Category Archives: Humanitarian Tech

Using AIDR to Collect and Analyze Tweets from Chile Earthquake

Wish you had a better way to make sense of Twitter during disasters than this?

Type in a keyword like #ChileEarthquake in Twitter’s search box above and you’ll see more tweets than you can possibly read in a day let alone keep up with for more than a few minutes. Wish there way were an easy, free and open source solution? Well you’ve come to the right place. My team and I at QCRI are developing the Artificial Intelligence for Disaster Response (AIDR) platform to do just this. Here’s how it works:

First you login to the AIDR platform using your own Twitter handle (click images below to enlarge):

AIDR login

You’ll then see your collection of tweets (if you already have any). In my case, you’ll see I have three. The first is a collection of English language tweets related to the Chile Earthquake. The second is a collection of Spanish tweets. The third is a collection of more than 3,000,000 tweets related to the missing Malaysia Airlines plane. A preliminary analysis of these tweets is available here.

AIDR collections

Lets look more closely at my Chile Earthquake 2014 collection (see below, click to enlarge). I’ve collected about a quarter of a million tweets in the past 30 hours or so. The label “Downloaded tweets (since last re-start)” simply refers to the number of tweets I’ve collected since adding a new keyword or hashtag to my collection. I started the collection yesterday at 5:39am my time (yes, I’m an early bird). Under “Keywords” you’ll see all the hashtags and keywords I’ve used to search for tweets related to the earthquake in Chile. I’ve also specified the geographic region I want to collect tweets from. Don’t worry, you don’t actually have to enter geographic coordinates when you set up your own collection, you simply highlight (on map) the area you’re interested in and AIDR does the rest.

AIDR - Chile Earthquake 2014

You’ll also note in the above screenshot that I’ve selected to only collect tweets in English, but you can collect all language tweets if you’d like or just a select few. Finally, the Collaborators section simply lists the colleagues I’ve added to my collection. This gives them the ability to add new keywords/hashtags and to download the tweets collected as shown below (click to enlarge). More specifically, collaborators can download the most recent 100,000 tweets (and also share the link with others). The 100K tweet limit is based on Twitter’s Terms of Service (ToS). If collaborators want all the tweets, Twitter’s ToS allows for sharing the TweetIDs for an unlimited number of tweets.

AIDR download CSV

So that’s the AIDR Collector. We also have the AIDR Classifier, which helps you make sense of the tweets you’re collecting (in real-time). That is, your collection of tweets doesn’t stop, it continues growing, and as it does, you can make sense of new tweets as they come in. With the Classifier, you simply teach AIDR to classify tweets into whatever topics you’re interested in, like “Infrastructure Damage”, for example. To get started with the AIDR Classifier, simply return to the “Details” tab of our Chile collection. You’ll note the “Go To Classifier” button on the far right:

AIDR go to Classifier

Clicking on that button allows you to create a Classifier, say on the topic of disaster damage in general. So you simply create a name for your Classifier, in this case “Disaster Damage” and then create Tags to capture more details with respect to damage-related tweets. For example, one Tag might be, say, “Damage to Transportation Infrastructure.” Another could be “Building Damage.” In any event, once you’ve created your Classifier and corresponding tags, you click Submit and find your way to this page (click to enlarge):

AIDR Classifier Link

You’ll notice the public link for volunteers. That’s basically the interface you’ll use to teach AIDR. If you want to teach AIDR by yourself, you can certainly do so. You also have the option of “crowdsourcing the teaching” of AIDR. Clicking on the link will take you to the page below.

AIDR to MicroMappers

So, I called my Classifier “Message Contents” which is not particularly insightful; I should have labeled it something like “Humanitarian Information Needs” or something, but bear with me and lets click on that Classifier. This will take you to the following Clicker on MicroMappers:

MicroMappers Clicker

Now this is not the most awe-inspiring interface you’ve ever seen (at least I hope not); reason being that this is simply our very first version. We’ll be providing different “skins” like the official MicroMappers skin (below) as well as a skin that allows you to upload your own logo, for example. In the meantime, note that AIDR shows every tweet to at least three different volunteers. And only if each of these 3 volunteers agree on how to classify a given tweet does AIDR take that into consideration when learning. In other words, AIDR wants to ensure that humans are really sure about how to classify a tweet before it decides to learn from that lesson. Incidentally, The MicroMappers smartphone app for the iPhone and Android will be available in the next few weeks. But I digress.

