Pictures: Humanitarian UAV Mission to Vanuatu in Response to Cyclone Pam

Aéroport de Port Vila – Bauerfield International Airport. As we land, thousands of uprooted trees could be seen in almost every direction.

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Massive roots were not enough to save these trees from Cyclone Pam. The devastation reminds us how powerful nature is.

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After getting clearance from the Australian Defense Force (ADF), we pack up our UAVs and head over to La Lagune for initial tests. Close collaboration with the military is an absolute must for humanitarian UAV missions. UAVs cannot operate in Restricted Operations Zones without appropriate clearance.

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We’re in Vanuatu by invitation of the Government’s National Disaster Risk Management Office (NDMO). So we’re working very closely with our hosts to assess disaster damage and resulting needs. The government and donors need the damage quantified to assess how much funding is necessary for the recovery efforts; and where geographically that funding should be targeted.

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Ceci n’est pas un drone; what we found at La Lagune, where the ADF has set up camp. At 2200 every night we send the ADF our flight plan clearance requests for the following day. For obvious safety reasons, we never deviate from these plans after they’ve been approved.

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Unpacking and putting together the hexacopters can take a long time. The professional and certified UAV team from New Zealand (X-Craft) follows strict operational check lists to ensure safety and security. We also have a professional and certified team from Australia, Heliwest, which will be flying quadcopters. The UAV team from SPC is also joining our efforts. I’m proud to report that both the Australian & New Zealand teams were recruited directly from the pilot roster of the Humanitarian UAV Network.

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The payload (camera) attached to our hexacopters; not exactly a GoPro. We also have other sensors for thermal imaging, etc.

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Programming the test flights. Here’s a quick video intro on how to program UAVs for autonomous flights.

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Night falls fast in Vanuatu…

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… So our helpful drivers kindly light up our work area.

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After flawless test flights; we’re back at “HQ” to program the flight paths for tomorrow morning’s humanitarian UAV missions. The priority survey areas tend to change on a daily basis as the government gets more information on which outlying islands have been hardest hit. Our first mission will focus on an area comprised of informal settlements.

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Dawn starts to break at 0500. We haven’t gotten much sleep.

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At 0600, we arrive at the designated meeting point, the Beach Bar. This will be our base of operations for this morning’s mission.

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The flight plans for the hexacopters are ready to go. We have clearance from Air Traffic Control (ATC) to fly until 0830 as manned aircraft start operating extensively after 0900. So in complex airspaces like this one in Vanuatu’s Port Vila, we only fly very early in the morning and after 1700 in the evening. We have ATC’s direct phone number and are in touch with the tower at all times.

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Could this be the one and only SXSW 2015 bag in Vanuatu?

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All our multirotor UAVs have been tested once again and are now ready to go. The government has already communicated to nearby villages that UAVs will be operating between 0630-0830. We aim to collect aerial imagery at a resolution of 4cm-6cm throughout our missions.

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An old basketball court; perfect for take-off & landing.

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And of course, when we’re finally ready to fly, it starts to pour. Other challenges include an ash cloud from a nearby volcano. We’ve also been told that kids here are pro’s with slingshots (which is one reason why the government informed local villagers of the mission; i.e., to request that kids not use the UAVs for target practice).

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After some delays, we are airborne at last.

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Operating the UAViators DJI Phantom…

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… Which I’m using purely for documentary purposes. In coming days, we’ll be providing our government partners with a hands-on introduction on how to operate Phantom II’s. Building local capacity is key; which is why this action item is core to the Humanitarian UAV Network’s Code of Conduct.

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Can you spot the hexacopter? While there’s only one in the picture below, we actually have two in the air at different altitudes which we are operating by Extended Line of Site and First Person View as a backup.

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More aerial shots I took using the Phantom (not for damage assessment; simply for documentary purposes).

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Can you spot the basketball court?

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Large clouds bring back the rain; visibility is reduced. We have to suspend our flights; will try again after 1700.

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Meanwhile, my Phantom’s GoPro snaps this close up picture on landing.

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Stay tuned for updates and in particular the very high resolution aerial imagery that we’ll be posting to MapBox in coming days; along with initial analysis carried out by multiple partners including Humanitarian OpenStreetMap (HOT) and QCRI‘s MicroMappers. Many thanks to MapBox for supporting our efforts. We will also be overlaying the aerial imagery analysis over this MicroMappers crisis map of ground-based pictures of disaster damage in order to triangulate the damage assessment results. Check out the latest update here.

