The Death and Life of Great American Cities

It is so easy to blame the decay of cities on traffic migrants ... or the whimsies of the middle class. The decay ... of cities goes deeper and is more complicated. It goes right down to what we think we want, and to our ignorance about how cities work. The forms in which money is used for city building-or withheld from use-are powerful instruments of city decline to day. The forms in which money is used must be converted instruments of regeneration from instruments buying violent cataclysms to instruments buying continual, gradual, complex and gentler change.

But when such districts are purposely “renewed to bring back the middle class,” or if they are the objects of conservation, to retain a population that has not yet deserted, the need to provide very extensive car accommodations immediately becomes a chief and overriding consideration. The existing deadness and thinness of use are thereby reinforced.

The Great Blight of Dullness is allied with the blight of traffic congestion.

The more territory, planned or unplanned, which is dull, the greater becomes the pressure of traffic on lively districts. People who have to use automobiles to use their dull home territory in a city, or to get out of it, are not merely capricious when they take the cars to a destination where the cars are unnecessary, destructive and a nuisance to their own drivers.

Territories exhibiting the Great Blight of Dullness need to be supplied with whatever conditions they lack for generating diversity. This is their basic need, regardless of traffic. But it is an aim which becomes impossible to further, if accommodations for huge numbers of cars get first consideration, and other city uses get the leftovers. A strategy of erosion by automobiles is thus not only destructive to such city intensity as already exists; it also conflicts with nurturing new or additional intensity of use where that is needed.

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Thus does erosion, little by little, subtract reasons for using an eroded district, and at the same time make it less lively, less convenient, less compact, less safe, for those who continue to have reason to use it. The more concentrated and genuinely urban an area, the greater the contrast between the smallness of what is delivered and the significance of what is lost by the process of erosion.

If vehicular traffic in cities represented some fixed quantity of need, then the action of providing for it would produce a satisfying and fulfilling reaction. Something, at least, would be solved. But because the need for more vehicles grows with the palliatives, the solution keeps receding

A striking statement of the positive feedback traffic process—or part of it—was worked out by Victor Gruen in 1955, in connection with his Fort Worth plan. Gruen, in order to understand the size of problem he had in hand, began by calculating the potential business that Fort Worth’s currently underdeveloped and stagnating—but traffic-jammed—downtown ought to be doing by 1970, based on its projected population and trading area. He then translated this quantity of economic activity into numbers of users, including workers, shoppers and visitors for other purposes. Then, using the ratio of vehicles per downtown users current in Fort Worth, he translated the putative future users into numbers of vehicles. He then calculated how much street space would be required to accommodate the numbers of these vehicles apt to be on the streets at any one time.

He got an outlandish figure of roadbed needed: sixteen million square feet, not including parking. This is in comparison with the five million square feet of roadbed the underdeveloped downtown now possesses.

But the instant Gruen had calculated his sixteen million square feet, the figure was already out of date and much too small. To obtain that much roadbed space, the downtown would have to spread out physically to an enormous extent. A given quantity of economic uses would thereby be spread relatively thin. To use its different elements, people would have to depend much less on walking and much more on driving. This would further increase the need for still more street space, or else there would be a terrible mess of congestion. Differing uses, necessarily strung out in such relatively loose fashion, would be so far from one another that it would become necessary to duplicate parking spaces themselves, because uses bringing people at different hours would not be sufficiently compact for much staggered use of the same accommodations.*2 This would mean spreading the downtown even thinner, in turn requiring still more use of cars, traveling greater absolute distances internally. Very early in the process, public transportation would be thoroughly inefficient, from both the customer’s and the operator’s point of view. In short, there would be no coherent downtown, but a great, thin smear, incapable of generating the metropolitan facilities, diversity and choices theoretically possible for the population and economy concerned.

As Gruen pointed out here, the more space that is provided cars in cities, the greater becomes the need for use of cars, and hence for still more space for them.

Dr. Weaver lists three stages of development in the history of scientific thought: (1) ability to deal with problems of simplicity; (2) ability to deal with problems of disorganized complexity; and (3) ability to deal with problems of organized complexity.

Problems of simplicity are problems that contain two factors which are directly related to each other in their behavior—two variables—and these problems of simplicity, Dr. Weaver points out, were the first kinds of problems that science learned to attack:

Speaking roughly, one may say that the seventeenth, eighteenth and nineteenth centuries formed the period in which physical science learned how to analyze two-variable problems. During that three hundred years, science developed the experimental and analytical techniques for handling problems in which one quantity—say a gas pressure—depends primarily upon a second quantity—say, the volume of the gas. The essential character of these problems rests in the fact that…the behavior of the first quantity can be described with a useful degree of accuracy by taking into account only its dependence upon the second quantity and by neglecting the minor influence of other factors.

These two-variable problems are essentially simple in structure…and simplicity was a necessary condition for progress at that stage of development of science.

It turned out, moreover, that vast progress could be made in the physical sciences by theories and experiments of this essentially simple character…It was this kind of two-variable science which laid, over the period up to 1900, the foundations for our theories of light, of sound, of heat, and of electricity…which brought us the telephone and the radio, the automobile and the airplane, the phonograph and the moving pictures, the turbine and the Diesel engine and the modern hydroelectric power plant…

It was not until after 1900 that a second method of analyzing problems was developed by the physical sciences.