Yolanda TweetClicker4

As you and/or your volunteers classify tweets based on the Tags you created, AIDR starts to learn—hence the AI (Artificial Intelligence) in AIDR. AIDR begins to recognize that all the tweets you classified as “Infrastructure Damage” are indeed similar. Once you’ve tagged enough tweets, AIDR will decide that it’s time to leave the nest and fly on it’s own. In other words, it will start to auto-classify incoming tweets in real-time. (At present, AIDR can auto-classify some 30,000 tweets per minute; compare this to the peak rate of 16,000 tweets per minute observed during Hurricane Sandy).

Of course, AIDR’s first solo “flights” won’t always go smoothly. But not to worry, AIDR will let you know when it needs a little help. Every tweet that AIDR auto-tags comes with a Confidence level. That is, AIDR will let you know: “I am 80% sure that I correctly classified this tweet”. If AIDR has trouble with a tweet, i.e., if it’s confidence level is 65% or below, the it will send the tweet to you (and/or your volunteers) so it can learn from how you classify that particular tweet. In other words, the more tweets you classify, the more AIDR learns, and the higher AIDR’s confidence levels get. Fun, huh?

To view the results of the machine tagging, simply click on the View/Download tab, as shown below (click to enlarge). The page shows you the latest tweets that have been auto-tagged along with the Tag label and the confidence score. (Yes, this too is the first version of that interface, we’ll make it more user-friendly in the future, not to worry). In any event, you can download the auto-tagged tweets in a CSV file and also share the download link with your colleagues for analysis and so on. At some point in the future, we hope to provide a simple data visualization output page so that you can easily see interesting data trends.

AIDR Results

So that’s basically all there is to it. If you want to learn more about how it all works, you might fancy reading this research paper (PDF). In the meantime, I’ll simply add that you can re-use your Classifiers. If (when?) another earthquake strikes Chile, you won’t have to start from scratch. You can auto-tag incoming tweets immediately with the Classifier you already have. Plus, you’ll be able to share your classifiers with your colleagues and partner organizations if you like. In other words, we’re envisaging an “App Store” of Classifiers based on different hazards and different countries. The more we re-use our Classifiers, the more accurate they will become. Everybody wins.

And voila, that is AIDR (at least our first version). If you’d like to test the platform and/or want the tweets from the Chile Earthquake, simply get in touch!

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Note:

  • We’re adapting AIDR so that it can also classify text messages (SMS).
  • AIDR Classifiers are language specific. So if you speak Spanish, you can create a classifier to tag all Spanish language tweets/SMS that refer to disaster damage, for example. In other words, AIDR does not only speak English : )

Welcome to the Humanitarian UAV Network

UAViators Logo

The Humanitarian UAV Network (UAViators) is now live. Click here to access and join the network. Advisors include representatives from 3D Robotics, AirDroids, senseFly & DroneAdventures, OpenRelief, ShadowView Foundation, ICT4Peace Foundation, the United Nations and more. The website provides a unique set of resources, including the most comprehensive case study of humanitarian UAV deployments, a directory of organizations engaged in the humanitarian UAV space and a detailed list of references to keep track of ongoing research in this rapidly evolving area. All of these documents along with the network’s Code of Conduct—the only one of it’s kind—are easily accessible here.

UAViators 4 Teams

The UAViators website also includes 8 action-oriented Teams, four of which are displayed above. The Flight Team, for example, includes both new and highly experienced UAV pilots while the Imagery Team comprises members interested in imagery analysis. Other teams include the Camera, Legal and Policy Teams. In addition to this Team page, the site also has a dedicated Operations page to facilitate & coordinate safe and responsible UAV deployments in support of humanitarian efforts. In between deployments, the website’s Global Forum is a place where members share information about relevant news, events and more. One such event, for example, is the upcoming Drone/UAV Search & Rescue Challenge that UAViators is sponsoring.

