The following are technical
notes to assist the user in understanding how The Atlas of Canada
mapped the Service Industries module. They have been organized
into the following topics:
Service-employment Data and Mapping
Commercial Land-use Data and Mapping
Service-employment Data and Mapping
Employment Data
The employment data used in the maps come from the 1996 Census
of Population (20% sample data) produced by Statistics Canada. (The
maps of growth of service employment use similar measures for 1986.)
Employment of an area is defined as the number of employed residents
at the date of census (refer to the glossary
or the 1996 Census Dictionary for more information on census-related
terminology).
The service-employment data are aggregated into the two-digit categories
of the standard industrial classification (SIC)
for each census
subdivision (municipality), and then aggregated into the set
of urban and rural geographic units used for the maps. The various
kinds of services and service combinations require different groups
of the two-digit categories. There are nine standard industrial
classification categories in wholesale; seven categories in retail;
seven financial categories; seven categories in the commercial services,
including business service; education; health; and three categories
of government. The service maps differentiate between the private-sector
services (wholesale, retail, finance, personal and leisure services)
and the public-sector services (education, health and public administration).
Each of these two broad groups is then further broken down into
sectors for mapping.
The maps of change in employment took advantage of the fact that
the standard industrial classifications were unchanged between 1986
and 1996, so the categories are comparable over time. Because the
geographic units have changed, however, the data were first geo-coded
to each census subdivision in each time period, and then the data
for each time period were aggregated into the 1996 geographic units.
Geographical Units
Since the number of workers is measured at the place of residence
and the maps are concerned with the place of work, it is necessary
to aggregate the residential locations into a geographic unit that
links workplace with residence. In this way, the number of persons
reported as employees will approximate the number of workers employed
by local businesses. The most appropriate geographic units to use
are the urban areas defined by Statistics Canada. The census
metropolitan areas include the largest cities (with at least
100 000 population in the central city), and the census
agglomerations are the smaller centres with population of at
least 10 000. As well, some of the census metropolitan areas and
census agglomerations have been grouped together into consolidated
census metropolitan areas, in which the component units are called
primary
census agglomerations and primary
census metropolitan areas.
Tables 1-1 to 1-5 summarize the number of geographic units of various
kinds and sizes that appear on these maps. The urban places total
159, with a small number of very large places and larger numbers
of smaller centres - a typical distribution of city sizes. Altogether,
urban places contribute 77.8% of the total population in 1996.
Table 1-1. Metropolitan Areas and Census Agglomerations Geographical
Unit, 1996
Metropolitan Areas and Census Agglomerations Geographical
Unit, 1996
10 000
to 30 000 |
53 |
950
000 |
278
000 |
3.3 |
30 000
to 100 000 |
49 |
2
495 000 |
757
000 |
8.7 |
100 000
to 300 000 |
17 |
2
571 000 |
803
000 |
8.9 |
300 000
to 1 000 000 |
6 |
2
983 000 |
1
051 000 |
10.3 |
Subtotal |
125 |
8
999 000 |
2
889 000 |
31.2 |
|
Table 1-2. Consolidated Census Metropolitan Areas Geographical
Unit, 19961
Consolidated Census Metropolitan Areas Geographical Unit,
1996
10 000
to 30 000 |
10 |
206
000 |
61
000 |
0.7 |
30 000
to 100 000 |
12 |
599
000 |
187
000 |
2.1 |
100 000
to 300 000 |
4 |
659
000 |
227
000 |
2.3 |
300 000
to 1 000 000 |
5 |
2
975 000 |
1
089 000 |
10.3 |
Over 1
000 000 |
3 |
9
011 000 |
3
037 000 |
31.2 |
Subtotal |
34 |
13
450 000 |
4
601 000 |
46.6 |
|
Table 1-3. Total Urban Places Geographical Unit, 1996
Total Urban Places Geographical Unit, 1996
10 000
to 30 000 |
63 |
1
156 000 |
339
000 |
4.0 |
30 000
to 100 000 |
61 |
3
094 000 |
944
000 |
10.8 |
100 000
to 300 000 |
21 |
3
230 000 |
1
030 000 |
11.2 |
300 000
to 1 000 000 |
11 |
5
958 000 |
2
140 000 |
20.6 |
Over 1
000 000 |
3 |
9
011 000 |
3
037 000 |
31.2 |
Subtotal |
159 |
22
449 000 |
7
490 000 |
77.8 |
|
Table 1-4. Rural Residuals Geographical Unit, 1996
Rural Residuals Geographical Unit, 1996
10 000
to 30 000 |
146 |
2
828 000 |
630
000 |
9.8 |
30 000
to 100 000 |
80 |
3
569 000 |
875
000 |
13.0 |
Subtotal |
226 |
6
397 000 |
1
505 000 |
22.2 |
|
Table 1-5. Canada Total Geographical Unit, 1996
Canada Total Geographical Unit, 1996
Total |
385 |
58
847 000 |
8
995 000 |
100.0 |
|
Source: Statistics
Canada. 1996 Census of Population
1 Includes Hull, Quebec as a separate unit within the
Ottawa-Hull consolidated census metropolitan area.