In the meantime, more information on this Humanitarian UAV Mission to Vanuatu–spearheaded by the World Bank in very close collaboration with the Government and SPC–can be found on the Humanitarian UAV Network (UAViators) Ops page here. UAViators is an initiative I launched at QCRI following Typhoon Haiyan in the Philippines in 2013. More on UAViators and the use of humanitarian UAVs in my new book Digital Humanitarians.

Important: this blog post is a personal update written in my personal capacity; none of the above is in any way shape or form a formal communique or press release by any of the partners. Official updates will be provided by the Government of Vanuatu and World Bank directly. Please contact me here for official media requests; kindly note that my responses will need to be cleared by the Government & Bank first.

Artificial Intelligence Powered by Crowdsourcing: The Future of Big Data and Humanitarian Action

There’s no point spewing stunning statistics like this recent one from The Economist, which states that 80% of adults will have access to smartphones before 2020. The volume, velocity and variety of digital data will continue to skyrocket. To paraphrase Douglas Adams, “Big Data is big. You just won’t believe how vastly, hugely, mind-bogglingly big it is.”

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And so, traditional humanitarian organizations have a choice when it comes to battling Big Data. They can either continue business as usual (and lose) or get with the program and adopt Big Data solutions like everyone else. The same goes for Digital Humanitarians. As noted in my new book of the same title, those Digital Humanitarians who cling to crowdsourcing alone as their pièce de résistance will inevitably become the ivy-laden battlefield monuments of 2020.

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Big Data comprises a variety of data types such as text, imagery and video. Examples of text-based data includes mainstream news articles, tweets and WhatsApp messages. Imagery includes Instagram, professional photographs that accompany news articles, satellite imagery and increasingly aerial imagery as well (captured by UAVs). Television channels, Meerkat and YouTube broadcast videos. Finding relevant, credible and actionable pieces of text, imagery and video in the Big Data generated during major disasters is like looking for a needle in a meadow (haystacks are ridiculously small datasets by comparison).

Humanitarian organizations, like many others in different sectors, often find comfort in the notion that their problems are unique. Thankfully, this is rarely true. Not only is the Big Data challenge not unique to the humanitarian space, real solutions to the data deluge have already been developed by groups that humanitarian professionals at worst don’t know exist and at best rarely speak with. These groups are already using Artificial Intelligence (AI) and some form of human input to make sense of Big Data.

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How does it work? And why do you still need some human input if AI is already in play? The human input, which can be via crowdsourcing or a few individuals is needed to train the AI engine, which uses a technique from AI called machine learning to learn from the human(s). Take AIDR, for example. This experimental solution, which stands for Artificial Intelligence for Disaster Response, uses AI powered by crowdsourcing to automatically identify relevant tweets and text messages in an exploding meadow of digital data. The crowd tags tweets and messages they find relevant and the AI engine learns to recognize the relevance patterns in real-time, allowing AIDR to automatically identify future tweets and messages.

As far as we know, AIDR is the only Big Data solution out there that combines crowdsourcing with real-time machine learning for disaster response. Why do we use crowdsourcing to train the AI engine? Because speed is of the essence in disasters. You need a crowd of Digital Humanitarians to quickly tag as many tweets/messages as possible so that AIDR can learn as fast as possible. Incidentally, once you’ve created an algorithm that accurately detects tweets relaying urgent needs after a Typhoon in the Philippines, you can use that same algorithm again when the next Typhoon hits (no crowd needed).

What about pictures? After all, pictures are worth a thousand words. Is it possible to combine artificial intelligence with human input to automatically identify pictures that show infrastructure damage? Thanks to recent break-throughs in computer vision, this is indeed possible. Take Metamind, for example, a new startup I just met with in Silicon Valley. Metamind is barely 6 months old but the team has already demonstrated that one can indeed automatically identify a whole host of features in pictures by using artificial intelligence and some initial human input. The key is human input since this is what trains the algorithms. The more human-generated training data you have, the better your algorithms.

My team and I at QCRI are collaborating with Metamind to create algorithms that can automatically detect infrastructure damage in pictures. The Silicon Valley start-up is convinced that we’ll be able to create a highly accurate algorithms if we have enough training data. This is where MicroMappers comes in. We’re already using MicroMappers to create training data for tweets and text messages (which is what AIDR uses to create algorithms). In addition, we’re already using MicroMappers to tag and map pictures of disaster damage. The missing link—in order to turn this tagged data into algorithms—is Metamind. I’m excited about the prospects, so stay tuned for updates as we plan to start teaching Metamind’s AI engine this month.

