Tag Archives: Map

From Russia with Love: A Match.com for Disaster Response

I’ve been advocating for the development of a “Match.com” for disaster response since early 2010. Such a platform would serve to quickly match hyperlocal needs with relevant resources available at the local and national level, thus facilitating and accelerating self-organization following major disasters. Why advocate for a platform modeled after an online dating website? Because self-organized mutual-aid is an important driver of community resilience.

Russian Bell

Obviously, self-organization is not dependent on digital technology. The word Rynda, for example, is an old Russian word for a “village bell” which was used by local communities to self-organize during emergencies. Interestingly, Rynda became a popular meme on social media during fires in 2010. As my colleague Gregory Asmolov notes in his brilliant new study, a Russian blogger at the time of the fires “posted an emotional open letter to Prime Minister Putin, describing the lack of action by local authorities and emergency services.” In effect, the blogger demanded a “return to an old tradition of self-organization in local communities,” subsequently exclaiming “bring back the Rynda!” This demand grew into a popular meme symbolizing the catastrophic failure of the formal system’s response to the massive fires.

At the time, my colleagues Gregory, Alexey Sidorenko & Glafira Parinos launched the Help Map above in an effort to facilitate self-organization and mutual aid. But as Gregory notes in his new study, “The more people were willing to help, the more difficult it was to coordinate the assistance and to match resources with needs.” Moreover, the Help Map continued to receive reports on needs and offers-of-help after the fires had subsided. To be sure, reports of flooding soon found their way to the map, for example. Gregory, Alexey, Glarifa and team thus launched “Virtual Rynda: The Help Atlas” to facilitate self-help in response to a variety of situations beyond sudden-onset crises.

“We believed that in order to develop the capacity and resilience to respond to crisis situations we would have to develop the potential for mutual aid in everyday life. This would rely on an idea that emergency and everyday-life situations were interrelated. While people’s motivation to help one another is lower during non-emergency situations, if you facilitate mutual aid in everyday life and allow people to acquire skills in using Internet-based technologies to help one another or in asking for assistance, this will help to create an improved capacity to fulfill the potential of mutual aid the next time a disaster happens. [...] The idea was that ICTs could expand the range within which the tolling of the emergency bell could be heard. Everyone could ‘ring’ the ‘Virtual Rynda’ when they needed help, and communication networks would magnify the sound until it reached those who could come and help.”

In order to accelerate and scale the matching of needs & resources, Gregory and team (pictured below) sought to develop a matchmaking algorithm. Rynda would ask users to specify what the need was, where (geographically) the need was located and when (time-wise) the need was requested. “On the basis of this data, computer-based algorithms & human moderators could match offers with requests and optimize the process of resource allocation.” Rynda also included personal profiles, enabling volunteers “to develop an online reputation and increase trust between those needing help and those who could offer assistance. Every volunteer profile included not only personal information, but also a history of the individual’s previous activities within the platform.” To this end, in addition to “Help Requests” & “Help Offers,” Rynda also included an entry for “Help Provided” to close the feedback loop.

Asmolov1

As Gregory acknowledges, the results were mixed but certainly interesting and insightful. “Most of the messages [posted to the Rynda platform dealt] with requests for various types of social help, like clothing and medical equipment for children, homes for orphans, people with limited capabilities, or families in need. [...]. Some requests from environmental NGOs were related to the mobilization of volunteers to fight against deforestation or to fight wildfires. [...]. In another case, a volunteer who responded to a request on the platform helped to transport resources to a family with many children living far from a big city. [...]. Many requests concern[ed] children or disabled people. In one case, Rynda found a volunteer who helped a young woman leave her flat for walks, something she could not do alone. In some cases, the platform helped to provide medicine.” In any event, an analysis of the needs posted to Rynda suggests that “the most needed resource is not the thing itself, but the capacity to take it to the person who needs it. Transportation becomes a crucial resource, especially in a country as big as Russia.”

Alas, “Despite the efforts to create a tool that would automatically match a request with a potential help provider, the capacity of the algorithm to optimize the allocation of resources was very limited.” To this end, like the Help Map initiative, digital volunteers who served as social moderators remained pivotal to the Virtual Ryndal platform. As Alexey notes, “We’ve never even got to the point of the discussion of more complex models of matching.” Perhaps Rynda should have included more structured categories to enable more automated-matching since the volunteer match-makers are simply not scalable. “Despite the intention that the ‘matchmaking’ algorithm would support the efficient allocation of resources between those in need and those who could help, the success of the ‘matchmaking’ depended on the work of the moderators, whose resources were limited. As a result, a gap emerged between the broad issues that the project could address and the limited resources of volunteers.”

