We can collect big data via smart sensors, but if we don’t have hypothesis we are testing or investments we are evaluating, we will end up with only the raw materials necessary to address some of the largest urban challenges. For example, without an analysis plan, we can place sensors that measure air quality and be left scratching our heads on how to clean polluted air. Without an analysis plan, we can be alerted to broken streetlights, but we will not know whether repairing outages really does reduce traffic crashes and fatalities.
Each of seven city finalists to the Smart City Challenge of the US Department of Transportation proposed to incorporate sensor-based technologies into the transportation network. According to their plans, these sensor-based technologies will enable connected vehicles and universal free WiFi networks. They also, very importantly, will collect an unprecedented amount of data about air quality, traffic safety, and congestion.
Alongside the technologies they aim to implement, a few cities outlined the goals they aim to achieve with these sensors. For example:
- Austin: “…Provide robust data and safety information to more efficiently operate and maintain the mobility network and communicate with travelers to make more informed decisions and provide future connected vehicles with real time information needed for guidance and optimized travel.”
- Kansas City: “…these sensors will help support efficient movement of traffic, identify requirements for dynamic tasking of buses or other public transportation assets, and improve responsiveness of public safety resources.”
But none of the Smart City finalists outlined a data analytics plan.
This, despite being clearly required in the notice of funding. A plan would detail the methods and processes that cities will use to meet their safety goals, alongside the organizational structure (which many did include: E.g., Pittsburgh) that will perpetuate these outcomes.
If we have no plan on how to evaluate this data, the aspirations of cities to generate knowledge from information will fall short. We end up with big data and no purpose for it.
photo credit: Rafael Parr