Tag Archives: Model

Humanitarian UAV Network: Strategy for 2014-2015

The purpose of the Humanitarian UAV Network (UAViators) is to guide the responsible and safe use of small UAVs in humanitarian settings while promoting information sharing and enlightened policymaking. As I’ve noted in the past, UAVs are already being used to support a range of humanitarian efforts. So the question is not if, but rather how to facilitate the inevitable expanded use of UAVs in a responsible and safe manner. This is just one of many challenging questions that UAViators was created to manage.

UAViators Logo

UAViators has already drafted a number of documents, including a Code of Conduct and an Operational Check-List for the use of UAVs in humanitarian settings. These documents will continue to be improved throughout the year, so don’t expect a final and perfect version tomorrow. This space is still too new to have all the answers in a first draft. So our community will aim to improve these documents over time. By the end of 2014, we hope to have a solid version of the of Code of Conduct for organizations and companies to publicly endorse.


In the meantime, my three Research Assistants (RA’s) and I are working on (the first ever?) comprehensive evaluation of 1) Small UAVs; 2) Cameras; 3) Payload Units; and 4) Imagery Software specifically for humanitarian field-workers. The purpose of this evaluation is to rate which technologies are best suited to the needs of humanitarians in the field. We will carry out this research through interviews with seasoned UAV experts coupled with secondary, online research. Our aim is to recommend 2-3 small UAVs, cameras, payload units and software solutions for imagery processing and analysis that make the most sense for humanitarians as end users. These suggestions will then be “peer reviewed” by members of the Humanitarian UAV Network.

Following this evaluation, my three RA’s and I will create a detailed end-to-end operational model for the use of UAVs in humanitarian settings. The model will include pre-flight guidance on several key issues including legislation, insurance, safety and coordination. The pre-flight section will also include guidance on how to program the flight-path of the UAVs recommended in the evaluation. But the model does not end with the safe landing of a UAV. The operational model will include post-flight guidance on imagery processing and analysis for decision support as well as guidelines on information sharing with local communities. Once completed, this operational model will also be “peer reviewed” by members of the UAViators.

Credit Drone Adventures

Both deliverables—the evaluation and model—will be further reviewed by the Advisory Board of UAViators and by field-based humanitarians. We hope to have this review completed during the Humanitarian UAV Experts Meeting, which I am co-organizing with OCHA in New York this November. Feedback from this session will be integrated into both deliverables.

Our plan is to subsequently convert these documents into training materials for both online and onsite training. We have thus far identified two sites for this training, one in Southeast Asia and the other in southern Africa. We’re considering a potential third site in South America depending on the availability of funding. These trainings will enable us to further improve our materials and to provide minimum level certification to humanitarians participating in said trainings. To this end, our long-term strategy for the Humanitarian UAV Network is not only to facilitate the coordination of small UAVs in humanitarian settings but also to provide both training and certification in collaboration with multiple humanitarian organizations.

I recognize that the above is highly ambitious. But all the signals I’m getting from humanitarian organizations clearly demonstrate that the above is needed. So if you have some expertise in this space and wish to join my Research Assistants and I in this applied and policy-focused research, then please do get in touch. In addition, if your organization or company is interested in funding any of the above, then do get in touch as well. We have the initial funding for the first phase of the 2014-2015 strategy and are in the process of applying for funding to complete the second phase.

One final but important point: while the use of many small and large UAVs in complex airspaces in which piloted (manned) aircraft are also flying poses a major challenge in terms of safety, collision avoidance and coordination, this obviously doesn’t mean that small UAVs should be grounded in humanitarian contexts with far simpler airspaces. Indeed, to argue that small UAVs cannot be responsibly and safely operated in simpler airspaces ignores the obvious fact that they already have—and continue to be responsibly & safely used. Moreover, I for one don’t see the point of flying small UAVs in areas already covered by larger UAVs and piloted aircraft. I’m far more interested in the rapid and local deployment of small UAVs to cover areas that are overlooked or have not yet been reached by mainstream response efforts. In sum, while it will take years to develop effective solutions for large UAV-use in dense and complex airspaces, small UAVs are already being used responsibly and safely by a number of humanitarian organizations and their partners.


