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Cities and Their Vital Systems: Infrastructure Past, Present, and Future (1988)

Chapter: 2 The Dynamic Characterization of Cities

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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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Suggested Citation:"2 The Dynamic Characterization of Cities." National Research Council. 1988. Cities and Their Vital Systems: Infrastructure Past, Present, and Future. Washington, DC: The National Academies Press. doi: 10.17226/1093.
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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

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

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

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.

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.

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.

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.

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.

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.

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.

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.

THE DYNAMIC CHARACTERIZATION OF CITIES 0.6 I 0.5 CO 0.4 ~n z o a) 0.1 0.3 33 o.o '20 '25 '30 '35 '40 '45 '50 '55 '60 '65 '70 '75 '80 '85 Year (1900's) FIGURE 2-17 Telephone numbers per capita in Austin, 1920-1985. The exponential rise in postal receipts in Austin (Figure 2-20) might not be so steep if this chart were further normalized by the corresponding year's postage rates and indexed for inflation. Current levels of postal revenues could be a strong indicator of the nature and level of the business of the city. It would also be interesting to examine the levels of overnight private letter and package delivery services. Size rather than quantity is the most pertinent question when looking at the hospitals and banks in a city. For instance, data regarding the number of hospital beds or the number of admissions may prove to be more 80 60 40 20 O '50 '55 '60 '65 '70 '75 '80 '85 Year (1900's) FIGURE 2-18 Annual water consumption per capita in Austin, 1950-1985.

20 CO - lo 10 ._ 8 or I ~ _ := '25 '30 '35 lIER]IAN, ARDEKANI, GOVIND, AND DONA 'at '45 '50 '55 '60 '65 '70 '75 '80 '85 Year (1900'S) FIGURE 2-19 Annual power consumption per capita in Austin, 1925-1985. revealing than the number of institutions. Similarly, for banks the number of customers or net assets and liabilities may be more meaningful than the number of banks. The number of hotels and motels in relation to the size of the permanent population is one measure of the transient population of a city. As in the case of hospitals and banks, however, the question of size confounds the issue. Data on hotel revenues and the number of beds available in a city might provide more definite answers. For example, data based on the 150 , __ == CI5; , . rid' _ _ co100- __ C', _ ~ _, ~ _ _ ~' __ 'a,= = cry50- _ _ ~' = = An, _ who ~' = _ , _ _ . o '10 '15 '20 '25 '30 '35 '40 '45 '50 '55 '60 '65 '70 '75 '80 '85 Year (1 900's) FIGURE 2-20 Annual postal receipts per capita in Austin, 1910-1985.

THE DYNAMIC CHARACTERIZATION OF CITIES 35 combined numbers of hotels and motels do not indicate the sharp increase in the number of motels after World War II and the decreasing number of hotels during the same period. COMPARISON OF CITIES In this study the first step in characterizing the dynamics of cities was to compare current demographic, socioeconomic, and infrastructural char- acteristics of several cities. Such comparisons are also fruitful in identi- fying key variables that may be used to characterize a single city through time. One city was selected from each of eight major urban regions of North America. The eight regions are the Boston-New York-Washington mega- lopolis (Bosnywash), the Great Lakes, California, the urban South, Flor- ida, the Gulf Shore, the Ohio Valley, and the Northwest (Figure 2-21 and Table 2-11. The selection of these regions is based on Maurice Yeates's North American Urban Patterns (1980) in which he subdivides North America into major urban regions using three criteria: (1) population density, (2) commuting patterns, and (3) size. For the current study, the cities of New York, Chicago, Los Angeles, Atlanta, Miami, Houston, Cincinnati, and Seattle were chosen to represent the eight regions, re- spectively. Recent data were used to examine 55 variables (see Table 2- 2) across the eight cities. DATA COLLECTION AND INTERPRETATION The values of the variables listed in Table 2-2 were extracted from numerous sources and therefore are not associated with a single year. An attempt was made, however, to maintain the same year for a single variable across the eight cities. In addition, most of the data are as recent as 1980. The values of the variables for each of the eight cities, together with their years, units, and sources, are given in Appendix B. Note that no values are reported for bus ridership (variable 40) in the eight cities. Nevertheless, to emphasize the importance of considering bus ridership as a characteristic variable, bus ridership has been included in the tables of Appendix B. Closer examination of the data in Appendix B reveals several dominant characteristics among the eight cities under study. New York City, for example, far exceeds the others in population density (23,2X5 persons/sq ml), followed by Chicago (13,1741; notable tall buildings (118 buildings above 20 stories), followed by Chicago (451; number of theaters (671), followed by Chicago (1691; hotel rooms (100 per 1,000 population), fol- lowed by Chicago (441; average residential electric bill ($90 per month),

36 U) 1 J&: _ S ~ 3 , ~ ° .a o; - ~, - r / to to Cat . - _ ~ 0 Z o . ~, Cat

THE DYNAAlIC CHARACTERIZA TION OF CI TIES TABLE 2-1 Distribution of Population Among the Major Urban Regions of North America, 1975-1976 37 Size Population (in thousands Density (persons Major Urban Region (in thousands) of square miles) per square mile) Bosnywash 44,384 102.58 432.7 Lower Great Lakes 37,569 128.38 292.6 California 19,668 67.75 290.3 Urban South 12,669 105.39 120.2 Plonda 7,440 35.96 206.9 Gulf Shore 6,790 42.69 159.1 Ohio Valley 6,150 19.20 320.3 Northwest 5,738 45.94 124.4 SOURCE: Yeates (1980). followed by Miami ($531; percentage of perimeter on water (76 percent), followed by Seattle (59 percent); amount of revenue per capita ($2,370), followed by Atlanta ($8731; city expenditures per capita ($2,036), fol- lowed by Atlanta ($9731; and workers using public transportation (242 per 1,000 population), followed by Chicago (1891. New York City also ranks first in miles of freeway (0.37 mi/sq ml), followed by Los Angeles (0.33~; and miles of sewer lines (20.4 mi/sq mid, followed by Chicago (18.91. The above characteristics identify New York as the most compact city among the cities studied. Yet New York is a considerably less compact city than that proposed by Dantzig and Saaty (1973) in Compact City A Plan for a Liveable Urban Environment and less compact than the more decentralized version of this concept recently set forth by Beck (19861. Dantzig and Saaty proposed that a circular city 8,840 feet (ft) in radius and 16 levels high could comfortably house nearly 2 million people. This 480-ft-high domed city would have a total floor area of 141 sq mi, on a land area of 8.81 sq mi, for a population density of 227,100 people/sq mi, compared with New York's 23,285 people/sq mi. The city would be capable of housing 600,000 families (300 families per 1,000 population) in homes (each 2,400 sq It in area) or apartments (each with a floor area of 1,400 sq ft). There would be three main transport modes in the compact city: hori- zontal mass transit (trams), vertical mass transit (elevators), and private electric automobiles. The horizontal transit system would have 200 transit cars with a total capacity of 737,280 passengers per day and a total track length of about 62 mi (0.44 mi/sq ml). There would also be 256 elevator banks with 32 cages per bank providing a vertical transportation capacity

38 HERMAN, ARDEKANI, GOVIND, AND DONA TABLE 2-2 Variablesa Studied in Each City No. Variable No. Variable l Pon,~l~tion 28 Hospital beds 29 Airports 30 Airport plane departures 31 Enplaned passengers 32 Parks 33 Stadiums 34 Cemeteries 35 Churches 36 TV stations 37 Radio stations 38 39 40 41 42 43 44 45 46 2 Area 3 Population density 4 Perimeter 5 Form factor 6 Percentage of perimeter on water 7 Median age 8 Average income 9 Revenue 10 Expenditures 11 Bonded debt 12 Households 13 Families 14 Workers employed in central business district 15 Workers using public transit 16 Total employment 17 School enrollment 18 Postal revenue 19 Library budget 20 Library books 21 Libraries 22 Schools 23 Colleges and universities 24 Notable tall buildings 25 Hotels 26 Hotel rooms 27 Hospitals and clinics Theaters Bus transit route length Buses Rail transit track length Rail cars Transit ridership Car registrations Length of freeways Length of nonfreeway roads 47 Bridges 48 Tunnels 49 Water lines 50 Water use 51 Sewer lines 52 Sewage treated 53 Solid waste generation 54 Telephones 55 Residential electric bills aSee Appendix A for definitions of selected variables. Of 300,000 passengers per minute. The 32 radial and 26 concentric road- ways would be used by 10,000 electric automobiles (0.005 per capita) as well as bicycles on protected, parallel rights-of-way. A total of 3,130 m of roads (22.2 mi/sq mi) would be needed. The total cost of constructing this city is estimated to be $36.5 billion in 1969 dollars. The city would contain 27 sq mi of parks (8.67 acres per 1,000 population) and 6.5 sq mi of work space. All offices and businesses would be concentrated in the central core exchange and the midplaza atrium. The city dwellers could be adequately served by 1,500 physicians and 32 clinics. In contrast to New York, the cities of Los Angeles and Houston can be characterized as highly decentralized and thus are least like a compact city. Los Angeles is ranked highest in number of hospitals and clinics

