Tag Archives: Geofeedia

GeoFeedia: Ready for Digital Disaster Response

GeoFeedia was not originally designed to support humanitarian operations. But last year’s blog post on the potential of GeoFeedia for crisis mapping caught the interest of CEO Phil Harris. So he kindly granted the Standby Volunteer Task Force (SBTF) free access to the platform. In return, we provided his team with feedback on what features (listed here) would make GeoFeedia more useful for digital disaster response. This was back in summer 2012. I recently learned that they’ve been quite busy since. Indeed, I had the distinct pleasure of sharing the stage with Phil and his team at this superb conference on social media for emergency management. After listening to their talk, I realized it was high time to publish an update on GeoFeedia, especially since we had used the tool just two months earlier in response to Typhoon Pablo, one of the worst disasters to hit the Philippines in the past 100 years.

The 1-minute video is well worth watching if you’re new to GeoFeedia. The plat-form enables hyper local searches for information by location across multiple social media channels such as Twitter, Youtube, Flickr, Picasa & now Instagram. One of my favorite GeoFeedia features is the awesome geofeed (digital fence), which you can learn more about here. So what’s new besides Instagram? Well, the first suggestion I made last year was to provide users with the option of searching by both location and topic, rather than just location alone. And presto, this now possible, which means that digital humanitarians today can zoom into a disaster-affected area and filter by social media type, date and hashtag. This makes the geofeed feature even more compelling for crisis response, especially since geofeeds can also be saved and shared.

The vast majority of social media monitoring tools out there first filter by key-word and hashtag. Only later do they add location. As Phil points out, this mean they easily miss 70% of hyper local social media reports. Most users and org-anizations, who pay hefty licensing fees to uses these platforms, are typically unaware of this. The fact that GeoFeedia first filters by location is not an accident. This recent study (PDF) of the 2012 London Olympics showed that social media users posted close to 170,000 geo-tagged to Twitter, Instagram, Flickr, Picasa and YouTube during the games. But only 31% of these geo-tagged posts contained any Olympic-specific keywords and/or hashtags! So they decided to analyze another large event and again found the number of results drop by about 70% when not first filtering by location. Phil argues that people in a crisis situation obviously don’t wait for keywords or hashtags to form; so he expects this drop to happen for disasters as well. “Traditional keyword and hashtag search thus be complemented with a geo-graphical search in order to provide a full picture of social media content that is contextually relevant to an event.”

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One of my other main recommendations to Phil & team last year had to do with analytics. There is a strong need for an “Analytics function that produces summary statistics and trends analysis for a geofeed of interest. This is where Geofeedia could better capture temporal dynamics by including charts, graphs and simple time-series analysis to depict how events have been unfolding over the past hour vs 12 hours, 24 hours, etc.” Well sure enough, one of GeoFeedia’s major new features is a GeoAnalytics Dashboard; an interface that enables users to discover temporal trends and patterns in social media—and to do so by geofeed. This means a user can now draw a geofeed around a specific area of interest in a given disaster zone and search for pictures that capture major infrastructure damage on a specified date that contain tags or descriptions with the words “#earthquake”, “damage,” “buildings,” etc. As Phil rightly points out, this provides a “huge time advantage during a crisis to give a yet another filtered layer of intelligence; in effect, social media that is highly relevant and actionable ‘bubbling-up to the top’ of the pile.” 

Analytics Screen Shot - CES Data

I truly am a huge fan of the GeoFeedia platform. Plus, Phil & team have been very responsive to our interests in using their tool for disaster response. So I’m ex-cited to see which features they build out next. They’ve already got a “data portability” functionality that enables data export. Users can also publish content from GeoFeedia directly to their own social networks. Moreover, the filtered content produced by geofeeds can also be shared with individual who do not have a GeoFeedia account. In any event, I hope the team will take into account two items from my earlier wish list—namely Sentiment Analysis and GeoAlerts.

A Sentiment Analysis feature would capture the general mood and sentiment  expressed hyper-locally within a defined geofeed in real-time. The automated Geo-Alerts feature would make the geofeed king. A GeoAlerts functionality would enable users to trigger specific actions based on different kinds of social media traffic within a given geofeed of interest. For example, I’d like to be notified if the number of pictures posted within my geofeed that are tagged with the words “#earthquake” and “damage,” increases by more than 20% in any given hour. Similarly, one could set a geofeed’s GeoAlert for a 10% increase in the number of tweets with the words “cholera” and “diarrhea” (these need not be in English, by the way) in any given 10-minute period. Users would then receive GeoAlerts via automated emails, Tweets and/or SMS’s. This feature would in effect make the GeoFeedia more of a mobile and “hands free” platform, like Waze for example.

My first blog post on GeoFeedia was entitled “GeoFeedia: Next Generation Crisis Mapping Technology?” The answer today is a definite “Yes!” While the platform was not originally designed with disaster response in mind, the team has since been adding important features that make the tool increasingly useful for humanitarian applications. And GeoFeedia has plans for more exciting develop-ments in 2013. Their commitment to innovation and strong continued interest in supporting digital disaster response is why I’m hoping to work more closely with them in the years to come. For example, our AIDR (Artificial Intelligence for Disaster Response) platform would really add a strong Machine Learning com-ponent to GeoFeedia’s search function, in effect enabling the tool to go beyond simple keyword search.

