2000115-Strengthening-Communities-with-Neighborhood-Data

Progress in Data and Technology 97
Big data come in many shapes. Data from social media, such as
Twitter feeds or mapping FourSquare check-ins, are the most visible.
The Livehoods Project, a research project from the School of Computer
Science at Carnegie Mellon University, is combining social media
data and machine-learning techniques to develop new methodologies
to portray social patterns across cities. Other data, like the geographic
features in Open Street Map, may be crowdsourced, that is created by
many distributed contributors (MapBox 2013).
Another type of big data is imagery; infrared pictures can map
the surface temperature across the city (Environmental Protection
Agency n.d.).
Mobile devices are now a common tool for conducting observational
surveys and collecting other primary data. SeeClickFix, a private firm, is
one national example. This mobile application (app) allows anyone to
report nonemergency issues (e.g., potholes, broken streetlights) to their
local governments; local governments have also developed their own
apps that can upload photographs and record geographic coordinates
to submit with the request. Cell phones and mobile tracking devices can
also generate data themselves, charting preferred routes for a bicyclist
or the volume of communications throughout the day. Physical sensors
can provide another rich source of data, measuring such diverse items
as air quality and automobile and pedestrian traffic. The potential for
better understanding neighborhood patterns through data from mobile
phones and other sensors will increase as these devices are connected to
the Internet, forming the Internet of Things (IoT).
However, the applications of big and unstructured data for public
policy in general, and neighborhood indicators in particular, are more
promise than practice at this point. The barriers include the obvious
lack of advanced technical knowledge, but also the need to learn more
about how the new indicators could inform action on community issues.
These new data also introduce the risk of distorting the real picture if
not interpreted correctly, because social media participation varies by
demographic and economic groups. They also raise concerns about privacy
protections, especially for data mining being conducted outside of
universities without structured review processes. Even with these hurdles,
the insights to be gained from new sources of data provide enough
incentive for researchers and practitioners to learn how to collect, organize,
and interpret them, and their use will undoubtedly gain momentum
in the coming years.