Wellbeing Toronto (WT) was developed to meet the needs of a variety of users from decision-makers that need data to support neighbourhood level planning; residents that want information to better understand the communities they live, work, and play in; and to businesses needing indicators to learn more about their customers, or planning their business.

WT is an interactive evidence-based information system, using the latest innovative visualization mapping tools.

WT provides a “common fact base” to users so they can make better informed decisions based on a set of reliable data.

WT is both a data visualization tool as well as public information participation tool. It enhances transparency by providing data as part of the City’s Open data Strategy.

WT creates a forum to discuss neighbourhood level issues across interest areas (domains), and not providing data only through a single domain perspective.

WT provides a central place to share information. It fosters horizontal (between Divisions and NGO sector), as well as vertical (between levels of government) in areas of knowledge transfer, and data sharing. This is particularly vital in an environment of shrinking resources to purchase data (avoids data duplication, fosters collaboration), and knowledge sharing.

In an environment of fiscal constraint, WT can be a strategic tool in ensuring service levels, while maximizing efficiencies through better knowledge of community needs within a place-based context.

In its basic philosophy of transparency and accountability, it is a way to further provide free data for the community as part of the City’s Open Data Strategy. It is not Open Data in its true sense (pure raw data), but rather Open Data within a business context (access to data within the framework of liveable neighbourhoods).

WT moves away from the prescriptive approach (telling people what the “at risk” neighbourhoods are within a “black box’), but rather allows and empowers citizens to define their own “at risk” communities themselves within a transparent application based on their own criteria.

Many indicator initiatives are developed to measure a specific issue. Indicators are therefore specifically chosen to measure that issue. WT does not measure a particular issue, but instead provides indicators within a framework (neighbourhood wellness) where indicators can be custom chosen, combined and weighted to measure a variety of issues across different domains from housing to transit.

WT uses municipal information in new innovative ways that help people understand the diverse neighbourhoods in Toronto. Data are collected from many City departments are brought together in one place to provide a comprehensive understanding of Toronto neighbourhoods.

WT empowers users to not just pick and map indicators, but to allow themselves to combine and place importance (weight) on the data they wish to use.

The benefits of WT will accrue over the long term as a tool to gauge change over time. It is an objective evidence based diagnostic tool to help measure neighbourhood wellbeing over time and not just a point in time.

Current activity on the web about WT

Please keep the following in mind when using the Wellbeing Toronto application. Maps, charts and other data can be used and interpreted in many ways. This mini-guide will help with the most common questions.

The City is not responsible for misinterpretation or misuse of data in any way, nor does it endorse any results a user may create using the application. Combining indicators to create a Composite Index does not automatically mean the result is statistically accurate or meaningful in any way. The user is responsible for explaining any linkages between indicators.

  1. Read all footnotes and sources carefully. These notes will contain useful information about the date of the data, exclusions, limitations, and other important notations.
  2. Indicator data in Wellbeing Toronto is aggregated to 140 neighbourhoods. Please note that there are often differences within this geography that are not shown on the maps. For example, a neighbourhood on a map may indicate a high concentration of a particular ethnic group, whereas the concentration may actually be in only a few small blocks within that neighbourhood.
  3. The indicator data inside Wellbeing Toronto is scaled with a range of 1 to 100. Tabular data is presented as unscaled raw numbers. Please consult Appendix A for more methodological details about how the application converts raw numbers to scaled numbers to create the Composite Index.
  4. Maps that portray averages may be subject to outliers – a few large or small numbers that differ significantly from the majority of numbers – thus affecting the overall average. Tabular data should be consulted alongside the map to determine if outliers may be affecting the overall outcome.
  5. An average (the mean) is different from a median (the midpoint in a dataset where half of the data values fall below and half above the given value). Medians are preferred when examining indicators like Income because an pure averages (mean) can skew the dataset if a few people have extremely high incomes (as in common in real life).
  6. Some numbers may be subject to a high degree of volatility. Very small numbers will always show a high degree of change over time, or proportion to a total. For example, there may be 2 murders in a neighbourhood one year and 3 the next; in percent terms the rate went up by 50%, but next year if the number drops to 1, the percent reduction will be 66%. Care should be taken with small numbers.
  7. To reduce the volatility of small numbers some indicators were smoothed out over time. Usually a 3-year average was applied to show average trends over a number of years, which reflects the reality somewhat better than a single point in time.
  8. Generally the smaller the geography, the more likelihood of data suppression. This is particularly true of some variables such as income, or cross-tabulations (income by ethnicity). Data suppression occurs where there are too few people to safely guard their identity under existing privacy legislation, and so the data is removed (suppressed).
  9. “Blank” geographic spaces indicate a lack of data for that particular location, either due to data suppression on the part of Statistics Canada or simply missing data for that area.
  10. Wellbeing Toronto uses 2 types of data distribution: Equal Interval and Quantile. The pattern of data on the map may be quite different between these 2 types and thought should be given to deciding which distribution type is best for the indicators chosen. The two types differ as follows:

    Equal Interval

    The range of data values between the minimum and maximum is divided into classes of equal length. For example, if 5 classes are used and the minimum data value is 0 and the maximum data value is 9, then each class will have a length of 2 and the classes will be 0-1, 2-3, 4-5, 6-7, and 8-9. Each class may have different numbers of values or no values.


