19 Web Of Shopping*

. . . this pattern defines a piecemeal process which can help to locate shops and services where they are needed, in such a way that they will strengthen the Mosaic Of Subcultures (8), Subculture Boundaries (13), and the decentralized economy needed for Scattered Work (9) and Local Transport Areas (11).

Shops rarely place themselves in those positions which best serve the people's needs, and also guarantee their own stability.

Large parts of towns have insufficient services. New shops which could provide these services often locate near the other shops and major centers, instead of locating themselves where they are needed. In an ideal town, where the shops are seen as part of the society's necessities and not merely as a way of making profit for the shopping chains, the shops would be much more widely and more hornogeneously distributed than they are today.

It is also true that many small shops are unstable. Two-thirds of the small shops that people open go out of business within a year. Obviously, the community is not well served by unstable businesses, and once again, their economic instability is largely linked to mistakes of location.

To guarantee that shops are stable, as well as distributed to meet community needs, each new shop must be placed where it will fill a gap among the other shops offering a roughly similar service and also be assured that it will get the threshold of customers which it needs in order to survive. We shall now try to express this principle in precise terms.

The characteristics of a stable system of shops is rather well known. It relies, essentially, on the idea that each unit of shopping has a certain catch basin‹the population which it needs in order to survive‹and that units of any given type and size will therefore be stable if they are evenly distributed, each one at the center of a catch basin large enough to support it.

Catch basins.

The reason that shops and shopping centers do not always, automatically, distribute themselves according to their appropriate catch basins is easily explained by the situation known as Hotelling's problem. Imagine a beach in summer time‹and, somewhere along the beach, an ice-cream seller. Suppose now, that you are also an ice-cream seller. You arrive on the beach. Where should you place yourself in relation to the first ice-cream seller? There are two possible solutions.

 

Two approaches to the ice-cream problem.

In the first case, you essentially decide to split the beach with the other ice-cream seller. You take half the beach, and leave him half the beach. In this case, you place yourself as far away from him as you can, in a position where half the people on the beach are nearer to you than to him.

In the second case, you place yourself right next to him. You decide, in short, to try and compete with him‹and place yourself in such a way as to command the whole beach, not half of it.

Every time a shop, or shopping center opens, it faces a similar choice. It can either locate in a new area where there are no other competing businesses, or it can place itself exactly where all the other businesses are already in the hope of attracting their customers away from them.

The trouble is, very simply, that people tend to choose the second of these two alternatives, because it seems, on the surface, to be safer. In fact, however, the first of the two choices is both better and safer. It is better for the customers, who then have stores to serve them closer to their homes and work places than they do now; and it is safer for the shopkeepers themselves since - in spite of appearances - their stores are much more likely to survive when they stand, without competition, in the middle of a catch basin which needs their services.

Let us now consider the global nature of a web which has this character. In present cities, shops of similar types tend to be clustered in shopping centers. They are forced to cluster, in part because of zoning ordinances, which forbid them to locate in so-called residential areas; and they are encouraged to cluster by their mistaken notion that competition with other shops will serve them better than roughly equal sharing of the available customers. In the "peoples" web we are proposing, shops are far more evenly spread out, with less emphasis on competition and greater emphasis on service. Of course, there will still be competition, enough to make sure that very bad shops go out of business, because each shop will be capable of drawing customers from the nearby catch basins if it offers better service - but the accent is on cooperation instead of competition.

 

The existing web and the peoples' web.

To generate this kind of homogeneous people's web, it is only necessary that each new shop follow the following three-step procedure when it chooses a location: 1. Identify all other shops which offer the service you are interested in; locate them on the map. 2. Identify and map the location of potential consumers. Wherever possible, indicate the density or total number of potential consumers in any given area. 3. Look for the biggest gap in the existing web of shops in those areas where there are potential consumers.

 

The gap in services.

Two colleagues of ours have tested the efficiency and potential stability of the webs created by this procedure. ("Computer Simulation of Market Location in an Urban Area," S. Angel and F. Loetterle, CES files, June 1967.) They chose to study markets. They began with a fixed area, a known population density and purchasing power, and a random distribution of markets of different sizes. They then created new markets and killed off old markets according to the following rules. (1) Among all of the existing markets, erase any that do not capture sufficient business to support their given size; (2) among all of the possible locations for a new market, find the one which would most strongly support a new market; (3) find that size for the new market that would be most economically feasible; (4) find that market among all those now existing that is the least economically feasible, and erase it from the web; ( 5 ) repeat steps (2) through (4) until no further improvement in the web can be made.

Under the impact of these rules, the random distribution of markets at the beginning leads gradually to a fluctuating, pulsating distribution of markets which remains economically stable throughout its changes.

Now of course, even if shops of the same kind are kept apart by this procedure, shops of different kinds will tend to cluster. This follows, simply, from the convenience of the shopper. If we follow the rules of location given above - always locating a new shop in the biggest gap in the web of similar shops - then, within that gap there are still quite a large number of different possible places to locate: and naturally, we shall try to locate near the largest cluster of other shops within that gap, to increase the number of people coming past the shop, in short, to make it more convenient for shoppers.

The clusters which emerge have been thoroughly studied by Berry. It turns out that the levels of clustering are remarkably similar, even though their spacing varies greatly according to population density. (See Geography of Market Centers and Retail Distribution,B. Berry, Englewood Clifis, New Jersey: Prentice-Hall, Inc., 1967, pp. 32-33.) The elements in this web of clustering correspond closely to patterns defined in this language.

Therefore:

When you locate any individual shop, follow a three-step procedure: 1. Identify all other shops which offer the service you are interested in; locate them on the map. 2. Identify and map the location of potential consumers. Wherever possible, indicate the density or total number of potential consumers in any given area. 3. Look for the biggest gap in the existing web of shops in those areas where there are potential consumers. 4. Within the gap in the web of similar shops, locate your shop next to the largest cluster of other kinds of shops.

We estimate, that under the impact of this rule, a web of shopping with the following overall characteristics will emerge:

	Population	Distance Apart (Miles)
Magic Of The City (10)	300,000	10*
Promenades (31)	50,000	4*
Shopping Streets (32)	10,000	1.8*
Markets Of Many Shops (46)	4,000	1.1*
Corner Groceries (89)	1,000	0.5*
* These distances are calculated for an overall population density of 5000 per square mile. For a population density of D persons/square mile, divide the distances by the square root of D/5000....

A Pattern Language is published by Oxford University Press, Copyright Christopher Alexander, 1977.