sensing city potential through social data @ icmu2014 panel

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

Facebook

Flickr

Foursquare

Twitter

Clusteringby sklearn

Namingby place API

Facebook

Yahoo

Foursquare

Wikilocation

Twitter

Google

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).