The massive cicada bloom that spread across the eastern seaboard this spring is winding down, but its end heralds another gradually emerging entity: citizen sensing. The Cicada Tracker—a community data-gathering initiative for documenting the noisy insects’ emergence from their burrows—was a rousing success, and it should encourage data innovators across the country to think about what a few motivated citizens and some commodity hardware can do for their communities.
The tracker was devised at a hackathon by John Keefe, a data journalist working for New York public radio station WNYC. The device was a simple piece of open hardware, consisting of an Arduino microcontroller, a temperature sensor, LEDs, resistors and wiring.
For around $80 and some careful construction, it enabled ordinary folks to measure soil temperature, which is a reliable indicator for exactly when the cicadas will surface. After measuring the temperature, people could then send that data—along with their locations and eventually any cicada sightings—to the WNYC team, who created an interactive map to visualize the emerging swarm.
Harvard’s Nieman Journalism Lab reports that the Cicada Tracker organizers received nearly 1,500 temperature readings and an additional 2,000 sightings, enough to make the data useful to scientists. UConn evolutionary biologist Chris Simon, who uses cicada tracking data for her research, told Nieman that mapping the temperature readings by hand might have taken months, while WNYC’s system publishes updates in real-time.
Citizen sensing efforts have begun to crop up in a broad range of fields. One recent example is the Radiation Detection Hardware Network app, which was conceived in the wake of the 2011 Fukushima nuclear disaster. The app allowed users with handheld Geiger counters to contribute their measurements to a large interactive map. An older effort is NOAA’s Citizen Weather Observer Program, launched in 2002, which allows users to contribute measurements from their private weather stations.
The concept of outsourcing some of the more mundane aspects of scientific research is not new in itself; today’s citizen sensing initiatives are, in part, an outgrowth of distributed computing efforts such as SETI@home and Folding@home, which allowed users to help process massive data sets by using the computing power of their idle processors. Now, with citizens able to contribute sensing capabilities in addition to data processing, the range of applications for these crowdsourced science projects has increased considerably.
The citizen sensing model could lend itself to numerous municipal ends. From public utility monitoring to natural resource management, government interventions in data-rich environments may be limited in their effectiveness by the costs of data collection. Citizen sensing presents a potent model for distributing some of those sensing costs to community members who are interested and happy to help out. Each individual’s effort is small, but the aggregate data from all the citizens can add up to quite a swarm.