The various nominal data sources were entered into digital format and used to create a retail database that details the retail landscape as it changes over time. Each source was entered into separate tables within the database and offers a specific set of information which. The rows of the table contain the individual
records while the columns detail the various attributes entered (See Table 2.2 for the variables entered from each source). In addition to the information entered from the sources, additional data was added to many of the tables. As discussed below, classification of the land-uses and retail types was added. Once the records were geocoded, the distance between each point and the peak value intersection (PVI) at Richmond and Dundas Streets was calculated and included in the table using the GIS tools discussed later in this chapter.
The analysis of business histories is much like that of personal histories found in demographic research (for examples of urban historical demographic research see: Thernstrom and Knights 1970; Pooley 1979; Gilliland 1998). Each individual retailer is equivalent to a person in traditional demographic model. They have a date of birth, the day the company was formed, and a date of death, marked by its closure due to bankruptcy or sale (unless remain in existence to this day). Thus, the number of new stores (births) and store closures (deaths) for a given year can be calculated, as can the tenure of the store (life expectancy). The businesses can be grouped by type in a similar fashion to those classifications applied to groups
of people. Just as people may be grouped by sex, religion, or occupation, stores can be generally grouped by the goods they sell.
Whereas a demographer uses the census or alphabetical city directory, the business directory is the primary source used by the business historian. Stores were grouped by the type of goods sold, for example, butchers, bakers, and chandlers. The
business directory also lists services and industries; however, this work is solely interested in the retailers, which are defined as those businesses from which one could obtain a physical good to consume off site. If a service is provided in addition to the good it is still considered a retail outlet. A butcher, for example, provides both a good and a service; the piece of meat, as well as his work in preparing that piece by dressing the animal and dividing the meat into portions as dictated by the customer. A butcher is considered a retailer since the customer did take away a physical good from the premise; similarly florists and tailors are also considered retailers despite the high level of service they provide. Services which did not provide a physical good to be removed from the shop were not considered. Thus, banks and other financial institutions were not included since they dealt in abstract notions of finance and money, represented on paper. Hotels provide a physical room in which to stay, but it was not removed from the premise. Similarly, restaurants and saloons were not considered as retailers since their services were primarily consumed on site. Although dealing with physical goods, wholesalers were not considered retailers since they did not sell to the general public.
Determining which businesses fit this definition of retailer is challenging for some types. For example, it was difficult to classify the butter dealers listed in the1881 directory since they could either be businesses open to the public or
wholesale distributers dealing with the local grocery stores. These elusive categories were the exception rather than the norm, and would not significantly impact the results if classified incorrectly. The majority of businesses were quite easy to delineate, such as dry goods stores and grocers.
TABLE 2.2 The attributes used in the database created from the nominal sources.
Source Years Sample Size Data Entered From Source Additional Data Business
Directories 1864, 1881, 1916, 1958, 2004
Complete Category Business
Name Proprietor Street Number Street Name Retail /Wholesale Retail Classification Distance to the PVI Street Directories Annually 1880- 1930 Dundas from Ridout to East of Wellington and Richmond from York to Carling Name Street
Number Street Land-Use Type Retail Classification Distance to the PVI
Tax
Assessments 1916, 1929 Dundas from Ridout to East of Wellington and Richmond from York to Carling
Street Side Cross
Street Street # Name Business Name/ Other Metric Conversion Width Metric Conversion Length Width (ft, inches) Length (ft, inches) Value/Foot Land Value Bldg. Value Total Assess Shopping Centre Directories 1981, 1985, 1991, 1996, 2001, 2006, 2010
Complete Name Address Type Year
Opened GLA Levels Retail of Distance to the PVI
No. Stores Food Court Enclosed No. Parking Spaces Annual Sales Sales/ Sq. Ft. Vehicle
Traffic People Traffic Avg. Non-anchor rent C.A.M.
Year Last
Sold Previous Owner Owners
Upon accounting for all of the retailers, they were divided into groups by the type of goods sold. Most generally the retailers were divided into those selling food stuffs, fashion goods and all others types of merchandise. Fashion retailers included tailors, milliners, dressmakers, ready-to-wear stores and dry goods houses, jewellers and shoe-makers. Food retailers included grocers, butchers, bakers, provision dealers, fruit, and fish and game shops. Neither tobacco nor alcohol shops were considered in the food category since they were considered not a food-stuff even though they are ingested. These fell into the other category, a miscellany of those stores which did not fit these classifications. The original business directory groupings were maintained in the databases for a finer level of categorization. This allowed jewellers, for example, to be distinguished from dry goods stores.