Census subdivisions not included in the urban areas were assigned
to census
divisions as residual
census divisions. Those census division residuals with population
less than 10 000 were combined with adjacent census division residuals.
Twenty-eight out of 254 census division residuals were combined
in this way, resulting in a total of 226 rural units. For the most
part, these rural units are small in size with a maximum population
of 87 000 (for example, Haldimand-Norfolk, Ontario).
In order to recognize the enormous size variation among the urban
centres, which range in population from 10 000 to more than 4 million
(Toronto, Ontario), the cities (and residual census divisions) are
represented by symbols that are proportional to their population.
To be precise, the area of the symbol is proportional to the population
being represented. The cities are represented by circles and the
residual census divisions by squares. The values for service specialization
assigned to each location are derived from the regression equations
for the cities, and the appropriate values are then calculated for
each of the residual census divisions. Thus, the cities collectively
define the specialization scale and the rural areas are assigned
to the scale afterwards. After the ranges of values for each quintile
have been determined, the residual census divisions are assigned
to their legend categories according to their values.
Regression Analysis Procedure
In order to overcome the influence of market distribution on the
location of services, it is necessary to develop a series of ratios
or indices that compare the level of service activity to the size
of the market. This could be done simply by calculating service
activity on a per-capita basis, or per million dollars in market
income. The resultant map would still be related to the city size
distribution; however, because larger cities consistently have higher
ratios of service activity, it is best to compare the observed level
of service activity (the actual employment) to an expected level
of service activity, as predicted by some combination of market
measures.
The statistical procedure for generating the expected levels of
service employment is called regression.
It uses information for the entire set of cities to generate a kind
of average or predicted value for each individual city, based on
that city's market characteristics. In this case, the market measures
used to predict service activity are employment, population and
income per capita.
How is the level of employment estimated on the basis of population
and income? The usual method is regression analysis, and a log-linear
regression model should be applied because the distributions of
city size and income per capita are log-normal. The model estimates
the amount of commercial employment that should be generated in
a community of given population and income level, and compares the
estimate to the observed value. The difference (either positive
or negative) is a measure of centrality.
The regression has the form:
log (employment) = A + B1 log (population) + B2
log (income per capita)
All three variables in the analysis (employment, population, income
per capita) are converted to logarithms so that the intercept A
is interpreted as a measure of scale (technically, the amount of
employment when population and income per capita are zero). The
regression coefficients B1 and B2 indicate
the relationship between employment and the two independent variables.
The coefficient R2 is the coefficient of determination
that indicates what proportion of the variance in employment is
explained by the other two variables.
Consider the equation for total commercial employment (wholesale,
retail, finance and commercial services):
log (employment) = -3.060 + 1.039 log (population) + 0.507 log
(income/capita)
R2 = 0.989
The fact that the regression coefficient for population (1.039)
is greater than one indicates that the commercial employment increases
disproportionately with population. A city that is ten times larger
than another (plus one on a log scale) will have eleven times more
service employment. In contrast, the regression parameter for income
per capita is less than one, which suggests that an increase in
income per capita is only partially captured as local consumption.
The money may be taxed, invested elsewhere, spent in larger cities
or used for travel. Finally, R2 indicates that the equation
predicts 98.9% of the variation in commercial employment, which
is a substantial achievement. The variations mapped in this set
of plates are fairly minor deviations within the overall patterns.
The equation provides an ‘all other things being equal’
prediction of local commercial employment, to which each urban area
can be compared. The city with the largest residual (more jobs than
expected), and therefore the greatest relative centrality, is Grande
Prairie, an agricultural community serving a prosperous agricultural
region in the Peace River district of Alberta. The residual from
a log relationship is actually the logarithm of the ratio of the
observed to expected values, and the value for Grande Prairie translates
into a ‘surplus‘ of 36.0% of the predicted commercial
employment. This amounts to about 2160 jobs more than the expected
total of 6005; in other words, Grande Prairie as a market is 36%
larger than it appears to be. At the other end of the list, the
largest negative residual occurs in Kitimat, British Columbia, which
generates 36.7% fewer jobs than predicted (1405 jobs instead of
2225). As a prospective store location, the Kitimat market is substantially
smaller than it appears.
Maps of Growth in Services
Maps of growth pose different problems in analysis and mapping.