To this end, Gregory readily admits that “the initial definition of the project as a general mutual aid platform may have been too broad and unspecific.” I agree with this diagnostic. Take the online dating platform Match.com for example. Match.com’s sole focus is online dating; Airbnb’s sole purpose is to match those looking for a place to stay with those offering their places; Uber’s sole purpose is matching those who need to get somewhere with a local car service. To this end, matching platform for mutual-aid may indeed been too broad—at least to begin with. Amazon began with books, but later diversified.

In any case, as Gregory rightly notes, “The relatively limited success of Rynda didn’t mean the failure of the idea of mutual aid. What [...] Rynda demonstrates is the variety of challenges encountered along the way of the project’s implementation.” To be sure, “Every society or community has an inherent potential mutual aid structure that can be strengthened and empowered. This is more visible in emergency situations; however, major mutual aid capacity building is needed in everyday, non-emergency situations.” Thanks to Gregory and team, future digital matchmakers can draw on the above insights and Rynda’s open source code when designing their own mutual-aid and self-help platforms.

For me, one of the key take-aways is the need for a scalable matching platform. Match.com would not be possible if the matching were done primarily manually. Nor would Match.com work as well if the company sought to match interests beyond the romantic domain. So a future Match.com for mutual-aid would need to include automated matching and begin with a very specific matching domain. 

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See also:

  • Using Waze, Uber, AirBnB, SeeClickFix for Disaster Response [link]
  • MatchApp: Next Generation Disaster Response App? [link]
  • A Marketplace for Crowdsourcing Crisis Response [link]

Live: Crowdsourced Crisis Map of UAV/Aerial Videos for Disaster Response

The first version of the Humanitarian UAV Network’s Crisis Map of UAV/aerial videos is now live on the Network’s website. The crowdsourced map features dozens of aerial videos of recent disasters. Like social media, this new medium—user-generated (aerial) content—can be used by humanitarian organizations to complement their damage assessments and thus improve situational awareness.

UAViators Map

The purpose of this Humanitarian UAV Network (UAViators) map is not only to provide humanitarian organizations and disaster-affected communities with an online repository of aerial information on disaster damage to augment their situational awareness; this crisis map also serves to raise awareness on how to safely & responsibly use small UAVs for rapid damage assessments. This explains why users who upload new content to the map must confirm that they have read the UAViator‘s Code of Conduct. They also have to confirm that the videos conform to the Network’s mission and that they do not violate privacy or copyrights. In sum, the map seeks to crowdsource both aerial footage and critical thinking for the responsible use of UAVs in humanitarian settings.

UAViators Map 4

As noted above, this is the first version of the map, which means several other features are currently in the works. These new features will be rolled out incrementally over the next weeks and months. In the meantime, feel free to suggest any features you’d like to see in the comments section below. Thank you.

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  • Humanitarian UAV Network: Strategy for 2014-2015 [link]
  • Humanitarians in the Sky: Using UAVs for Disaster Response [link]
  • Humanitarian UAV Missions During Balkan Floods [link]
  • Using UAVs for Disaster Risk Reduction in Haiti [link]
  • Using MicroMappers to Make Sense of UAV/Aerial Imagery During Disasters [link]

Taking the Pulse of the Boston Marathon Bombings on Twitter

Social media networks are evolving a new nervous system for our planet. These real-time networks provide immediate feedback loops when media-rich societies experience a shock. My colleague Todd Mostak recently shared the tweet map below with me which depicts tweets referring to “marathon” (in red) shortly after the bombs went off during Boston’s marathon. The green dots represent all the other tweets posted at the time. Click on the map to enlarge. (It is always difficult to write about data visualizations of violent events because they don’t capture the human suffering, thus seemingly minimizing the tragic events).

Credit: Todd Mostak

Visualizing a social system at this scale gives a sense that we’re looking at a living, breathing organism, one that has just been wounded. This impression is even more stark in the dynamic visualization captured in the video below.

This an excerpt of Todd’s longer video, available here. Note that this data visualization uses less than 3% of all posted tweets because 97%+ of tweets are not geo-tagged. So we’re not even seeing the full nervous system in action. For more analysis of tweets during the marathon, see this blog post entitled “Boston Marathon Explosions: Analyzing First 1,000 Seconds on Twitter.”