THE DYNAMIC CHARACTERIZATION OF CITIES 39 (822), followed by Chicago (123); number of airports (4), followed by Chicago and Seattle (3); television stations (18), followed by Chicago (9); and radio stations (72), followed by Seattle (481. Houston has the greatest perimeter ( 1 87 miles), followed by Los Angeles (144~; highest average annual income ($10,958 per capita), followed by Cincinnati ($9,534), largest employment (519 per 1,000 population), fol- lowed by Seattle (5061; and greatest number of public schools (1,944), followed by Miami (1,0501. A more systematic approach in comparing the eight cities under study is through a statistical technique known as factor analysis (Kendall, 1975), which has been used by Golob et al. (1972) to compare cities' needs for arterial roadways. In this technique a large number of variables are clus- tered into a few groups, each of which contains a set of mutually highly correlated variables. Each group is then represented by the variable that explains the greatest amount of variation in the data across, in this case, the cities studied. Therefore, a large number of variables can be reduced to only a few pertinent variables, which are used to characterize and compare the cities. Statistical Analysis Identifying the nine most descriptive variables to characterize the eight cities involves a twofold procedure. In the first stage the dimensionality imposed by the set of 40 variables for which data are available for all eight cities is reduced by clustering the variables into nine disjoint subsets or clusters such that any one cluster contains at least two but not more than seven variables. In the second stage the clusters are subjected to factor analysis to estimate the relative leverage exerted by each variable in the cluster. The outcome of the two-step process gives the ranking of variables within a cluster on the basis of the common variance explained. Using this approach, we start by establishing broad categories under which the variables can be uniquely classified. Each category that contains more than seven (one less than the number of cities studied) variables is further subdivided into two subcategories: (1) wants and (2) needs. An exception is the transportation category, which is also divided into wants and needs even though it includes only six variables. This has been done to emphasize the importance of considering other transportation variables, listed in Table 2-2, in any future analyses of this type. Although this clustering technique will not guarantee clusters with highly correlated variables, it is far more rational than a methodology based entirely on statistical considerations. In correlation methods of clustering, many variables, including those that are logically unrelated, may each fit

40 HERMAN, ARDEKANI, GOVIND, AND DONA into a cluster equally well. The correlation matrix for the variables to be clustered is shown in Table B-9 (Appendix B). To rank the variables in clusters so that a single variable can be used to explain a large body of data, the principal-component approach in factor analysis is used. Its primary concern is to resolve a set of variables linearly in terms of a small number of underlying factors, which are themselves uncorrelated. Mathematically, the principal-component approach is an attempt to rep- resent a variable Xj in terms of several factors Fogs as in the following model: Xj= aloft + aj2F2 + + ajnFn, where the coefficient ajn, the factor loading, is an indication of how significant the associated factor is in representing the observed variable. The underlying characteristics of the observed variables, their inter- correlations, and the amount of variance individually explained, among other results, can be derived by analyzing the matrix of the factor loadings. Note that the total variance explained by a factor, the so- called eigenvalue, is numerically equal to the sum of the squares of the loading on that factor. An important property of the principal-component approach is that each component makes a maximum contribution to the sum of the variances of all the observed variables. In essence the first principal component has the largest possible variance; every succeeding principal component also has to have the maximum sample variance, subject, however, to the condition that it be statistically uncorrelated with any of the preceding components. Although all the components are required to reproduce the total variance, for practical purposes, only a few components may be retained if they account for a large percentage of the total variance. The statistical method used in this study computes eigenvalues for all principal components, but only those components with eigenvalues greater than 1.0 are considered in the analysis (SAS Institute Inc., 19824. On the basis of the retained principal components, the observed variables are then ranked according to their communality estimate, which is the amount of variance held in common with the other variables in the cluster. This amount is numerically equal to the sum of the squares of factor loadings (Kendall, 19751. The results of the analysis are shown in Table 2-3. Data Presentation and Results The factor analysis technique was used to select nine characteristic variables: (1) median age of the city population, (2) length of perimeter,

THE DYNAMIC CHARACTERIZATION OF CITIES TABLE 2-3 Factor Analysis Results Showing the Members of Each Cluster and Their Communality Estimates 41 Eigenvalues of Principal Communalitya Cluster Variables Components Estimates 1. Demographic Median age 2.415600 0.872 Population density 1.313225 0.854 Population 0.883587 0.800 Families 0.340437 0.621 Households 0.047151 0.582 2. Geographic Perimeter 2.377001 0.969 Area 1.161406 0.920 Form factor 0.457670 0.837 Percentage of perimeter on water 0.003923 0.813 3. Health Hospital beds 2.313200 0.893 Cemeteries 0.564199 0.836 Hospitals 0.122600 0.584 4. Utilities Telephones 1.321853 0.847 Water use 1.145786 0.837 Electric bill average 0.532361 0.783 5. Economic Expenditures 3.200759 0.975 Revenues 2.122249 0.970 Postal revenue 0.818851 0.942 Bonded debt 0.651224 0.741 CBDb employment 0.175524 0.604 Employment 0.030281 0.573 Average income 0.001112 0.517 6. Transportation Enplaned passengers 2.164236 0.960 (wants) Airplane departures 0.964486 0.822 Tunnels 0.856362 0.208 Airports 0.014916 0.173 7. Transportation Car registrations 1.754869 0.877 (needs) Workers on public transit 0.245131 0.877 8. Social/cultural/ Theaters 2.566701 0.985 recreational Libraries 2.292634 0.963 (wants) TV stations 1.076829 0.953 Library books 0.683973 0.818 Stadiums 0.343463 0.750 Library budgets 0.032994 0.739 Radio stations 0.003407 0.727 Continued

42 TABLE 2-3 Continued HERMAN, ARDEKANI, GOVIND, AND DONA E~genvalues of Principal Communalitya Cluster Variables Components Estimates 9. Social/cultural/ Tall buildings 2.974723 0.814 recreational Enrollment 1.351481 0.771 (needs) Hotel rooms 0.876889 0.748 Universities and colleges 0.666752 0.683 Hotels 0.122761 0.664 Churches 0.007394 0.645 aBased on the retained factors with eigenvalues greater than 1.0. bCBD = Central business district. (3) number of telephone sets per capita, (4) hospital beds per 1,000 pop- ulation, (5) city expenditures per capita, (6) number of enplaned passen- gers, (7) per capita car registrations, (8) number of theaters, and (9) number of buildings taller than 20 stories. The values of these variables were normalized for each city according to the highest value of each variable across the eight cities in the study. The normalized values in turn were used to plot "snowflake" diagrams for the eight cities. Snowflake dia- grams have been used by Herman and Montroll (1972) to characterize countries by the distribution of a country's labor force across agricultural, transportation, commerce, manufacturing, service, and construction in- dustries. The diagrams are formed by assigning one radial axis of a polygon to each characteristic variable (Figure 2-221. For each of the eight cities in the current study, the value of each variable is plotted on a scale from zero to one along the respective axes. Diagrams plotted in this way can be powerful graphical representations of the growth and development of a city. They can also be used to compare cities with each other and to examine changes in a single city over time. Figure 2-23 shows the resulting diagrams for the eight cities studied. The variable represented by each axis of the phase diagrams is given in the figure caption and has the highest communality estimate in its re- spective cluster (Table 2-31. As can be seen the phase diagrams for Atlanta and Chicago are alike in shape although the area enclosed by Chicago's polygon is smaller. This shrinkage is attributable to the normalization of four of the variables on a per capita basis and to Chicago's denser pop- ulation. The difference in size between these two diagrams can be an indication that, on a per capita basis, the people of Atlanta are better

TlIE DYNAAlIG CHARACTERIZATION OF CITIES 43 Yugoslavia t1961) Philippines (1960) Mexico (1960) Portugal (1960) \V Argentina (1960) France (1962) me, my ,~, ~7 Australia (1961) Canada (1961) Finland (1960) Japan (1960) ~ = West Germany (1961) United Kingdom (1951) If United States (1960) 3 2 4 5 6 1 FIGURE 2-22 Six variable snowflakes for several countries for 1960. The axes as numbered on the lower right corner are as follows: 1. Agriculture; 2. Trans- portation; 3. Commerce; 4. Manufactunng; 5. Service; and 6. Construction (Source: Herman and Montroll, 19721. served than those of Chicago by the amenities represented in the diagrams. The similarity in shape and the difference in size of the snowflake diagrams are expected to remain relatively the same if other variables in clusters 2 through 9 of Table 2-3 are to be plotted on their respective axes, given that the pairwise correlations among variables are positive. Therefore, the similarity in the general shape of the diagrams for Chicago and Atlanta indicates that these two cities have similar characteristics. The relative size of the diagrams, independent of their similarity in shape, may be an indication that Chicago and Atlanta are at different positions on the same general evolutionary track. The diagrams for Houston and Los Angeles are also somewhat similar

44 HERMAN, ARDEKANI, GOVIND, AND DOIVA ,:7\ Atlanta ~ ,~ Cincinnati ~ Los Angeles\/ Houston - \,1 New York Seattle NO 1: Median age (1982) 3 4 2: Perimeter (1985) 2 ~ / 3: Phone sets per capita (1986) \~/~5 4: Hospital beds per 1,000 population (1982) 1 71( 5: Expenditure per capita(1964) 9/ | \ 6 6: Enplanedpassengers(1984) 1 7 7: Car registrations per capita (1964) 8 8 Theaters (1985) 9: Tall buildings (1985) FIGURE 2-23 Nine variable snowflakes. The axes numbered on the left corre- spond to the variables numbered on the right. in shape. They are both stretched out along axes 2 and 7, and both show small to intermediate values along axes 3, 4, 5, 8, and 9. Houston, however, is characterized by a lower number of per capita car registrations (axis 7), fewer enplaned passengers (axis 6), and a younger population (axis 1) than Los Angeles. The diagrams for Cincinnati and Seattle are similar in shape and, unlike

