Youthful Cities has created 7 unique indexes involving more than 75 cities from across the globe. 



Primary and secondary data were collected from a number of sources. PIVOT Canada collected primary data by talking to key sources in person, by email, and by telephone. Secondary data collection was done largely through online research. Our sources include census reports, municipal offices and websites, non-governmental organizations, academic sources (e.g. journal articles, development indexes, and reports), and other online sources (e.g. crowdsourcing sites like expatistan.com). Data was collected between October 2020-April 2021 and reflects this time period specifically. COVID-related data was collected in November 2020 and consequently reflect the situation during that time. 

Datapoint sources can be found on the PIVOT Hub. The additional indicator sources can be found by clicking the button below


  • Where necessary, data is converted to metric units
  • All cost-based data is normalized to Canadian Dollars and measured against the minimum wage of each municipality, January 1st to December 31, 2020
  • Since Youthful Cities was motivated by a desire to measure cities from the perspective of youth. Consequently, all cost-based data is normalized to Canadian Dollars and measured against the minimum wage of each municipality, January 1st to December 31, 2020. 


  • Measures in the index are varying in units such as cost, distance, time, or yes/no answers. 
  • In order to standardize the raw scores and apply even weighting, all measures will be scaled between 1 and 0. This is done through data normalization through min-max scaling which is applied to measurements by first determining if a higher or lower value of the overall indicator is better for youth. Eg. In the case of affordability, such as education and housing, lower values (costs) are better for youth. For indicators like “number of youth employment centres per capita”, a higher value is better for youth. 
  • In the case where one measurement in a scale does not adhere to the higher or lower value determinant, its values are inverted. E.g. In the case of the Cell Phone access scale, the question “Do you need a credit card to apply for a plan?”, easier access to a cellphone plan is better for youth, thus the yes/no score is inverted.
  • If a higher value is preferred, the formula (𝑥-𝑥_min)/(𝑥_max-𝑥_min) is applied
  • If a lower value is preferred, the formula 1-(𝑥-𝑥_min)/(𝑥_max-𝑥_min) is applied


  • Min-max normalization is highly influenced by the maximum and minimum values as the highest are assigned the value of 1.00 and the lowest 0.00 while the remainder 25 values (out of 27) received a proportionate value between 0.00 and 1.00. 
  • By scaling varied measures (and their units) to values between 0.00-1.00 we are able to:
    • Combine them into our Indicators that are scales 
    • Compare measurements regardless of the original unit of measurement
    • Ensure that measurements with larger raw values will not skew our scoring 
    • Compare data and ensure that each indicator is of equal weight to another
  • Quantifying and normalizing qualitative measures and that can minimize or result in lost nuances especially when it comes to data pertaining to gender, race, equity, neurodiversity, and ability. Values and data should not be analyzed alone, the purposes of quantification and normalization in this report serve as a way to compare aspects of cities to one another. 


Each city’s points were then translated into weighted scores based on the importance rankings determined for each category. Importance rankings came from the PIVOT Hub’s survey database, which asked 3000+ youth between the ages of 15-29 about the importance of the topics used in our index for youth. Below are the questions asked. Due to the fact that Income Generation is a topic created for this Indexes purposes with 4 out 8 indicators derived from the PIVOT Hub’s Cost of Living Indicators, the weighting for Cost of Living was utilized for it. The values listed are out of 10.


It is important to represent the local reality and experiences of youth in their cities. Pivot 2020 and Youthful Cities also acknowledges that Provincial governments set municipal boundaries. Most provinces have amalgamated cities over the past two decades, however British Columbia did not. For the Pivot Hub, Vancouver and Victoria were treated uniquely. Data was collected in all the municipalities that make up the Census Metropolitan Area (CMA) for each city but that data is represented separately in the Pivot Hub. To allow for better comparisons, the project also chose to merge the data from each CMA to form a data point for Metro Vancouver and Metro Victoria. The following rules were used to merge data and are noted in the Pivot Hub.

Merge rule options:

  • Sum of all municipal totals
  • Average of Yes/No
  • Average
  • Weighted average
  • Minimum value across all municipalities
  • Maximum value across all municipalities

*For the purposes of this report “Vancouver” encompasses Burnaby, City of Vancouver, Coquitlam, Delta, Langley, Maple Ridge, New Westminster, North Vancouver, Pitt Meadows, Port Coquitlam, Port Moody, Richmond, Surrey, West Vancouver, and White Rock.
*For the purposes of this report “Victoria” encompasses the City of Victoria, Colwood, Esquimalt, Langford, Oak Bay, and Saanich.


YouthfulCities is a social enterprise, and therefore we will publicly release the scoring but not the individual data points. Sources for each of the data points can be found on the PIVOT Hub. For indicators that were not collected by PIVOT sources can be found in appendix 1 below. If there is interest in the data points, please feel free to contact us. The data will be available for youth in order to encourage the use of the index for improving their cities. A small fee will be applied to this service for non-youth. A short explanation as to which data is requested and the data’s intended use will be part of the request process.

All scores were calculated within a Google sheet and human error is possible.