I was reading an article that referenced a book that caught my mind recently.

It tracked the change of metaphors mankind used to describe the brain as their understanding of the world changed.

The water clock was used as a metaphor for how the brain worked in early Greek times. The Middle Ages introduced gears and mechanisms. Then the telegraph followed by the telephone, to the current metaphor of the brain working as a computer.

This leads us down the rabbit hole of understanding our cities as being just computerized data points. For example, street-level face-recognition surveillance creates data points intended to, amongst many intents, keep the citizens safe.

I know a person who never pays for street parking by occasionally buying a new license plate, effectively resetting the debt to zero. His behaviour never becomes a data point.

It is challenging to identify the ‘data points’ to pursue white-collar crime. Data points guide where more police presence should be increased, for example. However, hidden tax-sheltered white-collar crime, which affects every tax-paying citizen, is effectively protected from scrutiny. It deserves closer attention based on the monetary losses to the citizens.

We fall into the trap of thinking data points are somehow objective and therefore unquestionable. We tend to not question what we are examining. Why are we tracking the crime stats in the inner city vs. crime stats of white-collar crime? If we don’t focus on white-collar crime, the problem doesn’t exist.

A more current parallel is the cessation of most testing for covid-19. If we don’t test, it appears that covid barely exists and, therefore, not much of a problem.

This is a vast topic and concept to tackle in a short Blog. I’ve just scratched the surface. But I hope you’ll give this a bit of a think. Think of your home community and recall one data point, metric, statistic or number that you remember recently, describing a sliver of your community, town or city.

Now ask yourself how valid, then applicable, this number is for urban planning. My neighbouring big city tracks the number of people caught getting free rides on public transit. The fines have been many times greater than a parking ticket compared to the loss of revenue in each situation.

How useful is the statistic you chose for effecting change? What bias is present in this data point?

It is challenging to escape the current modern numbers. But without stretching too far, think of another data point that might help city planners guide us to a better 25 years vision of communities.

The book referenced is A City Is Not a Computer: Other Urban Intelligences by Shannon Mattern.

Photo by Tingey Injury Law Firm on Unsplash

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