By trawling scientific list-serves, Chinese fish market websites, and local news sources, ecologists think they can use human beings as sensors by mining their communications….
The six billion people on Earth are changing the biosphere so quickly that traditional ecological methods can’t keep up. Humans, though, are acute observers of their environments and bodies, so scientists are combing through the text and numbers on the Internet in hopes of extracting otherwise unavailable or expensive information. It’s more crowd mining than crowd sourcing.
Much of the pioneering work in this type of Internet surveillance has come in the public health field, tracking disease. Google Flu Trends, which uses a cloud of keywords to determine how sick a population is, tracks epidemiological data from the Centers for Disease Control. Less serious projects — like this map of a United Kingdom snowstorm based on Tweets about snow — have also had some success tracking the real world.
These research efforts seem to indicate that people are good sensors, but pulling the information from what they post in human-readable formats and transforming it into quantitative models of the world is tough. The Global Public Health Intelligence Network has developed an epidemic warning system that pulls in data from news wires, web sites, and public health mailing lists. The GPHIN, which is probably the most advanced and uses highly variegated information, only picks up on about 40 percent of the 200 to 250 outbreaks that the World Health Organization investigates each year.
Nonetheless, Daw and and his co-authors from the Stockholm Univeristy Resilience Centre, say traditional ecological monitoring has its problems, too. Humans can make huge changes to ecosystems faster than the standard methods of data collection can keep up.
“The challenge is that existing monitoring systems are not at all in tune with the speed of social, economical and ecological changes,” the researchers write on their blog.
By looking at human data, not just fisheries and ecological readings, they think they’ll be able to detect ecosystem tipping points before they happen.