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Stunning Wind Map of Hurricane Sandy

Surface wind data from the National Digital Forecast Database is updated on an hourly basis. More galleries of stunning wind maps here.

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Map: 24 hours of Tweets in New York

The map below depicts geo-tagged tweets posted between May 4-5, 2013 in the New York City area. Over 36,000 tweets are posted on the map (click to enlarge). Since less than 3% of all tweets are geo-tagged, the map is missing the vast majority of tweets posted in this area during those 24 hours.

New York Tweets 24 hours

Contrast the above with the 1-month worth of tweets (April-May 2013) depicted in the map below. Again, the visualization misses the vast majority of tweets since these are not geo-tagged and thus not mappable.

New York 1 Month Tweets

These visuals are screenshots of Harvard’s Tweetmap platform, which is publicly available here. My colleague Todd Mostak is one of the main drivers behind Tweetmap, so worth sending him a quick thank you tweet! Todd is working on some exciting extensions and refinements, so stay tuned as I’ll be sure to blog about them when they go live.

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Egypt Twitter Map of iPhone, Android and Blackberry Users

Colleagues at GNIP and MapBox recently published this high-resolution map of iPhone, Android and Blackberry users in the US (click to enlarge). “More than 280 million Tweets posted from mobile phones reveal geographic usage patterns in unprecedented detail.” These patterns are often insightful. Some argue that “cell phone brands say something about socio-economics – it takes a lot of money to buy a new iPhone 5,” for example (1). So a map of iPhone users based on where these users tweet reveals where relatively wealthy people live.

Phones USA

As announced in this blog post, colleagues and I at QCRI, Harvard, MIT and UNDP are working on an experimental R&D project to determine whether Big Data can inform poverty reduction strategies in Egypt. More specifically, we are looking to test whether tweets provide a “good enough” signal of changes in unemployment and poverty levels. To do this, we need ground truth data. So my MIT colleague Todd Mostak put together the following maps of cell phone brand ownerships in Egypt using ~3.5 million geolocated tweets from October 2012 to June 2013. Red dots represent the location of tweets posted by Android users; Green dots – iPhone; Purple – Blackberry. Click figures below to enlarge.

Egypt Mobile Phones

Below is a heatmap of the % of Android users. As Todd pointed out in our email exchanges, “Note the lower intensity around Cairo.”

Egypt Android

This heatmap depicts the density of tweeting iPhone users:

Egypt iPhone users

Lastly, the heatmap below depicts geo-tagged tweets posted by Blackberry users.

BB Egypt

As Todd notes, “We can obviously break these down by shyiyakha and regress against census data to get a better idea of how usage of these different devices correlate with proxy for income, but at least from these maps it seems clear that iPhone and Blackberry are used more in urban, higher-income areas.” Since this data is time-stamped, we may be able to show whether/how these patterns changed during last week’s widespread protests and political upheaval.

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Global Heat Map of Protests in 2013

My colleague Kalev Leetaru recently launched GDELT (Global Data on Events, Location and Tone), which includes over 250 million events ranging from riots and protests to diplomatic exchanges and peace appeals. The data is based on dozens of news sources such as AFP, AP, BBC, UPI, Washington Post, New York Times and all national & international news from Google News. Given the recent wave of protests in Cairo and Istanbul, a collaborator of Kalev’s, John Beieler, just produced this digital dynamic map of protests events thus far in 2013. John left out the US because “it was a shining beacon of protest activity that distracted from the other parts of the map.” Click on the maps below to enlarge & zoom in.

World

Heat Map Protests

Egypt

Egypt Protests

India

GDELT India

As Kalev notes, “Right now its just a [temporally] static map, it was done as a pilot just to see what it would look like in the first place, but the ultimate goal would be to do realtime updates, we just need to find someone with the interest and time to do this.” Any readers want to take up the challenge? Having a live map of protests (including US data) with “slow motion replay” functionality could be quite insightful given current upheavals. In the meantime, other stunning visualizations of the GDELT data are available here.

And to think that the quantitative analysis section of my doctoral dissertation was an econometric analysis of protest data coded at the country-year level based on just one news source, Reuters. I wonder if/how my findings would change with GDELT’s data. Anyone looking for a dissertation topic?

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