Post on 07-Apr-2018
8/4/2019 The Poster for UIA2011 Academic Presentation
http://slidepdf.com/reader/full/the-poster-for-uia2011-academic-presentation 1/1
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: kousukekikuchi@toki.waseda.jp
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
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
LookPhotoRest ShoppingWaitEat
Ikebukuro
Kanamemachi
LookPhotoRest ShoppingWaitEat
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