TlIE DYNAMIC CHARACTERIZA TION OF CI TIES 45 those of Chicago and Atlanta, are also similar in size. These similarities could suggest that Cincinnati and Seattle are at the same stage of evolution in their infrastructure. Dissimilarities can be noted along axes 3, 4, and 6; Cincinnati has a considerably higher number of telephones in use per capita, a higher number of hospital beds per 1,000 population, and a greater number of enplaned passengers, respectively. Once again it must be noted that these similarities and dissimilarities are expected to persist among most of the variables in the clusters represented by each axis. In essence, it is expected that a roughly similar diagram shape can be obtained if the variable plotted on a given axis is replaced by another variable in its cluster, provided a positive correlation exists between substituting vari- ables. In contrast to the other diagrams, those of Miami and New York show little resemblance to each other or to any other city in the study. Miami has high values along axes 1, 3, 4, 6, and 7; and low values along axes 2, 5, 8, and 9. New York City has high values along all axes except axes 3 (telephones) and 7 (car registrations per capita), implying that the city may not be as well served by its utilities and private transportation infra- structure as the other cities in the study. It is remarkable that these snowflake diagrams characterize the cities under study in ways that are somewhat intuitive and certainly not sur- prising. For example, it is expected that Atlanta, Chicago, Los Angeles, and New York have high numbers of enplaned passengers. It is also not surprising that Atlanta, Cincinnati, and Miami all have relatively high numbers of hospital beds per 1,000 population and that New York is characterized by high per capita expenditures and numerous tall buildings. What is not so intuitive are all the variables that are correlated and clustered in the nine groups. Any attempt to explain the causality of these corre- lations is currently premature, considering that at present data are limited to the eight cities in this study. Analysis of even these limited data, however, begins to indicate the value of using snowflake diagrams to characterize cities. Diagrams of a city over time can provide valuable inflation about its growth, decay, and evolution in relation to variables that mold its character. These dia- grams could also provide insight into the age distribution of a city's key infrastructural elements such as sewer mains and bridges. Such time studies are currently being conducted for Roanoke, Virginia, using many of the variables considered in this study (Ardekani and Dona, 1987~. The proposed framework discussed in this chapter provides a means for characterizing the state of development of a city both over time and in relation to other cities. A classification scheme may then be formulated wherein all cities belonging to a given group exhibit basic similarities. It

46 HERMAN, ARDEKANI, GOVIND, AND DONA is thus feasible to examine the time evolution of these cities in relation to a particular set of variables and to make comparisons among them. Similarities can also be observed between two cities at different times. For example, the current characteristics of a city may resemble those of another city in the past. These comparisons might in turn be indicative of the future evolutionary path of the city in question. Such inferences about a city's future state are strongest when a wide array of variables is examined and the interrelations and feedbacks among these variables are accounted for. Of great importance are the effects produced by social, political, and economic changes, which can com- pletely alter a city's evolutionary trend. Note that it is inevitable that technological substitutions will occur, enabling a city to evolve toward greater complexity and causing a totally different infrastructural character to emerge (see Chapter 74. Several other recommendations may also be made regarding extensions of the research described in this chapter. The significance of the underlying statistical analysis, for example, would be enhanced through the study of considerably more cities. The list of variables may also be expanded to encompass a larger number of city characteristics. On the other hand, the list of variables may be shortened to consider only a specific aspect of cities, such as their demography, transportation, commerce, or utilities. Finally, it is imperative that interpretations of the shape and size of the snowflake diagrams be extended to recognize views of a city from the perspective of various disciplines such as demography, geography, eco- nomics, and environmental health. ACKNOWLEDGMENTS The authors are indebted to the National Academy of Engineering for its financial support of a graduate student who pursued the data collection task. They are also grateful for the invaluable help of David Lee Han and Hans Seyffert, Jr., in collecting data and assisting with the production of the artwork. Finally, the authors would like to ac- knowledge Michael Allen Hoffman and Andrezei Blinkow for their assistance in collecting data on the city of Austin. REFERENCES Ardekani, S. A., and E. L. Dona. 1987. Characterizing the development of cities. Submitted to the ASCE Journal of Urban Planning and Development. Atlanta Department of Community Development. 1984. City of Atlanta Comprehensive Development Plan 1984-1988. Atlanta.

THE DYNAMIC CHARACTERI7ATION OF CITIES 47 Automotive Safety Foundation. 1964. Urban Freeway Development in Twenty Major Cities. Washington, D.C. Beck, A. 1986. Utopia under glass. Journal of Civil Engineering 56(7):54-56. Bergman, E., and T. Pohl. 1975. Geography of the New York Metropolitan Region. Dubuque, Iowa: Kendall/Hunt Co. Blumenfeld, H. 1971. Modern Metropolis: Its Origins, Growth, Characteristics, and Plan- ning, Selected Essays, P. D. Spreiregen, ed. Cambridge, Mass.: MIT Press. City Directory of Austin, 1959 to 1985. Dallas, Tex.: R. L. Polk and Co. City Directory of San Antonio, 1900 to 1985. Dallas, Tex.: R. L. Polk and Co. City of Atlanta. 1985. Metropolitan Area Rapid Transit Authority Annual Report. City of Chicago. 1982. Portfolio for the Future: Chicago Long Range Infrastructure Planning Needs. Chicago: Metropolitan Housing and Planning Council. City of Chicago. 1984. Metropolitan Sanitary District of Chicago Maintenance and Op- erations Department Annual Report. City of Los Angeles. 1985. Statistical Report for the Fiscal Years 1976-1985. Los Angeles Department of Water and Power. City of Seattle. 1982. Seattle Engineering Annual Report. Dantzig, G. B., and T. L. Saaty. 1973. Compact City A Plan for a Liveable Urban Environment. San Francisco: W. H. Freeman. Federal Aviation Administration. 1985. Airport Activity Statistics of Certified Route Air Carriers for 12 Months Ending Dec. 1984. Washington, D.C.: FAA Office of Manage- ment Systems. General Directory of the City of Austin, 1900 to 1985. Galveston, Tex.: Morrison and Fourmy. Golob, T. F., E. T. Canty, and R. L. Gustafson. 1971. Towards the Stratification and Clustering of Metropolitan Areas. General Motors Research Publication No. GMR-1093. Warren, Mich. Golob, T. F., E. T. Canty, and R. L. Gustafson. 1972. Classification of Metropolitan Areas for the Study of New Systems of Arterial Transportation. General Motors Research Publication No. GMR- 1225. Warren, Mich. Gottmann, J. 1961. Megalopolis The Urbanized Northeastern Seaboard of the United States. Cambridge. Mass.: MIT Press. Grossman, D. 1979. The Future of New York City's Capital Plant. Washington, D.C.: Urban Institute. Hendrickson, C. 1986. A note on trends in transit commuting in the United States relating to employment in the central business district. Transportation Research 20A(1):33-37. Herman? R., and E. W. Montroll. 1972. A manner of characterizing the development of countries. Proceedings of the National Academy of Sciences 69(10):3019-3023. Houghton Mifflin Co. 1986. Information Please Almanac, 39th ed. Boston. Houston Chamber of Commerce. 1985. Information pamphlet. Kendall, M. 1975. Multivariate Analysis. London: Charles Griffin & Company, Ltd. Le Corbusier. 1967. The Radiant City. New York: Grossman-Orion Press. Le Corbusier. 1971. The City of Tomorrow. Cambridge, Mass.: MIT Press. Los Angeles City Planning Commission. 1981. Los Angeles City Planning 1781-1981. Los Angeles. Martin, J., J. Avery, and S. Collins. 1983. The Book of American City Rankings. New York: Facts on File Publications. Metcalf and Eddy, Inc. 1979. Wastewater Engineering: Collection, Treatment, Disposal and Reuse, G. Tchobanoglous, ed. New York: McGraw Hill. Miami-Dade Water and Sewer Authority. 1985. Component Unit Fiscal Report. Sept. 30.

48 HERMAN, ARDEKAIVI, GOVIND, AND DONA Newspaper Enterprise Association. 1985. World Almanac and Book of Facts. New York. Prigogine, I., P. Allen, and R. Herman. 1978. The evolution of complexity and the laws of nature. In Goals in a Global Community, Vol. II, E. Laszlo and J. Bierman, eds. Elmsford, N.Y.: Pergamon Press. SAS Institute Inc. 1982. SAS User's Guide: Statistics. Cary, N.C.: SAS Institute, Inc. Seattle Water Department. 1984. 1984 Annual Report. Seattle. Showes, V. 1979. World Facts and Figures. New York: John Wiley & Sons. Sinnreich, M. 1980. New York, World City. Cambridge, Mass.: Oelgeshlager, Gunn and Hain. Sunbank Marketing Department. 1985. Perspective '85. Miami. U.S. Bureau of the Census. 1983. County and City Data Book. Washington, D.C. Unibook, Inc. 1980. Houston, City of Density. O. M. Nergal, ed. New York: Macmillan. Yeates, M. 1980. North American Urban Patterns. New York: Halsted Press.

THE DYNAMIC CHARACTERI7ATION OF CITIES APPENDIX A Selected Study Variables: Explanatory Notes 49 Population Data for this variable are based on the results of the Census of Population and Housing, conducted as of April 1, 1980. Persons enum- erated were counted as inhabitants of their usual place of residence, which was not necessarily the same as a legal residence, voting residence, or domicile. Residence rules were established when a place of residence was not readily apparent (e.g., children in boarding schools below the college level were counted at their parental home, whereas college students were counted at their college residence). Area These data reflect annexations and boundary changes as of January 1, 1980. Included are dry land and land temporarily or partially covered by water such as marshlands, swamps, and river floodplains. Generally, streams, sloughs, estuaries, canals less than one-eighth of a statute mile in width, lakes, reservoirs, and ponds less than 40 acres in area are also included. Form Factor The ratio of the measured perimeter to the minimum pos- sible perimeter for the same area. Average Income These data are based on the aggregate personal income in the city divided by the city resident population. Personal income is the income received from all sources, measured before the deduction of in- come and other personal taxes but after the deduction of personal contri- butions for social security, government retirement, and other social insurance programs. Revenue All revenues except utility, liquor stores, and insurance trust revenues. Also included are all tax revenues and intergovernmental rev- enues, which are amounts received from other governments as fiscal aid or as reimbursements for the performance of general government functions and other specific services for the paying government. Expenditure All city expenditures other than those for utilities, liquor stores, and employee retirement or other insurance trusts. Bonded Debt All long-term credit obligations of the government and its agencies and all interest-bearing, short-term (l-year) credit obligations unpaid at the close of the fiscal year. Although bonded debt includes judgments, mortgages, revenue bonds, general obligation bonds, notes,

so HERMAN, ARDEKANI, GOVIND, AND DONA and interest-bearing warrants, it excludes noninterest-bearing obligations, interfund obligations, amounts held in a trust, advances and contingent loans from other governments, and individual benefits from employee retirement funds. Households Year-round occupied housing units. A housing unit can be a house, an apartment, a group of rooms, or a single room occupied as separate living quarters. The occupant may be a single family, one person living alone, two or more unrelated families, or related persons who share a household. Employment The total employed labor force divided by the total popu- lation. The labor force consists of persons at least 16 years old working as paid employees or in their own business or profession. Enrollment The number of persons at least 3 years old who have attended regular school or college at any time since February 1980. Regular school in this instance means nursery school, kindergarten, elementary school, and schooling that leads to a high school diploma or college degree. Tall Buildings Buildings at least 300 ft high or having at least 20 stories. Building height is measured from the sidewalk to the roof and includes the penthouse and tower (if present); the number of stories begins at street level. Hotels Both hotels and motels registered in the 1977 Census of Service Industries (Martin et al., 19831. Hospitals Facilities that have at least six beds and are licensed by the state as hospitals or that are operated by a federal or state agency (and therefore not subject to state and local licensing laws). Institutions and services commonly referred to as rest homes, nursing homes homes, and sanitariums are excluded. , old-age Hospital Beds Beds, cribs, and pediatric bassinets regularly maintained for inpatients during a 12-month period. Library Books Major library holdings reported in terms of volumes per 1,000 persons. Colleges and Universities All accredited, undergraduate degree-granting institutions that have a total institutional enrollment of 600 or more.

