Data Story: Phil Harris of Geofeedia

Data Stories is Gnip’s ongoing series telling the stories of the people and companies that are doing groundbreaking work in social data. This week we’re interviewing Phil Harris, CEO of Geofeedia, a company that allows you to search and monitor social media by location. Geofeedia is a recent Gnip customer, and I love what they’re doing. The inherent value of Geofeedia was made clear to me when we received a media request looking for all social media that was geotagged close to the finish line of the Boston Marathon. Content + location creates powerful stories and Geofeedia is making it easier to find the right ones. 

1. What social data sources do you wish had geotagged data?
Our business is built on the fundamental premise of open source social data aggregation.  Or, I should say, every source. That said, there are currently major social data sources that provide public location data based on location identifier versus geotag. We will accommodate location id to integrate these data sources, but I strongly believe that over time, the benefits of more precise geo-location tagging on social media content will encourage these services to move towards geotagging. When they do, we’re exceptionally well positioned to translate that evolution into benefit for our clients.

2. If you’re a user, what do you think is the advantage of sharing your geodata?
We’ve barely scratched the surface of how geodata will deliver value to consumers. I believe the rapidly growing penetration of smartphones and adoption of geo-centric applications such as navigation will create a rich ecosystem of geo-data driven benefits. I am speaking with major consumer brands who believe that they will be able to create and maintain consumer relationships via location based social media in ways that will deliver significant value back to the individual user.

3. What can you find with Geofeedia that you can’t find on other platforms?
I know from analyzing our data with active customers that a significant amount of user generated content is missed by traditional keyword or hashtag centric monitoring tools. We complement these platforms to ensure relevant location based content is delivered to our customers in real-time.

4. Only a small portion of social media is geotagged, do you think this will change in the future?
I do. We’re seeing an increase every quarter, but as brands start rolling out compelling reasons for consumers to geotag their content, I believe geotagged social media will become the default.

5. How do you think Geofeedia will be used for good?
The leading businesses I’m speaking with consider Geofeedia as a tool to improve their overall customer experience. Understanding an individual social media conversation at a moment in time at a given location drastically improves the ways brands can serve their customers. Also, numerous public safety agencies are using Geofeedia to improve their ability to respond to natural disasters and other scenarios where real-time, location based social media awareness delivers great value.

6. How will real-time geo monitoring affect a brand’s ability to connect with their customers?
Like I said, the major brands with whom I’m speaking are evaluating how to improve their overall customer experience across all touch points – sales, customer service, loyalty – through real-time location based monitoring, analysis and engagement. I do believe that real-time, location based social media engagement will drastically improve a brand’s ability to have a meaningful, new type of relationship with their customers and become a de facto element of their communication mix.

SGI Launches Global Twitter Heartbeat, Powered by Gnip

File this under cool news.

SGI’s Big Brain Computer has created a Global Twitter Heartbeat, allowing the supercomputer to analyze the Twitter stream for sentiment and geolocation to create a Twitter heartbeat telling us how the world is feeling based on emotions communicated via Twitter. Not only is this a cool undertaking by the folks at SGI, but we’re proud to announce that it is powered by Gnip’s decahose Twitter stream.

To make this happen, SGI partnered with Kalev H. Leetaru of the University of Illinois and Dr. Shaowen Wang of the CyberInfrastructure and Geospatial Information (CIGI) Laboratory at the University of Illinois at Urbana-Champaign.

This isn’t just some simple stream.  The SGI supercomputer analyzes every Tweet to assign location (not just GPS-tagged tweets, but processing the text of the Tweet itself) and tone values, then visualizing the conversation in a heat map that puts Tweet location, Tweet density and tone into a unified geospatial perspective. The entire process from ingestion to data analysis to producing the heat map runs at a speed that allows visualization of a map frame per second.

To see it live, check out SGI’s Facebook page.

You can also see videos of the Twitter Heartbeat for the Presidential Elections and Hurricane Sandy.

Geosocial Data: Patterns of Everyday Life

My love for checking in and thus, geolocation, began after SXSW of 2009 while I racked up points and worked hard to become the leader of Boulder, ultimately losing to Eric Wu. Since then, my views on geolocation have evolved, and I have become especially enamored with the way geosocial data allows us to leave trails of the lives we and others are living. At its best, geolocation + social connects us to friends we are close to by letting us know who is near and collectively, social data can identify common interests and patterns of behavior we couldn’t see in the past.

Since 2008, Foursquare has evolved becoming a service with 50 million users and two billion check-ins and a facelift launching tomorrow, Twitter has opened up a geolocation API, Facebook Places launched and continues to evolve, Highlight launched and Gowalla was acquired by Facebook. All of these advancements have happened in a couple of short years. Geotagging allows these new crop of social networks to add your geographic location via metadata and now you can add location to tweets, photos, videos, etc.

Patterns of My Life

Every time I check in and share my location, I start leaving a trail of my day-to-day life. This trail, at its most basic, serves as a virtual diary of where I went and with whom. Timehop emails me each day to tell me what I did a year ago, while services such as Rewind.Me allow me to search my patterns and how I stack up against others.

Tripmeter lets me see my virtual trail and the how I travel throughout the day based on Foursquare and Facebook checkins, similar to what Route does. Where Do You Go even lets you heatmap where you most often visit (hint: I hate South Boulder).

Foursquare Heat Map

Checkins Are a Moving Census

But collectively, the patterns woven by geosocial data are incredibly telling and act as a living census. Intriguingly, researchers from Carnegie Mellon have created what they call “Livehoods” which are neighborhoods defined on not only on geographic proximity, but also based on social geotagged data. Essentially, the similarities are based on where people check in. While the data only includes those using geolocation, it shows that people who check into a local restaurant and a similar bar create cultural neighborhoods. This data is more than just an intellectual curiosity. Companies can analyze customer patterns to focus marketing efforts, identify companies to partner with and determine new brick-and-mortar locations.

Example of Livehood Data

I particularly love the idea of an app using Foursquare data called “When Should I Visit?” that tells you when is a good time to visit London tourist attractions based on Foursquare checkins. Other use cases for this type of social data could tell people when to visit high-traffic destinations such as the DMV. I love knowing when not to be somewhere as much as knowing what locations and parties are trending.

HealthMaps uses geosocial data and news reports to help track epidemics as they pop up. The mapping system was created by a team of researchers, epidemiologists and software developers from Children’s Hospital Boulder to monitor real-time epidemics as they break out. Rumi Chunara, worked on this project and also helped use geosocial data to track how cholera spread in Haiti. (Rumi will be speaking at Gnip’s social data conference, Big Boulder, about social data in public service.) Geosocial data has unlimited uses in the cases of health epidemics and natural disasters.

Companies are starting to create passive geolocation checkins such as EpicMix from Vail Resorts, which enables skiers to automatically check in using the RFID tags on their ski lifts. The system tells users how much they skied, where they skied, their vertical ascents and where their friends are on the mountain. During the last Coachella, 30,000 concertgoers used RFID bands from Intellix to checkin and update their Facebook status on various portals spaced throughout concert grounds. Near field communication is another way social data provides amazing patterns.

Geosocial data allows us insight into the patterns of everyday people, and the applications for this are endless.