Collective Background Extraction for Station Market Area by using Location Based Social Network

Kousuke KIKUCHI, PhD StudentWaseda University@kousukekikuchi

Oct 24 2012

Outline

IntroductionPrevious methods to study citySensoring on cityLBSN ~ another sensoring method on cityHypotheses and premisePurpose

Data Collection and VisualizationMarket Analysis MethodResultsConclusionFuture study

Introduction

Previous Methods

e,g. Oral History, Image of City

e,g. Person Trip, Mathematical Model

Introduction

space syntax the image of the city

quantitative qualitative

Introduction

Previous Methods

e,g. Oral History, Image of City

Problems:Limited number of examineesProbability for examinees to change their behavior

e,g. Person Trip, Mathematical Model

Problems:Ignorance of city dwellers’

diversity

How to sublate them?Sensoring the information with content!

Introduction

Sensoring Methods

Making sensor node

Utilization of “Big Data”

Introduction

Realtime Rome Project, MIT SENSEable City Lab

Introduction

Twitter:opens the data which can be accessed through APIcontains user name, time, text, location data (but only few

percent)has keywords related to daily lifecan be analyzed to evaluate not only place, but also user itself.

We think the premise to be suitable for twitter analysis.

Introduction

Premise

Our background never change Our background will appear in keywords of Social mediaCollection of our background will be the background in some area

City will emerge distinctive keywords feature.

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

Data Collection and Visualization

Data Collection

Visualization

Market Analysis Method

Results

Results

sophisticated

Similar characteristi

cs

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.

Contact

twitter: @kousukekikuchifacebook: Kousuke.Kikuchiacademia: KousukeKikuchi