When first announcing this initiative,  I duly noted that launching such a network will at first raise more questions than answers, but I welcome the challenge and believe that members of UAViators are well placed to facilitate the safe and responsible use of UAVs in a variety of humanitarian contexts.

Acknowledgements: Many thanks to colleagues and members of the Advisory Board who provided invaluable feedback and guidance in the lead-up to this launch. The Humanitarian UAV Network is result of collective vision and effort.

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See also:

  • 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]
  • Crowdsourcing Analysis of UAV Imagery for Search and Rescue [link]

Humanitarians Using UAVs for Post Disaster Recovery

I recently connected with senseFly’s Adam Klaptocz who founded the non-profit group DroneAdventures to promote humanitarian uses of UAVs. I first came across Adam’s efforts last year when reading about his good work in Haiti, which demonstrated the unique role that UAV technology & imagery can play in post-disaster contexts. DroneAdventures has also been active in Japan and Peru. In the coming months, the team will also be working on “aerial archeology” projects in Turkey and Egypt. When Adam emailed me last week, he and his team had just returned from yet another flying mission, this time in the Philippines. I’ll be meeting up with Adam in a couple weeks to learn more about their recent adventures. In the meantime, here’s a quick recap of what they were up to in the Philippines this month.

MedAir

Adam and team snapped hundreds of aerial images using their “eBee drones” to create a detailed set of 2D maps and 3D terrain models of the disaster-affected areas where partner Medair works. This is the first time that the Swiss humanitarian organization Medair is using UAVs to inform their recovery and rehabilitation programs. They plan to use the UAV maps & models of Tacloban and hard-hit areas in Leyte to assist in assessing “where the greatest need is” and what level of “assistance should be given to affected families as they continue to recover” (1). To this end, having accurate aerial images of these affected areas will allow the Swiss organization to “address the needs of individual households and advocate on their behalf when necessary” (2). 

ebee

An eBee Drone also flew over Dulag, north of Leyte, where more than 80% of the homes and croplands were destroyed following Typhoon Yolanda. Medair is providing both materials and expertise to build new shelters in Dulag. As one Medair representative noted during the UAV flights, “Recovery from a disaster of this magnitude can be complex. The maps produced from the images taken by the drones will give everyone, including community members themselves, an opportunity to better understand not only where the greatest needs are, but also their potential solutions” (3). The partners are also committed to Open Data: “The images will be made public for free online, enabling community leaders and humanitarian organizations to use the information to coordinate reconstruction efforts” (4). The pictures of the Philippines mission below were very kindly shared by Adam who asked that they be credited to DroneAdventures.

Credit: DroneAdventures

At the request of the local Mayor, DroneAdventures and MedAir also took aerial images of a relatively undamaged area some 15 kilometers north of Tacloban, which is where the city government is looking to relocate families displaced by Typhoon Yolanda. During the deployment, Adam noted that “Lightweight drones such as the eBee are safe and easy to operate and can provide crucial imagery at a precision and speed unattainable by satellite imagery. Their relatively low cost of deployment make the technology attainable even by small communities throughout the developing world. Not only can drones be deployed immediately following a disaster in order to assess damage and provide detailed information to first-responders like Medair, but they can also assist community leaders in planning recovery efforts” (5). As the Medair rep added, “You can just push a button or launch them by hand to see them fly, and you don’t need a remote anymore—they are guided by GPS and are inherently safe” (6).

Credit: DroneAdventures

I really look forward to meeting up with Adam and the DroneAdventures team at the senseFly office in Lausanne next month to learn more about their recent work and future plans. I will also be asking the team for their feedback and guidance on the Humanitarian UAV Network (UAViators) that I am launching. So stay tuned for updates!