THE DYNAMIC CHARACTERI7A TIOI!; OF CI TIES 51 Airports All airports (i.e., major, international, national, and regional) that support activities of certificated route air carriers. Departures All performed scheduled and nonscheduled departures. Stadiums All football and baseball stadiums. Tunnels Underwater vehicular tunnels with lengths of 3,000 ft or more and land vehicular tunnels of 2,450 It or more. Telephones All public and private installed telephones that can be con- nected to a central exchange. APPENDIX B Characteristics of Eight U.S. Cities (on the following pages J

52 c: Ct _ so: C) - 4 - ¢ o ;^ ._ .= Ct m o 00 Ct _ ~ ~ m Cal I_ V ~ . ~ ~ 00 00 C4~ ~ ~ ~ 0 00 - _ _ _ m ~ ~ A: .- .- ~L) my Cal Ct ~ _ _ of on I_ _ ~ ~0 0 00 ~ 00 ~ 00 00 00 ~ . _ . _ ~ ~ ~ ~ ~ ~00 _ _ I_ _ ~_ ~oN . ~ . ~4 C) C) CalU. U. Cal CO Cal C`O ~0 0 Is ~ lit ~ ~ 3 ;, u3 con ; c: ~ ~ ~ c: ·- Me .O V ~ ~ ~O (~ - ~ a~ (~\ ~ ~ ~ ~ ~ ~ C;N {4_ c~ ~ ° °N ~ ~ ~ _ _ - - ~ ~ ~ ~ ~ o o ~ ~ - · ~ ct ct cIN o ~ ~ 'e - ~ ~ ~ s~ s~ ~4 ;A, ~ ~ -` "' c,, ~ ~ ~ ~ -' - ' ~ ~ ~ ~ ~ ~ ~ ~ ~ c-) ~ ~ ~ o ~ ~ ~ ~ ~ ~ ~ ~ ~ ;: ~ m m m m m ·= - m ~ ~ ~ ~ ~: ~ ~ ~ ~ ~ :: ._.= . . . . ~ ~ ~ ~ ~ 0 ~ ~ 3 ;^ 3 3 ~ ~ 0 _ 0 ~ u~ ~ ~ _ _ ~ ~ O O O O O O ~ c~ a~ ~ u~ ~ [~ oo oo oo oo oo ~ oo oo oo oo oo oo oo oo oo oo oo oo oo oo r~ oo oo oo ~ ~ ~cn ~ ~ ~ ~ ~ ~ ~ c~ ~ ~ cr crx ~ c ~ ~ a~ ~ ~ ~ ~ c~ '___ ________~_____4_ ______`__ C) ~ ~ ~ ~ ~ ~ ~ 0 0 0 0 0 0 ~ 0 ° ~ ~ ~ ~ ~ ~ ~ 0 0 0 0 0 0 ~ ~ O P~ C~ ~ ~ V ~ ~ ~ O Oo Oo Oo 0 0 ~ ~ 0 0 ~ _ ~ _ _ _ _ _ =. CN ao . . . cr~ - ~ ~ ~ ~ ~ o~ ~ 0 0 ~ - ~ r ~oo oo ~ ~ oo oo ~oc-1 c-) <~ t- r- c~) o,o c<) ~ ~ - oo oo O oo ~ 0 ~ ~ - - ~ C~ ~- oo \~o .~= .O C ~. S ~C~ ~S~ .~C13 ~ =- ~.> c3 E D E~ g - ~ ~: ~ o ~, o ~ ,: E m ~ ~ ~ 3 ~ v: ~ ~ ~ ~ ~ ~ E~ ~ x 0 _ ~ ~ ~ u~ ~ ~ c~ ~ ~ ~ ~ ~

53 UP ~ oo oo Vie oo ~- At,. _ - ~=\ {ON => - ^~ ~ -~ ~ ^ ~Cry s=^ >) ant . ° s ^ =^ c^ ° No v~ CtCt - o ~- cat o o ° Ce ON ads ~° O So ~ ~ °° ° - an ~ (~N ~ °° 'I ~ ° ~ ° ~ ¢~ ~ ~ ~ .o - .o ~ ~- ~_ Cat c~c E ~ ° ~ °- c~ i_ C, ° ~, · ~ c ~ ~ c ~ c ~ )2 C t C ~ ~ C C o 0 ~ ~ 0 0 0 ~ ~,, ~ o ~ ~ O .= Q O ~ ~ ~ a) ~ ~ ~ ~= Z ~ ~ ~ ~ ~ Z ~ ~ ~ ~ ·o ~Z ~ ~ ~ ~ ~ ~ ~ _ ~ ~ ~ ~ ~ ~ ~ U~ ~ ~ ~U~ ~ ~\0 C~ oo ~ oo oo oo oo oo oo oo o0 oo oo oo oc ~oo oo ~ oo oo oo ~ ~ cr~ a~ cr ~ a~ ~ a~ _____________ _ _ __ __ __ 5 ,, C o C E ~ E ~ ^° ~ -- ~E5 -- =5 ~ '= O oo oo ~ - . . . . . O - - O ~ ~ oo ~ oo ~ ~ ~ _ t_ ~ ', ~ ~ _ ~0 - oo ~ - ~-~ -c~ ~ ..... . ooo ~ ~ o -~t -~ - cn . _ eO ~ ~ G -- ~ _ o E x X ¢ ~ ~ ~ c~ ~ c' ~ ~ ~ ~ ~ o~ ~ ~ ~ ~ c~ ~ E~ 3 oo ~ O - ~ ~ ~ ~ ~ r~ oo cr 0 - ~ ~ ~ ~ ~o ~ oo ~O - c~ c~ ~ ~ c~ ~ ~ c~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~n ~ . CD o _ Ct oc 11 . ~

54 so ._ so Cal ;> o o ._ V sC~- C~ A m Em UP 00 on cr ~ O =^ =^ 00 C<S 00 OC 00 00 00 00 00 (~) 0 0 CSx ~ ~ ~ ~ ~ ~00 lo lo _ ~_ _ ~ ~ . ^ m ^ art ^= ~ ~ ~~ O O cn ~ ~ ~ =; ~ ~ ~ ~ cat So: ~ ~ C- ~ ~ - Mo ·_ ~ ~ ~En) ~ ~ c) C) ~C) L) ~ AL ° ~ ~ ~ _ C) . _ · ~ c X 0o 00 X c c c c c 00 X ~ "c ·_ C o0 oC .~ ~ ~ 00 X c) ~(:,) m ~ ~ ~ ~ m m ~ m m ~ ,, m m ~ ~ . ~,: :,: ~ ~ ~ ,,~ ,,: c,~ ~, ,, ,~ ~ ~ 3 3 ,~ ,~ j ~ ~ ~ ~ ~ ~ ~ ~ CC X ~ ~ ~ ~ ~ ~ Z Z O _ O ~ ~ u~ ~ _ _ _ _ O O O O O O u~ 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 D0 00 C~ ~C1N ~ ~ C~ ~ ~ ~ C~ ~__ ______~_______, ~ ~ ~ ~ ~ U~ 00 o0 ~ O0 00 00 ~ cr ax ~ ~ ~ cr~ _ ________ O O O O O O ~ O O O ~ s~ ~ ·= ·= ·= ~ ~ ~ p~ ~ ~ .= .~ ~ C~ ~ ~- ~ ~ ~ ~ ~ ~ O O O O O O ~ O O ~ - ~VVV8$8888 ~V8 8 ~ ~ c~, 0~ ~ ~ ~ ~ ~ =, _ _ _ _ _ _ _ _ _ _ c~ t u~, - c~ O ~ ~ O ~ ~ ~ ~ a~ - r- ~ 1- ~C-] ~ 00 - ~ ~ -) cr ~oo - 1 - 00 ~C ~5\ O ~ ~ O ~ ~ ~ ~ c ~- ~ c~ - ~Ir, ( ~oo _ o _ o . ~ ~.o Ct . ~ ,. ~=._ ~.> ·_ ct ~ 5 ~ _ D _ O ~ ~O G e E 0 ~ 0 _ ~ c ~ u~ ~ ~ oo c

