Tag Archives: ICT

Tracking farmers’ mobility silently – Holy grail?

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 .

Quantifying the Impact of Human Mobility on MalariaHuman movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal. Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation could greatly improve malaria control programs. Here, we use spatially explicit mobile phone data and malaria prevalence information from Kenya to identify the dynamics of human carriers that drive parasite importation between regions.

Embedly Powered

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.

Farmers with mobile phones that they use to look up price information about their crops

Farmers with mobile phones that they use to look up price information about their crops (IICD via Flickr)

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!

Reference

Personal Technology: Phoning in Data

There was an interesting article published in Nature last week about the use of mobile phones for research. It starts out quite basic but then gives some nice examples that could spark some new ideas. http://www.nature.com/news/2009/090419/full/458959a.html#B1

Personal technology: Phoning in data : Nature NewsPublished online 22 April 2009 | Nature 458, 959-961 (2009) | doi:10.1038/458959a News Feature Far from being just an accessory, mobile phones are starting to be used to collect data in an increasing number of disciplines. Roberta Kwok looks into their potential.

Embedly Powered

via Nature

The data/methodology derived from the following health mapping project seems especially valuable:

Last June, Albert-László Barabási and his colleagues at Northeastern University in Boston, Massachusetts, published a study in Nature  that analysed the movements of 100,000 mobile-phone users (1). Eagle is now working with Barabási’s group and others to examine phone-operator data from a range of geographic areas, including records for millions of mobile-phone users in Europe and two East African countries. Eventually, Eagle hopes to detect common behavioural patterns, such as changes in movement or calling frequency, that occur during disease outbreaks, which could help alert public-health officials to the early stages of an epidemic.

It would be interesting to see if movement or calling frequency could be used to detect behavioural patterns before food shortages, harvests, etc.

(1) González, M. C., Hidalgo, C. A. & Barabási, A.-L. Nature 453, 779–782 (2008)