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A new Method for Analyzing the Relationship between City and Human Behavior using geo-tagging Social

Networking Services

Kousuke Kikuchi, Ph.D Student, Creative School of Science and Engineering, Waseda University

Hiromu Okutsu, Employee with no title, Lifestyle Research and Design Center, Panasonic Electric Works Co., Ltd.

Atsushi Enta, Asistant Professor, Faculty of Science and Engineering, Tokyo University of Science

Hitoshi Watanabe, Professor, Faculty of Science and Engineering, Waseda University

Background and Objectives

The purpose of this thesis clarifies a methodology of analyzing and

visualizing the spatio-temporal distributions of human behaviors

elated to daily life by using Twitter. Although a number of field

tudy methods of human behaviors in a city, little research was related

o geo-tagged social networking services. Twitter has the possibility

o analyze user’s behavior. Fig.1 illustrates the scheme of this thesis.

Method

Firstly, two programs were fabricated to achieve the data collection:

making a list of geo-tagged users, and gleaning its users’ data. Fig.2

llustrates the procedures to attain the geo-tagged users and their

weets. Secondly, Excel sorted the determined keywords of “Verb”

and “Object” related daily life and outputted into files. Thirdly, we

utilized kernel density estimation to evaluate the comparative

assessment of these keywords. In “Verb,” three-dimensional kernel

density estimation evaluated the time-space density. Finally, we

visualized the assessments by using Voxler and Google Earth.

Results

n conclusion, this methodology indicates the locality of each area by

comparison between adjacent areas or inner area. However, three

imitations occurred in this method. First, unconscious bias remains

because only smart phone users can embed geo-location data. Second,

discontinuous history of users’ data disproves the human behavior.

Finally, this method cannot analyze real-time.

Future Study

By the overcoming of these limitations, the digital research will be a

ool for not only architects or urban planner but also citizens to design

heir city. Also, clalifying human behavior in realtime will enable the

user to change their behavior in real-time.

Contact Author: Kousuke Kikuchi

e-mail: [email protected]

Cycli ng Ru nning Walking Strolling

Denen Chofu

Imperial Palace

Meiji Jingu

Komazawa

Park Meguro

Asakusa

Shinjuku

Ikebukuro

Fig.1 The scheme of the thesis

A c c u m u l a t i o n

E x t r a c t i o n

Behavior

Behavior

Behavior

Behavior

A n a l y s i s time

・text・time

・geotag・user

F e e d b a c k

Twitter

Streaming

API

geo-tagged

userlist

2,630

a program for making

geo-tagged userlist

nine programs for

getting each tweets

of geotag users

user:

2,630

Query

Return

user:

geo-tagged tweets:

time period:

2,630

199,546

Jul. 19, 2010 - Dec. 5, 2010 (139days)

Fig.2 The summary of data collection Fig.3 All geo-tagged tweets in Tokyo

Shibuya

Omotesando

Harajuku

Ebisu

LookPhotoRest ShoppingWaitEat

AkihabaraOchanomizu

Ueno

Otemachi


Ikebukuro

Kanamemachi


14:30Yurakucho

20:00Ginza

22:30Yurakucho

0:00

24:00

Longitude

Latitude

Fig.4 Activities in Shibuya Fig.5 Activities in Akihabara Fig.6 Activities in Ikebukuro

Fig.8 Space and time distribution in Ginza

Fig.7 Photo and Shopping in Roppongi

Fig.9 Objects in Tokyo

Akasaka

Roppongi

Tamachi

Photo Shopping