ss v) upup °° ~) ~ r, aN cat <~N cat - o =^ $^ ~ ~ o \~) in, ~o To · ~· ~ · ~ · ~ · ~ · ~ ~o ~ ~- ° ~ ~ ~ ~ ca ^ ON O. ~(~' ~v, _Cal .- .e .s .E ~ ^ ^ ^ c ~o ao ao ao ~ ~ ~ ~ .= .= .= .= ~ g ~ ~ ~ O .O .O ~ ·O ·O ·O ·O ~ ·O ·O C) g O O =o ~~ ~ V ~ =o ~ ~ ~O C<S ~ ~ ~ C<S ~ ~ ~ ~ ~ C) ~0 ~o ~ O O O ~ O exS O O =) 3 t ~ ~ ~ ~ 3 :^ ~ ~ ~ :~ >` ;;~. _ >` ;^, ;;~` 3 >` ~ >. :~.3 ~ z ~ ~ ~ ~ ~ z ~ ~ ~ ~ 3 3 ~ ~ ~ ~ z ~ ~ ~ ~ ~ 1 - ~ ~ d ~ ~ ~ ~ ~ '=) ~ C~ 1 00 00 00 00 00 00 00 00 00 00 00 00 , ~ ~ a~ ~ ~ c~ ~ c~ 1____________ · $, ,,, ~=, O a' ~ O~ O ~Qo~ o ° ~ _8^ Oo ~o 0 ~o ^ _ _ _ C) o _ _ C) _ _ ~ o ~ ~_ . . . . . . o ~ ~ o _ ~ o o U3 cn ~L~ Ct , o" ,_ ~ ' ~ ~ ~ ~ L~ ~ c~ U~ ~ ~ ~ ~o ~ ~o oo oo oo oo oo ~ oo oo oo oo C ~ os _________ __ : - C) ~ ~ Ct ~ ~ E ~ ~ ,~ ~ ~ cr~ == 0 ~ ~ ~ ~ O v · - 8 8 _ - - ~ o ~ o ~ _ _ oo _ ~ _ cr, ~ c~ . . . . o o o ~ o o - C~ C) _ ~ ~ ~ ~ ~ C o cr~ =~ ~V V ~ V ~ - E~ oo 3N 00 . .. . o oo o oo ~o o _ ~ _ {~\ In ~ _ D e -E ~ ~-E :, D ~L~ C 2 `~, v ~ ~ E~ m m ~ ~ ~ v ~ v: m ~ 3 3 CQ ~ c~ ~ ~: oo cr 0 - ~ ~ ~ ~n ~ ~ oo a, 0 - ~ c~ ~ ~ ~o ~ oo ~ 0 - ~ ~ ~ u~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ u~ ~ u~ ~

56 a' c: ._ . ~ .= ._ o ._ A) D . ~ id l m C) o0 o0 ~_ ~ 0 Cat ~ -} -] Cal -} ~ -] 00 lo ~ ~ ~ ~ ~ ~ o 0 ~ ~ ~ of ~ ~ ~ Ct _ ~_ _ ~ _ _ _ - ~ ._ ._ ~(o_ C) C) con ~1 con con con can con con con ~^ O O cons con c Cat Cat Cat Cat Cat Cat Cat _ cat ~ ~ ~ ~ ~ ~ o0 ~ ~ ~ C: C) ~ ~ ~-0 ~ ~ 53 - , -, -) -, G) C) ~ O C) ~ ~ ~ C) C) ~ ~ ~ ~ ~ ~ ~ 00 ~ ~ ~ ~ - ~ ~,, (~N (~N t~ -, ;> O ~ ~ ~ ~ ~ _ ~ ~ _ - _ · ~ ~ ~ ~ ~ ~ ~ ~ ~ _ . ~ _ - ~O ~ ~ - - Ce ~ Ct ~ C~ ~ Ce ~ ~ ~ O O ~ ~ ~ ~ C~S ~ ~ ~ Q) o C~ ~ ~ o ~ C) ~ ~ ~ ~ ~ C) ~ ~ ~ O ~ C~ ·O cn = ~ ~ ~ ~ m ~ ~ - - m ~ o~ ~ ~ ~ <~ ~ ~ ~ ~ ~ ~ ~V ~ ·- ~ ~ O ~ ~ 3 cn .- . _ . u~ ~ ~ u~ u~ v~ c, c~ c~ ~ ~ 3 c:~ c~ ct = ~X~= ~mVZZ~ O~O ~~~~_0~O000u ~o~ oo oo oo oo oo oo oo oo cOo oo oo oo oo oo oo oo oo oo oo oo ~ oo oo oo [~ ~ ~ ~tm ~ ~ c~ c~ ~ ~ a~ ~ c~ o~ ~ a~ ~ ~ a~ c~ ___ _______________. ___~___ - °n Ei c' c~ cn - ~L o . . . . . . . C~ Cn . _ . _ . _ . _ ~ ~ ~ ~ ~ ~ '~ ~ ~ v v v v 8 8 8 8 8 8 VV 8 <,, ~ - . - , - , o o o o o o - ,- , o =, _ _ _ ~ _ _ _ o o 8 - _~ ~oo .. .. oo ~_ C~ ~ o ~ _ ~ oo ~ ~ ~ ~ ~ ~ C~ o ~ o ~ _ o oo c ~ ~ ~ \0 a~ ~ 0 ~ ~ ~ - ~ ~- ~O ~ ~ ~ a ~u~ oo r~ - , ~ ~ - ~ ~ c~, t- - , U~ ~C~ oo . ~cn cn C ~._ ~' - ;^ ~ .Q ~;> ~ C-;S ~ ~C) ~ E 3 _ _ e y O E e Y O E E ~O . E ;~ ee j c ~ E ~, ~ c e E ° c ~ Y ° O ~ O~ ~ 0 ~° D ,~ _ 3 ~ v~ ~ ~ ~ ~ v~ v E~ tc: ~ 0 - ~ ~ ~ ~ ~ ~ oo - ~'~oo~`_________ o~ 0-~ ~ ~ ~ ~o _ ~ c~ ~ ~ ~ ~ ~

57 °° v: an, ~] (~N ~an ~{~\ an (~\ ~ c ~ ~ ~ c - 0 ~x ~ ° ~ cat co an ~ U) ~ an ~ ~O ~ c .° .° .° ~ = ~ ~ ~ ~ ~ 0= c I S _, --, ~ ~ c ~ ~ ~ ' E ' e _ c c ~ ~ ~ ~ ~ us ~ ~ ~ ~ ~ us oo oo . oo oo oo oo oo oo oo or oo oo ~ ~ ~ ~ an ~ ~ ~ ~ ~ ~ _4 ~ ~ ~ _I _ co oo oooo oo an ~ ~_ ~ -a 3 °° o ~ ~-,, E -E ~-=, c -~=- O~ DO _ 8 - - 0- 0 0 ~ _ ~ 000 00 ~ c ~O O .. . .. . . O ~ - 00 ~ - - ON ~ ~ ~ ~O O ~ CS~ O _ ~_ - u, - ~c> c c ° c ~ =- ~ E ~ ~ E =,,-~-= ' ~ ° ' ~ = ~ = 3 3 o c cc ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ m m ~ ~ ~ c: ~ ~Q m ~ 3 ~ ~ ~ v, ~ ~ 00 ~ O - ~ ~ ~ ~ ~ ~ 00 ~ O - ~ ~ ~ ~ ~ ~ 00 ~N O - u ~ ~ ~ u~

58 o o o .~ m m C) Cal C) . c: 3 C) . ~ Cat :> Hi a' oo oo o o oo C<s oo oo oo oo oo oo oo \Io ._ _ ~_ - ~- - off . ~ . =^ m ~ ~ ~ In ~'^' ~- 0 0 ~r ~ r ~ r ~ ~ t ~ r ~ Ct ~ U: Cal ~i C ~ O O ~ ~C) ~ ~ C ~0 a) ~ =: (~ ~) ~) ~) =: ~ ~ ~ ~: ~ ~ ~ ~ .C) ~ oo oo .e ~ ~= O ~O O O O O - ~ O O G) '~ ~ · · ~^ ^ -s -t ~ -t _t ~ ~ _t ~ V) ~ ~ ~ cr ~ rL~ ~,L C`3-- - -- Ct Ct Ct Ct ces o o C<' C<'-'---=` O - m ~ ~ ~ ~ ~ m m m ~ ·e ·= m m ~ ~3 ~ ~ 3 ~ ~ ~ ~ ~ ~ ~ ~ ~> ~ ~ ~ ~ ~ ~ ~ v~ ~ ~ ~ ~ =0 ~ 3 3 ,,: 0 - 0 ~ - - - - 0 ~ 0 0 0 0 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 o0 00 00 00 ~ 00 00 00 ~ ~ ox ~ ~ ~ ~ ~ ~ c~ ~ ~ ~ ~ a~ ~ ~ c ~cn os o~ ___ _____I__________ ________ - U3 · _ ~ 0 . . . . .. 0 0 0 0 0 0 ~ 0 ·~-~ ·~ · - ~ ~ ~ ~ ~ ~ ·~·~ ~ ~ ~ v ~ ~ ~ o oo o o oo o ~ oo ~ ~^^^^^^~3r~^ l, - - - - - -- . ~ oo ~ ~ ~ ~ oo oo ~ - ~ oo ~ ~ ~o ~ ~ oo oo ~ ~ ~ ~ oo oo ~ ~o oo - ~ ~ ~ oo c~ ~- ~ ~ ~ - ~ t- ~ c ~- - ~ oo - =\ ~ ~t ~ - ) ~ ~ - ~cr ~- ~o - - cr _ - D e ~, ;^ c) ~ ~- ~ e · y 0 D ° o ' E, ° E o o o y ' y - o E _ - ° E _ o - E E E E ~E a ~ ° 0 ~ ~ ~ O ~ D _ m :r ~ ~ 3 ~ c~ ~ ~ ~ ~ ~ ~ ~ X tr: 0 - ~ ~ ~ ~ ~ ~ 00 -~-) ~t-oocr~---__~__~ ~ 0 _ ~ ~ ~ u~ _

