 8 and 10
and produce a BCD output data set in card image
form on unit 11. The standard EMPIRIC output data
set has been "dummied out" (//FT09F001 DD DUMMY).
181
-------
5.) Output Data Sets
UNIT 9 LABEL *COMVAR SAMPLE
UNIT 9 LABEL * COLUMN 1
UNIT 9 LABEL * COLUMN 2
UNIT 9 LABEL * COLUMN 3
UNIT 9 LABEL * COLUMN 4
UNIT 9 LABEL * COLUMN 5
UNIT 9 LABEL * COLUMN 6
UNIT 9 PARAMETER- ID#=
YEAR=1960
UNIT 11 LABEL *COMVAR SAMPLE
UNIT 11 LABEL *
UNIT 11 LABEL * COLUMN 1
UNIT 11 LABEL * COLUMN 2
UNIT 11 LABEL * COLUMN 3
UNIT 11 LABEL * COLUMN 4
UNIT 11 PARAMETER - ID#=
YEAR=1960
FORMAT FOR BCD DATA
(I3.F6.0)
EMPIRIC DATASET ON UNIT
SUBREGION VARIABLES
1 2
1 220000. 22000.
2 415000. 55000.
3 205000. 184000.
4 190000. 91000.
5 140000. 9000.
6 290000. 83000.
7 230000. 22000.
8 80000. 5000.
9 185000. 35000.
10 95000. 4000.
TOTALS 2050000. 510000.
EMPIRIC DATASET ON UNIT
SUBREGION VARIABLES
1 2
1 9565. 956.5
2 0.2231E 05 2957.
3 0. 1015E 05 9109.
4 9406. 4505.
5 0.1029E 05 661 .8
6 0. 1374E 05 3934.
7 0.1278E 05 1222.
80.0 0.0
9 6469. 1224.
100.0 0.0
TOTALS 0.9472E 05 0-2457E 05
RUN RAW DATA FOR THE FOLLOWING VARIABLES
1960 POPULATION
1960 WHITE COLLAR EMPLOYMENT
1960 BLUE COLLAR EMPLOYMENT
1960 TOTAL EMPLOYMENT
1960 ACCESSIBILITY TO POPULATION
1960 ACCESSIBILITY TO EMPLOYMENT
60,SUBDISTRICTS= 1 0 .VARIABLES^
RUN DENSITIES FOR THE FOLLOWING
(SUBREGIONS 8 AND 10 VALUES
1960 POPULATION' •
1960 WHITE COLLAR EMPLOYMENT
1960 BLUE COLLAR EMPLOYMENT
1960 TOTAL EMPLOYMENT
60,SUBDISTRICTS= 1 0, VARIABLES=
9
3 4 5
21000. 43000. 139963.
110000. 165000. 228669.
40000. 224000. 214657.
24000. 115000. 196850.
38000. 47000. 121945.
7000. 90000. 172577.
36000. 58000. 184564.
14000- 19000. 75996.
18000. 53000. 165163.
5000. 9000. 88379.
313000. 823000. 1588759.
11
3 4
913.0 1870.
5914. 8871.
1980. 0. 1109E 05
1188. 5693.
2794. 3456.
331.8 4265.
2000. 3222.
0.0 0.0
629.4 1853.
0.0 0.0
0.1575E 05 0.4032E 05
6,
VARIABLES
SET = 0)
4,
6
69863.
138501.
154807.
137335.
61288.
97898.
110007,
31039
87012.
36010.
923760.
182
-------
Section 2
Tape Index and Data File Information
The table on the following page describes the files on the
EIC tape. The tape is 9 track, standard labeled (SMP 243)at
1600 bpi. All of the files were transferred to the tape using the
IBM utility program IEHMOVE.
The second table shows the dataset names of the ten data files on
the EICCEQ tape, as well as the corresponding EMPIRIC names. When the
user prints out the datasets, the original EMPIRIC name will appear with
the data. The last four columns contain the information needed by the
EMPIRIC program control cards (cf. , Section 1).
183
-------
00
ENVIRONMENTAL IMPACT CENTER, INC.
MAGNETIC TAPE INDEX TMP 243
Table C.I.
DATE
SERIAL
EICCEQ*
* All
FILE DSNAME (ON TAPE)
1
2
3
4
5
6
7
8
9
10
11
12
files
EMPIRIC**
EMPIRIC. DECKS
BOSTON'.DATA1960
BOSTON. DATA19 70
BOSTON. REVISED19 70
DENVER. DATA1960
DENVER.UTILITY .DATA
DENVER. DATA19 70
MINN . STPAUL . DATA1960
MINN . STPAUL . DATA1970
MINN . STPAUL . COMPLETE .LANDUSE
WASHDC. ALL. DATA
transferred to tape using the IBM
RECFM
• •- .
VBS
VBS
VBS
VBS
VBS
VBS
VBS
VBS
VBS
VBS
utility
LRECL
208
208
208
968
84
968
852
388
128
1164
IEHMOVE.
BLKSIZE NOTES
(PDS) EMPIRIC LOAD MODULES
(PDS) EMPIRIC FORTRAN SOURCE
7294
7294
7294
7292
7292
7292
3647
3647
7294
7294
** Load modules created for an IBM 370 computer.
-------
Table C.2.
DESCRIPTION OF EMPIRIC DATASETS
oo
Information needed to read EMPIRIC data
DSNAME*
BOSTON. DATA1960
BOSTON. DATA19 70
BOSTON. REVISED1970
DENVER. DAT A19 60
DENVER . UTILITY . DATA
DENVER. DATA19 70
MINN . STPAUL . DATA1960
MINN . STPAUL .DATA19 70
MINN . STPAUL . COMPLETE .LANDUSE
WAS HOC. ALL. DATA
NUMBER OF
IDENTIFICATION VARIABLES
EMPIRIC NAME** NUMBER (IDENT) (NOVA)
FINAL . CALIB . DATA19 60
FINAL . CALIB . DATA19 70
REV.CALIB.DATA1970
CG.B340.Y6070
RN.B04.UTIL
CG.B340.Y7080
THIRD60.VARABLS
THIRD70.VARABLS
COMPLETE. DISTRICT .LANDUSE
BASE110.Y6068CHG
6010
7010
1
340
4
340
311
312
126
0
50
50
50
240
6
240
211
95
30
289
(see Secti
NUMBER OF
SUBREGIONS
(NSUB) YEAR
125
125
125
183
183
183
95
95
108
110
-r
-
-
6070
0
-
0
0
0
0
* DATASET NAME (to be used for retrieval).
