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Where You Go Is Who You Are

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Clay Smith
Clay Smith

From the Where 2.0 (2011) Conference, a discussion of how the places you go influences what kind of person you are.

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Where you go is who you are April 21, 2011 Clay Smith @smithclay
Youre an average of the 5 people you spend the most time with --Jim Rohn, motivational speaker guy Where 2.0, Where You Go Is Who You Are April 21, 2011
What are the 5 places you spend the most time in? Where 2.0, Where You Go Is Who You Are April 21, 2011
5 places you spend the most time in. 5 people you spend the most time with. Where 2.0, Where You Go Is Who You Are April 21, 2011
5 places you spend the most time in. 5 people you spend the most time with. Your check-ins Where 2.0, Where You Go Is Who You Are April 21, 2011
My foursquare check-ins in Chicago over a few months from wheredoyougo.net. Where I am in a given day Going Out 18% Home 46% Work 36% Where 2.0, Where You Go Is Who You Are April 21, 2011
Where 2.0, Where You Go Is Who You Are April 21, 2011
Ch an s CVS -in ce ck of e he m re? fc th ee ro Events, tin be airports g fri m ' e Nu nd s Community spaces, parks, etc Restaurants, bars Where 2.0, Where You Go Is Who You Are April 21, 2011
Neighborhood Bar Check-in Airport Check-in "Shared sociocultural framework" or macro-social order Ironic Check-in, French Laundry amusing badges Check-in Adventure Check-in Where 2.0, Where You Go Is Who You Are April 21, 2011
Check-ins are special. They have interactional potency. We can use check-ins to build better applications. Where 2.0, Where You Go Is Who You Are April 21, 2011
Case Study: I'm hungry. Recommendation Engines Experts The Masses Where 2.0, Where You Go Is Who You Are April 21, 2011
We can make recommendation engines smarter with geo-context. Whats the SpotRank of the places you typically eat dinner/ lunch at the times you typically eat dinner? Do you prefer locations that have a high SpotRank vs a low SpotRank? And the big one... Where have you been? And where have other people been that have been to similar places as you? All of these question can be answered with publicly- available APIs. More or less. Where 2.0, Where You Go Is Who You Are April 21, 2011
Exploiting social symmetries and asymmetries Applications should use this knowledge to make interesting decisions, outside of the scope of (most?) recommendation engines. Example 1: All youre friends are visiting airports. But youre not. Why not get some travel recommendations? (social symmetries) Example 2: The past few weeks, all your friends have been going to the same 3 dive bars. Why not recommend a new wine bar, with a 20% off coupon? (social asymmetries) Where 2.0, Where You Go Is Who You Are April 21, 2011
The power of memory and location When we check-in somewhere, we create a persistent record of that event. Imagine you checked in at every location where an important event in your life took place. What would that enable us to do? We dont have great memory of all the places weve been. Where 2.0, Where You Go Is Who You Are April 21, 2011
Check-ins are driven by strong social forces. Were just beginning to understand how to use this data. The data we share on location-based social networks is a critical part of our online identity, and and incredibly rich context. Social data is growing exponentially. And theres an API. Where 2.0, Where You Go Is Who You Are April 21, 2011
Lets use this information to build smarter applications Facebook has a sense of humor. Example of something that could get smarter. Where 2.0, Where You Go Is Who You Are April 21, 2011

