I had the opportunity to visit Zipline’s field-testing site in San Francisco last year after the company participated in an Experts Meeting on Humanitarian UAVs (Aerial Robotics) that I co-organized at MIT. The company has finally just gone public about their good work in Rwanda, so I’m at last able to blog about it on iRevolutions. When I write “finally”, this is not meant to be a complaint; in fact, one aspect that really drew me to Zipline in the first place is the team’s genuine down-to-earth, no-hype mantra. So, I use the word finally since I now finally have public evidence to backup many conversations I’ve had with humanitarian partners on the topic of cargo delivery via aerial robotics.
As I had signed an NDA, I was (and still am) only allowed to discuss information that is public, which was basically nothing until today. So below is a summary of what is at last publicly known about Zipline’s pioneering aerial robotics efforts in Rwanda. I’ve also added videos at the end.
Zipline’s Mission: to deliver critical medical products to health centers and hospitals that are either difficult or impossible to reach via traditional modes of transportation
Zipline Fleet: 15 aerial robotics platforms (UAVs) in Rwanda.
Aerial Robotics platform: Fixed-wing.
Weight of each platform: 10-kg.
Power: Battery-operated twin-electric motors.
Payload capacity: up to 1.5kg.
Cargo: Blood and essential medicines (small vials) to begin with. Eventually cargo will extend to lifesaving vaccines, treatments for HIV/AIDS, malaria, tuberculosis, etc.
Range: Up to 120 km.
Flight Plans: Pre-programmed and monitored on the ground via tablets. Individual plans are stored on SIM cards.
Flight Navigation: GPS using the country’s cellular network.
Launch Mechanism: Via catapult.
Maximum Speed: Around 100 km/hour.
Landings: Zipline’s aerial robot does not require a runway.
Delivery Mechanism: Fully autonomous, low altitude drop via simple paper parachute. Onboard computers determine appropriate parameters (taking into account winds, etc) to ensure that the cargo accurately lands on it’s dedicated delivery site called a “mailbox”.
Delivery Sites: Dedicated drop sites at 21 health facilities that can carry out blood transfusions. These cover more than half of Rwanda.
Takeoff Sites: Modified shipping containers located next to existing medical warehouses.
Delivery Time: Each cargo is delivered within 1 hour. The aerial robot takes about 1/2 hour reach a delivery site.
Flight Frequency: Eventually up to 150 flights per day.
Weather: Fixed-wings can operate in ~50km/hour winds.
Regulatory Approval: Direct agreements already secured with the Government of Rwanda and country’s Civil Aviation Authority.
Earthquakes can cripple communication infrastructure and influence the number of voice calls relayed through cell phone towers. Data from cell phone traffic can thus be used as a proxy to infer the epicenter of an earthquake and possibly the needs of the disaster affected population. In this blog post, I summarize the findings from a recent study carried out by Microsoft Research and the Santa Fe Institute (SFI).
The study assesses the impact of the 5.9 magnitude earthquake near Lac Kivu in February 2008 on Rwandan call data to explore the possibility of inferring the epicenter and potential needs of affected communities. Cellular networks continually generate “Call Data Records (CDR) for billing and maintenance purposes” which can be used can be used to make inferences following a disaster. Since the geographic spread of cell phones and towers is not randomly distributed, the authors used methods to capture propagating uncertainties about their inferences from the data. This is important to prioritize the collection of new data.
The study is based on the following 3 assumptions:
1. Cell tower traffic deviates statistically from the normal patterns and trends in case of an unusual event.
2. Areas that suffer larger disruptions experience deviations in call volume that persist for a longer period of time.
3. Disruptions are overall inversely proportional to the distance from the center(s) of a catastrophe.
Based on these assumptions, the authors develop algorithms to detect earthquakes, predict their epicenter and infer opportunities for assistance. The results? Using call data to detect when in February 2008 the earthquake took place yields a highly accurate result. The same is true for predicting the epicenter. This means that call activity and cell phone towers can be used as a large-scale seismic system.
As for inferring hardest hit areas, the authors find that their “predicted model is far superior to the baseline and provides predictions that are significantly better for k = 3, 4 and 5″ where k represents the number of days post-earthquake. In sum, “the results highlight the promise of performing predictive analysis with existing telecommunications infrastructure.” The study is available on the Artificial Intelligence for Development (AI-D) website.
In the future, combining call traffic data with crowdsourced SMS data (see this study on Haiti text messages) could perhaps provide even more detailed information on near real-time impact and needs following a disaster. I’d be very interested to see this kind of study done on call/SMS data before, during and after a contested election or major armed conflict. Could patterns in call/SMS data in one country provide distinct early warning signatures for elections and conflict in other crises?
I thrive when working across disciplines, building diverse cross-cutting coalitions to create, translate and apply innovative strategies driven by shared values. This has enabled the 20+ organizations I’ve worked with, and those I’ve led, to accelerate meaningful and inclusive social impact.
Which is why I've been called a social entrepreneur and a translational leader by successful innovators. President Clinton once called me a digital pioneer, while recent colleagues describe me as kind, dedicated, values-driven, authentic, creative, ethical, and impactful.