** Name of dataset appearing on EMPIRIC labels preceding data (see Section 1, p. III.89 for structure of
datasets). EMPIRIC names given here for reference only (these will appear on printout of a dataset
using the EMPIRIC software).
-------
Section 3
Contents of 'Datasets
The label information which appears at the beginning of each
\
dataset is reproduced on the following pages. For the specific definition
of a particular variable, the user is referred to Peat, Marwick, Mitchell &
Company. In addition, the Environmental Impact Center can refer the user
to the appropriate government official in each city.
Preceding the datasets of each city is a copy of the best available
analysis district map for that city. For. larger maps the user should
contact the Environmental Impact Center.
186
-------
EIC ANALYSIS ZONES FOR BOSTON
(Zone numbers correspond to alphabetical
order of towns. Note Boston itself
contains 12 zones starting with 112)
Figure B.3.
187
-------
IMP 243iape File Number 3:
DATASET NAME: BOSTON.DATA1960
EMPIRIC NAME: FINAL.CALIB.DATA1960
*
*
*
*
#
#
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
»
*
*
*
*
*
#
•*
t
*
*•
•Ir
1
2
3
4
5
6
7
8
9
10
11
12
(INT
13
14
15
16
17
18
(TOT
19
20
21
22
23
24
25
26
27
78
29
30
31
32
33
34
35
36
37
38
39
40
41
4?
43
44
45
45
47
48
49
50
ACRES
ACRES
MILES
MILES
MILES
MILES
MILES
MILES
OF
OF
Or
COMMUTER RAH.
RAPID TRANSIT
SERVED BY PU3LIC WATER
SERVED BY PUBLIC SEWER
OF PRIMARY (LINKTED-ACCESS, NON-INTERSTATE) ROADS
OF INTERSTATE HIGHWAYS
0=" PRIMARY PLUS INTERSTATE HIGHWAYS
RAPID TRANSIT RIGHTS-OF-WAY
RIGHTS-OF-WAY
PLUS COMMUTER RAIL siGHTS-OF-WAY
(BLANK)
NU'-'3ER OF FULL HIGHWA" INTERCHANGES
NUMBER OF PARTIAL HIGHWAY INTERCHANGES-
NUV3ER OF FULL PLUS PARTIAL HIGHWAY INTERCHANGES
ERCKANGES IMPLY ACCESS TO LOCAL STREETS).
MILES FROM CENTER OF POPULATION TO NEAREST HIGHWAY INTERCHANGE
NUMBER OF HIGHWAY RAMPS (I.E.* TO/FROM LOCAL-ACCESS STREETS)
TOTAL ACRES WITHIN 2 WILES OF H-IGHWAY INTERCHANGE
TOTAL ACRES WITHIN 1 MILE OF HIGHWAY INTERCHANGE
TOTAL ACRES WITHIN 1/2 MILE OF HIGHWAY INTERCHANGE
TOTAL ACRES WITHIN 1/2 MILE OF RAPID TRANSIT STATION
AL ACRES EXCLUDES WATEK AREA IN ITEMS 15-19).
(BLANK)
(BLANK)
'DRY' MANUFACTURING (ID EMPLOYMENT
(STANDARD' INDUSTRIAL CLASSIFICATION CODES 19,205,21,227,228 ,23,24,25»
27.301,302,311EXCEPT 311>,32(EXCEPT 324 AND 329),332»234 ,339,
341EXCEPT 347! ,25,36,37(EXC£PT 372 AND -373 ) ,33 ,.39 (EXCEPT 394 ))
'WET' MANUFACTURING (J2) EMPLOYMENT
(SIC 20IEX-CEPT 205 AND 206 ), 22 ( EXCEPT 227 A.-JD 228 ) ,264 ,265 .266,267 i
28»29(EXCEPT 291) ,30(EXCEPT 301 AND 302),311,324,329,331,335 ,335,347»
372,373,394)
'WET' MANUFACTURING (13) EMPLOYMENT
(SIC 206,261,262»263»Z91)
TOTAL MANUFACTURING EMPLOYMENT
INDUSTRIAL (NOr.'~MAf'UFACTU^I NG) EMPLOYMENT (SIC 01-17* 40-50)
COMMERCIAL (INCLUDING GOVERNMENT) EMPLOYMENT (SIC 52-94)
TOTAL EMPLOYMENT
TOTAL POPULATION
POPULATION IN GROUP QUARTERS
TOTAL HOUSEHOLDS
RESIDENTIAL ACRES
COMMERCIAL (INCLUDING INTENSIVE INSTITUTIONAL) ACRES
INDUSTRIAL (MANUFACTURING) ACRES
INDUSTRIAL (f»JON-MAWUFACTURINGl ACRES
EXT&MSIVE INDUSTRIAL ACRES
ACRES OF STREETS AND HIGHWAYS (INCLUDING MAJOR PARKING FACILITIES)
EXTENSIVE INSTITUTIONAL ACRES
ACRES OF RESTRICTED OPEN SPACE (E.G., RECREATIONAL)
VACANT ACRES
TOTAL ACRES
LOW JAlCOWfe HOUSEHOLDS (0-15 PERCENT ILE)
LOWER MIDDLE UUCOM£ HOUSEHOLDS US"-55", PERCENTILE>
LOW PLUS L3UER MIDDLE 1/VCOwS HOUSEHOLDS
UP?s a PUDDLE INtO«£ HOUSEHOLDS ($S-8Q PERCENTILE)
HI-SH INCOME HOUSEHOLDS <8
-------
IMP 243 Tape Pile Number 4
DATAS1T NAME: BOSTON.DATA1970
EMPIRIC NAME: FINAL.CALIB.DATA1970
* -
*
*•
*
*
*•
*•
*
-*
*
*
*
*
*
*
*
•*
*
*
I
2
3
4
5
6
7
8
<3
10
11
12
(INT
13
14
15
16
17
' 18
ACRES SERVED BY PUBLIC WATER
ACRES SERVED 5Y PUBLIC SEV.ER
MILES OF PRIMARY (LIMITED-ACCESS, NON-INTERSTATE) ROADS
MILES OF INTERSTATE HIGHWAYS
MILES OF PRIMARY PLUS INTERSTATE HIGHWAYS
MIL'ES OF RAPID TRANSIT R I GHTS-CF-WAY
MILES OF COMMUTER RAU R I GHTS-CF-wAY
MILES OF RAPID TRANSIT PLUS COMMUTER RAIL R IGHTS-OF-WA Y
MILES OF RAILROAD RIGHTS-OF-WAY
N'U'-'BER OF 'FULL HIGHWA'' INTERCHANGES /
NUMBER OF PARTIAL HIGHWAY INTERCHANGES
NUMBER OF FULL PLUS PARTIAL HIGHWAY INTERCHANGES
ERCHANGES IMPLY ACCESS TO LOCAL STREETS).
