Tag Archives: TED

Using Flash Crowds to Automatically Detect Earthquakes & Impact Before Anyone Else

It is said that our planet has a new nervous system; a digital nervous system comprised of digital veins and intertwined sensors that capture the pulse of our planet in near real-time. Next generation humanitarian technologies seek to leverage this new nervous system to detect and diagnose the impact of disasters within minutes rather than hours. To this end, LastQuake may be one of the most impressive humanitarian technologies that I have recently come across. Spearheaded by the European-Mediterranean Seismological Center (EMSC), the technology combines “Flashsourcing” with social media monitoring to auto-detect earthquakes before they’re picked up by seismometers or anyone else.

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Scientists typically draw on ground-motion prediction algorithms and data on building infrastructure to rapidly assess an earthquake’s potential impact. Alas, ground-motion predictions vary significantly and infrastructure data are rarely available at sufficient resolutions to accurately assess the impact of earthquakes. Moreover, a minimum of three seismometers are needed to calibrate a quake and said seismic data take several minutes to generate. This explains why the EMSC uses human sensors to rapidly collect relevant data on earthquakes as these reduce the uncertainties that come with traditional rapid impact assess-ment methodologies. Indeed, the Center’s important work clearly demonstrates how the Internet coupled with social media are “creating new potential for rapid and massive public involvement by both active and passive means” vis-a-vis earthquake detection and impact assessments. Indeed, the EMSC can automatically detect new quakes within 80-90 seconds of their occurrence while simultaneously publishing tweets with preliminary information on said quakes, like this one:

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In reality, the first human sensors (increases in web traffic) can be detected within 15 seconds (!) of a quake. The EMSC’s system continues to auto-matically tweet relevant information (including documents, photos, videos, etc.), for the first 90 minutes after it first detects an earthquake and is also able to automatically create a customized and relevant hashtag for individual quakes.

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How do they do this? Well, the team draw on two real-time crowdsourcing methods that “indirectly collect information from eyewitnesses on earthquakes’ effects.” The first is TED, which stands for Twitter Earthquake Detection–a system developed by the US Geological Survey (USGS). TED filters tweets by key word, location and time to “rapidly detect sharing events through increases in the number of tweets” related to an earthquake. The second method, called “flashsourcing” was developed by the European-Mediterranean to analyze traffic patterns on its own website, “a popular rapid earthquake information website.” The site gets an average of 1.5 to 2 million visits a month. Flashsourcing allows the Center to detect surges in web traffic that often occur after earthquakes—a detection method named Internet Earthquake Detection (IED). These traffic surges (“flash crowds”) are caused by “eyewitnesses converging on its website to find out the cause of their shaking experience” and can be detected by analyzing the IP locations of website visitors.

It is worth emphasizing that both TED and IED work independently from traditional seismic monitoring systems. Instead, they are “based on real-time statistical analysis of Internet-based information generated by the reaction of the public to the shaking.” As EMSC rightly notes in a forthcoming peer-reviewed scientific study, “Detections of felt earthquakes are typically within 2 minutes for both methods, i.e., considerably faster than seismographic detections in poorly instrumented regions of the world.” TED and IED are highly complementary methods since they are based on two entirely “different types of Internet use that might occur after an earthquake.” TED depends on the popularity of Twitter while IED’s effectiveness depends on how well known the EMSC website is in the area affected by an earthquake. LastQuake automatically publishes real-time information on earthquakes by automatically merging real-time data feeds from both TED and IED as well as non-crowdsourcing feeds.


Lets looks into the methodology that powers IED. Flashsourcing can be used to detect felt earthquakes and provide “rapid information (within 5 minutes) on the local effects of earthquakes. More precisely, it can automatically map the area where shaking was felt by plotting the geographical locations of statistically significant increases in traffic […].” In addition, flashsourcing can also “discriminate localities affected by alarming shaking levels […], and in some cases it can detect and map areas affected by severe damage or network disruption through the concomitant loss of Internet sessions originating from the impacted region.” As such, this “negative space” (where there are no signals) is itself an important signal for damage assessment, as I’ve argued before.

remypicIn the future, EMSC’s flashsourcing system may also be able discriminate power cuts between indoor and outdoor Internet connections at the city level since the system’s analysis of web traffic session will soon be based on web sockets rather than webserver log files. This automatic detection of power failures “is the first step towards a new system capable of detecting Internet interruptions or localized infrastructure damage.” Of course, flashsourcing alone does not “provide a full description of earthquake impact, but within a few minutes, independently of any seismic data, and, at little cost, it can exclude a number of possible damage scenarios, identify localities where no significant damage has occurred and others where damage cannot be excluded.”

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EMSC is complementing their flashsourching methodology with a novel mobile app that quickly enables smartphone users to report about felt earthquakes. Instead of requiring any data entry and written surveys, users simply click on cartoonish-type pictures that best describe the level of intensity they felt when the earthquake (or aftershocks) struck. In addition, EMSC analyzes and manually validates geo-located photos and videos of earthquake effects uploaded to their website (not from social media). The Center’s new app will also make it easier for users to post more pictures more quickly.


