Demo Soofi
- 2. Yosemite trip, May 2011 We had a great time…
- 3. Though it might have been better if the scenery had looked less like this alanak
- 5. The problem I want to go hiking at a time/day that works for me, but that also minimizes the size of the crowds.
- 6. The problem I want to go hiking at a time/day that works for me, but that also minimizes the size of the crowds. I would like to predict the crowd size for a specific location and a range of future dates.
- 7. The problem I want to go hiking at a time/day that works for me, but that also minimizes the size of the crowds. I would like to predict the crowd size for a specific location and a range of future dates. Then I can use that prediction to make an intelligent choice about when to take my trip.
- 8. How do we predict crowds right now? Government data Often aggregated Not always immediately accessible Check-ins Sparse coverage Prior knowledge/Intuition Not always validated
- 10. There’s another way. We can crowdsource this problem!
- 12. CrowdSkippr: Inner workings From flickr.com, extract the total number of photos taken at a given time/place). Extract data on temperatures from NOAA.gov for a given time/place. Using this information, create a prediction of how heavy the crowds will be at a given future time/place. TM
- 13. Gradient Boosting Regression Predictors Day of week (Flickr) Holiday flag (Flickr) Day of year (Flickr) Daily temperature (NOAA) Response Number of photos taken (Flickr) (proxy for size of crowd)
- 14. Photos (or visitors) per month normalized by total Wait: Is # photos a good proxy for # visitors? Photos Visitors
- 15. Photos (or visitors) per month normalized by total Wait: Is # photos a good proxy for # visitors? Photos Visitors 0 1 2 3 4 5 6 7 0 0.5 1 1.5 x100000 x 10000 R2 =0.89 No. Photos Num.Visitors
- 16. Day of Year Temperature Day of Week Yosemite National Park Holiday Relative Feature Importance 0 0.4 0.6 0.8 10.2
- 17. Thanks for your time! I’m Wafa.
- 18. For all 28-day windows in a given year, the median difference between crowd size on predicted and actual best days is 4.6%. (On the days that are predicted to have the lowest crowds, the crowd size is 29% of the worst possible crowds within that window.) Validation: Rocky Mountain National Park Predicted crowd size Actual crowd size (test data)
Editor's Notes
- #10: On the internet, everyone has a cell phone, people leave a digital footprint of themseves wherever they go, let’s see if we can use these footprints to create a prediction of where people will be in the future.