Tech Matters: Using tech for positive social change

Thanks to commenter Differance bringing up in response to to our last post on this topic that made me want to take a new tack. You're absolutely right that information quality comes from people and that data's fitness for a particular purpose is very contextual. To continue in this direction, let’s look at how people use this information. The people who are in most need of information about humanitarian disasters are the organized responders. [Commenter Iraqi Bootleg might have some very helpful ideas/examples here.] They are especially in need of big picture information that will help guide their response to do the most good with the resources employed. Civil authorities, humanitarian organizations, military units with a humanitarian mission, all hopefully have well-trained and experienced professionals in positions to make these critically important decisions. Let’s call our example professional Captain Lopez. Successful approach to crowdsourcing data: Captain Lopez’ ...

A recent guest Beneblog explains why we believe a correlation found between SMS text messages and building damage by researchers was not useful. Some of the questions we received made us realize we need to be clearer about why this is important. Why did we bother analyzing this claim? Why does it matter? Thanks to Patrick Ball, Jeff Klingner and Kristian Lum for contributing this material (and making it much clearer). We’re reacting to the following claim: “Data collected using unbounded crowdsourcing (non-representative sampling) largely in the form of SMS from the disaster affected population in Port-au-Prince can predict, with surprisingly high accuracy and statistical significance, the location and extent of structural damage post-earthquake.” While this claim is technically correct, it misses the point. If decision makers simply had a map, they could have made better decisions more quickly, more accurately, and with less complication than if they had tried to use crowdsourci...

Guest Beneblog by Patrick Ball, Jeff Klingner, and Kristian Lum After the earthquake in Haiti, Ushahidi organized a centralized text messaging system to allow people to inform others about people trapped under damaged buildings and other humanitarian crises. This system was extremely effective at communicating specific needs in a timely way that required very little additional infrastructure. We think that this is important and valuable. However, we worry that crowdsourced data are not a good data source for doing statistics or finding patterns. An analysis team from European Commission's Joint Research Center analyzed the text messages gathered through Ushahidi together with data on damaged buildings collected by the World Bank and the UN from satellite images. Then they used spatial statistical techniques to show that the pattern of aggregated text messages predicted where the damaged buildings were concentrated. Ushahidi member Patrick Meier interpreted the JRC results as su...