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See also:

  • Calling All UAV Pilots: Want to Support Humanitarian Efforts? [link]
  • How UAVs are Making a Difference in Disaster Response [link]
  • Grassroots UAVs for Disaster Response (in the Philippines) [link]

 

Launching a Search and Rescue Challenge for Drone / UAV Pilots

My colleague Timothy Reuter (of AidDroids fame) kindly invited me to co-organize the Drone/UAV Search and Rescue Challenge for the DC Drone User Group. The challenge will take place on May 17th near Marshall in Virginia. The rules for the competition are based on the highly successful Search/Rescue challenge organized by my new colleague Chad with the North Texas Drone User Group. We’ll pretend that a person has gone missing by scattering (over a wide area) various clues such as pieces of clothing & personal affects. Competitors will use their UAVs to collect imagery of the area and will have 45 minutes after flying to analyze the imagery for clues. The full set of rules for our challenge are listed here but may change slightly as we get closer to the event.

searchrescuedrones

I want to try something new with this challenge. While previous competitions have focused exclusively on the use of drones/UAVs for the “Search” component of the challenge, I want to introduce the option of also engaging in the “Rescue” part. How? If UAVs identify a missing person, then why not provide that person with immediate assistance while waiting for the Search and Rescue team to arrive on site? The UAV could drop a small and light-weight first aid kit, or small water bottle, or even a small walkie talkie. Enter my new colleague Euan Ramsay who has been working on a UAV payloader solution for Search and Rescue; see the video demo below. Euan, who is based in Switzerland, has very kindly offered to share several payloader units for our UAV challenge. So I’ll be meeting up with him next month to take the units back to DC for the competition.

Another area I’d like to explore for this challenge is the use of crowdsourcing to analyze the aerial imagery & video footage. As noted here, the University of Central Lancashire used crowdsourcing in their UAV Search and Rescue pilot project last summer. This innovative “crowdsearching” approach is also being used to look for Malaysia Flight 370 that went missing several weeks ago. I’d really like to have this crowdsourcing element be an option for the DC Search & Rescue challenge.

UAV MicroMappers

My team and I at QCRI have developed a platform called MicroMappers, which can easily be used to crowdsource the analysis of UAV pictures and videos. The United Nations (OCHA) used MicroMappers in response to Typhoon Yolanda last year to crowdsource the tagging pictures posted on Twitter. Since then we’ve added video tagging capability. So one scenario for the UAV challenge would be for competitors to upload their imagery/videos to MicroMappers and have digital volunteers look through this content for clues of our fake missing person.

In any event, I’m excited to be collaborating with Timothy on this challenge and will be share updates on iRevolution on how all this pans out.

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See also:

  • Using UAVs for Search & Rescue [link]
  • Crowdsourcing Analysis of UAV Imagery for Search and Rescue [link]
  • How UAVs are Making a Difference in Disaster Response [link]
  • Grassroots UAVs for Disaster Response [link]

Grassroots UAVs for Disaster Response

I was recently introduced to a new initiative that seeks to empower grassroots communities to deploy their own low-cost xUAVs. The purpose of this initiative? To support locally-led disaster response efforts and in so doing transfer math, science and engineering skills to local communities. The “x” in xUAV refers to expendable. The initiative is a partnership between California State University (Long Beach), University of Hawaii, Embry Riddle, The Philippine Council for Industry, Energy & Emerging Technology Research & Development, Skyeye, Aklan State University and Ateneo de Manila University in the Philippines. The team is heading back to the Philippines next week for their second field mission. This blog post provides a short overview of the project’s approach and the results from their first mission, which took place during December 2013-February 2014.

xUAV1

The xUAV team is specifically interested in a new category of UAVs, those that are locally available, locally deployable, low-cost, expendable and extremely easy to use. Their first field mission to the Philippines focused on exploring the possibilities. The pictures above/below (click to enlarge) were kindly shared by the Filipinos engaged in the project—I am very grateful to them for allowing me to share these publicly. Please do not reproduce these pictures without their written permission, thank you.

xUAV2

I spoke at length with one of the xUAV team leads, Ted Ralston, who is heading back to the Philippines the second field mission. The purpose of this follow up visit is to shift the xUAV concept from experimental to deployable. One area that his students will be focusing on with the University of Manila is the development of a very user-friendly interface (using a low-cost tablet) to pilot the xUAVs so that local communities can simply tag way-points on a map that the xUAV will then automatically fly to. Indeed, this is where civilian UAVs are headed, full automation. A good example of this trend towards full automation is the new DroidPlanner 2.0 App just released by 3DRobotics. This free app provides powerful features to very easily plan autonomous flights. You can even create new flight plans on the fly and edit them onsite.

