If what we measure is what we value, then we need to measure what we want to see in the world.
When we compare a map of outcomes like health, education, housing and income, we are certain to find that those areas where our metrics are highest will be those ‘green’ areas on Redlining maps, while the areas performing the worst will be historically red. So long as we measure only this, our solution will always be to simply raise the red areas up to the level of green.
But if you know anything about redlining, you know that green and red areas are interconnected. You can’t get red areas without other places being green, because they are part of one system. So we have a simple question. Could we also measure something that would give a green area a bad rating? What about something like ‘social isolation?’ Indeed these are largely white, wealthy areas with gross homogeneity. They are badly isolated.
If we think about the neighborhoods we want to see in an ideal world, we may describe them as a thriving, diverse communities where many different people work, access opportunity and create together sustainably. Yet this narrative doesn’t describe ‘green’ areas. So why do those areas show up at the top of all our metrics? What should we be measuring so that the world we want to see gets ranked highest?
If our metrics showed this diverse and collaborative community at the top, then we know the solution is to bring both ‘red’ and ‘green’ communities closer to this ideal. We would look to the whole system.
These outcomes, like social integration and cohesion, agency, trust, creative and cultural production, a sense of place, access to opportunity, civic engagement, happiness and well being, can only be gathered through a robust and ongoing process of community engagement and crowd-sourced data mapping. Using tools like Participatory Action Research, predictive analysis and anthropological approaches become critical to shifting systems of measurement, as well as giving communities the power to decide what is important to measure.
We want to be sure that we have the ability to measure, and therefore to value, the world we want to see. This is true in historically redlined places as well as small towns and suburban places. This is why community-driven data is so important if we want to break out of the system that says wealthy, white and isolated neighborhoods will always be the best.