The employment data include the number of workers in each service
category and each geographic unit for the years 1986 and 1996. The
difference in the employment totals (1996 value minus 1986 value)
is called the absolute growth; the absolute growth divided by the
1986 value is called the growth rate.
The procedures used to map growth in service employment are quite
simple. For each service sector or combination of sectors, the 1996
employment data for each location are compared to the 1986 employment
for the same sector. The map of absolute growth shows where the
growth has taken place across the country over the decade, among
regions and within regions, as the growth in service employment
has both responded to, and stimulated growth in, the urban markets.
The maps of absolute growth use symbols (circles) for each city
or residual census division (squares) that are proportional in area
to the number of jobs added. Positive and negative growth are shown
in different colours.
Given the enormous variation in the size of cities, however, a
map of absolute growth in service employment is unable to communicate
events within a particular city. Toronto is far larger than Orangeville
(Ontario) and both are growing by different amounts, but is one
city doing better than the other? The growth rates tell us how many
jobs each city has added relative to its size, so that one can be
compared against the other. The maps of relative growth begin with
the set of symbols that represents the population of cities and
residual census divisions, as in the maps of specialization, but
the colours of the symbols represent quintiles of growth rates as
defined for the cities.
Commercial Land-use Data and Mapping
Commercial structure can be defined as the geographic distribution
of commercial activity within a metropolitan area. This includes
the number, size and location of various kinds of commercial concentrations,
such as downtown, shopping centres or pedestrian-oriented strips.
The six elements of commercial structure that are shown in these
maps are described and defined below.
Elements of Commercial Structure
- Downtown: This is the concentration of activities
that serves the entire urban region and includes specialized retail,
financial and business services, as well as public-sector facilities.
These activities may be organized into subareas according to function
and market. Typically, downtown is the oldest part of the city
and the most accessible location overall. But every downtown is
different!
- Shopping Centres: These are privately owned
and operated facilities housing a number of stores, mostly retail,
that are linked together by pedestrian flows within the mall.
Centres are often isolated from other commercial facilities by
parking lots and streets. Shopping centres vary widely in size,
so that types of stores, store sizes and location vary with shopping-centre
size.
- Pedestrian Strips: These are streets containing
individually owned stores - a mixture of retail and service -linked
together by pedestrian flows and closely integrated within a local
market. Some are specialized by function (antiques) or to serve
a special market. They tend to emerge in the older built-up areas
of cities, or as former town centres in newer suburbs.
- Arterial Strips: These are stores located on
an arterial road or highway that provides access for customers
who come in cars. Customer linkages among the various stores are
rare; customers come from the market area served by the road system.
The stores share a common requirement for visibility and accessibility.
Activities include auto sales and repair, fast food and small
strip plazas.
- Industrial Zones: These are extensive areas
zoned for industrial use that may also include wholesalers, big-box
stores, and auto and other services. There are no internal links
among stores and visibility gives way to low rent as a location
advantage. Customers must seek out these facilities.
- Dispersed Stores: These activities remain after
all the commercial polygons have been designated, and the stores
assigned to various categories. Dispersed stores include traditional
isolated activities, such as service stations and convenience
stores, as well as more conventional store clusters that are simply
too small to qualify as polygons, especially in smaller cities.
Big-box stores may fall into this group.
Note that office buildings with concentrations of business and
financial services can be part of any of these elements, but the
offices shift the functional composition of the business location
toward these sectors (and reduce the share of corporate outlets).
Data source: Simmons and Kamikihara, 2002a
Data for Commercial Structure
The process begins with more than 1 million business entries for
Canada in an electronic telephone directory. The stores were classified
by type of commercial activity (standard industrial classification),
and 650 000 stores were extracted that belonged to the commercial
service categories: retail, finance, business, leisure and personal
services. (Wholesalers were excluded because of the confusion with
manufacturing firms.) For each of the target cities, the stores
were geo-coded (located on a map) within their respective cities.
Given the geography of stores for each city, a geography of commercial
locations was then imposed in the form of commercial polygons. A
polygon is a commercial area composed of at least 25 stores, and/or
50 000 square feet of floor area in the case of malls. Analysts
defined the boundaries of each polygon on the map by hand in order
to best separate commercial and noncommercial land uses (Simmons
and Kamikihara, 2002a).
Polygons have been identified for 81 of Canada's largest cities,
including all census metropolitan areas and all census agglomerations
with populations of more than 30 000. The maps of commercial land
use show the proportion of stores in each city that belongs to each
of the six types of commercial location. Each map shows circles
proportional to the size of the census metropolitan area or census
agglomeration. The cities are ranked according to each commercial
location variable and assigned to quintiles, which are colour coded.
Back to previous
page |