DroidPlanner.png

So the xUAV team will focus on developing software for automated take-off and landing as well as automated adjustments for wind conditions when the xUAV is airborne, etc. The software will also automatically adjust the xUAV’s flight parameters for any added payloads. Any captured imagery would then be made easily viewable via touch-screen directly from the low-cost tablet.

xUAV3

One of the team’s top priorities throughout this project is to transfer their skills to young Filipinos, given them hands on training in science, math and engineering. An equally important, related priority, is their focus on developing local partnerships with multiple partners. We’re familiar with ideas behind Public Participatory GIS (PPGIS) vis-a-vis the participatory use of geospatial information systems and technologies. The xUAV team seeks to extend this grassroots approach to Public Participatory UAVs.

xUAV4

I’m supporting this xUAV initiative in a number of ways and will be uploading the team’s UAV imagery (videos & still photos) from their upcoming field mission to MicroMappers for some internal testing. I’m particularly interested in user-generated (aerial) content that is raw and not pre-processed or stitched together, however. Why? Because I expect this type of imagery to grow in volume given the very rapid growth of the personal micro-UAV market. For more professionally produced and stitched-together aerial content, an ideal platform is Humanitarian OpenStreetMap’s Tasking Server, which is tried and tested for satellite imagery and which was recently used to trace processed UAV imagery of Tacloban.

Screen Shot 2014-03-12 at 1.03.20 PM

I look forward to following the xUAV team’s efforts and hope to report on the outcome of their second field mission. The xUAV initiative fits very nicely with the goals of the Humanitarian UAV Network (UAViators). We’ll be learning a lot in the coming weeks and months from our colleagues in the Philippines.

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Analyzing Tweets on Malaysia Flight #MH370

My QCRI colleague Dr. Imran is using our AIDR platform (Artificial Intelligence for Disaster Response) to collect & analyze tweets related to Malaysia Flight 370 that went missing several days ago. He has collected well over 850,000 English-language tweets since March 11th; using the following keywords/hashtags: Malaysia Airlines flight, #MH370m #PrayForMH370 and #MalaysiaAirlines.

MH370 Prayers

Imran then used AIDR to create a number of “machine learning classifiers” to automatically classify all incoming tweets into categories that he is interested in:

  • Informative: tweets that relay breaking news, useful info, etc

  • Praying: tweets that are related to prayers and faith

  • Personal: tweets that express personal opinions

The process is super simple. All he does is tag several dozen incoming tweets into their respective categories. This teaches AIDR what an “Informative” tweet should “look like”. Since our novel approach combines human intelligence with artificial intelligence, AIDR is typically far more accurate at capturing relevant tweets than Twitter’s keyword search.

And the more tweets that Imran tags, the more accurate AIDR gets. At present, AIDR can auto-classify ~500 tweets per second, or 30,000 tweets per minute. This is well above the highest velocity of crisis tweets recorded thus far—16,000 tweets/minute during Hurricane Sandy.

The graph below depicts the number of tweets generated since the day we started collecting the AIDR collection, i.e., March 11th.

Volume of Tweets per Day

This series of pie charts simply reflects the relative share of tweets per category over the past four days.

Tweets Trends

Below are some of the tweets that AIDR has automatically classified as being Informative (click to enlarge). The “Confidence” score simply reflects how confident AIDR is that it has correctly auto-classified a tweet. Note that Imran could also have crowdsourced the manual tagging—that is, he could have crowdsourced the process of teaching AIDR. To learn more about how AIDR works, please see this short overview and this research paper (PDF).

AIDR output

If you’re interested in testing AIDR (still very much under development) and/or would like the Tweet ID’s for the 850,000+ tweets we’ve collected using AIDR, then feel free to contact me. In the meantime, we’ll start a classifier that auto-collects tweets related to hijacking, criminal causes, and so on. If you’d like us to create a classifier for a different topic, let us know—but we can’t make any promises since we’re working on an important project deadline. When we’re further along with the development of AIDR, anyone will be able to easily collect & download tweets and create & share their own classifiers for events related to humanitarian issues.

