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Finding Wormholes with Flickr Geotags
Maarten ClementsMarcel ReindersArjen de VriesPavel Serdyukov
December 3rd, 2009GIS
03/12/2009 2Maarten Clements
Maarten Clements
• PhD: personalized retrieval in Social Media• Faculty of EEMCS – ICT group. • Supervisors
º Marcel Reinders – Prof. Bioinformatics (and more)
º Arjen de Vries – CWI, Prof. MM Dataspaces
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Location prediction
• Predict relevant locationsº Location Locationº User Location
• Why?
Flickr: MarsW Flickr: msokal
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2
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Location prediction
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Flickr
• Foto sharing website º Billions of photosº Active community:
º Tags, Geotags, Favorites, Comments…
20092008
32.3M
91.4MGeotags in flickr
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Flickr
• Using Flickr API to collect data:º http://www.flickr.com/services/api/
• Strategy to find people who geotag:• First collected top cities in 2008
1. 'New York, NY, United States'2. 'London, England, United Kingdom'3. 'San Francisco, California, United States'4. 'Paris, Ile-de-France, France'5. …8643. Lo Verdes, Canary Islands, Spain
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Place
Pho
tos
Total nr. of photos in 2008
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Flickr
• Repeat:º Select a city based on full distributionº Get a photo at this location (geotagged)º Select the user who made the photoº Get all this users photos
City
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Longitude
Latit
ude
-180 -108 -36 36 108 180
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Flickr
Users: 36,264 Photos: 52,425,279Geo Tags: 22,710,496
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Latit
ude
-17 -8.2 0.6 9.4 18.2 27
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55.4
49.8
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Flickr
TagsTitlesTime stampsSocial networkDescriptionsGroups
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Flickr
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User
Geo
Tag
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Photos
All Geo-tagsUnique Geo-tags
Round to 1000th degree
Clustered 100km
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Wormholes
• Places that are similar but not necessarily spatially close.
• Use user travel patterns to detect these places• Assumptions
º Users have a certain travel preferenceº Users make photos at places they like
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Wormholes
• Given a target location, find relevant users• Weigh Euclidean distance with normal
distribution
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Wormholes
• Given a target location, find relevant users• Weigh Euclidean distance with normal
distribution• Aggregate data over all users, using computed
weightsº 2000x4000 histogram, example 4x8:
User 1:User 2:User 1+2:
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Convolution:
Wormholes
• Given a target location, find relevant users• Weigh Euclidean distance with normal
distribution• Aggregate data over all users, using computed
weights• Compute convolution with Gaussian kernel• Compute difference with expected geotag
distribution
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Wormholes
• Result
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Wormholes
• Sigma determines how many users we call Relevant
σσ
Many relevant users Few relevant users
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Evaluation
• Find ground truth data: Wikipedia, GeoNames
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Evaluation
• Rank predicted peaks and compute precision• Is there a mountain in a range of 3cells around
the predicted peak?
0 10 20 30 40 50 60 70 80 90 1000
0.05
0.1
0.15
0.2
Avera
ge
Pre
cision
σ (km)
So… Does it work?
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Evaluation (manual)
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Evaluation (manual)
σ = 100km
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Evaluation (manual)
σ = 20m
Target: Tour Eiffel
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Evaluation (manual)
σ = 20m
Target: Tour Eiffel
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Evaluation (manual)
σ = 80m
Target: Tour Eiffel
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Evaluation (manual)
σ = 80m
Target: Tour Eiffel
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Evaluation (manual)
Target: Tour Eiffel
σ = 300m
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Evaluation (manual)
Target: Tour Eiffel
σ = 300m
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Evaluation (manual)
σ = 60m
Target: Pere Lachaise
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Evaluation (manual)
σ = 60m
Target: Pere Lachaise
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What next?
• User Location • Query exists of multiple points (instead of 1)• Get rid of grid based prediction
º Compute kernel convolution peaks directly from continuous geotag data.
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What next?
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What next?
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Conclusions
• We have proposed a new method to predict similar locations based on geotags.
• Scale parameter can be used to predict relevant locations at different scales.
• ECIR’10: Comparing different user aggregation methods
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http://ict.ewi.tudelft.nl/~maarten/wormholes/[email protected]
http://ict.ewi.tudelft.nl/~maarten/wormholes/[email protected]