so up in In ~ ~ ~ cry ^ - - - ~oN ~- - - - c -o ~ c) ~ ~x C) Cal~ C) _ ~ ~ ~ _ · _ ~ ~ ~ ~ ~ o~ ~ ~ oo o oo oo oo its o ~ ~ ~ ~ oo u, ·= ·= ·= ~ ~ c) ~ ~ ~ ~at o o o o - c: To of ~a no o o o ~ ~ .c ~ .- .- ~c c c ~ o <. ~S ~, a ~ C C C cc ~=^ ~4 ~,, C) ~ ~ C-) c ~ =-= Cn Ct Ct ~ ~ ~ ~ ~ ~ C) O o ~00 00 o ~ ~ ~ z ~ ~ ~ ~ ~ z 3 ~ ~ ~ 3 ~ ~ ~ ~ ~ ~ ~ ~ _ ~ ~ ~ u~ ~ ~ ~ ~n ~ ~0 0 ~ ~ ~ ~ ~ ~ oo oo oo oo oo oo oo oo co oo oo oo oo oo ~oo ~ oo oo oo oo oo c~ ~ ~ ~ ~ ~ ~ ~ C~ a~ ~ ~ ~ ~ ~ ~ a~ ____________ __ _ _ ______ ct ct ~ 0 o ~ c' o ~ o ~ >~= o~ 80 8 8 ~ 8-oS G) Cd ~ Ct cr~ c ~ g c~ E E 8 c~ g °~_ v c 0 ~ 0 ~ _ _ ._ ~ ~ ~ ~ ~ ~ 0 CQ V V: ~ ca ~ oo oo 0 ~ 0 oo ~ `.o ~ ~ _ ~ - 0 ~ 0 . . . . . . . . . . . . ON ~ ~ O~ 00 ~ - ) - - ~ O ~C) a' 0 0 0 0 a~ ~ - - oo u~ ~ 0 ~ ~v~ - -_ ^ 7 7 C~ U ~C.) U ~e ~' 5 E e ° a c - e E ,' E ~ c ~ 0 ' ° 3 ~ c ° ~ C c E4 c cY E ~ ~ ~~ e 0 a e 3 3 ~ C x ~ ~ ~ ~ ~ v, ~ ~ E~ ~ ~ m m ~ ~ ~ v ~ v: m ~ 3 3 v, v, cn a r~ oo a~ 0 - ~ ~ ~ u~ ~ ~ oo ~ 0 - ~ C~ ~ ~ ~ ~ ~ ~ ~ - . ~ c~ r~ oo ~ 0 - , ~ ~ ~ ~ ~ ~ ~n ~ v~ ~ v~

60 C) C) C Ct .~ Ct Cal _ o to so LU m . ho UP 00 of 00 at 0 ~ - ) ~ - ) ~- ) ~ ~ 00C13 00 00 00 00 050 010 010 \= ° ° ° at 5, ~ ~ _So _ _ _ _ _ _ _ ~ . ~ . ~. ~ Car C~3 =^ ~ Cal ~O 0 0 · ~ ~ ~ ~ ,~I, I, ;> ~ ~ ~ ·- ~·O ¢ ¢ ~ ~ ~ ~ ~ ~ ~ 00 SO V ~ ~ ~ C,) ~ ~ =\ ~0 ~ ~ ~ · - · = ·- At oo oo oo Go ~ ~ ~ ~ ~ oo Do ~ ~ .- ~ Go Do . - ~ ~' ~ ~ o oo o o o o o - - o o ~ · ~ - - (~\ ~ ~: c: ~ - - - - - ~ ~ ~ ~ ~ ~ ~ ~ ~ - · ^ C: c: ~r: ~ ~ ~ ~ ~ ~ · - - ~ ~ =. ~ ~ 5= ~ ~ ~ ~ ~ c:: . . . . . ~ ~ ~ ~ ~ ~ ~ ~ 3 ~ 3 3 ~ ~ 3 3 ~ ~ ~ 3 X 1:: ~ ~ ~ ~ ~ c~ ~ Z Z ~ ~ Z 0 _ 0 ~ ~ v~ ~ ~ 0 0 0 0 ~ 0 0 0 ~ ~ oo oo oo oo oo oo oo oo oo oo oo oo oo oo oo oo ~ oo ~ ~ ~o~ ~ cr~ ~ cr~ ___ _______________ ·~ ~ =, =, ~ ~ ~ ~ ~ ~ 0 0 0 0 0 0 0· ~ ~ ·~4~ ~-~. =, =, ~ ~ ~ ·~ · - ~ ~ ~ ~ ~ ~ 0 0 0 0 0 0 5 c'3 ~ ~ ~ ~ ~ ~ o o o Oo O O O ~ ~ ~ ~ ~ ~ ~ ~ r~ v, oo oo ~ oo oo oo r~ ~ oo ~ ~ ~ ~ ~ ~ a~ _________ . . O O . _ ~P~ O ~ O (,,9- o - - ~N00 - ~ ~ O~ .. . . . .. o - , ',~t _ ~ ~ =\ _ - ~ C~ ~t ~ O O ~ -M0 ~ ~ O - ~ ~ ~ ~ O ~t - ~ ~ t- ~] cr~ oo c<~ ~ oo t- ~ ~ - ~ - oo ~ - ) - ~ u~ ~ ~ ~ ~ ~ - O r ~u~ 00 ~D ._ C~ ~.O Ct . ~C~ . ~C<5 ~ ~.> e c ~ ° °e D e o c e E ~, e O ~e :' _ e o o D o, 2 c a e e o - o D O o- O D _- E E e ~ e ~ ° ~E 2 ~ 0 0_ ~ O c r~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 3 ~ u: ~ ~ ~ ~ u, ~ ~ ~ ~ ~ a~ 0 - _

61 ~ ~ v) ~o o ( oo oo oo ~ ' ~ct ~ - ~ ~o . ~. O O =^ :°< ~ e e .0 :~, =^ ~ . ~ . ~. 00 0 00 00 00 C,3 ~0 ', O · s ~', ', O~ c~ ^ (~N =\ cr~ ^ ~ ~s ~ 00 00 o0 :3 : : ~ . ~ . ~`= .. ~ ~ a E ° c 0 G e Y c u => ~ '( ~ c e. ~ u u u ~^ = ~ E ~ ~c ~ ~u c o C V C C C C O o O o V . . . c -0 c c a 0 0 _ e- c. e c c D O , 3 >, 0 0 5 E A O 3 0 0 0 - z u - _ u < z u 3 u U _ ~ ~ ~ ', Ir, ~ ~ ~ ~ ~ ~C ~u~ ~ 0 u~ oo oo oo oo oo oo oo oo oo oo oo ~ oo oo oo oo oo oo oo oo cr~ ~ ~ ~ ~ ~ crx ~ ~ ~ a ~ ~a ~c~ ~ ~ a~ - - - ~ - - ~ - ~ - - ~ - - - - ~ - ~ - - - - ~ ~ ~o =. · ~ ~ . - ~ ~ °=° ~ ~~ g ~ r ~ ~ c E E E == E a~ ~ c, O . 00_ ~ ~ ~ oo 00~ ~ ~ ~00 .. . .. . . . . ~ ~t ~r~ c-~ ~ -~ - O oo c~ ~O O~ o~` _~ ~ ~_ _ 1-_ - cn ~L) cn ~ v, e 1gE ~ ° ~ ~ ~ :? ~ ~ c~ A >, oo 0- 0 - ~1 ~) ~ ') ~ oo ON O -~ ~ ~ d" tr C~ ~ ~ C~ ~ oo~ ~ .. .. . U~ o~ -O - _ oo- ~') __ · _~ U) ~ ~ _ v: m ~ 3 3 - ._ D C) . s ~C~3 ~ _ - C.) ~s:: oc - C.) C) Ct 3 3 o ~ ~ u~ u: c~ ~ ~ oo a~ 0 _ (-I (~) ~I- t~, u~ ~ ~ v~ ~

62 .E cd · - o .o 1 m m Em Cal Ct - Cal . - Z C) =: 00 00 0 _ _ ~ ~ m] ~ - ) - ) ~ ~ 00C;S 00 00 00 00 00 00 00 \~) ° ° cr ~ ~ as AN ~C7- ON Do ~ ~ _ ~_ _ _ ~4 _4 _ _ O~ . ~ . ~ _ ~ ~ Cal CD Cal Us Cal ~0 0 'I · us coo 3 Coo U. C,, C,, ;> ~ con us ~CQ ~ ~ ~ ~ ~ ~ ·c ~·o C) _ ~ ~ ~ ~ C) ~ ~ cL, 00 V 'J V V V V V V V ~ ~0 C) ~ - ~m) ~C) C) (1) C) C~ ~ Mo C) O C-) ~ ~ - 0 00 - ~ o o o o o ~ ~- o O ~ · ~ - ~ C ~ - - _ _ - _ · ~ ~ ~ ~ ~ ~ ~ ~s ~ ~ o ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~o ~o m ~: ~ ~ ~mmmmm ~ cmm~ E3 =: ~ ~ ~ =~= ~ ~ ~ ~ ~ . . . . . ~ ~ . . . O ·- - 3 ~ ~ ·- ·- c~ ce c~s V ~ v~ cq v~ ~ ~ ~ ~ v ~, ~ ~ O ~ ~ 3 t ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ·V O - O ~ ~ ~ ~ ~ O O O O O O O O O ~ oo oo o0 oo oo o0 oo oo oo oo oo oo oo oo o0 oo oo oo ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ o~ ~ ~ ~ ~ a~ '___ ____.~__~____ U, . _ s:. ~0 C.)· - & V, o - oo_ O _ ;` ._ C~ ~: OO ._._ __ ~Ct __ ~ ~ & O ~ O P~ ~ ~ - 3 o o c~ ct: ~ ·- r4 ·- ~ 0 ~ _ ~ - , ~ ~ ~ ~ oo ~ c~3 ~ ~ & & ~ ~ ~ ~ ~ ~ && ~ _ 0) 43) (_) (_) (_) (_) o Oo Oo O o o O~ 00 =, _____~_ _ _ _ _ ~00 ~ ~ .. . . 00 00 ~ ~ O ~ O ~ 00 ~ ~ ~ 00 U~ ~ ~ ~ ~ 00 oo ~ ~ ~n ~ c~ U~ C) C > ~ _ · ~ O C~ t4 ~ -0 ~ ~ . C) ~ ,., ''' 4' '' "' E ._ C'3 ~: ct c' _ ~ _ c~ S r ~ ;~` & _ _ O ~ ~ O -& O ~ ~ t~ ,4 ° 0 v 3 ~ v' O ~ ~ ~ ~ v~ ~ ~ 00 ~_____~_ ~ ~ ~ u~ ~ ~ oo oo ~ oo oo oo r~ ~ cr ~ ~ ~ ~ ~ cn ~ _ _ _ ~ _ ~ _ _ o~ . 0 ~ ~ 0 ~ - oo ~ _ ~_ _ ~0~ ~_ u, ._ - ._ c~ ;> ._ ~: U3 oo c ~ ct ,<D c~ ~ · _ O ~ c ~ O ce c~ V ~ c' ~ c~ ~: {: ~ 0 :>, ~ 0 C) ~ D c`' s ~ ~ ~ Ct ~ ~ ~ c ~"D D D O ·_ ._ ._ ~] C`3 o ~0 _ ~ O O 0N 0 - ~ ~ ~ U~ ~ ~ ~ C~ ~ ~ C~

