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).
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.
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.