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Acknowledgements: Many thanks to Imran for collecting and classifying the tweets. Imran also shared the graphs and tabular output that appears above.

Calling all UAV Pilots: Want to Support Humanitarian Efforts?

I’m launching a volunteer network to connect responsible civilian UAV pilots who are interested in safely and legally supporting humanitarian efforts when the need arises. I’ve been thinking through the concept for months now and have benefited from great feedback. The result is this draft strategy document; the keyword being draft. The concept is still being developed and there’s still room for improvement. So I very much welcome more constructive feedback.

Click here to join the list-serve for this initiative, which I’m referring to as the Humanitarian UAViators Network. Thank you for sharing this project far and wide—it will only work if we get a critical mass of UAV pilots from all around the world. Of course, launching such a network raises more questions than answers, but I welcome the challenge and believe members of UAViators will be well placed to address and manage these challenges.

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Crowdsourcing the Search for Malaysia Flight 370 (Updated)

Early Results available here!

Update from Tomnod: The response has literally been overwhelming: our servers struggled to keep up all day.  We’ve been hacking hard to make some fixes and I think that the site is working now but I apologize if you have problems connecting: we’re getting up to 100,000 page views every minute! DigitalGlobe satellites are continuing to collect imagery as new reports about the possible crash sites come in so we’ll keep updating the site with new data.

Beijing-bound Flight 370 suddenly disappeared on March 8th without a trace. My colleagues at Tomnod have just deployed their satellite imagery crowdsourcing platform to support the ongoing Search & Rescue efforts. Using high-resolution satellite imagery from DigitalGlobe, Tomnod is inviting digital volunteers from around the world to search for any sign of debris from missing Boeing 777.

MH370

The DigitalGlobe satellite imagery is dated March 9th and covers over 1,000 square miles. What the Tomnod platform does is slice that imagery into many small squares like the one below (click to enlarge). Volunteers then tag one image at a time. This process is known as microtasking (or crowd computing). For quality control purposes, each image is shown to more than one volunteer. This consensus-based approach allows Tomnod to triangulate the tagging.

TomNod

I’ve long advocated for the use of microtasking to support humanitarian efforts. In 2010, I wrote about how volunteers used microtasking to crowdsource the search for Steve Fossett who had disappeared while flying a small single-engine airplane in Nevada. This was back in 2007. In 2011, I spearheaded a partnership with the UN Refugee Agency (UNCHR) in Somalia and used the Tomnod platform to crowdsource the search for internally displaced populations in the drought-stricken Afgooye Corridor. More here. I later launched a collaboration with Amnesty International in Syria to crowdsource the search for evidence of major human rights violations—again with my colleagues from Tomnod. Recently, my team and I at QCRI have been developing MicroMappers to support humanitarian efforts. At the UN’s request, MicroMappers was launched following Typhoon Yolanda to accelerate their rapid damage assessment. I’ve also written on the use of crowd computing for Search & Rescue operations.

TomnodSomalia

I’m still keeping a tiny glimmer of hope that somehow Malaysia Flight 370 was able to land somewhere and that there are survivors. I can only image what families, loved ones and friends must be going through. I’m sure they are desperate for information, one way or another. So please consider spending a few minutes of your time to support these Search and Rescue efforts. Thank you.

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Note: If you don’t see any satellite imagery on the Tomnod platform for Flight 370, this means the team is busy uploading new imagery. So please check in again in a couple hours.

See also: Analyzing Tweets on Malaysia Flight #MH370 [link]

Crisis Mapping without GPS Coordinates

I recently spoke with a UK start-up that is doing away with GPS coordinates even though their company focuses on geographic information and maps. The start-up, What3Words, has divided the globe into 57 trillion squares and given each of these 3-by-3 meter areas a unique three-word code. Goodbye long postal addresses and cryptic GPS coordinates. Hello planet.inches.most. The start-up also offers a service called OneWord, which allows you to customize a one-word name for any square. In addition, the company has expanded to other languages such as Spanish, Swedish and Russian. They’re now working on including Arabic, Chinese, Japanese and others by mid-January 2014. Meanwhile, their API lets anyone build new applications that tap their global map of 57 trillion squares.

