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OCR for page 22
The Dynamic Characterization
of Cities
ROBERT HERMAN, SIAMAK A. ARDEKANI,
SHEKHAR GOVIND, AND EDGAR DONA
The unprecedented rate of urban development over the last few gen-
erations has led to a wide variety of human problems, many of which
stem from the nature and growth of a city's infrastructure. Blumenfeld
(1971) has studied the consequences of urban sprawl, the process by which
a metropolis (Greek for mother city defined as the largest center of
activity in a region) develops satellite cities or suburbs and evolves into
a megalopolis (also Greek, meaning great city originally a town in the
Peloponnesus that the ancient Greeks unsuccessfully tried to develop into
a large city). Gottman (1961) recognized this phenomenon taking place
along the Boston-Washington corridor and saw in it new patterns in the
use of space. He argued that this reorganization of space was inevitable
because of the gregarious nature of opportunity-seeking people and leads
to a high concentration of white-collar and service-oriented workers.
Geographers have also looked at the growth of the metropolitan frame-
work. Adams (Chapter 6 in this volume) has used modes of transportation
to classify urban development into four eras or epochs: sail-wagon, iron
horse, steel rail, and auto-air-amenity. Each epoch sees unique metro-
politan growth patterns being molded by the transportation available. Ad-
ams has also speculated about the epoch yet to come, one in which
telecommunications and not transportation is the prime mover.
Dantzig and Saaty (1973) have looked at some of the problems that
have been identified in today's urban environment and have proposed new
ideas in space and time use. Their "compact city," which was designed
using a mathematically and architecturally elegant approach, makes full
~2
OCR for page 23
THE DYNAMIC CHARACTERI7ATION OF CITIES
~3
-
use of time and the third dimension in space in reorganizing urban activ
. .
tles.
Studies in urban planning have not been confined to printed words
and designs alone. The incorporation of Le Corbusier's ideas into the
building of the metropolises of Chandigarh (India), Brasilia (Brazil),
and the more recent example of Islamabad (Pakistan) suggests that ideas
in urban design have a practical side, too. Such incorporation also
demonstrates that if problems (especially those of scale) can be ac-
counted for in the design process, they can be addressed effectively
(Le Corbusier, 1967, 1971).
Any planning exercise that deals with the issues raised by modern
urban growth must account for the unique fingerprint of each city. This
requires grouping cities according to a "phylum," that is, an organic
group in which all members exhibit a basic similarity of ground plan
and evolve through similar stages, yet are completely dissimilar from
one another.
One of the grey areas in this arena is the classification of cities on the
basis of their infrastructure. It is characteristic of urban studies- to establish
a quantitative variable based on certain attributes of the city (such as
population or area). These variables are then most often viewed in isolation
from other variables, and cities are ranked or ordered on some common
scale. Attempts have also been made to stratify metropolitan areas into
relatively homogeneous groups on the basis of predetermined criteria (Go-
lob et al., 1 97 1 , 1 9721.
There have been few attempts to examine the evolutionary path of a
city and, using data across time and space, create a morphology for
different urban settings. A classification scheme introduced by Herman
and Montroll (1972) for characterizing countries and representing them
graphically (in the form of multiaxis phase diagrams) is extended for use
in this context to differentiate the form and structure of various cities.
In this chapter we shall establish a general framework for studies in the
taxonomy of cities. Taxonomic studies, in the classical sense, have gen-
erally been restricted to biosystematics the classification of living things
(a process in which historical data are relevant). However, if we accept
that evolution is the fundamental mode of change for both organic and
inorganic systems (as expounded by Herbert Spencer t1820-1903] and
Teilhard de Chardin t1881-19551 and discussed recently by Prigogine et
al. [19781), then it should be worthwhile to bring a historical perspective
to the investigation of a means for differentiating among cities.
