  "People want an authority to tell them how to value things. But they chose
this authority not based on facts or results. They chose it because it seems
authoritative and familiar." - The Big Short
  The pavement condition index is one such a familiar measure used by many US
cities to measure street quality and justify billions of dollars spent every
year on street repair. These billion-dollar decisions are based on evaluation
criteria that are subjective and not representative. In this paper, we build
upon our initial submission to D4GX 2015 that approaches this problem of
information asymmetry in municipal decision-making.
  We describe a process to identify street-defects using computer vision
techniques on data collected using the Street Quality Identification Device
(SQUID). A User Interface to host a large quantity of image data towards
digitizing the street inspection process and enabling actionable intelligence
for a core public service is also described. This approach of combining device,
data and decision-making around street repair enables cities make targeted
decisions about street repair and could lead to an anticipatory response which
can result in significant cost savings. Lastly, we share lessons learnt from
the deployment of SQUID in the city of Syracuse, NY.