Bio

Big Data Philanthropy for Humanitarian Response

My colleague Robert Kirkpatrick from Global Pulse has been actively promoting the concept of “data philanthropy” within the context of development. Data philanthropy involves companies sharing proprietary datasets for social good. I believe we urgently need big (social) data philanthropy for humanitarian response as well. Disaster-affected communities are increasingly the source of big data, which they generate and share via social media platforms like twitter. Processing this data manually, however, is very time consuming and resource intensive. Indeed, large numbers of digital humanitarian volunteers are often needed to monitor and process user-generated content from disaster-affected communities in near real-time.

Meanwhile, companies like Crimson Hexagon, Geofeedia, NetBase, Netvibes, RecordedFuture and Social Flow are defining the cutting edge of automated methods for media monitoring and analysis. So why not set up a Big Data Philanthropy group for humanitarian response in partnership with the Digital Humanitarian Network? Call it Corporate Social Responsibility (CRS) for digital humanitarian response. These companies would benefit from the publicity of supporting such positive and highly visible efforts. They would also receive expert feedback on their tools.

This “Emergency Access Initiative” could be modeled along the lines of the International Charter whereby certain criteria vis-a-vis the disaster would need to be met before an activation request could be made to the Big Data Philanthropy group for humanitarian response. These companies would then provide a dedicated account to the Digital Humanitarian Network (DHNet). These accounts would be available for 72 hours only and also be monitored by said companies to ensure they aren’t being abused. We would simply need to  have relevant members of the DHNet trained on these platforms and draft the appropriate protocols, data privacy measures and MoUs.

I’ve had preliminary conversations with humanitarian colleagues from the United Nations and DHnet who confirm that “this type of collaboration would be see very positively from the coordination area within the traditional humanitarian sector.” On the business development end, this setup would enable companies to get their foot in the door of the humanitarian sector—a multi-billion dollar industry. Members of the DHNet are early adopters of humanitarian technology and are ideally placed to demonstrate the added value of these platforms since they regularly partner with large humanitarian organizations. Indeed, DHNet operates as a partnership model. This would enable humanitarian professionals to learn about new Big Data tools, see them in action and, possibly, purchase full licenses for their organizations. In sum, data philanthropy is good for business.

I have colleagues at most of the companies listed above and thus plan to actively pursue this idea further. In the meantime, I’d be very grateful for any feedback and suggestions, particularly on the suggested protocols and MoUs. So I’ve set up this open and editable Google Doc for feedback.

Big thanks to the team at the Disaster Information Management Research Center (DIMRC) for planting the seeds of this idea during our recent meeting. Check out their very neat Emergency Access Initiative.

Geofeedia: Next Generation Crisis Mapping Technology?

My colleague Jeannine Lemaire from the Core Team of the Standby Volunteer Task Force (SBTF) recently pointed me to Geofeedia, which may very well be the next generation in crisis mapping technology. So I spent over an hour talking with GeoFeedia’s CEO, Phil Harris, to learn more about the platform and discuss potential applications for humanitarian response. The short version: I’m impressed; not just with the technology itself and potential, but also by Phil’s deep intuition and genuine interest in building a platform that enables others to scale positive social impact.

Situational awareness is absolutely key to emergency response, hence the rise of crisis mapping. The challenge? Processing and geo-referencing Big Data from social media sources to produce live maps has largely been a manual (and arduous) task for many in the humanitarian space. In fact, a number of humanitarian colleagues I’ve spoken to recently have complained that the manual labor required to create (and maintain) live maps is precisely why they aren’t able to launch their own crisis maps. I know this is also true of several international media organizations.

There have been several attempts at creating automated live maps. Take Havaria and Global Incidents Map, for example. But neither of these provide the customi-zability necessary for users to apply the platforms in meaningful ways. Enter Geofeedia. Lets take the recent earthquake and 800 aftershocks in Emilia, Italy. Simply type in the place name (or an exact address) and hit enter. Geofeedia automatically parses Twitter, YouTube, Flickr, Picasa and Instagram for the latest updates in that area and populates the map with this content. The algorithm pulls in data that is already geo-tagged and designated as public.

The geo-tagging happens on the smartphone, laptop/desktop when an image or Tweet is generated. The platform then allows you to pivot between the map and to browse through a collage of the automatically harvested content. Note that each entry includes a time stamp. Of course, since the search function is purely geo-based, the result will not be restricted to earthquake-related updates, hence the picture of friends at a picnic.

But lets click on the picture of the collapsed roof directly to the left. This opens up a new page with the following: the original picture and a map displaying where this picture was taken.

In between these, you’ll note the source of the picture, the time it was uploaded and the author. Directly below this you’ll find the option to query the map further by geographic distance. Lets click on the 300 meters option. The result is the updated collage below.

