Street Commerce

Creating Vibrant Urban Sidewalks

Andres Sevtsuk

• Publisher: University of Pennsylvania Press
• Year: 2020
• ISBN:
  9780812297089
• Topic: Social Sciences
• Subtopic: Urban Sociology
• Index: Social Sciences

A comprehensive analysis of the issues involved in planning for and facilitating successful street commerce Street commerce has gained prominence in urban areas, where demographic shifts such as increasing numbers of single people and childless "empty nesters, " along with technological innovations enabling greater flexibility of work locations and hours, have changed how people shop and dine out. Contemporary city dwellers are demanding smaller-scale stores located in public spaces that are accessible on foot or by public transit. At the same time, the emergence of online retail undermines both the dominance and viability of big-box discount businesses and drives brick and mortar stores to focus as much on the experience of shopping as on the goods and services sold. Meanwhile, in many developing countries, the bulk of urban retail activity continues to take place on the street, even as new car-oriented shopping centers are on the rise. In light of such trends, street commerce will play an important role in twenty-first-century cities, particularly in producing far-reaching benefits for the environment and local communities.Although street commerce is deeply intertwined with myriad contemporary urban visions and planning goals—walkability, quality of life, inclusion, equity, and economic resilience—it has rarely been the focus of systematic research and informed practice. In Street Commerce, Andres Sevtsuk presents a comprehensive analysis of the issues involved in implementing successful street commerce. Drawing on economic theory, urban design principles, regulatory policies, and merchant organization models, he conceptualizes key problems and offers innovative solutions. He provides a range of examples from around the world to detail how different cities and communities have bolstered and reinvigorated their street commerce. According to Sevtsuk, successful street commerce can only be achieved when the private sector, urban policy makers, planners, and the public are equipped with the relevant knowledge and tools to plan and regulate it.

CHAPTER 1
The Predictability, and Unpredictability, of Street Commerce

Despite the enormous variation in towns, neighborhoods, and businesses that one finds across the United States, a new field of urban science, pioneered by theoretical physicists who have turned to investigating cities as the new frontier for complexity research, is suggesting that there is surprising consistency to the pattern of commerce in any city or metropolitan area. Despite differing architectural styles, variations in businesses, and the unique characters of public spaces that frame them, these scientists suggest that the patterns of shops and services in different cities are actually systematic and predictable.

I am referring, in particular, to the work on urban scaling laws that has come out of the Santa Fe Institute for Complexity Studies and by scholars such as Luis Bettencourt, Jeffrey West, Jose Luis Lobo, and their colleagues.¹ In a 2007 paper published by the Proceedings of the National Academy of Sciences entitled “Growth, innovation, scaling, and the pace of life in cities,” Bettencourt and his colleagues claimed to have discovered universal laws that describe how material urban infrastructure and immaterial socioeconomic outputs vary with city size. They discovered that as urban populations double, the per capita provisions of physical infrastructure, including the number of shops and services that cities offer, do not. Larger cities obviously require more total miles of paved streets, longer linear networks of sewage pipes, and more retail stores than smaller cities, but if we compare these provisions per capita, then larger cities actually require fewer of them than smaller cities, suggesting that efficiencies and economies of scale come to play.

This “sublinear” scaling of infrastructure and amenities can be described with a precise trend line, which suggests that despite local architectural differences, characters, and flavors, the number of businesses in Keene, NH, and Concord, MA, is, in fact, quite predictable. The system of stores in a midsize city, such as Albuquerque, NM, is just a smaller scalar version of the same system in large metropolitan areas, such as New York or Los Angeles. Once we understand the properties of this pattern, we can develop an educated guess about how many businesses a town has just from knowing its population.

Besides scaling laws that predict the number of businesses in any town, scholars have found that businesses arrange themselves into clusters of predictable sizes. And recent data science work has additionally pointed out how regularly these clusters tend to locate with respect to each other.²

This chapter looks at macro patterns of retail and service establishments and discusses the regularities of these patterns across American cities. I will also discuss the limits of these regularities and illustrate how individual cities often deviate from the norm. Just as there are statistical commonalities in any group of human organizations, such commonalities are present in the macroscopic retail landscapes. But there are also unique traits that distinguish every retail cluster and metropolitan pattern of retail clusters from one another, which not only result from their unique historic, geographic, or climatic circumstances, but also from conscious policy, planning, and design choices that towns have adopted over time.

Different Scales of Street Commerce

Business establishments in the United States are officially tracked by North American Industry Categories, commonly known as NAICS codes. NAICS is the standard used by federal agencies for classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the US economy. The number of NAICS category digits indicates the level of detail in the establishment description. While the two-digit code “44” refers to the highest-level description, called “retail trade,” a three-digit code can distinguish “441”—“Motor Vehicle and Parts Dealers”—from “442”—“Furniture and Home Furnishings Stores.” A four-digit code, “4413,” goes into further detail, representing “Automotive Parts, Accessories, and Tire Stores.” The system goes all the way up to eight digits.