MILES FROM CENTER OF 'CPULATICN TO NEAREST HIGHWAY INTERCHANGE
NUMBER OF HIGHWAY SAM.'S (I.E., TO/FROM LOCAL-ACCESS STREETS)
TOTAL ACRES WITHIN 2 MILES OF HIGHWAY INTERCHANGE
'TOTAL ACRES WITHIN 1 NILE OF HIGHWAY INTERCHANGE
TOTAL ACRES WITHIN 1/2 MILE OF HIC-i'.vAY INTERCHANGE
TOTAL ACRES WITHIN l/Z KILE OF RAPID TRANSIT STATION
*• (TOTAL ACRES EXCLUDES WATE.l ARE\ IN ITEMS 15-19).
*
>
*
*
*
*
*
#
*•
*
*
*
*
*
%
*
*
*
*
*
*
*
*
«.
*
*
*
#
*
*
*
*
*
*
#
.19
20
21
22
23
24
25
26
27
28
29
32
31
32
33
34
35
36
3"?
38
39
40
41
42
43
. i.4
'»6
z.7
(BLANK)
(BLANK)
•DRY' MANUFACTURING HI) EMPLOYMENT
(STANDARD INDUSTRIAL CLASSIFICATION CODES 19 , 205 ,2 1 ,227, 228 .23 ,24,25 t
27.301,302, 3KEXCEPT 31 1 1 »32 I EXCEPT 324 AND 329 ; ,332 ,334 , 339 ,
34(EXCEPT 347) ,35,36, S7!EXCEPT 372 AND 373 ! .33 .39 ( EXCEPT 394) J
•WET' MANUFACTURING (!2) EMPLOYMENT
(SIC 20(£XC£PT 205 A\p 206 ) ,22 1 EXCEPT 227 AND 228 ! .264 , 265 ,266 ,267 «
28.29!EXCEPT 29 1 ) .30 ( EXCEPT 301 AMD 302 ) .3 1 1 .324,329 .33 1 . 335 ,336,347 »
372.373.394)
•WET' MANUFACTURING (13) EMPLOYMENT
1SIC 206, 261.262. 263, >91)
TOTAL MANUFACTURING EMPLOYMENT
INDUSTRIAL (NON-MANUFACTURING) EMPLOYMENT (SIC 01-17. 40-50)
COMMERCIAL (INCLUDING GOVERNMENT! EMPLOYMENT (SIC 52-S4)
TOTAL EMPLOYMENT
TOTAL POPULATION
POPULATION IN GROUP CJARTERS
TOTAL' HOUSEHOLDS
RESIDENTIAL ACRES
COMMERCIAL (INCLUDING INTENSIVE INSTITUTIONAL) ACRES
INDUSTRIAL MANUFACTURING) AC3ES
INDUSTRIAL [ jVON— MANUF ACTUR I No 1 AC3:iS
EXTrNSIV^ INDUSTRIAL ACRES
ACRES OF STREETS AND HIGHWAYS (INCLUDING MAJOR PARKING FACILITIES)
r'xTF.vSIVF INSTITUTIONAL ACRES
ACRES OF RESTRICTED CPE\ SPACE (E.G., f^CREAT ICNAL )
VACANT ACRES
TOTAL ACRES
LOW INCOT^C B0US£HOL£)f, 10~i3 ~ '-^i.ts* ilLc J
LOWER r/nDDlc l^COms HOU5£riOW>$ 4 15-55 FeRC£*VTlkE)
LOW PLUS LCWS8. jMiOtfi-^ ^'^^ H0^^!*f!i^3f- £ --IIP) '
UPPZR Mii^Le INW^CI ^'"^^ft^ p|pvc£/Lrit£}
t/ppm 'Hippie ptt*s wt&H I*XQ*£. ucus&ttct,os>
WfyBZR. Q? Ci>j*}WUT£A pUfiC STOPS
189
-------
IMP 243 Tape File Number 4
DATASET NAME: BOSTON.DATA1970
EMPIRIC NAME: FINAL.CALIB.DATA1970
* AS NUMBER OF RAPID TRANSIT STOPS
* 49 NUMBER OF COMMUTER RAIL PLUS RAPID TRANSIT STOPS
* 50 NUMBER OF RAPID TRANSIT STOPS WITHIN 1 MILE OF DISTRICT CENTROID
ID = 7010 NSUB = 125 NVA", = 50
190
-------
TMP 243 Tape File Number 5
DATASET NAME: BOSTON.REVISED1970
EMPIRIC NAME: REV.CALIB.DATA1970
#
*
*
* FOLLOWING VALUES MODIFIED
*
*
* 01-
* STR
* ICT
* 3
* 3
* 5
* 5
» 5
* 5
* 5
* 24
* 24.
* 44
* 44
» 49
* 49
* 65
* 65
* 68
* 68
* 68
* 68
* 71
* 71
* 101
* 101
* 101
* 117
* 117
* 117
* 117
VAR
IAB
LE
37
39
31
32
34
35
39
35
39
34'
39
37
39
37
39
31
32
33
39
31
39
32
37
39
34
35
33
39
NEW VALUE
170
5539
1107
2496
257
573
832-
273
10472
224
6100
157
2725
278
2047
1746
334
503
5369
1472
»
»
•
ft
»
*
*
»
*
»
*
*
»
»
*
*
*
•
»
*
7526.
170
611
2394
568
1100
955
73
*
»
*
*
*
*
*
10 =
1 NSUB = 123 NVA=?
50
191
-------
381
ENVIRONMENTAL IMPACT CENTER
ANALYSIS DISTRICTS
FOR
DENVER
(183 DISTRICTS)
Figure B.4.
-------
TMP 243 Tape File Number &
DATASET NAME: DENVER.DATA1960
EMPIRIC NAME: CG.B340.Y6070
Actual 1960 data is found in positions shown under the heading
"1960 Base Year." Data under the heading "1970 Forecast Year"
are not actual data but rather EMPIRIC forecast values. For
actual 1970 values, see dataset: DENVER.DATA1970.