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However, by no means all problems could be probed by this method of analysis. The life sciences, such as biology and medicine, could not be, as Dr. Weaver points out. These sciences, too, had been making advances, but on the whole they were still concerned with what Dr. Weaver calls preliminary stages for application of analysis; they were concerned with collection, description, classification, and observation of apparently correlated effects. During this preparatory stage, among the many useful things that were learned was that the life sciences were neither problems of simplicity nor problems of disorganized complexity; they inherently posed still a different kind of problem, a kind of problem for which methods of attack were still very backward as recently as 1932, says Dr. Weaver.

Describing this gap, he writes:

One is tempted to oversimplify and say that scientific methodology went from one extreme to the other…and left untouched a great middle region. The importance of this middle region, moreover, does not depend primarily on the fact that the number of variables involved is moderate—large compared to two, but small compared to the number of atoms in a pinch of salt…Much more important than the mere number of variables is the fact that these variables are all interrelated…These problems, as contrasted with the disorganized situations with which statistics can cope, show the essential feature of organization. We will therefore refer to this group of problems as those of organized complexity.

What makes an evening primrose open when it does? Why does salt water fail to satisfy thirst?…What is the description of aging in biochemical terms?…What is a gene, and how does the original genetic constitution of a living organism express itself in the developed characteristics of the adult?…

All these are certainly complex problems. But they are not problems of disorganized complexity, to which statistical methods hold the key. They are all problems which involve dealing simultaneously with a sizable number of factors which are interrelated into an organic whole.

In 1932, when the life sciences were just at the threshold of developing effective analytical methods for handling organized complexity, it was speculated, Dr. Weaver tells us, that if the life sciences could make significant progress in such problems, “then there might be opportunities to extend these new techniques, if only by helpful analogy, into vast areas of the behavioral and social sciences.”

In the quarter-century since that time, the life sciences have indeed made immense and brilliant progress. They have accumulated, with extraordinary swiftness, an extraordinary quantity of hitherto hidden knowledge. They have also acquired vastly improved bodies of theory and procedure—enough to open up great new questions, and to show that only a start has been made on what there is to know.

But this progesss has been possible only because the life sciences were recognized to be problems in organized complexity, and were thought of and attacked in ways suitable to understanding that kind of problem.

The recent progress of the life sciences tells us something tremendously important about other problems of organized complexity. It tells us that problems of this kind can be analyzed—that it is only sensible to regard them as capable of being understood, instead of considering them, as Dr. Weaver puts it, to be “in some dark and foreboding way, irrational.”

Now let us see what this has to do with cities.

Cities happen to be problems in organized complexity, like the life sciences. They present “situations in which a half-dozen or even several dozen quantities are all varying simultaneously and in subtly interconnected ways.” Cities, again like the life sciences, do not exhibit one problem in organized complexity, which if understood explains all. They can be analyzed into many such problems or segments which, as in the case of the life sciences, are also related with one another. The variables are many, but they are not helter-skelter; they are “interrelated into an organic whole.”

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Because the life sciences and cities happen to pose the same kinds of problems does not mean they are the same problems. The organizations of living protoplasm and the organizations of living people and enterprises cannot go under the same microscopes.

However, the tactics for understanding both are similar in the sense that both depend on the microscopic or detailed view, so to speak, rather than on the less detailed, naked-eye view suitable for viewing problems of simplicity or the remote telescopic view suitable for viewing problems of disorganized complexity.

In the life sciences, organized complexity is handled by identifying a specific factor or quantity—say an enzyme—and then painstakingly learning its intricate relationships and interconnections with other factors or quantities. All this is observed in terms of the behavior (not mere presence) of other specific (not generalized) factors or quantities. To be sure, the techniques of two-variable and disorganized-complexity analysis are used too, but only as subsidiary tactics.*2

In principle, these are much the same tactics as those that have to be used to understand and to help cities. In the case of understanding cities, I think the most important habits of thought are these:

1. To think about processes;

2. To work inductively, reasoning from particulars to the general, rather than the reverse;

3. To seek for “unaverage” clues involving very small quantities, which reveal the way larger and more “average” quantities are operating.

If you have gotten this far in this book, you do not need much explanation of these tactics. However, I shall sum them up, to bring out points otherwise left only as implications.

Why think about processes? Objects in cities—whether they are buildings, streets, parks, districts, landmarks, or anything else—can have radically differing effects, depending upon the circumstances and contexts in which they exist. Thus, for instance, almost nothing useful can be understood or can be done about improving city dwellings if these are considered in the abstract as “housing.” City dwellings—either existing or potential—are specific and particularized buildings always involved in differing, specific processes such as unslumming, slumming, generation of diversity, self-destruction of diversity.

This book has discussed cities, and their components almost entirely in the form of processes, because the subject matter dictates this. For cities, processes are of the essence. Furthermore, once one thinks about city processes, it follows that one must think of catalysts of these processes, and this too is of the essence.

The processes that occur in cities are not arcane, capable of being understood only by experts. They can be understood by almost anybody. Many ordinary people already understand them; they simply have not given these processes names, or considered that by understanding these ordinary arrangements of cause and effect, we can also direct them if we want to.

Why reason inductively? Because to reason, instead, from generalizations ultimately drives us into absurdities—as in the case of the Boston planner who knew (against all the real-life evidence he had) that the North End had to be a slum because the generalizations that make him an expert say it is.