Information analysis and risk management by cartography
ABSG Consulting Inc. Tokyo Japan
the Central Disaster Management Council: The Cabinet Office of Japanese government,
the earthquake risk is extremely high in Japan. Consequently, the necessity of effective risk
control to minimize the influence of such an earthquake is rising. Thus, studies to assess the vulnerability to
earthquake are required. When a great
earthquake causes extensive damage to the headquarters of a company, it is
difficult to continue business same as usual.
Therefore it is natural that a company wants to have a backup data
centre or a backup office in the light of the risk management.
this paper, the validity of the analysis by cartography is discussed taking the
case of choosing a backup data centre. A
quantitative analysis of PML (Probable Maximum Loss), for example, is effective
for the evaluation of risk. PML
represents the percent of the restoration cost of building after
earthquake. Then, it is desirable to
choose a building whose numerical value of PML is low. The resultant of PML shows us if it is a high
risk or not. However, the problem is the
case where there are similar PML for some candidate buildings. In what way are those building similar? For this question, I propose to use
cartography for information analysis. Especially, I introduce the Ordonnable Matrix that makes us
possible to show, all at once, a ensemble information and low end information.
evaluate 9 candidate sites for the backup data centre which escapes disaster at
the same time as the Headquarter on assumption that we are hit by 19 great
The value in each cell
indicates the seismic intensity based on the JMA seismic intensity scale (This
is a scale used in Japan.
JMA 7 is maximum.). Let the candidate sites
be the horizontal axis, and the earthquake sources the vertical axis. The candidate sites are arranged in order of
the distance from the Headquarter. The earthquake
source ID is arranged by number. From
Table 1, it is difficult to sort useful information,
compare the candidate sites, and select better one. Then, I propose the analysis by Ordonnable
is the illustration of the numerical value of each cell of a tabulation. The Table 2 shows that the darker of the
colour of the cell becomes, the greater the earthquake is. In this analysis, the understanding of the
whole table will be easy by the use of the six visual effects equivalent to the
JMA. And I take, other information, the solidness of the ground and add
it into Ordonnable Matrix as the width of each column of matrix. The solidness
is given in terms of 3 levels; firm, average, and soft. The softer the ground is, the easier it is to
quake, and I make the matrix wider so that it seems to do more harm. After
making the matrix in visual, in order to grasp the whole image, lines of matrix
are exchange to improve the visual effect.
candidate site A, which is located only 4 km away from the headquarter, has almost the
same earthquake hazard as that of the Headquarter against the expected 19 earthquakes. That is, there is a strong correlation of the
risk between the Headquarter and the site A.
The candidate site H or J is far away from the Headquarter, so the
possibility of a simultaneous damage by the same earthquake is low among them.
But the earthquake hazard is high. (The column widths are wide. This means that
these sites are easy to quake.) The site F is possible to take a better
candidate as a backup data centre. Because site F don’t have simultaneous disaster
with Headquarter and where the earthquake hazard in the site is low.
As this example, it is
often important to use appropriate information effectively in the aggregation
of information. In that case, the integration
information will be important. I want to
propose the cartography is a distinguishing method of information.
Jacques. Sémiologie graphique. Paris:
Mouton, 1966, 432p.
Jacques. La graphique et le traitement graphique de l’information (japanese)Tokyo: Heibon-sha, 1982,
Karl V. Earthquakes, Volcanoes, and Tsunami; An Anatomy of Hazards. NY: Skandia
group, 1982, 392p