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.
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.
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.