1960
•BASE
YEAR
t,'H~0 U
1)
2)>
K_- 3)
4)
5)
6)
71
8)-
9)
10)
11)
12)
13)
14)
15)
16)
17)
18)
19)
20)
21)
2Z)
23)
24)
Z57 ~
2o)
0)
1970
FORECAST
Y cAR
-S-E H.CTL D
121)
122)
1 23 )
124)
- 125) ;.:. :
126)
127) '-.-:;
128)
t
129)
130)
131)
132)
133)
134)
135)
136)
137)
- E38)
139)
~TWJ
141)
Ltt)
143)
144)
- 1 45T ~
146)
— r?rr
14b)
149)
15J)
s
Li
LMI
UML
UI
J- UR1 :-..:•••
•• ---SF^vw .
•^"'•'••HF.-' =-•'••'•.
.:X-X.THH •.:••.-.
HHl
HH2
HH3
HH4
HHS
HH6+-
POO-14
Plb-iy
P20-24
P2I3-29
P30-39
P50-64
Pbb-J-
PQPINrtH
GQ
IN
TPGP
jj
^ tay
TABLh TP4-1 .
t. *• —
'OEPENDEhtT VARIABLES , ^ '
LUW INCOME FAMILIES
LOWER MIDDLE INCOME FAMILIES
-UPPER .MIDDLE INCOME, FAMILIES
UPPER INCOME FAMILIES
•UNRELATEDv:lNDIVIDUAL HOUSEHOLDS
-:-:STRUCTUREJ;TYP E.: ••• ^ ~^:\i~ ~:'~- -n;-?rt -"•»'<:
-SINGLE FAMILY -HOUSEHOLDS
rMULTI-FAMIUiY HOUSEHOLDS ".;..-
; .TOTAL HOUSEHOLDS •...,-:-?•-:. ;:.^..-:
":'SIZE^- '"-v-'-i -•• -;.;i--v^.v; '• .•"-;:."!H-;!;V;-"
1-PER5UN HOUSEHOLDS ,
2-PERSON HOUSEHOLDS ,:•• ' ', ; ':
3-PERSON HOUSEHOLDS
4-PERSON HOUSEHOLDS • - i, "-
5-PcRSUN HOUSeHULUS ,. ... .
6-OR-MORE PERSON HOUSEHOLDS
FOFULAriON IN HUUStHULUS t BY AUt
00-14
15-19
20-24
Z5-Z9
30-39
•4-U-4V
50-64
AGE 65 Af4D ULUfcK ~~ ~
TOTAL POPULATION IN HOUSEHOLDS
ttlSCcLLAhiEGUb
POPULATION IN GROUP QUARTERS
INMATES OF INSTITUIIUINS
TOTAL POPULATION
DUMMY
DUMMY
jur-lMV
DUMMY
193
-------
TMP 243 Tape File Number 6
DATASET NAME: DENVER.DATA1960
EMPIRIC NAME: CG.B340.Y6070
P L 0 V M E N T
31)
32)
J3)
34)
35) .;••••- --••
-36).: -:
37) .
36) ••, .
39)
-
47) ..--.:
48) '".'"
-^ 49 )•••:.••"-"'''•'•'
••-?• 501 • "•••-
51)-
'• 52) ..;
53 ) - '•'•• ' '
54) "' '
55)
151)
152)
153)
154)
155)
156)
157)
158)
159)
160)
161 )
162)
163)
164)
165)
loo)
167)
168)
169)
170)
171)
172)
173)
174)
175)
HTCU
TRAuE
FIRE
SERV-" • :
GOVT-
A CHIN-
CON.
". RET
WH
MIL
TGE
•TE-ACM
• TE-AC
"TE
EON ILU
. EONCLU
. • EONSLU
EON PL U
• • .•' .. . ';' ,\
XILUF
XILUF
UcPriviL>tiS 1 VAKiABLti
MAN UP . , TRANSP . , COMMUN ., UTILITIES EMPL.
TR&Uc E1MPLOYMciv4f
FINANCE, INSURANCE, L REAL ESTATE EMPL
••"'•' SERVICES.''EMPLOYMENT"! ""'•' :: -^ ^-: '"'':":" '&-•*?>••
CIVILIAN GOVERMHENT EMPLOYMENT • • , .; -
AGRICULTURE £. MINING EMPLOYMENT
CONSTRUCTION EMPLOYMENT
RETAIL EMPLOYMENT . . -
WHOLESALE EMPLOYMENT -
MILITARY EMPLOYMENT '
TOTAL GOVERNMENT EMPLOYMENT - ;
TOTAL EMPL, LESS AG, CON, £ MIL EMPL
TOTAL EMPLOYMENT, LESS AG & CON
TOTAL EMPLOYMENT - .
OTHER EMPLOYMENT VARIABLES
EMPLOYMENT BY LAND USE TYPE
EMPLOYMENT ON INDUSTRIAL LAND USE
EMPLOYMENT ON -COMMERCIAL LAND USE •
EMPLOYMENT ON SERVICE LAND USE
EMPLOYMENT ON PUoLIC LAND USE
SPECIAL. EMPLOYMENT ...
MTCU ON PLU ' - ' •-'-•• " '""•-
GOVT ON SLU ' . - ...... .-: ..
GOVT ON ILU •••••• : .
SERV ON ILU •:.,...:. -;.. .
SERV ON PLU - ••• ./'. '
SPECIAL FACTORS- .
