“Smart” cities are reinventing themselves by taking a data-driven approach to governing, but smart city technology has so far addressed only a limited set of use cases. Transit and energy innovation has been the priority over longstanding afflictions like poverty and income inequality. Why haven’t we seen more examples of people-centric smart cities?
Infrastructure use cases are low-hanging fruit. For one, transportation and energy operate on a grid; a closed system, one where interactions are systematic and predicted with relative ease. People on the other hand, behave far less rationally. There are also existing private-sector solutions like smart phones, IoT devices, smart meters, cloud platforms and the like that make it easier to collect, store and analyze infrastructure data. Finally, for all intents and purposes, the smart city movement has been co-opted by the IBMs and Deloittes of the world who see it as an opportunity to rebrand their infrastructure software solutions and market them to cities.
In short, existing technology standards help drive down costs and create market conditions that allow smart infrastructure systems to proliferate.
People-centric smart city technology, however, has very few standards — which is why these systems are so new and why you have probably never heard of them.
Every time you interact with a government service, whether it’s attending public school or filing your taxes, a government agency collects data about you. “Integrated data systems” or IDS, link those data across government agencies, painting a detailed portrait of your interactions with government as you progress through life.
The privacy and security of these data are protected by an array of federal laws, and putting them to use, in a de-identified manner can have a transformative effect on how government does business.
In 2016, Speaker Paul Ryan and Senator Patty Murray created the Commission on Evidence-Based Policy Making — later signed in to law by President Obama. The goal of the Commission is to develop “a strategy for increasing the availability and use of data in order to build evidence about government programs, while protecting privacy and confidentiality.”
These data can be used to test the efficacy of government programs, introducing new cost/benefit metrics into the budgeting process. Combined with machine learning, these data can predict who is likely to benefit from an anti-eviction intervention or a job training program.
Integrated data allows Allegheny County, Pennsylvania, to train machine-learning models that predict negative outcomes for children and help prioritize limited case management resources. These data make it possible for the Camden Coalition to link hospital claims and criminal justice data to identify “super utilizers” and assign them appropriate services, ultimately saving millions of dollars.
Despite the awesome effort of philanthropies like the Laura and John Arnold Foundation — who alone has awarded more than $176 million to the cause — these systems are slow to get off the ground in all but a handful of major cities nationwide.
The sluggish pace of innovation is due, as new research from my colleagues at Penn suggests, to a lack of standards which makes it more expensive for cities to develop this technology. Integrated data systems can cost $2.5 million to $4 million. There is no off-the-shelf option. While federal HIPAA and FERPA laws provide a privacy standard, there is still great deal of research and development needed in order to convert privacy as an abstract principle into a set of defensible algorithms.
Finally, the biggest standard which has yet to take hold is a pipeline whereby governments convert policy conundrums into objective research questions then funnel data-driven conclusions into the budgeting process.
Transportation and energy are important smart city use cases, but in the near future, as globalization and automation brings about unprecedented economic upheaval, cities must do more to convert the data they already collect into actionable policy intelligence. In the smart city of the future, progress will be measured not only by the time it takes for your autonomous car to get you to work, but by the pace of economic productivity and social justice.
Ken Steif, PhD is the director of the Master of Urban Spatial Analytics program at the University of Pennsylvania. He is also the founder of Urban Spatial.