63 x a, a, a, ~- a, a, usor c ~ ~^ I ~D~ lo' ~ cot =\ (~\ =~is to 'o c^ O ,o no ~ C e i-~ IS ~ ~ cry ~.= C C ~ ~ .r ~ ~ ~ .c ~ ~ ~ C ~ ~ O ~ ~ E E E 0 A E 0 e y E _ ~ ~ ~ ~ ~ ~ ~ HE .c £ ~ ~ ~ > ~ ~: ~ ~ g g E c c: ~ ~ ~ ~ c ~ ~ ~ s ~,, ~ ~ E E c~ E x ~, E ~ ~ m I - ~ ~ ~ ~1 ~ ~ In ~ ~ ·n ~ In v) ~ ~ ~a~ ~o ~ ao 00 oo 00 oO 00 00 00 00 oO oo oo oO 00 00 ~ ~o oo 00 ~ 00 X oo cr ~ a~ cr cn ~ ~ crx ~ ~ ~ cr~ ~ ~ ~o~ c ~ a~ ___4__~___~__ _____ __ _ ~_~ ~ ~ °= O c' a' O ~Q 0 0 0 ° Oo 8 0 ~ __ _ ~ ~ ~ _ .. . . . _ ~ ~ ~ ~ ~ ~ ~ 00 ON _ _ ~t ~ oo _ cr~ c~ '~ ,,.) D s ~ ,,, e E ° 8 _ g 0 c E E S ^ ~g~igg - ~ r ._ 5 C~ Ct ._ ~S C,) ~ ~ =_-= C,) c5 O o E~ _ _ ~oo\.0 ~ ~ ~- ~ ~ . . . .. .. . O u~ u~ O OO ~cr~-~ ~ c ~_ u~ U3 a~ cn hn S c,t, c ~s: ° a. e ,°, E ~,, ~ E ~ : ~ m m =: ~ ~ ~ ~ ~ m E~ 3 - ._ D 4 - ·c c: C~ 4_ ~ C ~ V) .Ce 3 v~ u, v~ ~ ~ 0 - ~ ~ ~ ~ ~ ~ oo a~ 0 - ~ ~ ~ ~ ~ ~ oo ~O - c~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ u~ ~ u~

64 . o .~ m z C) 00 00 00 ~ _ _ O ~ ~ ~ ~ ~ ~ ~ ~8 o o oo en oo oo oo Go Do oo oo~ . . _ ~ ~ _ _ ~_ ~ . ~ . ~. m ~ ~ ~ ~ ~ ~ ~O O O ~ Cal ~ Cal Hi ~C~3 ~ ;-CD of Us Cat ~ ~ ~ ~ ~ C) ~ ~0 ~ -] - ~ -} ~ AL ~ ~ ~ ~ ~ ~ ~ ~ ~-} -] ou ~ ~ ~ ~ ~ -c x x ctS crN ~ - ~ ~ of ~ ~-0, ~ ao ~ - ~ c c - - c If cts - at {~S Cal At At ct ~ o o 4~ ~ ~ ~ ~ ~ - ~ ~ ~ ~ ~ ) ~/L) ~ r -t S r C-) ~ O ~ C Ct Ct C~ S m ~ ~: c ~ ~mmmmm.~=mm~ ~ ~ ~ ~ ~ =~= ~v' u' c~ ~ c: v' c~ v, 8 t ~ 0 ~ 3 =~X ~= ~ZZ~Z 0 _ 0 ~u ~ ~ _ _ _ 0 0 0 0 0 0 ~ c~ ~ u~ ~ ~ ~ ~ ~ oo oo oo oo oo oo oo oo oo oo oo oo oo oo oo oo oo oo oo oo ~ oo oo oo ~ ~ oo ~ ~ ox ~ ~ ~ a~ ~ ~ c~ ~ crx ~ o~ ~ cr ~ ~ a~ ~ cr~ cr ___ ______._________ _________ c~ ~ ~ ~ ~ ~ ~ o~ 0 0 0 0 0 ~ 0 0 ~ - c~ ce c~ . - . - . - .- ~ ~ ~ ~ ~ ~ . ~.= ~ ~ ~ E ~ ~ ~ g g g g 8 8 8 8 8 8 g g 8 8 _ _ _ _ _ n ~ ~ ~ ~ cr 0 ~D ~ ~ oo O C~ ~ ~O ~ oo ~ oo oo O O ~ o oo ~ ~ ~ ~ ~ - ~ ~ ~ ~ - ~ ~ ~ ~ - ~ o o ~ ~ ~- ~ ~ o - c~ ~ ~ c~ - ~ - - c~ ~ - - ) o~ r~ c ~c~ . ~c~ c ~Ce ~=-D ~·> e O ~ y ~ ~ - ~ ~ ~ ~ ~ E ~ ~ ~ ~ s ~ ~ =, ~ e t ~ m ~ ~ ~ 3 ~ ~ ~ ~ ~ ~ ~ ~ E~ ~ =: 0 - ~ ~ ~ ~ ~ ~ 00 - ~d~-ooON-________ cr 0 - ~ ~ ~ u~ _ ~ ~ ~ ~ ~ ~ ~ c~

65 Do oo - - - ~ oo - - ~ ^ =^ o \ o o o Ce of oo t ct ~ c) - to ~ ~ ~ °° Ho ~ cr ~ (~N ~ · ~ · ~ · c: - ~ c) - ~ - ~ . - . - . - ~ ~ ~ ~ E E E c, ~ °o ~ ~ ~ ~ o _ o :> ;~> ~ ~ ° I:: Cal ~ - ° of ~ ° ° ° ~ .~ ~ ~ ~ ~ Z ._ ~ ~ ~ ._ ~ ~ ~ ~ ~ US of oo oo oo of oo To oo Do oo oo ~ ~ at ~ ~ ~ ~ ~ ~ cr _ _ 1_ _ ~ ~ _ ~ _ _ ~ US of ~ ~o _ _ ~- ` 0 00 c~ o 0 · ~(~\ _ C - ;^ ~ 0 t0 v ~ ~ O C a C e c ~n V Z ;' ~ V m 0 ~ ~ 0 0 ~ ~ cr~ a~ oo ~0 ~ oo oo oo ~ r~ ~ oo oo ~ ~ ~ cr. ~ ~ c~ c ~ o~ _ _~____~_ ___ · ;~ ;~- C~ Og~g^ Smg O cr~ == O ~ E E E <, E ~ ~ c~ °^ °~ °^ ~o ~ g .- g _ - --VC) ~ ~ o~ ~ o- o _ U~ o ~ o vo _ - , ~oo ~ _ ~ ~ o oo U~ . . . . .. . . . . . . . . OoO ~ ~ - 0 ~ cr, ~00 0 ~ ~ oo ~ ~ ~c~ O ON ~ o ~ o - c~ : C c ~r ~ ~ c ~E - - ~ ,, 0 <,, E ~ e ,: O y ~: ~ E ~ O <o 07 y E ,: m ~ ~: ~ ~ c: ~ ~Q m E~ 3 3 ~ v, v' ~ ~ oo a~ 0 - ~ ~ ~ ~ ~ ~ oo ~ O - ~ ~ ~ In ~ ~ oo ~ 0 - ~ ~ ~ ~

66 G) L4 Cal _ I: 3 A em Cal ~ o .= 3 ~ Do m ~ z of ~4 o Cal 3 Cal 00 Cot - U' C,: . ~ S oo 00 `4 _ _ ~ · _ _ _ ` - ~ en L4 _ ~ __ m ~ ~ ~ . - 4- ~ V, =4 ct C) _ F4 F4 00 - 00 00 _ _ e~ ~ C) c: .= .~ ~ ~5 ^4 F. ~ ~ ~ C~ ~ 00 00 00 00 00 C~ ~ ~ ~ ~ _____ ~_ ~ ~ ~ c~ u, u, ca c'3 en u~ u~ c~ u S: C) C~ ~ ~ S~ S S~ S~ 5 1 ~4 ~ C4_~ -~ t4_ O O O O C ' ~t ~ ~S -~ - a, ~ ~ ~ ~ L4 ~ 4 ~ 4 ~ 4 ~ 4 r m m r r . . . . ~ V~ U) . . . . . ~ ~ ~ 3 ~ _ o-o U~ ~ V~ C-l ~ o o o o o o o o o ~ oo 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 X cr ~ ~o~ ~ ~ ~ ~ ~ ~ cr~ cr ~ ~ ~ c~ cr~ ___ _______________ ~ ~n ~ 00 00 oo ~ cr ~ _ _ _ 00 00 ~o ° ° ° _ _ C~ . ~ . ~. C~3 CD ~0 0 C) ~ ~ e~ ~ ~ ·~ ~·O ~ ~ (~ C~ ~4 c ~O 00 00 S S . ~ c4 oO oo L4 e4 e4 _ _ o o t4 . 0~ _ _ ~ ~= ~ _ _ O O t' (t et ~ Ct {~ ~ O L L (t ~ ·~4 ·~4 ~ ~ ~ ~ ~ ~ ~ ~ ~4 - 4 ~ ~ c~ v~ c~ c~ ~ ~ o ~ 3 3 ~ ~ 3 3 ~ ~F4 F4 V] (_) Z Z F4 ~4 Z ~] C~ ~ ~ ~ ~ t~ 00 00 ~ 00 00 00 ~ ~ 00 ~ 0\ ~ ~ ~ ~ ~ ~ C~ _________ o4 o4 o4 o4 o4 o4 o4 o. _ ~`: L4 =4 =4 =4 =4 ~4 ~ ~ ~ ~ =4 =4 L V) ~ ~ ~ ~ ~ ~ g O g O O O ~ g O O ~------ - _ {- ______ _ _ - ~O .. . ~ _ cr, ~ \0 ~ ~ . Oo u~ ~ ~ ~ - In ~ ~ ~ 00 0 ~ ~ ~ ~ 0 In O ~ ~ ~ ~ ~ - ~ _ _ 00 ._ ca C~ L4 O ~ - - 00 1- 00 - ~ _ ~ _ ~_ C) . _4 ._ C,3 ;;^ ~ . _4 ~4 . ~ c c ,, C ° E ~E E ~ E ' ~ E, ~ 2 2 ,, E r~ ~ ~ ~ 3 ~ u, ~ ~ ~ ~ rr, 0 - ~ ~ ~ ~ ~ r~ oo cr 0 - ~ ~ ~ ~ ~o _________ _~