What about typical criticisms (by now broken records) that social media is biased and unreliable (and thus useless)? What about the usual theatrics about the digital divide invalidating any kind of crowdsourcing effort given that these will be heavily biased and hardly representative of the overall population? Despite these already well known short-comings and despite the fact that our inchoate digital networks are still evolving into a new nervous system for our planet, the existing nervous system—however imperfect and immature—still adds value. TED and LastQuake demonstrate this empirically beyond any shadow of a doubt. What’s more, the EMSC have found that crowdsourced, user-generated information is highly reliable: “there are very few examples of intentional misuses, errors […].”

My team and I at QCRI are honored to be collaborating with EMSC on integra-ting our AIDR platform to support their good work. AIDR enables uses to automatically detect tweets of interest by using machine learning (artificial intelligence) which is far more effective searching for keywords. I recently spoke with Rémy Bossu, one masterminds behind the EMSC’s LastQuake project about his team’s plans for AIDR:

“For us AIDR could be a way to detect indirect effects of earthquakes, and notably triggered landslides and fires. Landslides can be the main cause of earthquake losses, like during the 2001 Salvador earthquake. But they are very difficult to anticipate, depending among other parameters on the recent rainfalls. One can prepare a susceptibility map but whether there are or nor landslides, where they have struck and their extend is something we cannot detect using geophysical methods. For us AIDR is a tool which could potentially make a difference on this issue of rapid detection of indirect earthquake effects for better situation awareness.”

In other words, as soon as the EMSC system detects an earthquake, the plan is for that detection to automatically launch an AIDR deployment to automatically identify tweets related to landslides. This integration is already completed and being piloted. In sum, EMSC is connecting an impressive ecosystem of smart, digital technologies powered by a variety of methodologies. This explains why their system is one of the most impressive & proven examples of next generation humanitarian technologies that I’ve come across in recent months.


Acknowledgements: Many thanks to Rémy Bossu for providing me with all the material and graphics I needed to write up this blog post.

See also:

  • Social Media: Pulse of the Planet? [link]
  • Taking Pulse of Boston Bombings [link]
  • The World at Night Through the Eyes of the Crowd [link]
  • The Geography of Twitter: Mapping the Global Heartbeat [link]

State of the Art in Digital Disease Detection

Larry Brilliant’s TED Talk back in 2006 played an important role in catalyzing my own personal interest in humanitarian technology. Larry spoke about the use of natural language processing and computational linguistics for the early detection and early response to epidemics. So it was with tremendous honor and deep gratitude that I delivered the first keynote presentation at Harvard University’s Digital Disease Detection (DDD) conference earlier this year.

The field of digital disease detection has remained way ahead of the curve since 2006 in terms of leveraging natural language processing, computational linguistics and now crowdsourcing for the purposes of early detection of critical events. I thus highly, highly recommend watching the videos of the DDD Ignite Talks and panel presentations, which are all available here. Topics include “Participatory Surveillance,” “Monitoring Rumors,” “Twitter and Disease Detection,” “Search Query Surveillance,” “Open Source Surveillance,” “Mobile Disease Detection,” etc. The presentation on BioCaster is also well worth watching. I blogged about BioCaster here over three years ago and the platform is as impressive as ever.

These public health experts are really operating at the cutting-edge and their insights are proving important to the broader humanitarian technology community. To be sure, the potential added value of cross-fertilization between fields is tremendous. Just take this example of a public health data mining platform (HealthMap) being used by Syrian activists to detect evidence of killings and human rights violations.

Another title for this post might have been “Here Come the Crowd-Sorcerers…” I’ll be following up with Crowd-Sorcerer sequels soon (to answer many readers who have been asking) but before  I do, I want to look at a prequel. In 2005, Charles Leadbeater gave what is without doubt one of my all time favorite TED Talks ever. The examples he shares—mountain bikes, telescopes and computer games—provide excellent insights into the opportunities and challenges that companies like Ushahidi face. This talk foretells what may very well be the future of crisis mapping.

If you don’t have 20 minutes to watch the talk, just continue reading since I tease out the most salient points in this post. Charles gave this talk in 2005, before Jeff Howe had even coined the term “crowdsourcing”;  before Brafman and Beckstrom’s book “Spider and the Starfish: The Unstoppable Power of Leaderless Organizations”; and way before Clay Shirky wrote his book “Here Comes Everybody: The Power of Organizing without Organizations.”

Charles starts by asking: who invented the mountain bike?  Not a company with a large R&D team. Nor a lone innovative genius in some garage. The mountain bike came from young users in northern California who were frustrated by  heavy traditional bikes and old racing bikes. So they hacked a few bikes and voila, the mountain bike was born. But it wasn’t until 10-15 years later that a small company thought to create a business out of these hacked bikes. Today, mountain bike sales account for some 65% of the bike market in the US alone.