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How about videos as a source of Big Data during disasters? I was just in Austin for SXSW 2015 and met up with the CEO of WireWax, a British company that uses—you guessed it—artificial intelligence and human input to automatically detect countless features in videos. Their platform has already been used to automatically find guns and Justin Bieber across millions of videos. Several other groups are also working on feature detection in videos. Colleagues at Carnegie Melon University (CMU), for example, are working on developing algorithms that can detect evidence of gross human rights violations in YouTube videos coming from Syria. They’re currently applying their algorithms on videos of disaster footage, which we recently shared with them, to determine whether infrastructure damage can be automatically detected.

What about satellite & aerial imagery? Well the team driving DigitalGlobe’s Tomnod platform have already been using AI powered by crowdsourcing to automatically identify features of interest in satellite (and now aerial) imagery. My team and I are working on similar solutions with MicroMappers, with the hope of creating real-time machine learning solutions for both satellite and aerial imagery. Unlike Tomnod, the MicroMappers platform is free and open source (and also filters social media, photographs, videos & mainstream news).

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So there you have it. The future of humanitarian information systems will not be an App Store but an “Alg Store”, i.e, an Algorithm Store providing a growing menu of algorithms that have already been trained to automatically detect certain features in texts, imagery and videos that gets generated during disasters. These algorithms will also “talk to each other” and integrate other feeds (from real-time sensors, Internet of Things) thanks to data-fusion solutions that already exist and others that are in the works.

Now, the astute reader may have noted that I omitted audio/speech in my post. I’ll be writing about this in a future post since this one is already long enough.

What to Know When Using Humanitarian UAVs for Transportation

UAVs can support humanitarian action in a variety of ways. Perhaps the most common and well-documented use-case is data collection. There are several other use-cases, however, such as payload transportation, which I have blogged about herehere and here. I had the opportunity to learn more about the logistics and operations of payload UAVs while advising a well-known public health NGO in Liberia as well as an international organization in Tanzania. This advising led to conversations with some of the leading experts in the UAV-for-transportation space like Google Project WingMatternet and Vayu for example.

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Below are just some of the questions you’ll want to ask when you’re considering the use of UAVs for the transportation of small payloads. Of course, the UAV may not be the most appropriate technology for the problem you’re looking to solve. So naturally, the very first step is to carry out a comparative cost-benefit analysis with multiple technologies. The map below, kindly shared by Matternet, is from a project they’re working on with Médecins Sans Frontières (MSF) in Papua New Guinea.

Credit: Matternet

Why does it take some 4 hours to drive 60km (40 miles) compared to 55 minutes by UAV? The pictures below (also shared by Matternet) speak for themselves.

Credit: Matternet

Credit: Matternet

Credit: Matternet

Any use of UAVs in humanitarian contexts should follow the Code of Conduct proposed by the Humanitarian UAV Network (UAViators), which was recently endorsed by the UN. Some of the (somewhat obvious) questions you’ll want to bear in mind as you carry out your cost-benefit analysis thus include:

  • What is maximum, minimum and the average distance that the UAV needs to fly?
  • How frequently do the UAVs need to make the deliveries?
  • How much mass needs to be moved per given amount of time?
  • What is the mass of individual packages (and can these be split into smaller parcels if need be)?
  • Do the packages contain a mechanism for cold transport or would the UAV need to provide refrigeration (assuming this is needed)?
  • What do the take-off and landing spaces look like? How much area, type of ground, size of trees or other obstacles nearby?
  • What does the typology between the take-off and landing sites look like? Tall trees, mountains, or other obstructions?
  • Regarding batteries, is there easy access to electricity in the areas where the UAVs will be landing?
  • Is there any form of cell phone coverage in the landing areas?
  • What is the overall fixed and variable cost of operating the payload UAVs compared to other solutions?
  • What impact (both positive and negative) will the introduction of the payload UAV have on the local economy?

While the payload weight is relatively small (1kg-2kg) for low-cost UAVs, keep in mind that UAV flights can continue around the clock. As one of my colleagues at the Syria Airlift Project recently noted, “If  one crew could launch a plane every 5 minutes, that would add up to almost 200kg in an eight-hour time period.”

