World Explorer: Visualizing Aggregate Data from Unstructured Text in Geo-Referenced Collections Shane Ahern, Mor Naaman, Rahul Nair* & Jeannie Yang Yahoo! Research Berkeley

Attraction Map of Paris Stanley Milgram, 1976. Psychological Maps of Paris

Stanley Milgram, 1976.

Psychological Maps of Paris

Attraction Map of Paris Y!RB, 2007.

Y!RB, 2007.

Flickr “geotagged” 20+ million images Can we do better?

20+ million images

Location-driven Modeling Derive meaningful data about map regions E.g., representative tags, photos

Derive meaningful data about map regions

E.g., representative tags, photos

Data Description

Issues Sparse data set Photographer bias In location In tags Incorrect data

Sparse data set

Photographer bias

In location

In tags

Incorrect data

Heuristics Number of photographs denotes the “importance” of a location Users will use a common subset of tags to describe objects/locations Concentrated tag usage indicates descriptiveness

Number of photographs denotes the “importance” of a location

Users will use a common subset of tags to describe objects/locations

Concentrated tag usage indicates descriptiveness

Algorithm Clustering: k-Means, get set of k clusters “ Document” C is bag of all tags in cluster For each tag in C calculate: TF = |P(C,t)| IDF = |P(R)| / |P(R, t)| UF = |U(C,t)|/|U(C)|

Clustering: k-Means, get set of k clusters

“ Document” C is bag of all tags in cluster

For each tag in C calculate:

TF = |P(C,t)|

IDF = |P(R)| / |P(R, t)|

UF = |U(C,t)|/|U(C)|

Scoring Score (t) = TF * IDF * UF Threshold values 30+ photographs Minimum 3 users Score > 1 Final dataset: (tag, score, latitude, longitude)

Score (t) = TF * IDF * UF

Threshold values

30+ photographs

Minimum 3 users

Score > 1

Final dataset: (tag, score, latitude, longitude)

DEMO

Precomputation Divide the world into equal sized non-overlapping tiles Compute and store the tags for each tile Repeat for different zoom levels

Divide the world into equal sized non-overlapping tiles

Compute and store the tags for each tile

Repeat for different zoom

levels

Retrieval Find the tile level closest in size to the request area Select the tiles that fully cover the request area Return the tags that fall within the request area

Find the tile level closest in size to the request area

Select the tiles that fully cover the request area

Return the tags that fall within the request area

User Study 10 subjects 6 female, 4 male Ages 20-60 Varying technical knowledge No geotagged photos of their own

10 subjects

6 female, 4 male

Ages 20-60

Varying technical knowledge

No geotagged photos of their own

Experiment tasks Vacation recap San Francisco tour Explore a new city

Vacation recap

San Francisco tour

Explore a new city

Recall Reminded the subject about locations “It brings out memories” “Oh my God! This place has the best restaurants” “We wanted to see the Polynesian Cultural Center"

Reminded the subject about locations

“It brings out memories”

“Oh my God! This place has the best restaurants”

“We wanted to see the Polynesian Cultural Center"

Discovery Participants discovered previously unknown locations and events “I’ve never heard of this festival” “There is car racing which I'd probably go see”

Participants discovered previously unknown locations and events

“I’ve never heard of this festival”

“There is car racing which I'd probably go see”

Needle & Haystack Excellent visualization of the Haystack Hard to find specific information “Where was Culver City again?” No way to search “I guess what I’m looking for are bull fighting pictures”

Excellent visualization of the Haystack

Hard to find specific information

“Where was Culver City again?”

No way to search

“I guess what I’m looking for are bull fighting pictures”

Other Responses Gets the “vibe” of a place Share with other people Tags did not always match the mental model of a location Wanted more tags Want more info about tags

Gets the “vibe” of a place

Share with other people

Tags did not always match the mental model of a location

Wanted more tags

Want more info about tags

Conclusions Extracted meaningful aggregate information from georeferenced data Allows users to explore locations in a new way Users like using the overview but also want the ability to search

Extracted meaningful aggregate information from georeferenced data

Allows users to explore locations in a new way

Users like using the overview but also want the ability to search

Future work Adding search capability Show photos in places with no tags Differentiate locations and events Apply to other types of georeferenced data

Adding search capability

Show photos in places with no tags

Differentiate locations and events

Apply to other types of georeferenced data

tagmaps.research.yahoo.com World Explorer Data API Visualization toolkit Trip Explorer Night Explorer

World Explorer

Data API

Visualization toolkit

Trip Explorer

Night Explorer

Questions? Rahul Nair [email_address] http://tagmaps.research.yahoo.com