Tag Archives: Study

How Can Digital Humanitarians Best Organize for Disaster Response?

I published a blog post with the same question in 2012. The question stemmed from earlier conversations I had at 10 Downing Street with colleague Duncan Watts from Microsoft Research. We subsequently embarked on a collaboration with the Standby Task Force (SBTF), a group I co-founded back in 2010. The SBTF was one of the early pioneers of digital humanitarian action. The purpose of this collaboration was to empirically explore the relationship between team size and productivity during crisis mapping efforts.


Duncan and Team from Microsoft simulated the SBTF’s crisis mapping efforts in response to Typhoon Pablo in 2012. At the time, the United Nations Office for the Coordination of Humanitarian Affairs (UN/OCHA) had activated the Digital Humanitarian Network (DHN) to create a crisis map of disaster impact (final version pictured above). OCHA requested the map within 24 hours. While we could have deployed the SBTF using the traditional crowdsourcing approach as before, we decided to try something different: microtasking. This was admittedly a gamble on our part.

We reached out to the team at PyBossa to ask them to customize their micro-tasking platform so that we could rapidly filter through both images and videos of disaster damage posted on Twitter. Note that we had never been in touch with the PyBossa team before this (hence the gamble) nor had we ever used their CrowdCrafting platform (which was still very new at the time). But thanks to PyBossa’s quick and positive response to our call for help, we were able to launch this microtasking app several hours after OCHA’s request.

Fast forward to the present research study. We gave Duncan and colleagues at Microsoft the same database of tweets for their simulation experiment. To conduct this experiment and replicate the critical features of crisis mapping, they created their own “CrowdMapper” platform pictured below.

Screen Shot 2016-04-20 at 11.12.36 AM Screen Shot 2016-04-20 at 11.12.53 AM

The CrowdMapper experiments suggest that the positive effects of coordination between digital humanitarian volunteers, i.e., teams, dominate the negative effects of social loafing, i.e., volunteers working independently from others. In social psychology, “social loafing is the phenomenon of people exerting less effort to achieve a goal when they work in a group than when they work alone” (1). In the CrowdMapper exercise, the teams performed comparably to the SBTF deployment following Typhoon Pablo. This suggests that such experiments can “help solve practical problems as well as advancing the science of collective intelligence.”

Our MicroMappers deployments have always included a live chat (IM) feature in the user interface precisely to support collaboration. Skype has also been used extensively during digital humanitarian efforts and Slack is now becoming more common as well. So while we’ve actively promoted community building and facilitated active collaboration over the past 6+ years of crisis mapping efforts, we now have empirical evidence that confirms we’re on the right track.

The full study by Duncan et al. is available here. As they note vis-a-vis areas for future research, we definitely need more studies on the division of labor in crisis mapping efforts. So I hope they or other colleagues will pursue this further.

Many thanks to the Microsoft Team and to SBTF for collaborating on this applied research, one of the few that exist in the field of crisis mapping and digital humanitarian action.

The main point I would push back on vis-a-vis Duncan et al’s study is comparing their simulated deployment with the SBTF’s real-world deployment. The reason it took the SBTF 12 hours to create the map was precisely because we didn’t take the usual crowdsourcing approach. As such, most of the 12 hours was spent on reaching out to PyBossa, customizing their microtasking app, testing said app and then finally deploying the platform. The Microsoft Team also had the dataset handed over to them while we had to use a very early, untested version of the AIDR platform to collect and filter the tweets, which created a number of hiccups. So this too took time. Finally, it should be noted that OCHA’s activation came during early evening (local time) and I for one pulled an all-nighter that night to ensure we had a map by sunrise.

Tracking Population Movements using Mobile Phones and Crisis Mapping: A Post-Earthquake Geospatial Study in Haiti

I’ve been meaning to blog about this project since it was featured on BBC last month: “Mobile Phones Help to Target Disaster Aid, says Study.” I’ve since had the good fortune of meeting Linus Bengtsson and Xin Lu, the two lead authors of this study (PDF), at a recent strategy meeting organized by GSMA. The authors are now launching “Flowminder” in affiliation with the Karolinska Institutet in Stockholm to replicate their excellent work beyond Haiti. If “Flowminder” sounds familiar, you may be thinking of Hans Rosling’s “Gapminder” which also came out of the Karolinska Institutet. Flowminder’s mission: “Providing priceless information for free for the benefit of those who need it the most.”

As the authors note, “population movements following disasters can cause important increases in morbidity and mortality.” That is why the UN sought to develop early warning systems for refugee flows during the 1980’s and 1990’s. These largely didn’t pan out; forecasting is not a trivial challenge. Nowcasting, however, may be easier. That said, “no rapid and accurate method exists to track population movements after disasters.” So the authors used “position data of SIM cards from the largest mobile phone company in Haiti (Digicel) to estimate the magnitude and trends of population movements following the Haiti 2010 earthquake and cholera outbreak.”

The geographic locations of SIM cards were determined by the location of the mobile phone towers that SIM cards were connecting to when calling. The authors followed the daily positions of 1.9 million SIM cards for 42 days prior to the earthquake and 158 days following the quake. The results of the analysis reveal that an estimated 20% of the population in Port-au-Prince left the city within three weeks of the earthquake. These findings corresponded well with of a large, retrospective population based survey carried out by the UN.

“To demonstrate feasibility of rapid estimates and to identify areas at potentially increased risk of outbreaks,” the authors “produced reports on SIM card move-ments from a cholera outbreak area at its immediate onset and within 12 hours of receiving data.” This latter analysis tracked close to 140,000 SIM cards over an 8 day period. In sum, the “results suggest that estimates of population movements during disasters and outbreaks can be delivered rapidly and with potentially high validity in areas with high mobile phone use.”

I’m really keen to see the Flowminder team continue their important work in and beyond Haiti. I’ve invited them to present at the International Conference of Crisis Mappers (ICCM 2011) in Geneva next month and hope they’ll be able to join us. I’m interested to explore the possibilities of combining this type of data and analysis with crowdsourced crisis information and satellite imagery analysis. In addition, mobile phone data can also be used to estimate the hardest hit areas after a disaster. For more on this, please see my previous blog post entitled “Analyzing Call Dynamics to Assess the Impact of Earthquakes” and this post on using mobile phone data to assess the impact of building damage in Haiti.