The study described in this chapter includes historical data for variables
that represent several infrastructural attributes of one city, namely, Austin,
Texas; it also examines the evolutionary track of the variables from the
OCR for page 24
24
HERMAN, ARDEKANI, GOVIND, Al!,'D DONA
turn of the century to the present, using as its sources the City Directory
of Austin (1959-1985) and the General Directory of the City of Austin
(1900-1985~. Similar variables are examined for eight other cities (At-
lanta, Chicago, Cincinnati, Houston, Los Angeles, Miami, New York,
and Seattle) for the present. Because pictorial representations of data
derived from a complex situation may provide insight into the mechanisms
of the system, the first set of data will be evaluated from this point of
view. The second series of data will be used to determine which variables
statistically discriminate or correlate best across the cities under study.
Our intention in this chapter is to explore a means of differentiating
among cities according to the mutually dependent areas of a city's current
character and its evolutionary path. Eventually, formal models might be
constructed to describe possible changes for a set of cities with similar
ground plans.
EVOLUTION OF A CITY
The figures that follow are pictorial representations of the evolutionary
path taken by certain attributes of the city of Austin. Comparisons across
both time and space require the use of data that are either normalized on
some scale or reduced to a dimensionless quantity. It is a common practice
to use population as a normalizing variable and to represent attributes on
a per capita (or its reciprocal) basis. Density functions in two dimensions
(involving area) have also been used previously for normalization. It would
be worthwhile to investigate density functions formed in one dimension
when the functions are normalized with respect to the total length of streets
in a city. Figures 2-1, 2-2, and 2-3 show how three basic variables-
population, area, and street miles have changed over time. It would be
interesting to observe the changes in lane miles, which reflect both road
width and length, rather than street miles; unfortunately, such data are
extremely difficult to obtain.
Figures 2-4, 2-5, and 2-6 show how the basic variables change with
respect to one another. The density of population shows an approximate
linear increase from 1900 to 1950 (Figure 2-41; from 1950 to 1980, how-
ever, the city's growth in area was faster than the corresponding growth
in population. The 1985 peak represents the spurt of growth the city has
had in the 1980s. Density of street miles may give a general indication
of the "efficiency" of land use in Austin (Figure 2-51. The decreasing
trend from 1950 onward suggests that even though the city acquired various
tracts, this land was not opened up to the same extent as land acquired
before 1950. Another interpretation is that the density of streets in the
inner city (areas included in the city limits until 1950), may not be greater
OCR for page 25
THE DYNAMIC C,IIARACTERI7AIfON OF CITIES
500000
Q
o
to
400000
300000
200000
1 00000
O
'10 '15 '20 '25 '30 '35 '40 '45 '50 '55 '60 '65 '70 '75 '80 '85
Year (1900'S)
FIGURE 2-1 Population of Austin, 1910 - 1985.
than the density of streets in the suburban areas adjoining the city (de-
veloped after 1950), and the decline comes simply from the inclusion of
tracts of undeveloped land. The density of population per street mile has
remained fairly constant from 1955 to 1980 (Figure 2-6), which supports
(although not conclusively) the argument that the effect is due to the
inclusion of undeveloped land. One immediate conclusion to be drawn
from Figure 2-6 is that growth in the number of street miles in the city
has lagged behind growth in population over the last 70 years. This state
150
100
. _
In
-
~ 50
'10 '15 '20 '25 '30 '35 '40 '45 '50 '55 '60 '65 '70 '75 '80 '85
Year (1 900's)
FIGURE 2-2 Area enclosed by the city limits of Austin, 1910-1985.
OCR for page 26
26
2000 - .
a,
o1 000
in
._
O
lIERMAN, ARDEKANI, GOVI1!iD, AND DONA
T =.l~4
~11~
. . . . . . . . . .
'15 '20 '25 '30 !35 '40 '45 '50 '55 '60 '65 '70 '75 '80 '85
Year (1900'S)
FIGURE 2-3 Miles of streets in Austin, 1915 - 1985.
ment reflects a simple measure of the increasing number of people sharing
the use of one unit of the transportation infrastructure.
