Collective Culture and Visualization of Spatiotemporal Information

Introduction Archiving with digital technology is indispensable
for historical cultural heritage items, but we also
have to apply this technology for culture that remains
difficult to form and store. This paper will argue three
important aspects of archiving regional culture, its representation,
sharing, and analysis. Then, in order to deal
with these aspects, we will introduce the environment
KACHINA-CUBE (KC) which we are developing and
evaluate findings of its application. We will start with
elaborating its design concept, then architecture and application,
referring to its related works.
Design Concept
In order to design KC as a tool for archiving regional
culture, we see the following three aspects important,
thus take them into consideration.
Fig. 1 Three levels of cultural representation
Representation Aspect
Developing Valsiner’s concept of culture (Valsiner,
2007), we argue that cultural representations appear in
three levels; personal, collective, and public (See Fig. 1).
The first, personal culture consists of personal memory
and knowledge. The second level of collective culture
can be considered collection of personal cultures. The
third level of public culture is public information, publicly
recognized and found in textbooks and dictionaries.
Public culture is well preserved. Because of complexity
and ambiguity of personal and collective cultures, however,
researchers are still struggling with how to preserve
these two, which should be understood by putting them
in their socio-cultural contexts. Taking a socio-cultural
approach, Wertsch (1998) advocates to treat narratives
as artifacts, the key to represent culture.
Sharing Aspect
W3C puts tremendous efforts to create standardized
frameworks for Web, and researchers in digital humanities
regard semantic web technology as one of the key
research fields. This kind of technology gives us various
chances to share data for other use. We believe archived
cultural data should be standardized to fit in this framework,
which allows users to access data and utilize them
in various platforms.
Analytical Aspect
To research history and culture in a specific region, oral
history plays an important role to make us understand
them. Collection of oral history in a specific area reveals
what kind of life experience people had and/or have in
the area, their similarities and differences.
Valsinar and Sato (2006) propose the concept of Trajectory
Equifinality Model (TEM) as a framework to
analyze personal experience which suggests diverse and
possible trajectories based on three concepts: Bifurcation
Point (BFP), Obligatory Passage Point (OPP), and
Equifinality Point (EFP). BFP is a point of each person’s
behavior branching or forking into new types of behavior
by his or her choice. OPP means, literally, Obligatory
Passage Point which most of the people have to go
through because of their own logic, institutions, and customs.
EFP is defined as the final state that individuals
equally reach from different initial conditions. Focusing
on OPP and EFP to investigates cultural recognition that
people in a particular region share, this paper describes
the KC system that assists users to seek through multiple
narratives and identify OPP/EFP in construction of the
regional culture.
Architecture
Giving consideration into these three aspects, we have
been developing KC system. As for the representation
aspect, we accept Wertsch’s propose and design the software
to hold data of spatiotemporal information.
We decided to design KC in three dimensions, two dimensions
for geographical information and another one for temporal information. In this virtual 3D space
(CUBE model) (see Fig. 2), users can post formal and
informal story fragments. Among them, we call formal
ones history fragments, and informal fragments story
ones. KC also supports researchers to make linkages
among fragments in periodical or logical order. We call a
set of cultural fragments storyline.
As for the sharing aspect, we apply RDF/OWL to define
our data. Its extensive and flexible definition is suitable
for our system and motivates other researchers to access
our data (Bray, 2001). We defined the data format as follows:
Fig. 2 Image of CUBE model
1. History fragment class: Objective information in
textbook or dictionary
2. Story fragment class: Subjective information such
as oral history
3. Storyline class: Aggregate of historical and story
fragments based on a specific context
4. Geography class: Geographical information of the
historical and story fragments
5. Temporal class: Time when the incidents told in
historical and story fragments occurred
Finally, the analytical aspect, KC implements the function
of OPP/EFP detector. OPP/EFP detector searches
fragments occurred in similar places or time. Using
OPP/EFP detector makes it possible to learn spatiotemporal
possibilities. This is the implementation of Sato’s
concept, and to understand a region in meta level, this
feature can be a strong analytical tool.
Application
As a test case, we applied the data of movie culture in
Kyoto Rakusai Area, a.k.a. Japan’s Hollywood, to our
system. We used oral history data collected by Tomita
and Itakura (2001). Each story that has spatial and/or
temporal information is stored to the system. Currently
we have oral history data from three storytellers who had
involved in movie industries in the area from 1910’s to
1930’s. In terms of the representation aspect, the story
fragments were well mapped on the 3D space, with their
spatiotemporal information. Using our storyline representation
visualizes connections among independent
fragments. As for the sharing aspect, we are still working
on definition of the data, hoping that this feature will
be available soon. As for the analytical aspect, OPP/EFP
detector displays different storytellers’ worlds with possible
alternatives experiences (see Fig. 3).
Fig. 3 Display of OPP/EFP detector
Conclusions
In this paper, we argue importance of not only archiving
collective culture but also standardized semantic Web as
a socio-cultural analytical tool which allows researchers
to access data and utilize them in various platforms.
Based on this argument, we developed KC, applying it
to actual research. As a result, our system demonstrates a
lot of potentials for research in various fields, which we
have to prove by developing further this software with
applications, as well as examining it in more case studies
of collective culture.
There are well-known research projects and Web systems
to deal with spatiotemporal information. For example,
the TimeMap Project in the University of Sydney
develops Web GIS that can visualize chronological data
and animate historical maps (Johnson, 2004). The SIMILE
Project in the Massachusetts Institute of Technology
develops the TimeLine system that can organize text and
pictorial data in chronological order. Our KACHINA
CUBE is significantly different from these previous Web
systems in the following two points:
1. Adoption of CUBE model (3D viewer that combined
map with timeline); and 2. Implementation of user interface suitable to contain
narratives and oral histories
With KC, therefore, we hope to contribute to further
development of regional archive system and digital humanities
in general.
References
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Tomita, M. and Itakura, F. (2001). Voices from Kyoto:
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