PROPORTION ON EXTENSIVE INDUSTRIAL
PRCPORTION ON EXTENSIVE PUBLIC LAND USE
LAND USE
ACREAGES
56)
37)
58)
59)
60)
61)
62)
63)
o4)
65)
oo)
67)
176)
177)
178)
179)
160)
181)
182)
183 )
134)
185)
1 'do )
187)
RLU
SFLU
MFLU
ELU
ILU
CLU
SLU
PLU
XILU
XPLU
P/R
STS
RESIDENTIAL LAND USE
SINGLE FAMILY" LAND USE
MULTI-FAMILY LAND USE
EMPLOYMENT LAND USE
INDUSTRIAL LAND USE — INTENSIVE
COMMERCIAL LAND USE
SERVICES LAND USE
PUBLIC LAND USE — INTENSIVE
INDUSTRIAL LAND USE — EXTENSIVE
PUBLIC LAND USE — EXTENSIVE
PARKS AND RECRtATlUN LAND USE
STREETS AND HIGHWAY RIGHT OF *AY
194
-------
TMP 243 Tape File Number 6
DATASET NAME: DENVER.DATA1960
EMPIRIC NAME: CG.B340.Y6070
. o b }
69-i
71)
72)
73)
75)
7o)
77)
7o)
a u)
oi)
d2)
ci 3 )
.-'}. 66)
87)
c>8)
39)
90)
T R
9iJ
92)
93)
9t)
95)
96)
97)
98)
99)
100)
101)
166) '
169)
190)
191)
192)
193)
195)
19o)
197)
198)
199 )
2 00 )
201 )
202)
203)
20-r)
203)
••;•••• 206) V:' ' .-•-
207)
209)
210)
A N, S PORTA
2 11)
212)
2 13-) — _ '
2 It )
215)
21t>)
217)
213)
219)
220)
221)
102) 222)
OPPORTUNITIES
M
10
103)
INUTES
15
223)
224)
AVAIL
USED
UNOEV
TOTLU
*RLU
SSFLU
SMF LU -
3ILU
*SLU
•SULU
loEVlL
NHnD
NSFO
NHFD
NED ; :
GHHD
GED
T I 0 N
HAHHW
HAHHW
HAEHW
HAENW
TAHHW
TAHNW
TAcHW
TAENH
•CAHHW
CAHNW
CAEHW
CAfcNW
• MM ' I N
OLIMM
OLM1MM
AVAILABLE OR DEVELOPABLE LAND"" " '" "'
• TOTAL USED LAND
UNDEVELOPABLE OR RESTRICTED LAND
TOTAL LAND AREA
RATIOS
PROPORTION RESIDENTIAL LAND USE
PROPORTION SINGLE FAMILY LAND USE
PROPORTION MULTI-FAMILY LAND USE
. PROPORTION EMPLOYMENT LAND USE
PROPORTION INDUSTRIAL LAND USE
PROPORTION COMMERCIAL LAND USE
PROPORTION SERVICE LAND USE
~~PRyPaRTTC~N PUBLIC CANT) USt
PROPORTION USED LAND
P'RGT>URTTCT«l WATTA"8 L t LAND" " " '
USED LA^4b• / USED + AVAILA3LE LAND
DENSITIES
NET HOUSEHOLD DENSITY
NET SINGLE FAMILY DENSITY
NET MULTI-FAMILY DENSITY
NET EMPLOYMENT-DENSITY"'." ': . :';, "•
/ v GROSS HOUSEHOLD DENSITY
GROSS EMPLOYMENT DENSITY -.
- DUMMY • - . - . ,,;:
DUMMY . ' :. :
(ALL UTILIZING BASE YEAR ACTIVITIES)
HIGHWAY ACCESSIBILITIES
TO HOUSEHOLDS, HOME TO WORK IMPEDANCE
TO HOUSEHOLDS, NON-HOME IMPtDANCE
TO EMPLOYMENT, HOME TO WORK IMPEDANCE
TO EMPLOYMENT, NON-HOME IMPEDANCE
TRANSIT ACCESSIBILITIES
AS ABOVE
. AS ABOVE • '• -,
.AS 'ABOVE
AS ABOVE ' - ,
•-' COMPOSITE ACCESSIBILITItS
AS ABOVE ,-'•'"
AS ABOVE • . '
AS ABOVE
AS AbUVh
(ALL UTILIZING FUTURE YEAR HIGHWAY NETWORK)
SYMBOLIC NAMtS '10' OR '151 AS APPROPRIATE
""OPP TO LOWER INCQMfc FAMILIES
OPP. TO LOWER MIDDLE INCOME FAMILIES
195
-------
TMP 243Tape File Number 6
DATASET NAME: DENVER.DATAl960
EMPIRIC NAME: CG.B340.Y6070
105)
106)
107)
105)
109)
ADD
110)
111)
112)
113)
115)
lio)
117)
118)-
119*
120)
225)
226)
227)
228)
229)
I T, I 0 N
230)
231)
232)
2J3)
234)
23o>
237)
233)
239)
240)
OUMIMM
OK I MM
OUR I MM
OHHMM
OtMPMM
A L SPA
OPP. TO UPPER MIDDLE INCOME FAMILIcS
OPP. TO UPPER INCOME FAMILIES
OPP TO TOTAL HOUSEHOLDS
GPP TO TOTAL EMPLOYMENT
C t '
DUMMY
DUMMY
DUMMY
DU71MY
DUMMY
DUMMY
DUMMY
DUMMY
DUMMY
DUMMY ....
DUMMY
196
-------
TMP 243Tape File Number 7
DATASET NAME: DENVER.UTILITY.DATA
EMPIRIC NAME: RN.B04.UTIL
1960 1970 Description
1 4 Water Service Area
2 5 Sewer Service Area
3 6 Total Area
197
-------
IMP 243Tape File Number 8
DATASET NAME: DENVER.DATA1970
EMPIRIC NAME: CG.B340.47080
Data in this file are arranged in the same fashion as that
for DENVER.DATA1960. Actual 1970 values are found in the
same positions as the 1960 values on the 1960 dataset.
198
-------
Figure B.5.
ENVIRONMENTAL IMPACT CENTER ANALYSIS ZONES
for Minneapolis-St. Paul
199
-------
IMP 243Tape File Number 9
DATASET NAME: MINN.STPAUL»DATA1960
EMPIRIC NAME: THIRD60.VARABLS
1 LJ Q
•^rg^'l^^ff^^^fclKHI Sl^^^i^^^^^^El^iSS2Si£222Ps?a;5:-'
3 .,l.vr.tr^-,i;,:,TO.-vUMY£y_^^^ ."1.
l'?a-4y^;_^rZfr .>lji*^^ ^
5 LLMI C = LIC + LHIQ
"3^S26^^SI^^^^Ui)'WJ93^Sl>'''r ***'**• ^'"^
POPULATION BY AGE
GPPQTR- GROOP
2-59 J
m£&®i$m
?l^«5.Tv»P®8ffli
CCMH = 1RET -tJSVCFJR +• GOVED
tS&t^'Xfrtf^&TltF^Ft^riTStiVB^^TtfV'^jf^
^TC^Al^S^^^4^:^*^^I^G«S«S4fciS^S^a!i^
iJu^3 7-^-i^^^^^^^SiP33^Kfc^^SSs!^^^;^SSii^^^^^^SliS^S^
39
~ -"
^S^^fe^A-t-NO US'Sii^g^^Sas^sgigiSi;
^ACQPJ
200
-------
IMP 243Tape File Number 9
DATASET NAME: MINN.STPAUL.DATA1960
EMPIRIC NAME: THIRD60.VARABLS
-.«™»«>.<.M., ,-«.•••, 1
rSITE|,(.^CCESSlBILIJIES MULTIPLIED BY USED AREA
,^^
^I&S2£2&83S3l»B£fi£&U MB I; Q*U S E D A
49 AHU*USEDAf . "•' " ~"""""""
ER*OS
1ACJlfS^::.E.G.V- 123 - 12.3 ACRES)
" ' •
.,.