Another indicator of the load imposed on infrastructure is the number
of motor vehicles in a city. Figure 2-7 shows that the total number of
registered vehicles in Austin has grown at an exponential rate that is much
faster than the growth in population. The number of vehicles per capita
demonstrates this fact (Figure 2-~. As might be expected from the pre-
ceding discussions, both the number of vehicles per street mile and the
number of vehicles per square mile have increased exponentially since
2000
. .
'10 '15 '20 '25 '30 '35 t40 t45 '50 t55 '60 t65 70 75 '80 '85
Year (19O0'S)
FIGURE 2-4 Population density of Austin' 1910 - 1985.
OCR for page 27
THE DYN~IC CHARACTERIZATION OF CITIES
20
-
-
0' 10
~n
o
u,
a)
._
hi.
O
27
'15 20 25 30 t3540 45 50 55 t60 65 70 75 80 85
Year (1 900's)
FIGURE 2-S Density of street miles in Austin, l91S-1985.
1950 (Figures 2-9 and 2-10~. It is worth noting that Figures 2-8, 2-9, and
2-10 all have a similar characteristic hump from 1920 to 1945; after 1950
they show exponential growth. This phenomenon indicates that strong
correlations exist between the normalizing variables and the number of
vehicles.
One of the more intriguing findings in this study arises from different
functions of the number of restaurants (Figures 2-1 1, 2-12, and 2-131.
Except for localized fluctuations, the ratio between the size of the pop-
ulation of Austin and the number of restaurants has been remarkably steady
400 - .
hi; 300
An
~ 200
o
-
es
-
~100
o
o
'15 '20 '25 '30 '35 '40 '45 '50 '55 '60 '65 '70 '75 '80 '85
Year (1900's)
FIGURE 2-6 Population per street mile in Austin, l91S-1985.
OCR for page 28
28
500000
400000
._
o
200000
Z 1 00000
300000
O
HERMAN, ARDEKAfII, GOVIND, AND DONA
= - ,,,.. ~
. ._ ~ ===
~ ~:~
~ A_ .
'20 '25 '30 '35 '40 '45 '50 '55 '60 '65 '70 '75 '80 '85
Year (1 900's)
FIGURE 2-7 Total number of vehicles in Austin, 1920-1985.
from 1900 to 1985 (mean = 678, standard deviation = 1591. Charts
showing the number of restaurants normalized by area (mean = 4.2,
standard deviation = 1.3) and street miles (mean = 0.4, standard de-
viation = 0.1), also exhibit a similar consistency over a long period of
time (Figures 2-12 and 2-131. It would be interesting to compare these
values for different cities, assuming that other cities display such char-
acteristic numbers as well.
The ratio of population to the number of restaurants could, for example,
reflect the degree to which a city is service oriented. Preliminary studies
of data from San Antonio indicate that a steady-state ratio of population
n
._
Q
' 0.6
In
.O
0.4
0.0
'20 '25 '30 '35 '40
'45 '50 '55 '60 '65 '70 '75 '80 '85
Year (1900's)
FIGURE 2-8 Number of vehicles per capita in Austin, 1920-1985.
OCR for page 29
THE DYNAMIC CHARACTERIZATION OF CITIES
400
-
:; 300
200
~n
-
c' 100
O
~9
1:P ~1 ~ =
-~-
I . . . '": t :i ·
'20 '25 '30 '35 '40 '45 '50 '55 '60 '65 '70 '75 '80 '85
Year (1900'S)
FIGURE 2-9 Number of vehicles per street mile in Austin, 1920-1985.
to the number of restaurants is not unique to Austin. In San Antonio the
number of people per restaurant has a mean of 809 and a standard deviation
of 359 (City Directory of San Antonio, 1900-1985~.
The level of sharing of selected services (auto dealers, doctors, lawyers,
and contractors) by the population (Figures 2-14 and 2-15) may represent
key factors in the growth of a city. The number of doctors, including both
physicians and dentists, indicates the level of health care available to the
city's inhabitants. A lower population number per contractor implies higher
4000
- 2000
In
-
C)
._
~1 000 ~
o
'20 '25 '30 '35 '40 '45 '50 '55 '60 '65 '70 '75 '80 '85
Year (1 900's)
FIGURE 2-10Number of vehicles per square mile in Austin, 1920-1985.