More Related Content

Where You Go Is Who You Are

  • 1. Where you go is who you are April 21, 2011 Clay Smith @smithclay
  • 2. Youre an average of the 5 people you spend the most time with --Jim Rohn, motivational speaker guy Where 2.0, Where You Go Is Who You Are April 21, 2011
  • 3. What are the 5 places you spend the most time in? Where 2.0, Where You Go Is Who You Are April 21, 2011
  • 4. 5 places you spend the most time in. 5 people you spend the most time with. Where 2.0, Where You Go Is Who You Are April 21, 2011
  • 5. 5 places you spend the most time in. 5 people you spend the most time with. Your check-ins Where 2.0, Where You Go Is Who You Are April 21, 2011
  • 6. My foursquare check-ins in Chicago over a few months from wheredoyougo.net. Where I am in a given day Going Out 18% Home 46% Work 36% Where 2.0, Where You Go Is Who You Are April 21, 2011
  • 7. Where 2.0, Where You Go Is Who You Are April 21, 2011
  • 8. Ch an s CVS -in ce ck of e he m re? fc th ee ro Events, tin be airports g fri m ' e Nu nd s Community spaces, parks, etc Restaurants, bars Where 2.0, Where You Go Is Who You Are April 21, 2011
  • 9. Neighborhood Bar Check-in Airport Check-in "Shared sociocultural framework" or macro-social order Ironic Check-in, French Laundry amusing badges Check-in Adventure Check-in Where 2.0, Where You Go Is Who You Are April 21, 2011
  • 10. Check-ins are special. They have interactional potency. We can use check-ins to build better applications. Where 2.0, Where You Go Is Who You Are April 21, 2011
  • 11. Case Study: I'm hungry. Recommendation Engines Experts The Masses Where 2.0, Where You Go Is Who You Are April 21, 2011
  • 12. We can make recommendation engines smarter with geo-context. Whats the SpotRank of the places you typically eat dinner/ lunch at the times you typically eat dinner? Do you prefer locations that have a high SpotRank vs a low SpotRank? And the big one... Where have you been? And where have other people been that have been to similar places as you? All of these question can be answered with publicly- available APIs. More or less. Where 2.0, Where You Go Is Who You Are April 21, 2011
  • 13. Exploiting social symmetries and asymmetries Applications should use this knowledge to make interesting decisions, outside of the scope of (most?) recommendation engines. Example 1: All youre friends are visiting airports. But youre not. Why not get some travel recommendations? (social symmetries) Example 2: The past few weeks, all your friends have been going to the same 3 dive bars. Why not recommend a new wine bar, with a 20% off coupon? (social asymmetries) Where 2.0, Where You Go Is Who You Are April 21, 2011
  • 14. The power of memory and location When we check-in somewhere, we create a persistent record of that event. Imagine you checked in at every location where an important event in your life took place. What would that enable us to do? We dont have great memory of all the places weve been. Where 2.0, Where You Go Is Who You Are April 21, 2011
  • 15. Check-ins are driven by strong social forces. Were just beginning to understand how to use this data. The data we share on location-based social networks is a critical part of our online identity, and and incredibly rich context. Social data is growing exponentially. And theres an API. Where 2.0, Where You Go Is Who You Are April 21, 2011
  • 16. Lets use this information to build smarter applications Facebook has a sense of humor. Example of something that could get smarter. Where 2.0, Where You Go Is Who You Are April 21, 2011

Editor's Notes

  • #2: I want to talk about how the places you go, particularly your check-ins on a location-based social network of choice, are an inherently “social action” that establish an individual’s identity. So, from that, we get the title “Where you go is who you are.” The next part of the title is how we can use this incredibly rich context to build awesome applications.\n\nI want to first explain how I arrived at the conclusion that “where you go is who you are,” go into why I think check-ins on location-based social networks are really special, and then elaborate on how I think we can use this information to build better applications. I am a software engineer, after all.\n
  • #3: Let’s start with social. Who do you spend the most time with? Naturally related to location.\n
  • #4: A more interesting question. These two questions are related.\n
  • #5: These overlap fairly closely, possibly even perfectly in some cases. The places you spend the most time in share space with the people you spend the most time with.\n
  • #6: Check-ins are a special case. Since they are passive (not always tracking your every move), they don’t particularly represent the places you spend the most time in.\n
  • #7: These aren’t a representation of everywhere I’ve been, but where I want people to know I’ve been. A small slice of where I actually spend my time.\n\nWhere you live and you work is also interesting, but these two places are mostly static.\n
  • #8: These aren’t a representation of everywhere I’ve been, but where I want people to know I’ve been. A small slice of where I actually spend my time.\n\nWhere you live and you work is also interesting, but these two places are mostly static.\n
  • #9: I’m really interested in what causes us to check-in certain places, since it’s a fair assumption that we don’t really check-in every place we ever visit.\n\nThis is one way I thought of breaking down the frequency of places I check-in to. It’s not backed up by strong data.\n
  • #10: Each of these check-ins references a different bit of socialcultural knowledge. Check-ins have to power to create social symmetries and asymmetries within a group, and are “social action.”\n
  • #11: Check-ins are special, because they so powerfully index shared socialcultural knowledge. They aren’t unique in this way (tweets, foursquare status updates essentially do the same thing), but a location is a heck of a lot easier to parse than natural language.\n
  • #12: All of these services have some “location-aware” capacity that can answer what’s essentially a local-search question. Which one does the best job?\n
  • #13: Recommendation engines for local searches are really compelling. But it’s a much, much more complicated problem that Netflix or Pandora. Fortunately, we can get lots of information from APIs about a particular location (thanks SimpleGeo!)\n\nOther examples:\nWhat’s the weather like? Is the weather improving or getting worse?\nAre there are major events going on in your neighborhood? Want to avoid them? Or not?\n
  • #14: Related to this local search recommendation engine problem is what happens when we start applying social ‘symmetries’ and ‘asymmetries’ to the data. Interesting stuff could happen, and we could get even more interesting recommendations.\n\nApplications should exploit social symmetries and asymmetries.\n
  • #15: Lastly, there’s the question of historical check-in data. I’m reminded of the best meal I’ve ever had, but details are fuzzy.\n\nThink it’s recognized in this conference that there’s a lot we can do with digital storytelling when we have a list of places a person has been. How do the places and experiences we’ve had shape us as people? Cool stuff possible here over long term.\n
  • #16: \n
  • #17: \n