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Naturally, Google and Matternet are not the only group out there developing UAVs for payload transportation. Amazon, DHL and others are prototyping the same technology. In addition, many of the teams I met at the recent Drones for Good Challenge in Dubai demo’ed payload solutions. One of the competition’s top 5 finalists was Drone Life from Spain. They flew their quadcopter (pictured above) fully autonomously. What’s special about this particular prototype is not just it’s range (40-50km with 2-3kg payload) but the fact that it also includes a fridge (for vaccines, organs, etc.,) that can be remotely monitored in real-time to ensure the temperature remains within required parameters.

At some point in your planning process, you’ll want to map the landing and take-off sites. The map below (click to enlarge) is the one we recently produced for the Tanzania UAV project (which is still being explored). Naturally, all these payload UAV flights would be pre-programmed and autonomous. If you’d like to learn more about how one programs such flights, check out my short video here.

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One other point worth keeping in mind is that UAVs need not be independent from existing transportation infrastructure. One team at the recent Drones for Good Challenge in Dubai suggested using public buses as take-off and landing points for UAVs. A university in the US is actually exploring this same use case, extending the reach of delivery trucks by using UAVs.

Of course, there are a host of issues that one needs to consider when operating any kind of UAV for humanitarian purposes. These include regulations, permits, risk assessments and mitigation strategies, fail safe mechanisms, community engagement, data privacy/security, etc. The above is simply meant to highlight some of the basic questions that need to be posed at the outset of the project. Needless to say, the very first question should always be whether the UAV is indeed the most appropriate tool (cost/benefit analysis) for the task at hand. In any case, the above is obviously not an exhaustive list. So I’d very much welcome feedback on what’s missing. Thank you!

How to Become a Digital Sherlock Holmes and Support Relief Efforts

Humanitarian organizations need both timely and accurate information when responding to disasters. Where is the most damage located? Who needs the most help? What other threats exist? Respectable news organizations also need timely and accurate information during crisis events to responsibly inform the public. Alas, both humanitarian & mainstream news organizations are often confronted with countless rumors and unconfirmed reports. Investigative journalists and others have thus developed a number of clever strategies to rapidly verify such reports—as detailed in the excellent Verification Handbook. There’s just one glitch: Journalists and humanitarians alike are increasingly overwhelmed by the “Big Data” generated during crises, particularly information posted on social media. They rarely have enough time or enough staff to verify the majority of unconfirmed reports. This is where Verily comes in, a new type of Detective Agency for a new type of detective: The Virtual Digital Detective.

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The purpose of Verily is to rapidly crowdsource the verification of unconfirmed reports during major disasters. The way it works is simple. If a humanitarian or news organization has a verification request, they simply submit this request online at Verily. This request must be phrased in the form of a Yes-or-No question, such as: “Has the Brooklyn Bridge been destroyed by the Hurricane?”; “Is this Instagram picture really showing current flooding in Indonesia”?; “Is this new YouTube video of the Chile earthquake fake?”; “Is it true that the bush fires in South Australia are getting worse?” and so on.

Verily helps humanitarian & news organizations find answers to these questions by rapidly crowdsourcing the collection of clues that can help answer said questions. Verification questions are communicated widely across the world via Verily’s own email-list of Digital Detectives and also via social media. This new bread of Digital Detectives then scour the web for clues that can help answer the verification questions. Anyone can become a Digital Detective at Verily. Indeed, Verily provides a menu of mini-verification guides for new detectives. These guides were written by some of the best Digital Detectives on the planet, the authors of the Verification Handbook. Verily Detectives post the clues they find directly to Verily and briefly explain why these clues help answer the verification question. That’s all there is to it.

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If you’re familiar with Reddit, you may be thinking “Hold on, doesn’t Reddit do this already?” In part yes, but Reddit is not necessarily designed to crowdsource critical thinking or to create skilled Digital Detectives. Recall this fiasco during the Boston Marathon Bombings which fueled disastrous “witch hunts”. Said disaster would not have happened on Verily because Verily is deliberately designed to focus on the process of careful detective work while providing new detectives with the skills they need to precisely avoid the kind of disaster that happened on Reddit. This is no way a criticism of Reddit! One single platform alone cannot be designed to solve every problem under the sun. Deliberate, intentional design is absolutely key.