Credit: What3Words

When I spoke with CEO Chris Sheldrick, he noted that their very first users were emergency response organizations. One group in Australia, for example, is using What3Words as part of their SMS emergency service. “This will let people identify their homes with just three words, ensuring that emergency vehicles can find them as quickly as possible.” Such an approach provides greater accuracy, which is vital in rural areas. “Our ambulances have a terrible time with street addresses, particularly in The Bush.” Moreover, many places in the world have no addresses at all. So What3Words may also be useful for certain ICT4D projects in addition to crisis mapping. The real key to this service is simplicity, i.e., communicating three words over the phone, via SMS/Twitter or email is far easier (and less error prone) than dictating a postal address or a complicated set of GPS coordinates.

Credit: What3Words

How else do you think this service could be used vis-à-vis disaster response?

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Quantifying Information Flow During Emergencies

I was particularly pleased to see this study appear in the top-tier journal, Nature. (Thanks to my colleague Sarah Vieweg for flagging). Earlier studies have shown that “human communications are both temporally & spatially localized following the onset of emergencies, indicating that social propagation is a primary means to propagate situational awareness.” In this new study, the authors analyze crisis events using country-wide mobile phone data. To this end, they also analyze the communication patterns of mobile phone users outside the affected area. So the question driving this study is this: how do the communication patterns of non-affected mobile phone users differ from those affected? Why ask this question? Understanding the communication patterns of mobile phone users outside the affected areas sheds light on how situational awareness spreads during disasters.

Nature graphs

The graphs above (click to enlarge) simply depict the change in call volume for three crisis events and one non-emergency event for the two types of mobile phone users. The set of users directly affected by a crisis is labeled G0 while users they contact during the emergency are labeled G1. Note that G1 users are not affected by the crisis. Since the study seeks to assess how G1 users change their communication patterns following a crisis, one logical question is this: do the call volume of G1 users increase like those of G0 users? The graphs above reveal that G1 and G0 users have instantaneous and corresponding spikes for crisis events. This is not the case for the non-emergency event.

“As the activity spikes for G0 users for emergency events are both temporally and spatially localized, the communication of G1 users becomes the most important means of spreading situational awareness.” To quantify the reach of situational awareness, the authors study the communication patterns of G1 users after they receive a call or SMS from the affected set of G0 users. They find 3 types of communication patterns for G1 users, as depicted below (click to enlarge).

Nature graphs 2

Pattern 1: G1 users call back G0 users (orange edges). Pattern 2: G1 users call forward to G2 users (purple edges). Pattern 3: G1 users call other G1 users (green edges). Which of these 3 patterns is most pronounced during a crisis? Pattern 1, call backs, constitute 25% of all G1 communication responses. Pattern 2, call forwards, constitutes 70% of communications. Pattern 3, calls between G1 users only represents 5% of all communications. This means that the spikes in call volumes shown in the above graphs is overwhelmingly driven by Patterns 1 and 2: call backs and call forwards.

The graphs below (click to enlarge) show call volumes by communication patterns 1 and 2. In these graphs, Pattern 1 is the orange line and Pattern 2 the dashed purple line. In all three crisis events, Pattern 1 (call backs) has clear volume spikes. “That is, G1 users prefer to interact back with G0 users rather than contacting with new users (G2), a phenomenon that limits the spreading of information.” In effect, Pattern 1 is a measure of reciprocal communications and indeed social capital, “representing correspondence and coordination calls between social neighbors.” In contrast, Pattern 2 measures the dissemination of the “dissemination of situational awareness, corresponding to information cascades that penetrate the underlying social network.”

Nature graphs 3

The histogram below shows average levels of reciprocal communication for the 4 events under study. These results clearly show a spike in reciprocal behavior for the three crisis events compared to the baseline. The opposite is true for the non-emergency event.Nature graphs 4

In sum, a crisis early warning system based on communication patterns should seek to monitor changes in the following two indicators: (1) Volume of Call Backs; and (2) Deviation of Call Backs from baseline. Given that access to mobile phone data is near-impossible for the vast majority of academics and humanitarian professionals, one question worth exploring is whether similar communication dynamics can be observed on social networks like Twitter and Facebook.

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