Data description

In the late summer of each year (usually around Labor Day) I record the name of each store at street-level between Spadina Avenue/Street and Christie Street/Grace Street on Bloor Street West in Toronto. I count a store to be at street level it if is at most half a story above or below ground.

I record also the nature of the goods or services that the store offers. These two characteristics can be determined relatively reliably. (In some cases, however, a store has no name in English or displays two signs, with different names; or, in the case of a restaurant, the name displayed differs from the name on the health certificate.)

Whether the store is the "same" business as the store at the same location (or a different location) in a previous year can be more difficult to determine. Sometimes a store changes hands but keeps the same name, and sometimes a store changes names but retains the same ownership (and engages in the same activities). I attempt to determine the persistence of stores from year to year, but may not do so with complete reliability. For example, I classify the store that used to be called "Kinko's" as the same business as the one that was called "FedEx Office" until it left the strip (although presumably its ownership changed), but classify the store that used to be called "Bloor Supersave" as a different business from the one now called "Bloor Superfresh".

Thus my dataset contains a set of businesses, and, for each year, a set of observations. To each business I assign a name, which is usually the name of the store the first year I observe it. Each observation for a particular year consists of a location, a business, and an observed name (which may differ from the name of the business, if the business remains the same but the name of the store changes).

I assign each business to one of the following broad categories.

  • Art, literature, music (Art supplies, photo finishing, books, records, CDs)
  • Bikes (Bikes, bike repair)
  • Business services
  • Cleaning services (Laundromat, laundry, dry cleaning)
  • Clothing (Clothes, lingerie)
  • Convenience
  • Department store (Department store)
  • Drugs (Drugs, drug paraphenalia)
  • Electronics (Electronics, computers)
  • Entertainment (Theater, cinema, video games)
  • Financial (Banks, tax preparation, money services)
  • Food (Supermarket, fruit and vegetables, butcher)
  • Home decoration (Art, posters, flowers)
  • Home furnishing (Furniture, beds, futons, kitchen equipment, home decoration, cushions, candles)
  • Home maintenance (Hardware)
  • Internet parlor (Internet parlor)
  • Media (Radio station, newspaper)
  • Medical (Doctor, dentist, medical lab, herbalist, drug store)
  • Miscellaneous (Environmental products, daycare center, watch repair, fortune teller)
  • Newsagent (Newspapers, magazines)
  • Personal adornment (Jewelry, tattoos)
  • Personal care (Barber, beauty clinic, spa, hairdresser, nail salon, cosmetics)
  • Pets (Pets, pet supplies)
  • Phones (Phones, phone services)
  • Political
  • Prepared food (Restaurants, cafés, fast food, pizza, bars, candies)
  • Recreation (Gym, yoga studio, dance studio)
  • Social services
  • Travel (Travel agent, ride service)
  • Variety (Variety store, dollar store, gifts)

In addition, I associate with every business one or more types, some of which are noted in parentheses after the categories in the list above. In some cases, I break down the types into subtypes. For example, I classify restaurants according to the cuisine they serve. In some cases, classifying a business is not straightforward. For example, the dividing lines between restaurants, cafés, bars, and fast food establishments are not clearcut.