In sum, our goal at Verily is to crowdsource Sherlock Holmes. Why do we think this will work? For several reasons. First, authors of the Verification Handbook have already demonstrated that individuals working alone can, and do, verify unconfirmed reports during crises. We believe that creating a community that can work together to verify rumors will be even more powerful given the Big Data challenge. Second, each one of us with a mobile phone is a human sensor, a potential digital witness. We believe that Verily can help crowdsource the search for eyewitnesses, or rather the search for digital content that these eyewitnesses post on the Web. Third, the Red Balloon Challenge was completed in a matter of hours. This Challenge focused on crowdsourcing the search for clues across an entire continent (3 million square miles). Disasters, in contrast, are far more narrow in terms of geographic coverage. In other words, the proverbial haystack is smaller and thus the needles easier to find. More on Verily here & here.

So there’s reason to be optimistic that Verily can succeed given the above and recent real-world deployments. Of course, Verily is is still very much in early phase and still experimental. But both humanitarian organizations and high-profile news organizations have expressed a strong interest in field-testing this new Digital Detective Agency. To find out more about Verily and to engage with experts in verification, please join us on Tuesday, March 3rd at 10:00am (New York time) for this Google Hangout with the Verily Team and our colleague Craig Silverman, the Co-Editor of the Verification Handbook. Click here for the Event Page and here to follow on YouTube. You can also join the conversations on Twitter and pose questions or comments using the hashtag #VerilyLive.

This is How Social Media Can Inform UN Needs Assessments During Disasters

My team at QCRI just published their latest findings on our ongoing crisis computing and humanitarian technology research. They focused on UN/OCHA, the international aid agency responsible for coordinating humanitarian efforts across the UN system. “When disasters occur, OCHA must quickly make decisions based on the most complete picture of the situation they can obtain,” but “given that complete knowledge of any disaster event is not possible, they gather information from myriad available sources, including social media.” QCRI’s latest research, which also drew on multiple interviews, shows how “state-of-the-art social media processing methods can be used to produce information in a format that takes into account what large international humanitarian organizations require to meet their constantly evolving needs.”

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QCRI’s new study (PDF) focuses specifically on the relief efforts in response to Typhoon Yolanda (known locally as Haiyan). “When Typhoon Yolanda struck the Philippines, the combination of widespread network access, high Twitter use, and English proficiency led to many located in the Philippines to tweet about the typhoon in English. In addition, outsiders located elsewhere tweeted about the situation, leading to millions of English-language tweets that were broadcast about the typhoon and its aftermath.”

When disasters like Yolanda occur, the UN uses the Multi Cluster/Sector Initial Rapid Assessment (MIRA) survey to assess the needs of affected populations. “The first step in the MIRA process is to produce a ‘Situation Analysis’ report,” which is produced within the first 48 hours of a disaster. Since the Situation Analysis needs to be carried out very quickly, “OCHA is open to using new sources—including social media communications—to augment the information that they and partner organizations so desperately need in the first days of the immediate post-impact period. As these organizations work to assess needs and distribute aid, social media data can potentially provide evidence in greater numbers than what individuals and small teams are able to collect on their own.”

My QCRI colleagues therefore analyzed the 2 million+ Yolanda-related tweets published between November 7-13, 2013 to assess whether any of these could have augmented OCHA’s situational awareness at the time. (OCHA interviewees stated that this “six-day period would be of most interest to them”). QCRI subsequently divided the tweets into two periods:

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Next, colleagues geo-located the tweets by administrative region and compared the frequency of tweets in each region with the number of people who were later found to have been affected in the respective region. The result of this analysis is displayed below (click to enlarge).

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While the “activity on Twitter was in general more significant in regions heavily affected by the typhoon, the correlation is not perfect.” This should not come as a surprise. This analysis is nevertheless a “worthwhile exercise, as it can prove useful in some circumstances.” In addition, knowing exactly what kinds of biases exist on Twitter, and which are “likely to continue is critical for OCHA to take into account as they work to incorporate social media data into future response efforts.”

QCRI researchers also analyzed the 2 million+ tweets to determine which  contained useful information. An informative tweet is defined as containing “information that helps you understand the situation.” They found that 42%-48% of the 2 million tweets fit this category, which is particularly high. Next, they classified those one million informative tweets using the Humanitarian Cluster System. The Up/Down arrows below indicate a 50%+ increase/decrease of tweets in that category during period 2.

