Outalot Blog

This looks to be the year that all sorts of location-based startups are coming out of the woodwork that let people do crazy things with their phones and locations.  And, incidentally or not, they’re also collecting a huge amount of interesting data. 

Outalot is one such resource:  We help people make decisions about where to go, at home or on their phones.  Without being too creepy (we take privacy very seriously, as everyone in this space should), there are some interesting trends that arise from anonymous data about how the service is used.  Below are a few questions an LBS service may be able to answer in broad terms.  Each one has a long list of possible commercial opportunities, sociology lessons, and city planning implications.  And anyway, it’s just interesting:

1. How far do people travel to get what they want? Do people trek farther on the weekends?  Is there a particular lunch area that people will travel 20 minutes to get to?
2. What day of the week do people go out, and in which neighborhoods?  Is Thursday a good night to go out downtown?  Is Sunday better for Astoria?
3. What different crowds overlap in the same neighborhoods?  That is, where are people from the Upper East Side mingling with people in Williamsburg.. and what disaster awaits if they find out?
4. How do trends change as the seasons change?  We figure people stay closer to home in the freezing months of winter, but how big is that effect in real terms?
5. How do trends change as the weather changes?  Do people stay at home more during cold days, or on hot and humid days?  How much does the appeal of outdoor space draw the crowds?  And what if the forecast is wrong — how far ahead are people planning?
6. What’s the disparity between how much a place is liked (or disliked), and how popular it is to actually visit?  Gramercy Tavern has a lot of positive ratings, but not many people seem to be regularly going there.  Why?  In this case, it really is a great place, but a tad pricey for a spur-of-the-moment dinner decision.  For what other reasons might this disparity appear?
7. Do iPhone users and BlackBerry users hang out in the same areas?  What does that say about the places they’re seen together — or separate?
8. How does the crime rate of an area affect people’s habits?  Is everyone steering clear of the dangerous areas, or is all the hot stuff getting started out there on the seedier side of town?
9. How much does cleanliness impact a place’s popularity?  The Dept. of Health provides health inspection reports for businesses, which one could overlay on popularity data.  Imagine if you could provide hard evidence to the sketchy neighborhood diner that their business would double if they just, er, got rid of the vermin. 
10. What are people searching for that they can’t find?  This is the most important one to us at the moment: it represents either a failure in our system and an opportunity to improve the information in our database, or a failure of the city to actually provide what someone wants.

This is just a starter list of possibilities.  Any others?