Transaction Management in Database Management System
ISAIA2012
1. Collective Background Extraction for
Station Market Area by using Location
Based Social Network
Kousuke KIKUCHI, PhD Student
Waseda University
@kousukekikuchi
Oct 24 2012
2. Outline
Introduction
Previous methods to study city
Sensoring on city
LBSN ~ another sensoring method on city
Hypotheses and premise
Purpose
Data Collection and Visualization
Market Analysis Method
Results
Conclusion
Future study
4. Introduction
quantitative qualitative
space syntax the image of the city
5. Introduction
Previous Methods
e,g. Person Trip, Mathematical e,g. Oral History, Image of City
Model
Problems:
Problems: Limited number of examinees
Ignorance of city dwellers’ Probability for examinees to
diversity change their behavior
How to sublate them?
Sensoring the information with content!
7. Introduction
Realtime Rome Project, MIT SENSEable City Lab
8. Introduction
Twitter:
opens the data which can be accessed through API
contains user name, time, text, location data (but only few percent)
has keywords related to daily life
can be analyzed to evaluate not only place, but also user itself.
We think the premise to be suitable for twitter analysis.
9. Introduction
Premise
Our background never change
Our background will appear in keywords of Social media
Collection of our background will be the background in some area
City will emerge distinctive keywords feature.
10. Introduction
This thesis fabricates the system to extrapolate the market
characteristics by assessing the user activities of Twitter and finds other
potential function in a city
15. Conclusion
We assembled the programs to collect and analyze big data from twitter.
We discovered users of Roppongi showed similar traits to Akihabara.
This similarity may not be explained by the previous methods.
We can construct the methodology on clarifying the potential of city.
We pledge to work further to detect the distinctive feature in each stations by
using more computational methods.