OCR for page 30
30
ce 800
in
a:
-
o
._
HERMAN, ARDEKANI, GOVIND, AND DONA
600
400
200
o
_w
_
'10 t15 20 25 t30 '35 t40 '45 t50 t55 t60 65 t70 75 '80 '85
Year (1 900's)
FIGURE 2- 1 1 Population per restaurant in Austin, 1910-1985.
construction activity. The number of law firms could indicate essential
aspects of the city and the type of activity on which it thrives.
Various conclusions may also be drawn by looking at the utility hookups
in a city (Figures 2-16 and 2-17~. One could reason that every dwelling
unit would probably have its own electric meter; in the case of water,
however, most apartment complexes do not have separate meters for each
apartment. For the most part, charges for this utility are assessed on a
fixed rate (which may be included in the rent of the apartment). Data on
6
._
~ 4
in
-
3
in
0
a:
1
. .
. .
'10 '15 '20 '25 '30 '35 '40 '45 '50 '55 '60 '65 '70 '75 '80 '85
Year (19OO'S)
FIGURE 2-12 Number of restaurants per square mile in Austin, 1910-1985.
OCR for page 31
THE DYNAMIC CHARACTERI7A TION OF CI TIES
0.5 - .
-0.4
0.3
_.
~0.2
CO
lo,0.1
~:
0.0
31
'15 '20 '25 '30 '35 '40 '45 '50 '55 '60 '65 '70 '75 '80 '85
Year (1900'S)
FIGURE 2-13 Number of restaurants per street mile in Austin, 1915-1985.
the number of electric meters and water meters could therefore be used
to characterize the composition of the housing market (Figure 2-161. Fur-
ther, the volume of telephone numbers in use may reflect not only the
average family size (assuming one telephone number per family) but also
the business activity of the city (Figure 2-17~.
Austin's annual per capita consumption of water appears to have re-
mained stable at 70,000 gallons since 1970 and shows no signs of in-
creasing (Figure 2-181. This variable could be used as an indicator of
geographical and climatological differences among cities. Another excel
4000
. ~
a)
3000
~5
o
- 2000
._
Q 1 000
O
'10 '15 '20 '25 '30 '35 '40 '45 '50 '55 '60 '65 '70 '75 '80
Year (1 900's)
FIGURE 2-14 Population per auto dealer in Austin, 1910-1985.
OCR for page 32
32
1,400
1,200
1,000 -
c
° 800
-
lo
600
400
200
O
HERMAN, ARDEKANI, GOVIND, A1!iD DONA
1 1
_ _
1 1 1 1 1111 1 1
T T T.
1 11
.
, .
'00 '05 '10 '15 '20 '25 '30 '35 '40 '45 '50 '55 '60 '65 '70 '75 '80
Year (1 900's)
| HI Population per Doctor
HI Population per Contractor ~ Population per Law Firm
FIGURE 2-15 Population per doctor, law firm, and contractor in Austin, 1900-
1980.
lent scale for the comparison of cities would be per capita annual power
consumption because it measures an infrastructural attribute that is often
of prime concern (Figure 2-191. Cities that use electrical energy for trans-
portation including escalators and elevators as well as rapid transit sys-
tems may show higher per capita consumption of electric power than
other cities. Weather and industry (among other factors) affect this variable
as well. For this reason, per capita power consumption might be an ex-
cellent discriminant among cities.
0.45
0.40
0.35
54 0.30
g 0.25
`~, 0.20
0.15
0.10
0.05 ~
moo ~
it,
_ .
~ l
~ l
'00 '05 '10 '15 '20 '25 '30 '35 '40 '45 '50 '55 '60 '65 '70 '75 '80 '85
Year (1 900's)
~ Electric Meters per Capita Cl5 Gas Meters per Capita [a Water Meters per Capita .
FIGURE 2-16 Electric, gas, and water meters per capita in Austin, 1900-1980.
OCR for page 60
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