And so, the mountain bike was created entirely by consumers, not by the mainstream bike market because they didn’t see the need, opportunity or have the incentive to create the mountain bike.

Charles argues that it is now possible to “organize without organizations: you don’t need an organization to organize, to achieve large and complex tasks like innovating new software programs” (hint hint). He notes that people (previously consumers now producers) are  increasingly becoming the source of big disruptive ideas. Some of these individuals are amateurs so “they do what they do for the love of it but they want to do it to very high standards.” They take their leisure very seriously, they refine their skills, they invest their own time, etc. This has huge organizational implications for many sectors.

Take astronomy for example. Some 30 years ago, only professional astronomers with huge and very expensive telescopes could see far into space. Today, individuals using “open source” telescopes and the Internet can do what only professional astronomers could do and help discover new stars, meteors at virtually no cost. “So there is a huge competitive argument about sustaining capacity for open source and consumer-driven innovation because it is one of the greatest competitive levers against monopoly,” says Charles.

As a former journalist, Charles recounts from a personal view the significant change that has happened in his profession. He describes the thrill of seeing others in the subway reading his article. At the same time though, he notes that readers only had two places where they could contribute: letters to the editor or the op-ed page. In the case of the former, editors would select the ones they liked, cut them in half and print them three days later. As for op-eds, if readers “knew the editor, been to school with them, slept with their wife, then they could write an article for the op-ed page.”

“Shock horror now, the readers want to be writers and publishers. That’s not their role, they’re supposed to read what we write! But they don’t want to be journalists. The journalists think that the bloggers want to be journalists. They don’t want to be journalists. They just want to have a voice, they want to have a dialogue, a conversation. They want to be part of that flow of information.

So there’s going to be tremendous struggle. But also there’s going to be tremendous movement, from the closed to the open. What you’ll see is two things that are critical, and these are two challenges for the open movement. The first is, can we really survive on volunteers? If this is so critical, do we not need this funded, organized, supported in much more structured ways? Can we really organize that just on volunteers?

And finally, what you will see is the intelligent, closed organizations moving increasingly in the open direction. So it’s not going to be a contest between two camps, but in-between them you’ll find all sorts of interesting places that people will occupy. New organization models coming about, mixing closed and open in tricky ways. […] And those organizational models it turns out are incredibly powerful and the people who can understand them will be very very successful.”

Charles ends his presentation with a final example, the biggest computer games company in China with 250,000,000 subscribers. The CEO of the company only employs 500 people to service these gamers. “How can this be?” asks Charles?

“Because basically he doesn’t service them, he gives them a platform, he gives them some rules, he gives them the tools and then he kind of orchestrates the conversation, he orchestrates the action. But actually a lot of the content is created by the users themselves. And this creates a kind of stickiness between the community and the company which is really, really powerful. […] So this is about companies built on communities that provide communities with tools, resources platforms with which they can share.”

Cymatic Insights for Crisis Mapping

I just came across Evan Grant’s fascinating TED 2009 talk on “Making Sound Visible through Cymatics.” Cymatics describes the process of visualizing sound. Sound waves create vibrations—patterns—that can be visualized on the surface of a plate covered with sand as depicted below.


In his talk, Evan demonstrates how different sound frequencies create distinctly different geometric sand patterns. As the sound frequencies increase, so does the complexity of the sand patterns themselves. He describes cymatics as a “looking glass into a hidden world” that can “unveil the substance of things not seen.”

For example, a lexicon of dolphin language is actually being created using cymatics by visualizing the sonar beams that dolphins emit. Cymatics can also be used to create natural art forms. The picture below is a visualization of Beethoven’s 9th Symphony created using a cymatic device. Cymatics can also recreate archetypal forms of nature such as snowflakes or starfish.


It’s not entirely clear what all this means. As Evan notes, cymatics is still a very young field and not many people are working in this line of research. Cymatics shows that sound has form and can effect form in matter. So Evan asks us to think about the universe forming, “about the immense sound of the universe forming, and to ponder on that … perhaps cymatics had an influence on the formation of the universe itself.” Watch Evan’s 5-minute TED Talk below.

In closing, Evan encourage us to apply our passions, knowledge and skills to areas like cymatics. I find this field very interesting because of the analogies with crisis mapping. As often mentioned on iRevolution, crisis mapping is about rendering otherwise hidden patterns visible to improve situational awareness and decision-making.

One can think of conflict processes as sound waves or vibrations and the “plates” as crisis mapping platforms like Ushahidi. We need to “vibrate” conflict data at different frequencies and to develop visual analytics—different templates for data visualization—in order to visualize patterns in a compelling fashion. One might call this the “String Theory” of Crisis Mapping.


A colleague and I tried analyzing conflict data as music back in 2006 when I was at the Santa Fe Institute (SFI). I had been inspired by the work of an Italian geophysicist who had taken seismic data (tremblings of the earth) and analyzed the data as music in order to look for “melodic” patterns. We used conflict event-data from Afghanistan but the result was not particularly music to my ears—but then again, neither is war.

Patrick Philippe Meier