How new data sources can build upon our understanding of cities and help them thrive.
At Spatial.ai we have a thing for cities. Many of us grew up playing Sim City 2000. Some of us dreamed of being architects. I certainly did. But like many, through a series of seemingly random circumstances, we all ended up in this world of software and bits. Little did we know, as we moved further away from the physical city — the city was quietly moving closer to the digital bits of the internet.
And so that is how we wound up here. We realized the city was speaking, every day, every hour, every second — telling you who it is, what it is doing, what people care about, even where it is suffering. Spatial was built to decode the language of the city. By analyzing the unstructured data pouring out of cities from a variety of sources, such as social media, we capture the evolving culture of communities and structure them into useful formats. Sometimes this is expressed in neighborhood personalities for real estate, sometimes through the social listening Ford does to understand cities, or recently as a cat that also happens to be an omniscient local.
What people find fascinating is the real-time nature of our data. Especially compared to census data which by the date of this writing is seven years old — the data can be as fresh as what was this place like today; complementing traditional location data and greatly expanding our understanding of cities.
What I’d like to focus on in this post is tracking one of the core tenants of thriving cities — education, while also pointing out how a lag in data can create limited understanding of a city’s trajectory. Let’s start with what is currently available, both house hunters and urban planners rely on the American Community Survey and census dataset to understand education in a city:
This shows the population with a college degree. Implication is clear. Live in dark purple areas and you will be around more educated folks. This is somewhat true, but a limited understanding.
Let’s look a little deeper.
Strange, The two most well known centers of education in a city are among the lightest colors of a map. Notice the downtown area (known as OTR) is similarly light, yet there are few places in the midwest that have the density of tech startups that this area has, and no where in Cincinnati with larger libraries, playhouses, and museums. In these areas lacking college graduates you’d be hard pressed to walk down the street without hearing someone talk about philosophy, design, or technology.
You see, the places where people choose to live lag way behind the places people choose to learn and do business. Further, our current access to educational and learning data lags far behind what is actually true.
We could argue that this map was not intended to be used for the purpose of understanding access to education in a city — and it is not. Unfortunately it the best thing urban planners, policy makers, house hunters and the like have.
There is room for a deeper understanding of the city. All you have to do is listen. Let’s look at an educational behavior map of Cincinnati over the past twelve months using Spatial data.
What you are looking at is not density of degrees or judgement on the quality of schools, this is a density of behaviors that are educational — I call this access to educational activity. Places to experience history, science, art, field trips. Generally places where folks are disproportionately talking about learning something new.
Some interesting things to note from this map.
In some cases the areas that are lighting up on this behavioral map of a city might be evidence that the city is nursing the wounds of an area once lacking in education. All the more interesting, and all the more reason to go be a part of that awakening.
I’d argue that this is not an indictment on the first map. It is a call for a broader understanding of cities, and a meshing of the tried and true data of old — and the voice of the digital city.
Imagine a world where people had access to both sets of data and could see them in parallel. Cities and their citizens might discover a few implications:
At Spatial, we have recently come to a deeper appreciation of how we can reshape cities to fit humans. Over the next few months we will continue building out city sensing capabilities to help cities build better versions of themselves.