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Artificial Intelligence Open Location Platform Editor's Picks Smart Cities Infrastructure Public Sector

Data takes the bias out of city planning. Or does it?

If you want to create a better city, you’re going to need as much data as possible. But it will take more than computers to build the cities of the future.

Take a moment to consider your city. Picture your favorite neighborhoods in your mind.

Is there a public park very close to the local university? Maybe all the houses on certain blocks are all the same height? Are the streets aligned to a grid, or do they twist and turn in every direction? Are the high-rise apartment buildings consolidated to one part of town?

Urban planners decide what goes where in any city. Naturally, there are some obvious practicalities in their process. Don’t build a waste management facility next to a hospital. Don’t plan a school next to an interstate highway.


But beyond the practical decisions, traditionally, many zoning decisions made by urban and/or city planners are based on their personal instincts or interests – which can introduce bias that has long-lasting effects.

As an example, consider a zoning commission in a city experiencing a population boom. Local residents may resist changing the existing zones to accommodate more people, so planners could decide to create new areas for housing. After a review, the commission determines the best options is to re-zone under-developed land close to the waterways for high-rise apartments.

There are two issues that arise from this example. The first is conscious bias: city officials understand that if they change zoning in a way that upsets their residents, they’ll likely face difficulty getting re-elected. That pressure influences their decision about how they decide to change neighborhoods, ultimately influencing those neighborhoods long into the future.

The second issue is a lack of environmental awareness. By not considering that the waterways are expanding due to higher rainfall, new housing near the river will be at risk of flooding. This results in lowering property value, and creating housing where families must endure the risk of their homes being damaged or destroyed by changing environment.

The answer is in the data

Objective, measurable data can help a city planner be confident that decisions about city expansion are efficient as well as equitable for the people that live there. We spoke to Leen Balcaen, Sr. Director of Industry Solutions at HERE Technologies to understand this more.

“The more data that you take into account, the better that city planners can make decisions about where to put what,” said Balcaen. “You can make better decisions… because eventually your plan will have to align with what the state plan is, and how those pieces of the puzzle will work together. As we move toward a digital world, the more data-driven we make it, the faster and easier it will be to build that world.”

It’s possible that a thoughtful urban planner could gather all the data about a city possible, then feed that data into a set of algorithms. The algorithms could then output the perfect data-driven recipe for a newly designed city.

The issue is, no one would ever accept it, and the city officials that enforced such a model would be run out of town.

Algorithms have difficulty understanding when the architecture in a certain neighborhood is historic to the city. They can overlook a property developer’s plan to tear down a strip mall and open a row of cafés and eateries. They have trouble predicting that a Neighborhood Residents Association doesn’t want high-rises in their area. There is an emotional quotient in city planning that AI cannot imitate.

“The emotional aspect elevates the need for extensive, thorough data,“ Balcaen explains. “The more data you have that informs a decision-making process, the more that conscious biases will be revealed when a planner comes a conclusion.”


In other words, if a data-driven model says an area should get a park, and a planner says that the area should instead get a high-rise apartment building, the reasoning behind the decision will become transparent. It will then be up to the planner, the local legislation and the city residents to decide whether that reason is acceptable.

Unfortunately, data doesn’t innately drive bias out of urban planning. But by utilizing information that is sourced from multiple sources and data types, data can play a key role in helping residents and legislators identify when biased decisions are being made, and use that insight to steer toward a better future.

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