See Also:

  • Welcome to the Humanitarian UAV Network [link]
  • How UAVs are Making a Difference in Disaster Response [link]
  • Humanitarians Using UAVs for Post Disaster Recovery [link]
  • Grassroots UAVs for Disaster Response [link]
  • Using UAVs for Search & Rescue [link]
  • Debrief: UAV/Drone Search & Rescue Challenge [link]
  • Crowdsourcing Analysis of UAV Imagery for Search/Rescue [link]
  • Check-List for Flying UAVs in Humanitarian Settings [link]

Surprising Findings: Using Mobile Phones to Predict Population Displacement After Major Disasters

Rising concerns over the consequences of mass refugee flows during several crises in the late 1970’s is what prompted the United Nations (UN) to call for the establishment of early warning systems for the first time. “In 1978-79 for example, the United Nations and UNHCR were clearly overwhelmed by and unprepared for the mass influx of Indochinese refugees in South East Asia. The number of boat people washed onto the beaches there seriously challenged UNHCR’s capability to cope. One of the issues was the lack of advance information. The result was much human suffering, including many deaths. It took too long for emergency assistance by intergovernmental and non-governmental organizations to reach the sites” (Druke 2012 PDF).

Forty years later, my colleagues at Flowminder are using location data from mobile phones to nowcast and predict population displacement after major disasters. Focusing on the devastating 2010 Haiti earthquake, the team analyzed the movement of 1.9 million mobile users before and after the earthquake. Naturally, the Flowminder team expected that the mass exodus from Port-au-Prince would be rather challenging to predict. Surprisingly, however, the predictability of people’s movements remained high and even increased during the three-month period following the earthquake.

The team just released their findings in a peer-reviewed study entitled: “Predictability of population displacement after the 2010 Haiti earthquake” (PNAS 2012). As the analysis reveals, “the destinations of people who left the capital during the first three weeks after the earthquake was highly correlated with their mobility patterns during normal times, and specifically with the locations in which people had significant social bonds, as measured by where they spent Christmas and New Year holidays” (PNAS 2012).

For the people who left Port-au-Prince, the duration of their stay outside the city, as well as the time for their return, all followed a skewed, fat-tailed distribution. The findings suggest that population movements during disasters may be significantly more predictable than previously thought” (PNAS 2012). Intriguingly, the analysis also revealed the period of time that people in Port-au-Prince waited to leave the city (and then return) was “power-law distributed, both during normal days and after the earthquake, albeit with different exponents (PNAS 2012).” Clearly then, “[p]eople’s movements are highly influenced by their historic behavior and their social bonds, and this fact remained even after one of the most severe disasters in history” (PNAS 2012).


I wonder how this approach could be used in combination with crowdsourced satellite imagery analysis on the one hand and with Agent Based Models on the other. In terms of crowdsourcing, I have in mind the work carried out by the Standby Volunteer Task Force (SBTF) in partnership with UNHCR and Tomnod in Somalia last year. SBTF volunteers (“Mapsters”) tagged over a quarter million features that looked liked IDP shelters in under 120 hours, yielding a triangulated country of approximately 47,500 shelters.

In terms of Agent Based Models (ABMs), some colleagues and I  worked on “simulating population displacements following a crisis”  back in 2006 while at the Santa Fe Institute (SFI). We decided to use an Agent Based Model because the data on population movement was simply not within our reach. Moreover, we were particularly interested in modeling movements of ethnic populations after a political crisis and thus within the context of a politically charged environment.

So we included a preference for “safety in numbers” within the model. This parameter can easily be tweaked to reflect a preference for moving to locations that allow for the maintenance of social bonds as identified in the Flowminder study. The figure above lists all the parameters we used in our simple decision theoretic model.

The output below depicts the Agent Based Model in action. The multi-colored panels on the left depict the geographical location of ethnic groups at a certain period of time after the crisis escalates. The red panels on the right depict the underlying social networks and bonds that correspond to the geographic distribution just described. The main variable we played with was the size or magnitude of the sudden onset crisis to determine whether and how people might move differently around various ethnic enclaves. The study long with the results are available in this PDF.

In sum, it would be interesting to carry out Flowminder’s analysis in combination with crowdsourced satellite imagery analysis and live sensor data feeding into an Agent Base Model. Dissertation, anyone?