^^E^^ga^^^S^'» b^^b^^Bgff^^^
• * v^.^^^^.---^N«.)«j«
v:"' '
^S^5iafesisy^^^ifeSfe>«£..:»i:f«SSi«i
ssisiSE&lb^.Vv'&HiW^,^^^^
sF£f*4.^^{;if/l|^C.A'ai->*s--i^
^?^^
icSs^i&-Sa^i^i!SfS^t^i-i^SJ;s££^^,
^DiMtigS£Ic^EtAc«a
146 U.SEDAC/JCTAC _
'-"•• - -"—'••- "-/.(.USEDAC-i-V
.Z..v ^ ~. — 1A.8,,.,_,.,,,,, ..USE.D AC./.
I1".JL5.0,11^
rzi52.'~
201
-------
IMP 243Tape File Number 9
DATASET NAME: MINN.STPAUL.DATA1960
EMPIRIC NAME: THIRD60.VARABLS
155 NCA*VACAC/(USEDAC+VACAC)
>::;i. „ „ : 1 56 ,:^;::;( NC A+NPA) *VACAC/ (US EDAC.+V AC AC )
,ACTI VI TY-LAND DEVE LCPABILITY INDICESv^(i^.^,, _,^
" 1 58 VAC AC*LMI Q>TCTHU""""" **" ~ """"*'~7 ~"
".•T^~ VV.>'-r.-Vl59' " VACAC*UMl^Q/TOTHU^--i:SI^SS^.;:J^S£
160 VACAC*HIC/TOTHU
162_ .VAC.AC*CONM/TOTEMP
' VAC AC*T-OT.E M p/.t CT Hu:'i5KEiSSii;:^^ss::
164 VACAC*TOTHU/TCTEMP., ,v ,
riGOM PO 5 LIE) -ACC E SS IB ILLXY-DE NSXIY-rtANQSDEVlEtDP. AS JLHTY Li
165 _ AEMP*VACAC*TCTHU/NRA
;;N3^^NCA*NPA)-^4iiSiiaa..^-...
;*INDUS/,NIA.
;j»-^^«i«
AEMP*(VACAC+USEDAO*TOTHU/NRA
AHU* (VACACTUS EDACJ^COMM/ (
AEMP*VACAC/TOTAC
rtlBI LIJI ESl-(llNWEJ&HlEm^^I7J^I^2,"
NOUS
HACCfM
^HAUNHIQ,
ZHAH11
.^CCESSIBILITI ESTMULXI PjXEQL
.HAT N,nii«;*ii<:PnAr J 7T ^, »«
r~ I -I -M-i 4*1^ V*J ^^ Aj-J- C«-L/ Mx^^MAoJMv^-Wvvoe&xraw^^Xt-i&iwiiiwc Wtwv'A ^Ac-*v^A«-«> X^MUT jt*v6«,v< wSSv
,^HACCfM*USEDACm _^^WM>r_ ^^^^^^^^
AE f^ P^US ED AC «^wj>%^^A^^^ix«*«>i^M^i<^us!K>v^^
|y*^^r^^'^^!^y"^^''^^>~^^y*^^r!iy^a'''lfl' '"»-8^"
.»^,«*«^*»iJ^:;^««^»t«.»li(^T..- ^**-vJ
192 HAU!JHIO*VACAC j
-^^•-^•^w~s5sjp?»f»fp^:j;7u(,^tiAr j:pw^~^r^rr^^>^*»^*^^r-^-i<><^^ 1
^,^^X^-^-'^;^^^^!^^^'<^^^^Ei^-r4r1 U-"* V AU- W i^->«WA.Wrf «u«^^ WU-K^^^M »-«w«-A«*«Wt**-*tA»^**W«**a *SW*«S««*v*f«»-^' '^-^ ^- ^
., ..
105 TAC C!*M
>**• •"* ^ '^^."^^'^^^^'^^^^'^'^L %? :^ *j r* "w*-^ •***'v. «ws«i««r^ ^w0^**51*^^ *^^^^^-^^
^X^-^JiwiJ^S^ji*^^ M^P^ ffl^&^tM 2ft^«™*.-™ -(^ «*^W>^»UMI«»^<*«^H^M^^
"r ca*rr^^:v^ie^^ 1 1 WUT
j^.J|L-^> ^.I.^j^^ri^aM»S^*^jb3«%g^^Vij^Vaafaiiivj .-/•» 4_i " f f .£
: 199., __________ ,_ ...... ._-,,..JAH U
^^JTACCNM*USEDAC-~--^ .'
'SUSEDAC., _
204. ..... __________ _TAUMHIQ*USEDAC _
'
JTRANSIJJ-.:-ACCES>SIBlLITIES MULTJ PLI ED ,,BY VACANT AR£A _
"""' 210 J^LZ™rilTAU WHl'S*V ACAC~"7"'Tl'r^'.Z™™
202
-------
IMP 243 Tape File Number 10
DATASET NAME: MINN.STPAUL.DATA1970
EMPIRIC NAME: THIRD70.VARABLS
^^^^^^IUW^^^UMi^%iHM^S^^^^^^SSSM2^^'""
,_POP,,ULA,TION BY AGE
ISfelllgQlliS^^
~*-3~>i»*»**w^iite%^,^M.c>'^v..T,^^^^
^25.4.4 ^^^^ /w ;'ww^™^^r^Mr^^.^-
':,,' • l r •
^--«Ml'SG^+^M
...... .._.___S VC
':••.'.'• '• 3T- «"~
'.(C O.MPQ SLT£)_ACC ESS 1 .8 1
,?.\-'-j»rA^ 3 8 ••.c^i
.,i--,i^i i.iipr. ^ ^ *^ ™-
^
""'
,.41,
'42 ,
43,.
vr;^^"'-«?,
wJ&»^^ii^^
»«;^.v.^^->.-*^^«^-^->^^
..,.AHU
203
-------
DATASET NAME: MINN.STPAUL.DATA1970
EMPIRIC NAME: THIRD70.VARABLS
IMP 243 Tape File Number 10
.(COMPOSITE.)., ^ACCESSIBILITIES MULJJ PL! ED. BY ,US
-•,-.»_.--•>. .4- A-».-,~>-^-.»- ->-i*usv-i>,>,«*V A I
-.^T,.—— -.•.-,.-,w-.- ~ U *
- ••- -> .-.^T;,.,—
_„._. H-O-- ,...,•«,-.. :
-,v--'- -^-j'~r";T': A f—11 n^.t"ir--r- r> Ji r* •• ••-''^*:;T;':^'-'-TV«^
^^^o*^ Ac M P ^USE-DA O^---.^.^^^;^,^
.__ALLHI G*USEDAC__.,,_,,...^_.OT_W__..^__,J
: G*U S-E DAC^-5j«ifAA:'?-£kiik.,;ii5^ii^S^ii^ssaff£ii^i;1;
._ ...... ..,.50 ..... „
^^ui- 5 1 •xxi
52
••^^r-^^-^^^- -^
.. .
