“These three characteristics – one, contagiousness; two, the fact that little causes can have big effects; and three, that change happens not gradually but at one dramatic moment – are the same three principles that define how measles moves through a grade-school classroom or the flu attacks every winter. Of the three, the third trait – the idea that epidemics can rise or fall in one dramatic moment – is the most important... The name given to that one dramatic moment in an epidemic when everything can change all at once is the Tipping Point.”

• Malcolm Gladwell, The Tipping Point, p. 9.

The plethora of individual events that happen during a large-scale humanitarian crisis are virtually endless. The sheer number of people whose daily lives are disrupted and uprooted presents a formidable challenge for aid agencies in evaluating and responding to the needs of individuals. For reasons of practicality (making the greatest use of finite resources – both physical and human), programs and aid delivery tend to be based on the perceived greatest need in defined areas, be that a housing unit, a neighbourhood, a town, and so on. But the information landscape is changing, and we are now able to communicate with large groups of affected individuals and have them communicate needs, at least in some way, to us. So, then, as we are increasingly able to receive, analyze, store and transmit large numbers of individual reports of events – how should this change the way we evaluate and respond to an affected population’s needs? The Ushahidi platform essentially allows us to do just this – collect, analyze, store and transmit individual streams of data. But to truly make sense of these individual reports, we need to be able to understand both how representative they are of the population in a given area, and we need to be able to analyze these data to understand when individual reports reach what Gladwell calls “the tipping point.” More importantly, once we understand the tipping point of crowdsourced crisis data, we need to figure out how to use this data to intervene for the best interests of the affected populations. First, let’s consider the data collection and analysis. Presumably, different events (food insecurity, lack of shelter, trauma, etc.) are going to manifest quite differently and with different intensity based on the population and area affected, as well as the cause of the crisis. Furthermore, the linguistic terms used in reports can further add to imprecision and uncertainty, corresponding to a diverse number of causes or events (“Help” “food” “medicine” etc.). Where Ushahidi differs from other forms of individual accounts is the ability to collect these reports in near real-time and to “dispatch” a team of on-the-ground rapporteurs to evaluate the situation. Furthermore, while perhaps not immediately actionable in isolation, a sharp increase in reports of similar events (“need food” or “need medicine”) in a similar geographic area should signal an aggregate change in what’s happening on the ground. Evaluating what is a meaningful or important shift, however, will require further exploration; in essence, we need to define the parameters of the crowdsourced tipping point. Beyond collecting and analyzing the data, there’s a need to ensure action is taken to address the issues Ushahidi identifies. Interactions with the search and rescue community during past events have demonstrated the value of the platform for meeting their needs. However, when considering large-scale protracted events (other than search and rescue functions), data collected through Ushahidi could likely address some challenges faced in the coordination of population needs assessments and aid delivery. It seems unreasonable to assume that Ushahidi could usurp current methods for completing detailed epidemiologic studies of population needs; rather, where the crisis-mapping platform should be deployed is in the identification of emerging events, particularly among displaced populations who may be outside of the view of aid groups (consider those individuals in an urban environment, who may be displaced among the local population). In these instances, reaching the crowdsourced tipping point should signal the need for a comprehensive needs assessment by aid agencies with the capability to do so, which would then provide more specific details of what is needed, by who and in what quantity. The potential for Ushahidi to complement and enhance existing humanitarian aid frameworks is immense. What is needed is careful consideration of how to achieve this and the establishment of partnerships with those in a position of coordination of humanitarian aid (major NGOs, the Cluster system, etc.). Furthermore, we must begin to elicit the support of experts with insight into how to aggregate and analyze individual streams of information – public health professionals, experts in crisis early warning, fuzzy logic experts, and humanitarian aid professionals who are ultimately the knowledge-users. Achieving this is a large undertaking, no doubt, but could drastically change the ways in which we evaluate and respond to crisis-affected populations’ needs.