Back in 2009, Silvia Renn (World Fish) blogged her review of a Nature news article describing the possibility of using mobile phone to monitor human behaviors and public health and noted the untapped potential of such data and technology to detect human behavioral patterns in agriculture. Following up on the story, Science recently published an article more deeply mined such mobile phone-based location data and attempted to quantify the impact of human mobility on malaria .
As Silvia earlier noted, I also think such mobile phone-based location data and its mining technology could start unlocking answers to so many research questions involving human, or farmers in our case, behavior. There are, of course, concerns on the subject and their privacy, but let’s put it aside for a moment.
On one possible example, not necessarily fully relying on costly surveys, we could exactly pinpoint when, how many times during the growing season, farmers visit which fields, located where, for how long (as long as they keep carrying the mobile phone, that is). We may still need some in situ surveys, but we will have much better ideas on which types of management event happens, when, where – so that we can ask a lot more informed questions and hopefully useful answers. At some point after harvest, we could also track their travel from the field to the market, the mode of transportation, travel time, etc, to understand their market accessibility and value-chain of the product. We can even guesstimate how much production they may have this season, based on their farming product transportation to the markets. We will have better ideas on the production cost side as well.
For the public health side, we’ve been hearing the location of hospital/clinic doesn’t mean much; how many doctors and nurses there are now is a lot more critical issue – but it’s very difficult to keep track. Imagine we can keep track people’s time to travel from their home/village to the health clinic, how busy the facility is, how long they stay in the facility, what’s their next destination (another clinic, bank, or going back home); good interpretation of such information could lead us to much better real-time understanding of what’s happening on their livelihood and when/whether to trigger an alarm.
Anybody wants to put more thoughts and construct a concept note together? Let me know!