Tech Matters: Using tech for positive social change

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...