We know see a lot more content relevant to the earthquake than we did after the initial search. Geofeedia only parses for recently published information, which adds temporal relevance to the geographic search. The result of combing these two dimensions is a more filtered result. Incidentally, Geofeedia allows you to save and very easily share these searches and results. Now lets click on the first picture on the top left.

Geofeedia allows you to create collections (top right-hand corner).  I’ve called mine “Earthquake Damage” so I can collect all the relevant Tweets, pictures and video footage of the disaster. The platform gives me the option of inviting specific colleagues to view and help curate this new collection by adding other relevant content such as tweets and video footage. Together with Geofeedia’s multi-media approach, these features facilitate the clustering and triangulation of multi-media data in a very easy way.

Now lets pivot from these search results in collage form to the search results in map view. This display can also be saved and shared with others.

One of the clear strengths of Geofeedia is the simplicity of the user-interface. Key features and functions are esthetically designed. For example, if we wish to view the YouTube footage that is closest to the circle’s center, simply click on the icon and the video can be watched in the pop-up on the same page.

Now notice the menu just to the right of the YouTube video. Geofeedia allows you to create geo-fences on the fly. For example, we can click on “Search by Polygon” and draw a “digital fence” of that shape directly onto the map with just a few clicks of the mouse. Say we’re interested in the residential area just north of Via Statale. Simply trace the area, double-click to finish and then press on the magnifying glass icon to search for the latest social media updates and Geofeedia will return all content with relevant geo-tags.

The platform allows us to filter these results further the “Settings” menu as displayed below. On the technical side, the tool’s API supports ATOM/RSS, JSON and GeoRSS formats.

Geofeedia has a lot of potential vis-a-vis humanitarian applications, which is why the Standby Volunteer Task Force (SBTF) is partnering with the group to explore this potential further. A forthcoming blog post on the SBTF blog will outline this partnership in more detail.

In the meantime, below are a few thoughts and suggestions for Phil and team on how they can make Geofeedia even more relevant and compelling for humanitarian applications. A quick qualifier is in order beforehand, however. I often have a tendency to ask for the moon when discovering a new platform I’m excited about. The suggestions that follow are thus not criticism at all but rather the result of my imagination gone wild. So big congrats to Phil and team for having built what is already a very, very neat platform!

  • Topical search feature that enables users to search by location and a specific theme or topic.
  • Delete function that allows users to delete content that is not relevant to them either from the Map or Collage interface. In the future, perhaps some “basic” machine learning algorithms could be added to learn what types of content the user does not want displayed or prioritized.
  • Add function that gives users the option of adding relevant multi-media content, say perhaps from a blog post, a Wikipedia entry, news article or (Geo)RSS feed. I would be particularly interested in seeing a Storyful feed integrated into Geofeedia, for example. The ability to add KML files could also be interesting, e.g., a KML of an earthquake’s epicenter and estimated impact.
  • Commenting function that enables users to comment on individual data points (Tweets, pictures, etc) and a “discussion forum” feature that enables users to engage in text-based conversation vis-a-vis a specific data point.
  • Storify feature that gives users the ability to turn their curated content into a storify-like story board with narrative. A Storify plugin perhaps.
  • Ushahidi feature that enables users to export an item (Tweet, picture, etc) directly to an Ushahidi platform with just one click. This feature should also allow for the automatic publishing of said item on an Ushahidi map.
  • Alerts function that allows one to turn a geo-fence into an automated alert feature. For example, once I’ve created my geo-fence, having an option that allows me (and others) to subscribe to this geo-fence for future updates could be particularly interesting. These alerts would be sent out as emails (and maybe SMS) with a link to the new picture or Tweet that has been geo-tagged within the geographical area of the geo-fence. Perhaps each geo-fence could tweet updates directly to anyone subscribed to that Geofeedia deployment.
  • Trends alert feature that gives users the option of subscribing to specific trends of interest. For example, I’d like to be notified if the number of data points in my geo-fence increases by more than 25% within a 24-hour time period. Or more specifically whether the number of pictures has suddenly increased. These meta-level trends can provide important insights vis-a-vis early detection & response.
  • Analytics function that produces summary statistics and trends analysis for a geo-fence of interest. This is where Geofeedia could better capture temporal dynamics by including charts, graphs and simple time-series analysis to depict how events have been unfolding over the past hour vs 12 hours, 24 hours, etc.
  • Sentiment analysis feature that enables users to have an at-a-glance understanding of the sentiments and moods being expressed in the harvested social media content.
  • Augmented Reality feature … just kidding (sort-of).

Naturally, most or all of the above may not be in line with Geofeedia’s vision, purpose or business model. But I very much look forward to collaborating with Phil & team vis-a-vis our SBTF partnership. A big thanks to Jeannine once again for pointing me to Geofeedia, and equally big thanks to my SBTF colleague Timo Luege for his blog post on the platform. I’m thrilled to see more colleagues actively blog about the application of new technologies for disaster response.

On this note, anyone familiar with this new Iremos platform (above picture) from France? They recently contacted me to offer a demo.