; A COM M-* V A C A C^^-
AEMP*VACAC
^- ; :• -7
- •
T*^ * t_ll !•*•»( A r" * IT <
SEWER SERVICE
"^r-. .^, ,- . -r^%7:--< >^-:-^'i,u-- '.v:-*>^*?y5
57
i-»T^-_i.i*-;.^«.i.«-_. -Y:vvr-;^*?^."5"<-i^';.'™tir^
. -^i>'-^^^-£^-~,^ • C3'CJi£^."£ii»fc~S-iwii^
?-*~;-K.'?--J.-~^..>^-:• VW--ST
i§gKER*IQJA.C,
~ 59 ^^
iittAUKBI£Q*USEOA€3
HAHU*USEDA,C
J
S^
^l^iy^wiigi^Eia
^?«?^n^^^srT^T!s^^
feas^-^i^sgi^^^^^^^^^^
-IAjLJKHig*yS.EDA.C,
{TRANS IJ JL. ACCESS IB .1 LI T IE S MU LT.JL g LI ED BY V. A .CANT™ A R E /S
L:*:~9o^J~*!iiiiiLi^AkND • '•" ' •' '"'" -•••'•j'-"-1*1"- •••-•
9.1 _._ JACCNM*VACAC
:'^;-~ 92 ^ -*:^*^ia*TA E M P* V AC A C-^^-^^^^^**-sa^a««=
93..___^__TALLPI Q*VACAC,r,^,^^^,f^m^^-,^,. .,.._.„]
'" 95 ~ """"'~IT'"''TAHU*VACAC...I. 111 ~
204
-------
IMP 243 Tape File Number 11
DATASET NAME: MINN.STPAUL.COMPLETE.LANDUSE
EMPIRIC NAME: COMPLETE.DISTRICT.LANDUSE
(Acres given in tenth of acres, e.g., 123 =12.3 acres)
1960 1962 1970
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
(IGNORE)
Residential Area
Commercial Area
Industrial Area
Public and Semi-Public Area
Recreational Area
Streets and Alleys
Water Area '
Vacant & Agricultural Area
Total Land Area
Note: Contains data for 11 districts which are not included in the
other two Minneapolis-St. Paul datasets.
The numbers of the 11 Extra Districts are: 1,2,3,10,11,13,14,25,
26,27,29.
205
-------
ENVIRONMENTAL IMPACT CENTER
ANALYSIS DISTRICTS
FOR
WASHINGTON B.C.
Figure B.6.
206
-------
IMP 243 Tape File Number 12
DATASET NAME: WASHDC.ALL.DATA
EMPIRIC NAME: BASE110.Y6068CHG
1960 1968 DELTA DESCRIPTION
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39'
40
41
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117.
118
119
120
121
122
123
124
125
126
127
128
129
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
FAMILIES WITH INCOMES OF *
FAMILIES WITH INCOMES OF
FAMILIES WITH INCOMES OF
FAMILIES WITH INCOMES OF
FAMILIES WITH INCOMES OF
FAMILIES WITH INCOMES OF
FAMILIES WITH INCOMES OF
FAMILIES WITH INCOMES OF
FAMILIES WITH INCOMES OF
FAMILIES WITH INCOMES OF
HOUSEHOLDS WITH INCOMES OF *
HOUSEHOLDS WITH INCOMES OF
HOUSEHOLDS WITH INCOMES OF
HOUSEHOLDS WITH INCOMES OF
HOUSEHOLDS WITH INCOMES OF
HOUSEHOLDS WITH INCOMES OF
HOUSEHOLDS WITH INCOMES OF
HOUSEHOLDS WITH INCOMES OF
HOUSEHOLDS WITH INCOMES OF
HOUSEHOLDS WITH INCOMES OF
LOWER INCOME FAMILIES
LOWER MIDDLE INCOME FAMILIES
UPPER MIDDLE INCOME FAMILIES
UPPER INCOME FAMILIES
1 PERSON HOUSEHOLDS
2 PERSON HOUSEHOLDS
3 PERSON HOUSEHOLDS
4 PERSON HOUSEHOLDS
5 PERSON HOUSEHOLDS
6+ PERSON HOUSEHOLDS
AGRICULTURAL
MANUFACTURING, TRANSPORTATION, COMMUNICATIONS,
& UTILITIES (MCTU)
RETAIL & WHOLESALE EMPLOYMENT (RETW)
FINANCE, INSURANCE, REAL ESTATE, SERVICE
(FIRES)
GOVERNMENT EMPLOYMENT (GOVT)
EMPLOYMENT ON RESIDENTIAL LAND
EMPLOYMENT ON INDUSTRIAL LAND
EMPLOYMENT ON INSTITUTIONAL LAND
EMPLOYMENT ON COMMERCIAL LAND
EMPLOYMENT ON AGRICULTURAL & VACANT LAND
RESIDENTIAL LAND USE (ACRES)
207
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IMP 243Tape File Number 12
DATASET NAME: WASHDC.ALL.DATA
EMPIRIC NAME: BASE110.Y6068CHG
I960 1968 DELTA DESCRIPTION
42 130 218 INDUSTRIAL LAND USE
43 131 219 COMMERCIAL LAND USE
44 132 220 INTENSIVE INSTITUTIONAL LAND USE
45 133 221 EXTENSIVE INSTITUTIONAL LAND USE
46 134 222 TOTAL INSTITUTIONAL LAND USE
47 135 223 PARKS
48 136 224 VACANT LAND
49 137 225 MISC. LAND USE
50 138 226 TOTAL LAND USE
51 139 227 USED LAND
52 140 228 USED & VACANT LAND
53 141 229 WHITE HOUSEHOLDS
54 142 230 NONWHITE HOUSEHOLDS
55 143 231 SINGLE FAMILY HOUSEHOLDS
56 144 232 MULTI FAMILY HOUSEHOLDS
57 145 233 TOTAL FAMILIES
58 146 234 TOTAL UNRELATED HOUSEHOLDS
59 147 235 TOTAL HOUSEHOLDS
60 148 236 TOTAL EMPLOYMENT
61 149 237 NET HOUSEHOLD DENSITY
62 150 238 NET EMPLOYMENT DENSITY
63 151 239 ALL ACTIVITY (HOUSEHOLDS & EMPLOYMENT)
64 152 240 NET ACTIVITY DENSITY
65 153 241 % LOWER INCOME FAMILIES
66 154 242 % LOWER MIDDLE INCOME FAMILIES
67 155 243 % UPPER MIDDLE INCOME FAMILIES
68 156 244 % UPPER INCOME FAMILIES
69 157 245 % LOWER & LOWER MIDDLE INCOME FAMILIES
70 158 246 % UPPER MIDDLE & UPPER INCOME FAMILIES
71 159 247 % FAMILY OF TOTAL HOUSEHOLDS
72 160 248 % UNRELATED HOUSEHOLDS OF TOTAL HOUSEHOLDS