At the four-digit level, there are 36 codes that make up street commerce—27 establishment categories that fall under “retail trade,” 3 categories that fall under “food services and drinking places,” 3 trades that fall under “personal and laundry services,” and another 3 trades under “repair and maintenance services” (Table 1). When I refer to “street commerce” in this book, I generally refer to these 36 NAICS categories, which represent the types of stores and services that anyone on the street can readily walk into to purchase goods or services without having to make an appointment. This definition of street commerce does not include office buildings and a series of other service-oriented establishments, such as legal, financial, or consulting businesses. It also does not include cultural amenities, such as theaters, cinemas, or concert houses, which function at specific times with advance ticketing. The categories cover shopping, eating, and personal services that are generally open for walk-ins during business hours and constitute some of the most common destinations for nonwork, school, or recreational trips in cities.³

Table 1. Thirty-six retail, food, and service categories that typically constitute street commerce.

#	NAICS	Description
1	4411	Automobile Dealers
2	4412	Other Motor Vehicle Dealers
3	4413	Automotive Parts, Accessories, and Tire Stores
4	4421	Furniture Stores
5	4422	Home Furnishings Stores
6	4431	Electronics and Appliance Stores
7	4441	Building Material and Supplies Dealers
8	4442	Lawn and Garden Equipment and Supplies Stores
9	4451	Grocery Stores
10	4452	Specialty Food Stores
11	4453	Beer, Wine, and Liquor Stores
12	4461	Health and Personal Care Stores
13	4471	Gasoline Stations
14	4481	Clothing Stores
15	4482	Shoe Stores
16	4483	Jewelry, Luggage, and Leather Goods Stores
17	4511	Sporting Goods, Hobby, and Musical Instrument Stores
18	4512	Bookstores and News Dealers
19	4521	Department Stores
20	4529	Other General Merchandise Stores
21	4531	Florists
22	4532	Office Supplies, Stationery, and Gift Stores
23	4533	Used Merchandise Stores
24	4539	Other Miscellaneous Store Retailers
25	4541	Electronic Shopping and Mail-Order Houses
26	4542	Vending Machine Operators
27	4543	Direct Selling Establishments
28	7223	Special Food Services
29	7224	Drinking Places (Alcoholic Beverages)
30	7225	Restaurants and Other Eating Places
31	8111	Automotive Repair and Maintenance
32	8112	Electronic and Precision Equipment Repair and Maintenance
33	8114	Personal and Household Goods Repair and Maintenance
34	8121	Personal Care Services
35	8123	Dry Cleaning and Laundry Services
36	8129	Other Personal Services

Figure 1. Log-log scatter plot of retail, food, and service establishments versus population size in 273 US metro areas where population is greater than 40,000 people.

To illustrate how the provision of these amenities scales up with metropolitan population, in Figure 1 I have plotted the number of amenities that fall into these 36 types in each US metropolitan area on the vertical axis and the corresponding 2010 population on the horizontal axis. Each black dot denotes one core-based statistical area (CBSA), which consists of an urban center of at least 10,000 people plus adjacent counties (or equivalents) that are socioeconomically tied to the urban center by commuting. There are over 900 CBSAs in the United States. In smaller cities, they tend to cover a single municipality, but in larger cities they often include several towns. Since CBSA sizes vary drastically between New York City, NY, Los Angeles, CA, or Chicago, IL, on the larger end, and Pahrump, NV, and Blackfoot, ID, on the smaller, I have plotted both axes on the graph in logarithmic scale—a scale where each successive mark on each axis is 10 times greater than the previous mark. The numbers on the axes therefore do not increase linearly as they move further from zero, but exponentially, each time multiplying the previous number by 10. This allows us to spread out all the varying town sizes along the graph instead of having a dense cluster of small towns on the left side and a few large metro areas on the right. The two topmost points on the right-hand side represent the New York-Northern New Jersey-Long Island CBSA, with a population of 18.9 million, and the Los Angeles-Long BeachAnaheim CBSA, with a population of 12.8 million.

There is strikingly little scatter in the graph—the number of retail, food, and service businesses in most metro areas correspond closely to the area’s population. As the number of residents increases, so does the number of amenities. The exponent 0.94, which characterizes the slope of the trend line, tells us that the relationship is sublinear, just like the physicists predict. An exponent of “1” would mean that when you double the population you also double the amenities. The slightly lower value of 0.94 tells us that when you double the population...

Table of contents

• Cover
• Title Page
• Copyright
• Contents
• Introduction
• Chapter 1. The Predictability, and Unpredictability, of Street Commerce
• Chapter 2. The Survival of Individual Stores
• Chapter 3. How Stores Cluster
• Chapter 4. Coordinated Clustering: Business Improvement Districts, Co-ops, and Malls
• Chapter 5. Location, Location, Location: How Retailers Gravitate to Homes, Workplaces, and Pedestrians
• Chapter 6. How Urban Design and Building Typologies Affect Retail Location Patterns
• Chapter 7. How Demographic Shifts and E-Commerce Are Reshaping the Retail Landscape
• Conclusion
• Appendix
• Notes
• Index
• Acknowledgments