Embed Size (px)
DESCRIPTION
Introduction of my researches in social sensing. Especially, the following two topics are explained. 1. Sightseeing spots retrieving 2. Photo spots recommendation
Citation preview
Urban Computing:
Sensing City Potential through Social DataYutaka Arakawa, ph.dAssociate ProfessorNara Institute of Science and Technology, JAPAN
A human works as a sensor
• Traditional Sensors• Mote, Micaz, Zigbee, Arduino, etc.• Numerical info. Ex:Temperature, Humidity
• Human Sensors • Twitter, Facebook, Foursquare, etc. (User generated contents)• Textual info. Ex: Delicious, Hot, Noisy
Crowdsourcing
Delicious!
So Exciting!
A Train is delayed.
I’m in Singapore
Learn from 3.11• 位置登録実績 MAP （コロプラ）
3
Access log of Location-based social
“GAME”
Safety status of 3G networks
Before 3.11 After 3.11
Research Target
What kind of information can be found from social data?
City Potential• Popular Sightseeing Spots• Popular Photo Spots• Popular Events• Movement of people • etc...
Our research results
1. Sightseeing spots retrieving
2. Photo spots recommendation
Our research results
1. Sightseeing spots retrieving• Where is the most attractive spot in the city?• Not only the location but also its name.• Social City Maps
2. Photo spots recommendation
Motivation: City Map
Latest map
Old traditional map
Only STARno text, no figure
3D figure, and title
Finding spots from social data The number of photo || Famousness
Clustering
Crandall, D., Backstrom, L., Huttenlocher, D. and Kleinberg, J.: “Mapping the world’s photos”, ACM WWW2009, pp. 761– 770.
Flickr provides good data. Twitter is noisy.
Our research issues
Analyze other social data, such asFoursquare, Facebook, etc.
Develop an android application as an evaluation platform
What attracts the people?
Is is really good spots?
Our developed application
A platform for publishing and evaluating UGM (User
Generated Maps)
Other research issues
Mean Shift Clustering
(Flat Kernel)
Source
Flickr
Foursquare
Clusteringby sklearn
Namingby place API
Yahoo
Foursquare
Wikilocation
Outputas a kml file
Research topic 1
how to eliminate a noise?
Research topic 2
how to estimate proper BW? how to change BW dynamically?
Research topic 5
how to decide a proper name of each cluster?
Research topic 7
how to evaluate ?
Research topic 6
how to select a representative image or additional information for the cluster ?
Research topic 4
How to take user’s preference into consideration?
Research topic 3
how to select only famous spots? how to quantify famousness?
Our research results
1. Sightseeing spots retrieving
2. Photo spots recommendation• City potential is represented by photos.• Place and setting recommendation for
amateur user.
Photo spot recommendation
Camera setting?(ISO, exposure,etc)
From where?Which season?
What time?How about a weather?
My experience @Singapore
Camera setting?(ISO, exposure,etc)
From where?Which season?
What time?How about a weather?
Architecture of our system
Flow
Screen shots
Various Conditions can be set.
Photos are selected from Flickr.
Screen shots
Navigation
Camera setting info.
Summary
Social Network Services Find city
potential(Our researches)
Ubiquitous city
Open data(Government)
Thank you!This work is partially supported by SCOPE(Strategic Information and Communications R&D Promotion Program).