Further complications arose in the classification scheme in determining which group to fit a business that had more than one category. Department stores, for example, sold clothing, furniture and often food stuffs. Thus, they could fit into each of the aforementioned categories. To overcome this problem the original
groupings in the business directory were used, such that even if it was known that a department store carried furnishings, but it was not listed under furnishings, it was not recorded in the category in the database. Similarly, if a store was reported more than once due to it being listed in different categories, then unique records were created for each listing. Thus, if a clothing store was listed under both men’s and women’s clothing, it received two records, one for each category. The duplicate records were marked in order that the numbers were not inflated when looking at all retailers and their locations.
Each business was given a unique identifier (ID) so that it could be
distinguished from the other listings and to help with the data management. If a store was present in both 1882 and 1883 it was given the same ID. This allows for the longevity of the stores to be discerned. These unique IDs are especially
important for tracing one business over time, as was done for the analysis of the central retail district since a continuous run of listings was available between 1880 and 1930.
Name changes complicated some of the management of the records within the database. Some years a business would be listed by the name of proprietor, and others by its formal name; for example Askin’s fruits or London City Fruits in
differing years. Since Askin was listed as the proprietor of London City Fruits, and the address was the same, it can be assumed that the business was the same and thus given the same ID. When looking for the same business the data was sorted by name which was complicated by the fact that some years Askin’s Fruits was referred to as T. Askin’s Fruits or Thomas Askin’s Fruits, thus moving the records at opposite ends of the database. Legal name changes also complicated the delineation of
businesses, especially by the addition of a new partner in the company. For example, the addition of Allan Grey to the company would have changed Thomas Askin’s Fruits to Grey and Askin’s Fruits or Askin and Grey – Fruit Dealers.
Businesses, unlike people, can be in many places at once. This is a result of the chaining of stores with several branches operating throughout the city. Each unique location is recorded as a separate record. Location IDs are given to distinguish each branch outlet. Thus a store with four branches would have four unique records, each containing the same business ID but unique location IDs. This coding allows for chains to be highlighted in the analysis.
The categorizing of businesses is one large problem when dealing with retail histories. Another is determining the locations of the stores. There were many instances in the directory listings where no street address was given. Many of these listed a building or intersection rather than a street address while others simply did not have any spatial information. Retailers often locate in prominent buildings that are generally known to the population. Thus, the directory compilers documented the location by building rather than address. Today a business might be said to be in Masonville Mall the location of which is standard knowledge for the residents of the city. Over time, this knowledge can be lost, creating difficulties in determining the location of these retailers. An example is the retailers whose addresses are listed as The Albion Building. Although this building still stands today along the west side of Richmond Street, north of Dundas, it is not commonly referred to by this name.
In order to determine the location of a specific building its name is first searched for in the city directories and tax assessment datasets. If this is to no avail, then the fire insurance plans are used, which indicate the names of some of the prominent buildings, the Albion included. Since the locations are unknown it is much like finding a needle in a haystack, having to scour through many sheets
reading each building’s name. Once located in the city directory, tax assessment or fire insurance plans, the building’s corresponding street address is added to the records’ for use in the geocoding process outlined in the next section.
Various software programs were used to create and manage these databases in digital format. For most purposes GIS is not the best way to produce and manage the raw, non-spatially referenced datasets. This is due to the complexity of the program and its limited data entry facilities. Furthermore, many people helped enter the data used in this study, many of whom were not familiar with GIS or had access to GIS software on their computers; thus, a more accessible software program was implemented for the data entry.
The spreadsheet program Microsoft Excel was generally used to enter the original sources. This was due to its relative light weight (in terms of memory and processing requirements), its ease of use, and wide availability and familiarity amongst those who were tasked with the data entry. Taking a laptop containing Excel spreadsheets to the archives is much easier than using either a database management system or the GIS software. The tables in which the data is entered are structured where each piece of data is given a separate field: addresses were stored as street number, street name, and street type, all in separate fields. Each row in the table corresponded with a new record.
Once entered, the data underwent a process of cleaning and error checking. This involves several strategies: fields are sorted alphabetically in order to
determine if mistakes were made in the entry of text, such as the spelling of street names or the inclusion of superfluous spaces. Numeric fields, such as street number, are also sorted to ensure that no unduly large figures or extraneous characters were entered. Random records are then selected to be checked with the original sources to ensure the precision of the work. After the data were geocoded, further checks ensure that the addresses were matched to correct location as discussed in the following section.
Many of the Excel datasheets were then transferred into the database management program Access, also made by Microsoft. This software allows for the datasheets to be linked across common attributes to form a relational database. Once this database was structured, these relationships allowed for queries to be written that drew attributes from two or more unique tables, combining them into a
new table. Queries are also written to update the data and automatically look for errors. Much of the analysis can be performed within the database management software. For example, the number of retailers per year in each category can be quickly determined using a relatively simple query. This software is suitable for performing many of the analyses which is found in the subsequent chapters; however, it was unable to examine the spatial aspect of the data. To exploit the spatial aspect of the data, the datasets were imported into the GIS software and spatially referenced.