The article Improving UN Responses to Humanitarian Crisesby the UN Chronicle documents the efforts that have been made to improve responses to complex humanitarian crises, including how they plan on incorporating new technologies into that improved effort. It talks about the two different phases that made up this effort:
1) Improving Coordination- one of the huge issues in humanitarian crises is that everyone responds at the same time, with similar intentions for what they do to alleviate suffering, with little coordination to figure out what is already being addressed and what still needs attention. The UN tried to remedy this by creating a humanitarian “cluster” system in 2005; this cluster consisted of UN agencies, NGO’s, and IGO’s. Within these organizations, each would be assigned a cluster: protection, camp coordination and management, water sanitation and hygiene, health, emergency shelter, nutrition, emergency telecommunications, logistics, early recovery, education and agriculture. Each cluster then coordinates with a main UN office for Humanitarian Affairs. These clusters are flawed in that the information they have is difficult to share, and their remains little coordination with the office of humanitarian affairs. Another huge problem is that many in the field were not aware that the clusters even existed, so they werent utilized for their specific function by many agencies on the ground.
2) This cluster system was especially proven to be flawed in the 2010 earthquake in Haiti. It was then that the United Nations Foundation, along with other partners published a report on Disaster Relief 2.0, or how new technologies would help make things like the cluster system more effective. These new technologies include crisis mapping (which we worked with in class this week), mobile technologies, geospatial data and active citizen-based reporting. All of these have played a role in improved quality of response and response time in disasters, but the mapping systems have been particularly useful. In Japan in 2011, crowdsource mapping played a huge role for relief workers as they tried to figure out their priorities for delivery of food, shelter and sanitation aid. Mapping was also used during the conflict in Libya to track fighting and the movement of refugees.
The UN report then continues to note how new technologies like the ones above not only are proving extremely useful in disaster settings, but also allow for new voices to play a role in disaster response. Through the use of new media and mapping sources, citizens and not just officials can play a role in reporting where and what needs help most immediately and urgently. The mapping that we plan to do for our final project is not only informative on how to use mapping systems like Open Street Map, also provide us with a skill that is becoming increasingly important in the aid world as a way to support official agencies on the ground.

After his original experience with HRELP, Mr. Munro proceeded to work on many ICT4D projects worldwide. For example, Mr. Munro was involved with the Mission 4636 service during the January 12th, 2010 earthquake in Haiti. With this service, Haitian’s were able to text their medical needs and receive aid. Mr. Munro helped to coordinate the translation and categorization of text messages that were received. With the help of Crowdflower, their crowdsourcing platform, Mr. Munro and his colleagues were able to translate the messages within ten minutes. Overall, the initiative was successful and they were able to process more than 80, 000 messages – “the first time that crowdsourcing had been used for real-time humanitarian relief and the largest deployment of humanitarian crowdsourcing to date.”Along with crowdsourcing efforts, one of Mr. Munro’s major areas of interest includes machine loading. In 2011, Mr. Munro worked as Chief Technology Officer at the Global Viral Forecasting, an initiative dedicated to predicting and preventing the emergence of new disease outbreaks. In particular, he worked with a system called EpidemicIQ. With the help of thirty labs worldwide, the team, currently, is able to gather information about epidemics and load them into the system to filter out what is relevant. The machine-loading technique gathers various types of information that it can then use to predict a certain epidemic arising in an area. For example, Google Flu trends determined that flu outbreaks could be predicted by simply tracking the symptoms that are usually searched.Beyond these experiences, Mr. Munro has worked in Sierra Leone as Chief Information Officer for Energy for Opportunity (EFO), an organization devoted to finding a safe and environmentally friendly way of providing electricity to communities throughout West Africa. He currently, “heads the IT services at EFO and does everything from developing software systems to training and acceptance testing” (EFO).When he is not involved in attending conferences or performing research, Mr. Munro enjoys blogging at Jungle Light Speed and traveling around the world.