Pitney Bowes is a leader in providing demographic data for the Canadian market.
The MapInfo Canadian Estimates and Projections database variables are “updated” from their Census 2011 values. MapInfo estimates and projections for Canada are updated annually. The reference date for the data is always July 1, which is considered the midpoint for the reference year.
The estimation and projection methodology involves a combination of top-down methods (national to census subdivision), using traditional demographic techniques, and bottom-up methods (dissemination area to census subdivision) using demographic techniques along with proprietary spatial modeling techniques. Significant efforts are applied to the task of integrating the latest Statistics Canada data into the MapInfo Canada demographic update process. The 2006 census information is used as the benchmark for all of the estimates and projections in this release
The principal population characteristics in the Estimates and Projections database are age and sex. These characteristics are derived through a cohort component method that takes into account the aging of the population, mortality rates by age and sex, fertility rates, and differential migration by age and sex.
The principal household characteristics in the Estimates and Projections database are age of household maintainer and household income. The distribution of households by age of maintainer is derived from the cohort component model results and the probabilities associated with a person in a given age group being the primary maintainer of a household as defined by Statistics Canada. Household Income is derived from an economic-demographic model developed in part by Strategic Projections, Inc. for higher levels of geography. For smaller geographic layers, including Dissemination Areas, income trends based on census information are combined with regional income trends in order to estimate and project household income. The distribution of households by income is derived from a process which advances census-based income distributions in a manner consistent with the trend in average income for a DA. All income figures are given in current year dollars for each year of the series. Note that this presentation does not involve the use of an income deflator to take into account inflation.
The Canada Expenditure Potential database is developed using Statistics Canada’s Survey of Household Spending (SHS) and MapInfo’s PSYTE Canada Advantage cluster system. The survey respondents are geocoded by Statistics Canada to their dissemination area (DA) of residence. Then, while maintaining strict confidentiality and data suppression standard, Statistics Canada aggregates and tabulates all SHS data by PSYTE Canada Advantage cluster. Coefficients are derived by MapInfo such that when applied against an independently derived estimate of aggregate household expenditures at the DA level, an estimate of detailed consumer expenditures is generated. Careful attention is paid to statistical reliability due to sample size, and in some cases imputations and substitutions are made to maintain reliability and consistency within the database.
Every market-focused company uses demographic data to target consumers where they live. But only the most strategic companies also target consumers where they work using workplace estimates. Why do some companies care about workplace estimates? Because consumers who spend a large percent of their waking hours working in an area that is different from where they live, also purchase products and services near their workplaces from restaurants, banks, dry cleaners, drug stores, and more. So, by determining the workplace populations of markets, companies gain much more precise market insight and, therefore, more profitable site selection capabilities.
Gain insight into disposable versus discretionary income. When it comes to getting a true grasp of your customer’s available funds, you must first understand both their disposable and their discretionary income.
Need versus want
The difference between disposable and discretionary income is basically the difference between need and want. Disposable income is essentially after-tax income, whereas discretionary income is the money that remains for spending or saving after households pay necessities. These necessities consist of taxes, food, housing, transportation, apparel, and out-of-pocket health care.
The Pitney Bowes Business Insight Disposable and Discretionary Income database provides you with the data to understand these available funds.
The Pitney Bowes Business Insight Disposable and Discretionary Income database subtracts tax estimates, derived in part from Statistics Canada’s Survey of Labor and Income Dynamics, to achieve disposable income estimates.
To determine discretionary income, Pitney Bowes Business Insight’s Disposable and Discretionary Income database begins with the disposable income figure and subtracts household necessity spending estimates. The estimate of household expenditures comes from our Canadian Expenditures, which are derived from linking Statistics Canada’s Survey of Household Spending with Pitney Bowes Business Insight’s segmentation data product.
The PSYTE HD® geodemographic segmentation system classifies Canadian neighbourhoods into mutually exclusive lifestyle groups—or ‘clusters’ based on select geodemographic metrics, location, and indicators of consumer and lifestyle behaviour.
PSYTE HD Canada is fundamentally a geo-demographic cluster system. Geo-demographic cluster systems, in contrast to household-based systems or hybrid systems, use the smallest area for which census data are published – in Canada, the census dissemination area – as a de facto neighbourhood base. Nevertheless, diversity within dissemination areas as “neighbourhoods” exists and will likely increase. Users will find, however, that PSYTE HD Canada captures much of that diversity and still provides a sound basis for sensible market segmentation strategies.
The basic assumption of clustering is that people with similar characteristics, preferences, and consumer behaviors tend to live in like neighbourhoods. However, as Canadian society changes and neighbourhoods evolve, cultural and economic diversity increases. The extent of diversity—whether socio-economic, ethnic, cultural, lifestyle, life-stage, or other dimension—is such that the new PSYTE HD Canada takes into account unprecedented levels of “within neighbourhood” differences as well as increased diversity overall. Nevertheless, users should discover that the fundamental drivers of consumer behaviors and lifestyles within each cluster are substantially similar.
PSYTE HD – Canadian Neighbourhoods
There are eight settlement context levels as noted in the following chart, which shows the distribution of “Canadian Elite” households across the settlement context schema:
A second advantage of this approach to settlement context is the ability of analysts to better visualize the dynamic nature of human settlements. Just as the earliest suburbs arose from the city environs of the last century, so new suburbs, exurbs, and mid-sized cities grow and extend their influence within a micro-region and beyond. Over time, as commuting patterns and employment hubs form more complex networks, the nature and extent of commercial activity evolves.
PSYTE HD – Clusters
This database breaks the population down to 59 distinct clusters. The clustering process starts with the grouping of DA’s into 300 mini-clusters or “atoms” based on key demographic themes or dimensions such as: age, dwelling type, family structure, education, employment characteristics, ethnicity, income, and mode of travel to work. The initial phase involves a sophisticated clustering algorithm which maximizes the similarities of DA’s within each atom while simultaneously maximizing the differences among DA’s across the atoms. Thus, the atoms represent the fundamental geo-demographic structure of Canada whereby each atom contains DA’s that are similar with respect to principal characteristics of demography and areal situation. The atoms become the key building blocks for the final geo-demographic clusters.Read the Methodology
An estimate of the working population aged 15 to 64 at the DA level based on labour force participation rates is subtracted from the 2006 Census of Canada Population and the current-year demographic Estimates and Projections. Next, up-to-date daytime employment estimates based on measures of business intensity and employment statistics are derived from business-related sources. Constraining factors include the reconciling of employment statistics to labour market totals at the CMA and provincial levels.
Estimated Daytime Population
Estimated Daytime Employees
Estimated Daytime Persons at Home
-0 to 14 years
-15 to 64 years
This database provides the summary of businesses in Canada. With over 100 variables, each table represents counts of businesses in Canada by 2-digit (US) SIC code for all levels of geography. Other variables include information such as total number of businesses, employees, sales and years in yellow pages.
The Canada Business Summary data is compiled from more than 300 telephone directories, government data sources, business magazines, newsletters and newspapers. Over 1.1 million Canadian businesses were geocoded and then summarized by standard census areas and postal FSAs. Use Canada Business Summary to understand the number and type of businesses within a geographic area.
The Canada Business Location file file is available to provide a database of individual business as geographic points that can be mapped and used for site location models.
PCensus Analyst is a powerful analyst tool that combines mapping, demographics and your data to create powerful analyses and compelling reports and visualizations.
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