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“In the first time period (roughly the first 48 hours), we observe concerns focused on early recovery and education and child welfare. In the second time period, these concerns extend to topics related to shelter, food, nutrition, and water, sanitation and hygiene (WASH). At the same time, there are proportionally fewer tweets regarding telecommunications, and safety and security issues.” The table above shows a “significant increase of useful messages for many clusters between period 1 and period 2. It is also clear that the number of potentially useful tweets in each cluster is likely on the order of a few thousand, which are swimming in the midst of millions of tweets. This point is illustrated by the majority of tweets falling into the ‘None of the above’ category, which is expected and has been shown in previous research.”

My colleagues also examined how “information relevant to each cluster can be further categorized into useful themes.” They used topic modeling to “quickly group thousands of tweets [and] understand the information they contain. In the future, this method can help OCHA staff gain a high- level picture of what type of information to expect from Twitter, and to decide which clusters or topics merit further examination and/or inclusion in the Situation Analysis.” The results of this topic modeling is displayed in the table below (click to enlarge).

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When UN/OCHA interviewees were presented with these results, their “feedback was positive and favorable.” One OCHA interviewee noted that this information “could potentially give us an indicator as to what people are talking most about— and, by proxy, apply that to the most urgent needs.” Another interviewee stated that “There are two places in the early hours that I would want this: 1) To add to our internal “one-pager” that will be released in 24-36 hours of an emergency, and 2) the Situation Analysis: [it] would be used as a proxy for need.” Another UN staffer remarked that “Generally yes this [information] is very useful, particularly for building situational awareness in the first 48 hours.” While some of the analysis may at times be too general, an OCHA interviewee “went on to say the table [above] gives a general picture of severity, which is an advantage during those first hours of response.”

As my QCRI team rightly notes, “This validation from UN staff supports our continued work on collecting, labeling, organizing, and presenting Twitter data to aid humanitarian agencies with a focus on their specific needs as they perform quick response procedures.” We are thus on the right track with both our AIDR and MicroMappers platforms. Our task moving forward is to use these platforms to produce the analysis discussed above, and to do so in near real-time. We also need to (radically) diversify our data sources and thus include information from text messages (SMS), mainstream media, Facebook, satellite imagery and aerial imagery (as noted here).

But as I’ve noted before, we also need enlightened policy making to make the most of these next generation humanitarian technologies. This OCHA proposal  on establishing specific social media standards for disaster response, and the official social media strategy implemented by the government of the Philippines during disasters serve as excellent examples in this respect.

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Lots more on humanitarian technology, innovation, computing as well as policy making in my new book Digital Humanitarians: How Big Data is Changing the Face of Humanitarian Action.

How to Counter Rumors and Prevent Violence Using UAVs

The Sentinel Project recently launched their Human Security UAV program in Kenya’s violence-prone Tana Delta to directly support Una Hakika (“Are You Sure”). Hakika is an information service that serves to “counteract malicious misinformation [disinformation] which has been the trigger for recent outbreaks of violence in the region.” While the Tana Delta is one of Kenya’s least developed areas, both “mobile phone and internet usage is still surprisingly high.” At the same time, misinformation has “played a significant role in causing fear, distrust and hatred between communities” because the Tana Delta is perhaps parado-xically also an “information-starved environment in which most people still rely on word-of-mouth to get news about the world around them.”

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In other words, there are no objective, authoritative sources of information per se, so Una Hakika (“Are You Sure”) seeks to be the first accurate, neutral and reliable source of information. Una Hakika is powered by a dedicated toll-free SMS short code and an engaged, trusted network of volunteer ambassadors. When the team receives a rumor verification request via SMS, they proceed to verify the rumor and report the findings back (via SMS) to the community. This process involves “gathering a lot of information from various different sources and trying to make sense of it […]. That’s where WikiRumours comes in as our purpose-built software for managing the Una Hakika workflow.”

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A year after implementing the project, the Sentinel team carried out a series of focus groups to assess impact The findings are particularly encouraging. In a way, the Sentinel team has formalized and stream-lined the organic verification process I describe here: How To Use Technology To Counter Rumors During Crises: Anecdotes from Kyrgyzstan. So where do UAVs come in?

The Sentinel team recently introduced the use of UAVs to support Una Hakika’s verification efforts and will be expanding the program to include a small fleet of multi-rotor and fixed wing platforms. Before piloting this new technology, the team carried out research to better understand local perceptions around UAVs (also referred to as Unmanned Aerial Systems, UAS):

“Common public opinion concerns in places like Europe and North America relate to the invasion of privacy, misuse by government or law enforcement, a related concern about an overbearing security state, and fears of an aviation disaster. Concerns found among residents of the Tana Delta revolve around practical issues such as whether the UAS-mounted camera would be powerful enough to be useful, how far such systems can operate, whether they are hampered by weather, how quickly a drone can be deployed in an emergency, and who will be in physical possession of the system.”