67 Up ~ Up <5 ~cr Do oo oo . ~ ;> ;> ;> _ ox ~~ \~) ~ ~ .,4 ~ ~ ~0 ~ ~=4 ~ a c O ~ to ~ ~= a ~ ~ ,~- ~ ~ ~ ~ oo 0 oo Do x ,3 ~0 cry oo ~ ~ at ~ ON At ^ O~ cry (~N ^ ~ ~ ~ _ ~ ~ ~ ~ ~ ~ ~ c, - _ _ at, ~ lo,, ~ A,. ~ ~ ~ ~ ~_ ._ ._ ._ E z a 0 0 ° -c ~ c~ ~ ~ ~ c ~.8 c c c . ~.=C a c c c c e. c ^= C~ a a | c ~ ~ ~ s ~ ~= s s ~ ~ E ~ E ~ E E ~ E ~ ~ ~t ~ ~ u) ~ v) ~ u~ u~ oo oo oo oo oo oo oo oo oo oo oo ~ ~ ~ ~ ~ ~ a~ ox o~ ~ - ~ - - - - - - - - ~ ~ o o g 8 8 _ 8 0 _ _' _ ~ _ _ C) _ _ oo oo oo oo oo c ~cr~ C5~ ~ o~ _ ~_ _ _ _ _ _ ~ Ce au c~ E ~ a ,~, m~ `t . ~ cr~ == 0 Z 0 ·- 8 . g _ ~ cn ~ o ~ o- _ _ Ct ~ ~ ~ ~ ~ ~ o V) C ~ ~ ( C~ _ ~V~ ~- -- =o ~ ~ ~t_ ~ _~ ~_u~ _ ~ ~ ~ . . . .. . . . . . . .. _ ~ oo ~ 0 0 oo 0 ~ c~ oo _ - - \~:} ~o ~ =\ _4 . t- _ oo _ 0 _ u~ - c ~D s - oC ° E Y E ° j E E o e ~ £-~ E ~c ~ ~= o u, c' ~ ~ o~ ~ m m ~ ~ ~ ~ ~ CQ ~ ~ 3 3 ~Q cn ~ ~ ~: oo ~ O _ ~ ~ ~ u~ ~ ~ oo ~ O - ~ ~ ~ ~D ~oo ~ O _ ~ ~ ~ u~ ~ ~ ~ c~ ~ ~ ~ ~ ~ c~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ v~ ~ u~ ~ u~

68 U. ._ ¢ o C~ =, CQ a~ ~: .= a, C~ C~ ~D . - o a~ C~ ._ o o .~ Ct o x Ct m m E~ 0 a~ 00 U~ 0 00 t_ C~ 0D D ~1 Ct > D D ~ Z O 1 1 1 O ~ O - O \0 0 - 1 1 1 1 O ~ ~ - ~ d - d=N . . . . . . . . 1 1 _ 1 1 1 O ~ - ~ - ~ ~ - O O _t 1 1 1 1 o o ~ - ~ o ~ U~ o - o _ _ 1 1 O - O ~ - - ~ ~ ~ ~ ~ O . . . . . . . . . . 1 1 1 1 o ~ _ o _ ~ U~ ~ ~ O ~ C~ . . . . . . . . . . . 1 1 1 1 o U) _ ~ ~ o ~ ~ _ oo ~ ~ _ ~ . . . . . . . . . . . . . 1 1 1 1 o _ _ ~ ~ ~ o U~ ~ _ ~ ~ _ ~ ~ 1 1 1 1 O ~ ~ ~ ~ ~ ~ _ ~ ~ ', ~ ~' C - , ,~, 1 1 1 1 1 1 - O - ~ ~ - X ~ o ~ V~ ~ oo ~ ~ ~ _ 1 1 1 1 1 1 1 1 o o ~ ~ V) ~ ~ O o - ~ ~ ~ ~ ~ ~ ~o 1 1 1 1 1 1 1 1 r~ oo cr 0 - ~ ~ ~ ~ ~ ~ 00 0N

69 0 ~ 0 ~ ~-~ _ ~ r ~ r ~- -~ ~0 oo-to oo ~ ¢-, - - 1 1 1 1 1 ~ - 0 ~ ~ ~ ~ ~-~ e~ r r ~ U~ ~ ~ _ ~-0 ~ 0 ~O .................. . . 1 1 1 1 1 1 1 1 1 1 1 1 1 1 - O \0 ~ O ~ O 0 - - - ~ 'd - ~) . . . . . . . . . . . . . . . . . . . 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 ~ ~ ~ r ~ ~ r ~-0 cot oo ~ ~ -~ 0 ~ cot r ~ . . . . . . . . . . . . . . . . . . . 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 I ~-r ~ 0-~ ~ ~ 0-~ ~ r oo r ~ O .. . . . . . . . . . . . . . . I I I i I I r ~ ~ ~ r .. 1 1 1 (~} -) -] Cod ~ ~ U) . . . . . . . . 1 1 1 1 1 1 1 1 1 1 1 1 1 1 cot ~ ~ cot-O r rot ~ ~ 0 ~ 0-~ cut ~- . . . . . . . . . . . . . . . . 1 1 1 1 1 1 1 1 1 1 - } ~ ~ ~ ~ _ - ) ~ ~ _ ~ ~t C~4 . . . . . . . . . . . . . . . . . . . 1 1 1 1 1 1 1 C~ CA o. . . . ~ ~ o.. . . . ~ ~ C . . , ~ c~ ~> c~ ~ _ _ _ ~ ~ ~ oo ~-~ r 1 1 1 1 1 1 1 1 1 - ~ C~ ~ ~ O ~ ~ ~ ~ ~ oo ~ ~ . . . . . . . . . . . . . . . 1 1 1 1 1 1 1 1 1 1 0 ~ _ ~ ~ ~ . . . . . 1 1 1 1 1 1 1 1 1 1 1 1 1 I _ 0 ~ ~ U~ ~ 0 0 ~ ~ C~ 0 ~ 0 ~ C~ . . . . . . . . . . . 1 1 1 1 1 1 1 1 r ~ -- - r = --- ~ -- r . . . . . . . . . . . . . . . . 1 1 1 1 1 oo ~ oo ~ ~ 0 ~-~ ~-~ ~-~ ~ r oo . . . . . . . . . . . . . . . . . . . . . 1 1 1 1 1 1 1 1 -~ r ~ ~ r ~ ~ ~ ~ oo u~ ~ ~ ~ ~--r" . .. . . . . . . . . . . . . . . . . . . . 1 1 1 1 1 1 0-~ ~ ~ ~ r~ oo ~ o-~ ~ ~ ~ r x ~ oo ~ ~ ~ ~ ~ ~ ~ ~ C~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ V~ 1 1 1 1

70 c m J ',: U~ O oo o0 C~ r~ C~ O 2 oo C) eD ~ ;> z o _ o _ 1 o, _ _ o ~ ~ X _ o ~ o _ ~o _ 1 1 1 1 o ~ ~ ~ ~ 1 1 1 1 1 o oo _ ~ ~ ~ - , _ _ 1 1 1 1 o ~ _ ~ o . . . . . . . . - 1 1 1 o oo ~ ~ ~ _ ~ C~) oo tr] - 1 1 1 1 1 1 1 . o U~ ~ ~ ~ ~ ~ _ ~ ~ _ - 1 1 1 1 1 1 o ~ ~ ~ V~ ~ ~ ~ ~ o _ 1 1 1 o ~ ~ ~ _ ~ ~ - , ~ _ ~ - , ~t _ 1 1 1 1 1 o ~ ~ o ~ ~ ~ ~ o ~ o ~ U~ o _ 1 1 1 O ~ ~ ~ ~ 0~ oo d- ~ ~ - d - Ox . . . . . . . . . . . . . . . - 1 1 1 1 1 1 1 oo o ~ ~ ~ ~ ~ ~ ~ ~ X o ~ _ ~ ~ _ . - 1 1 1 1 1 1 ~ O - ~ _ ~1 t_ c-3 ~ ~ ~D ................. E 1 1 1 1 1 1 1 1 1 .= O ~ oo oo ~ ~ ~ O X ~ ~ ~ ~ o ~ _ ~ ~ C) ....... ..... .... O 1 1 1 1 O O ~ O O ~ O ~ \0 ~ M0 ~ - ~ O . . . . . . . . . . . . . . . . . _ _ - 1 c~ o ~ - ~ ~ ~ - ~ U. ~ ~ ~ o ~ - ~ o _ ~ - CD ............ ... ... ~ 1 1 , 1 1 1 1 1 1 eD C) r~ oo oS o-~ ~ U~ oo ~ X o ~ ~ .= C4 C~ ~ ~ ~ ~ ~ ~ ~ ~ ~ V ~Ct 1 1 1 1 1 1 1

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Cities and Their Vital Systems asks basic questions about the longevity, utility, and nature of urban infrastructures; analyzes how they grow, interact, and change; and asks how, when, and at what cost they should be replaced. Among the topics discussed are problems arising from increasing air travel and airport congestion; the adequacy of water supplies and waste treatment; the impact of new technologies on construction; urban real estate values; and the field of "telematics," the combination of computers and telecommunications that makes money machines and national newspapers possible.

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