73 161 249 % HH SIZE 1-2
74 162 250 % HH SIZE 3-4
75 163 251 % HH SIZE 5+
76 164 252 % WHITE HOUSEHOLDS
77 165 253 % NONWHITE HOUSEHOLDS
78 166 254 % SINGLE FAMILY HOUSEHOLDS
79 167 255 % MULTI FAMILY HOUSEHOLDS
80 168 256 % VACMT LAND
81 169 257 % USED LAND
82 170 258 USED LAND/(USED & VACANT LAND)
83 171 259 PARK/RESIDENTIAL LAND
84- 172 260 PARK/HOUSEHOLDS
85 173 261 GROSS HOUSEHOLD DENSITY (SQ. MILES)
86 174 262 GROSS EMPLOYMENT DENSITY (SQ. MILES)
208
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IMP 243 .Tape File Number 12
DATASET NAME: WASHDC.ALL.DATA
EMPIRIC NAME: BASE110.Y60&8CHG
1960 1968 DELTA DESCRIPTION
87 175 263 GROSS ACTIVITY DENSITY (SQ. MILES)
88 176 264 EMPLOYMENT/HOUSEHOLDS
(% Change 1960 to 1968)
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
TOTAL FAMILIES
TOTAL UNRELATED INDIVIDUALS IN HOUSEHOLDS
TOTAL HOUSEHOLDS
TOTAL EMPLOYMENT
TOTAL ACTIVITY
LOWER INCOME FAMILIES
LOWER MIDDLE INCOME FAMILIES
UPPER MIDDLE INCOME FAMILIES
UPPER INCOME FAMILIES
VACANT LAND
USED LAND
WHITE HOUSEHOLDS
NONWHITE HOUSEHOLDS
SINGLE FAMILY HOUSEHOLDS
MULTI FAMILY HOUSEHOLDS
AGRICULTURAL EMPLOYMENT
MCTU EMPLOYMENT
RETW EMPLOYMENT
FIRES EMPLOYMENT
GOVT EMPLOYMENT
EMPLOYMENT ON RESIDENTIAL LAND
EMPLOYMENT ON INDUSTRIAL LAND
EMPLOYMENT ON INSTITUTIONAL LAND
EMPLOYMENT ON COMMERCIAL LAND
EMPLOYMENT ON AGRICULTURAL & VACANT LAND
Breakdowns not given in EMPIRIC report or on dataset labels.
209
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
EPORT NO.
EPA-600/5-75-013
|3. RCCIPIENT'S ACCESSION-NO.
TITLE AND SUBTITLE
SECONDARY IMPACTS OF TRANSPORTATION AND WASTEWATER
INVESTMENTS: RESEARCH RESULTS
'S. REPORT DATE
July 1975
6. PERFORMING ORGANIZATION CODE
. AUTHOR(S)
Bascom, S.E., Cooper, K.G., Howell, M.P.,
Makrides. A.C., and Rabe. F.T.
i. PERFORMING ORGANIZATION NAME AND ADDRESS
8. PERFORMING ORGANIZATION REPORT NO.
Environmental Impact Center
55 Chapel Street
Newton, Mass.
10. PROGRAM ELEMENT NO.
HIA095 21 ART 11
11. CONTRACT/GRANT NO.
EQC 317
12. SPONSORING AGENCY NAME AND ADDRESS
13. TYPE OF REPORT AND PERIOD COVERED
14. SPONSORING AGENCY CODE
EPA-ORD
15. SUPPLEMENTARY NOTES
16. ABSTRACT : : ~ ' — ; ;
This report is the second of a two-part research study. The first report involved an
extensive review of previous research pertaining to secondary effects of highways,
mass transit, wastewater treatment and collection systems,- and of land use models
which might be utilized to project secondary environmental effects. The report is
published under the title: "Secondary Impacts of Transportation and Wastewater
Investments: Review and Bibliography", (EPA fo. 600/5-75-002, January, 1975).
The second report, presents, in this publication, the results of original research on
the extent to which secondary development can be attributed to highways and wastewater
treatment and collection systems, and what conditions under which causal relations
appear to exist. Case studies of recent development trends were made in four metro-
politan regions: Boston, Massachusetts, Denver, Colorado, Washington, D.C., and
Minneapolis-St. Paul, Minnesota. Data for the four metropolitan regions were analyzed
using econometric techniques and simulation modeling. The data tape (TMP 243) is
stored with Optimum Systems Incorporated, Washington, D.C.
This report consists of four sections: an Introduction and Summary of Findings; a
technical documentation of case studies and econometric analysis; an evaluation of
the Findings and Suggestions for Further Research; and Appendices summarizing the
17.
dynamic model ana its
'ORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
c. COS AT I Field/Group
Analytical techniques; Regional/community
development; Land Ose; Water Resources
Planning; Data Collection, Storage, and
Retrieval; Environment; Highways Effects;
Investments; Wastewater Treatment
Wastewater Treatment;
Data Collection;
Analytical techniques
13. DISTRIBUTION STATEMENT
19. SECURITY CLASS '(This Report)
21. NO. OF PAGES
20. SECURITY CLASS (Thispage)
22. PRICE
EPA Form 2220-1 (9-73)
»U.S. GOVERNMENT PRINTING OFFICE3975 631-560/939 1-3
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