“For the most part, they [local residents] are genuinely curious, have a plethora of questions about the implementation of UAS in their communities, and are enthusiastic about the many possibilities. This genuine technological optimism makes the Tana Delta a likely site for one of the first programs of its kind. The Sentinel Project is conducting its UAS operations with the policy of ‘progress through caution,’ which seeks to engage communities within the proposed deployment while offering complete transparency and involvement but always emphasizing exposure to (and demonstration of) systems in the field with the people who have the potential to benefit from these initiatives. This approach has been extremely well received & has already resulted in improvements to implementation.”

While Una Hakika’s verification network includes hundreds of volunteer ambassadors, they can’t be everywhere at the same time. As the Sentinel team mentioned during one of our recent conversations, there are some places that simply can’t be reached by foot reliably. In addition, the UAVs can operate both day and night; wandering around at night can be dangerous for Una Hakika’s verification ambassadors. The Sentinel team thus plans to add InfraRed, thermal imaging capabilities to the UAVs. The core of the program will be to use UAVs to set up perimeter security areas around threatened communities. In addition, the program can address other vectors which have led to recent violence: using the UAVs to help find lost (potentially stolen) cattle, track crop health, and monitor contested land use. The team mentioned that the UAVs could also be used to support search and rescue efforts during periods of drought and floods.

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Lastly, they’ve started discussing the use of UAVs for payload transportation. For example, UAVs could deliver medical supplies to remote villages that have been attacked. After all, the World Health Organization (WHO) is already using UAVs for this purpose. With each of these applications, the Sentinel team clearly emphasizes that the primary users and operators of the UAVs must be the local staff in the region. “We believe that successful technology driven programs must not only act as tools to serve these communities but also allow community members to have direct involvement in their use”.

As the Sentinel team rightly notes, their approach helps to “counteract the paralysis which arises from the unknowns of a new endeavour when studied in a purely academic setting. The Sentinel Project team believes that a cautious but active strategy of real-world deployments will best demonstrate the value of such programs to governments and global citizens.” This very much resonates with me, which is why I am pleased to serve on the organization’s Advisory Board.

Could This Be The Most Comprehensive Study of Crisis Tweets Yet?

I’ve been looking forward to blogging about my team’s latest research on crisis computing for months; the delay being due to the laborious process of academic publishing, but I digress. I’m now able to make their  findings public. The goal of their latest research was to “understand what affected populations, response agencies and other stakeholders can expect—and not expect—from [crisis tweets] in various types of disaster situations.”

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As my colleagues rightly note, “Anecdotal evidence suggests that different types of crises elicit different reactions from Twitter users, but we have yet to see whether this is in fact the case.” So they meticulously studied 26 crisis-related events between 2012-2013 that generated significant activity on twitter. The lead researcher on this project, my colleague & friend Alexandra Olteanu from EPFL, also appears in my new book.

Alexandra and team first classified crisis related tweets based on the following categories (each selected based on previous research & peer-reviewed studies):

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Written in long form: Caution & Advice; Affected Individuals; Infrastructure & Utilities; Donations & Volunteering; Sympathy & Emotional Support, and Other Useful Information. Below are the results of this analysis sorted by descending proportion of Caution & Advice related tweets (click to enlarge).

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The category with the largest number of tweets is “Other Useful Info.” On average 32% of tweets fall into this category (minimum 7%, maximum 59%). Interestingly, it appears that most crisis events that are spread over a relatively large geographical area (i.e., they are diffuse), tend to be associated with the lowest number of “Other” tweets. As my QCRI rightly colleagues note, “it is potentially useful to know that this type of tweet is not prevalent in the diffused events we studied.”

Tweets relating to Sympathy and Emotional Support are present in each of the 26 crises. On average, these account for 20% of all tweets. “The 4 crises in which the messages in this category were more prevalent (above 40%) were all instantaneous disasters.” This finding may imply that “people are more likely to offer sympathy when events […] take people by surprise.”

On average, 20% of tweets in the 26 crises relate to Affected Individuals. “The 5 crises with the largest proportion of this type of information (28%–57%) were human-induced, focalized, and instantaneous. These 5 events can also be viewed as particularly emotionally shocking.”

