Scary smart “Smart Cities”

Many cities are waking up to the power of using data, Urbanful.org asks “what are we doing with it?” Great question. However, the examples provided are either scary not smart or and who the heck cares? None of the technologies really help cities become more accessible for the most part, way too many are about enforcement or the even creepier notion of “predictive policing” which sounds more like a step toward a “Minority Report” society.

We need smart deployment of smart technologies. What are the important needs of citizens and their cities. It’s not enough to track weather or the number of pedestrians with overpriced sensors; we need to answering the questions like “what is the walkability of that sidewalk” to determine the type of data and sensor technology that needs to be deployed.

Thoughtful deployment of microlocation beacons could help visually impaired users (but not track citizens) move down streets. Another use would to alert users to changed states like if a restroom is out-of-order if they opt into the service, or if they are in “discovery mode” be informed of all services around them. Microlocation needs to provide end users with the option of data not track citizens. Traffic signals won’t need to noisily chirp at around 10 thousand dollars an intersection, instead they could inform users of their status directly to the person’s smart device, informing them that it’s ok to cross the street and that they have 22 seconds to cross. Indeed, with appropriate validation, some users should be able to lengthen the crossing period just by the acknowledgment of a need to cross and their physical presence.

Each of these smart not creepy scenarios has considerable potential to make our cities more accessible and not just about policing or counting. The scenarios need to be better fleshed out, use-case scenarios need to be developed with people who are visually impaired or have limited mobility. When talking about “developed with” it doesn’t mean just citizens advisory groups, it means employing people with disabilities to develop the use-cases, develop the apps and use the data to make public space usable by more people.

Of the eight examples Urbaful.org gives as example only the use of the Tranquilien app (developed by Snips.net) by SNCF do predictive modeling for train usage comes close to demonstrating socially significant usefulness,

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