    The same number of data values are placed into each class. For example, if 5 classes are chosen, then one-fifth (or 20%) of the values would be placed into each class. A class cannot ever be empty. It may not always be possible to divide the data into the specified number of classes if the data values are not varied enough.

  11. Most maps deal with the largest populations for any given topic. If you do not see a indicator for your desired population, the population may be too small to include in our collection. Statistics Canada suppresses data for very small populations in order to guarantee confidentiality and anonymity in reporting. Very small samples are also prone to statistical error and so are suppressed. Wellbeing Toronto will continue to expand its collection of indicators over time.
  12. Ethnicity, race, visible minority and ancestry are separate concepts and should not be used interchangeably. For example, different techniques are used to collect data for the Visible Minority: Chinese and Ethnic Origin: Chinese categories, and so they are not the same nor are they comparable. Also, some languages such as Spanish comprise people that emigrated from many different Spanish-speaking countries – not only Spain. For detailed definitions of terms and concepts used in the demographics domain, please refer to the Census Dictionary, located at: http://www12.statcan.gc.ca/census-recensement/2006/ref/dict/azindex-eng.cfm.
  13. Aboriginal (aka Native Indian) populations are especially subject to undercounting. Extra care should be taken when including Aboriginal indicators in calculations. Please refer to the Aboriginal section of the Statistics Canada website for particulars:http://www.statcan.gc.ca/eng/subjects/aboriginal_peoples.
  14. On maps that show service locations, please note that maps show only physical geographic locations. Unless stated otherwise, no other information about access to services is provided (e.g. catchment areas, hours of service, eligibility for services). Confidential locations and agencies that have only post-office boxes are not shown. Therefore, it is not appropriate to simply conclude that an area is “well-served” due to an abundance of service locations.
  15. Spatial information is not survey-accurate, and should not be used for work requiring high precision or large-scale mapping (ex. Streets are shown as buffered centre lines and are not representative of true right-of-ways). Please contact Surveys & Mapping for the appropriate survey-level information.
  16. Numbers dealing with persons within the Census are often rounded to the nearest 5. For example, 1230 may in reality be 1228, 1231 or 1232 people. Neighbourhood data is often composed of smaller geographies added together, and this may compound the rounding, creating totals that may not conform to aggregate datasets released from Statistics Canada.This is why some totals do not add up perfectly, because rounding in different locations may produce slight variations.
  17. Copies of Wellbeing Toronto outputs such as maps and charts may be made for personal use only. Any commercial use requires permission from the City of Toronto.
  18. Indicators that are very similar should not be added together, as then you are simply doubling the data without adding any new information. This implicit double-weighting may apply to indicators that share similar populations but are named differently or count slightly different things, such as Language: Chinese and Visible Minority: Chinese. Another example would be Average Family Income and Pre-Tax Household Income; these are similar enough that adding them together does not produce good results. Consult the Census Dictionary for exact definitions of each indicator: http://www12.statcan.gc.ca/census-recensement/2006/ref/dict/azindex-eng.cfm.
  19. Indicators marked with the word ‘Category’ indicate the total population that answered the question pertaining to the indicator below the Category one. For example, the Mobility Category is all the people who answered the question about whether they moved that year. Non-movers and Movers are the two possible answers for this question. This is used to calculate percentages or rates for certain variables. So if you wanted to calculate the percentage of people who moved in a given year, you would divide the Movers indicator by the Mobility Category indicator. This use is recommended for advanced users.
  20. More detailed information about neighbourhoods can be found in the Neighbourhood Profiles and in the Toronto Social Atlas, located at www.toronto.ca/demographics.
  21. Normalization by population and area was not done on indicators in order to provide users with the raw data and to avoid implicit analysis within the application. Total population and area information is provided for those advanced users who want to normalize any given indicator by these two variables. This can be done by downloading the raw tabular data and manipulating it on the user’s computer in a spreadsheet or database application. Normalization is recommended for analysis of data strongly tied to either population or land area (e.g., crime or tree cover). Future versions of Wellbeing Toronto may support on-the-fly normalization for select indicators.
  22. Correlation does not mean causation. Just because Indicator A is high in one neighbourhood and Indicator B is also high does not mean that A causes B or that B causes A. The occurrence of phenomena that seem linked in a neighbourhood must be determined with more sophisticated statistical tests than just looking at a map.
  23. Geoprocessing and interpolation was performed on some indicators (such as the Library ones) in order to avoid boundary-averaging and other geospatial analysis problems where raw data did not precisely fit the neighbourhood geography. Please contact SPAR (spar@toronto.ca) for details.