Tweets related to Donations & Volunteering accounted for 10% of tweets on average. “The number of tweets describing needs or offers of goods and services in each event varies greatly; some events have no mention of them, while for others, this is one of the largest information categories. “

Caution and Advice tweets constituted on average 10% of all tweets in a given crisis. The results show a “clear separation between human-induced hazards and natural: all human induced events have less caution and advice tweets (0%–3%) than all the events due to natural hazards (4%–31%).”

Finally, tweets related to Infrastructure and Utilities represented on average 7% of all tweets posted in a given crisis. The disasters with the highest number of such tweets tended to be flood situations.

In addition to the above analysis, Alexandra et al. also categorized tweets by their source:

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The results depicted below (click to enlarge) are sorted by descending order of eyewitness tweets.

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On average, about 9% of tweets generated during a given crises were written by Eyewitnesses; a figure that increased to 54% for the haze crisis in Singapore. “In general, we find a larger proportion of eyewitness accounts during diffused disasters caused by natural hazards.”

Traditional and/or Internet Media were responsible for 42% of tweets on average. ” The 6 crises with the highest fraction of tweets coming from a media source (54%–76%) are instantaneous, which make “breaking news” in the media.

On average, Outsiders posted 38% of the tweets in a given crisis while NGOs were responsible for about 4% of tweets and Governments 5%. My colleagues surmise that these low figures are due to the fact that both NGOs and governments seek to verify information before they release it. The highest levels of NGO and government tweets occur in response to natural disasters.

Finally, Businesses account for 2% of tweets on average. The Alberta floods of 2013 saw the highest proportion (9%) of tweets posted by businesses.

All the above findings are combined and displayed below (click to enlarge). The figure depicts the “average distribution of tweets across crises into combinations of information types (rows) and sources (columns). Rows and columns are sorted by total frequency, starting on the bottom-left corner. The cells in this figure add up to 100%.”

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The above analysis suggests that “when the geographical spread [of a crisis] is diffused, the proportion of Caution and Advice tweets is above the median, and when it is focalized, the proportion of Caution and Advice tweets is below the median. For sources, […] human-induced accidental events tend to have a number of eyewitness tweets below the median, in comparison with intentional and natural hazards.” Additional analysis carried out by my colleagues indicate that “human-induced crises are more similar to each other in terms of the types of information disseminated through Twitter than to natural hazards.” In addition, crisis events that develop instantaneously also look the same when studied through the lens of tweets.

In conclusion, the analysis above demonstrates that “in some cases the most common tweet in one crisis (e.g. eyewitness accounts in the Singapore haze crisis in 2013) was absent in another (e.g. eyewitness accounts in the Savar building collapse in 2013). Furthermore, even two events of the same type in the same country (e.g. Typhoon Yolanda in 2013 and Typhoon Pablo in 2012, both in the Philippines), may look quite different vis-à-vis the information on which people tend to focus.” This suggests the uniqueness of each event.

“Yet, when we look at the Twitter data at a meta-level, our analysis reveals commonalities among the types of information people tend to be concerned with, given the particular dimensions of the situations such as hazard category (e.g. natural, human-induced, geophysical, accidental), hazard type (e.g. earth-quake, explosion), whether it is instantaneous or progressive, and whether it is focalized or diffused. For instance, caution and advice tweets from government sources are more common in progressive disasters than in instantaneous ones. The similarities do not end there. When grouping crises automatically based on similarities in the distributions of different classes of tweets, we also realize that despite the variability, human-induced crises tend to be more similar to each other than to natural hazards.”

Needless to say, these are exactly the kind of findings that can improve the way we use MicroMappers & other humanitarian technologies for disaster response. So if want to learn more, the full study is available here (PDF). In addition, all the Twitter datasets used for the analysis are available at CrisisLex. If you have questions on the research, simply post them in the comments section below and I’ll ask my colleagues to reply there.

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In the meantime, there is a lot more on humanitarian technology and computing in my new book Digital Humanitarians. As I note in said book, we also need enlightened policy making to tap the full potential of social media for disaster response. Technology alone can only take us so far. If we don’t actually create demand for relevant tweets in the first place, then why should social media users supply a high volume of relevant and actionable tweets to support relief efforts? This OCHA proposal on establishing specific social media standards for disaster response, and this official social media strategy developed and implemented by the Filipino government are examples of what enlightened leadership looks like.