1. Web Service Framework of Visualizing Sensor Information in
Disaster Management
Kun-Yen Tsai
Institute of Information Science
Academia Sinica
11529 Nankang, Taipei,
Taiwan, ROC
Kunyen811@iis.sinica.edu.tw
Jan-Ming Ho
Institute of Information Science
Academia Sinica
11529 Nankang, Taipei,
Taiwan, ROC
hoho@iis.sinica.edu.tw
I. INTRODUCTION
Dealing with large-scale disasters is a great challenge
that requires up-to-date information sharing and resource
arrangement in emergency services, such as fire
departments, police departments, etc. However, a large-
scale disaster not only might impact people and properties in
a city but also in a much broader area, e.g., even crossing
several continents. For example, in 2012, the super storm
Sandy had stricken the east coast of America. The hurricane
Sandy had at least affected 24 states from Florida to Maine
[1]. To minimize casualties and to provide timely rescues to
victims of a disaster, it is necessary to coordinate different
response teams such that their efforts are coherent and
efficient. Without a global view of the real-time situation in
a disaster, it might be hard for a command center to make
good decisions and responses. Thus we’ll study the problem
of visualizing real-time situations of a large-scale disaster in
this article.
With state-of-the-art ICT technologies, we may take
advantages of mobile and immobile sensors which have been
deployed in the field to share these real-time information in
real-time for disaster management purposes. These sensors
are primarily used to monitor environmental status. By
tracking the influence of a disaster upon the environment
through various kinds of sensors, e.g., rainfall water level,
and air and water pollution, we can detect the range of
damage area and make scientific and dynamic judgment on
the situation. For example, during and after the time of
disaster, location or movement information collected from
sensors carried by human, animals, or other moving vehicles
is a good indicator of road condition and also provides a way
to estimate the range of damage area. Through the aide of an
information fusion and visualization system of these sensors,
a disaster command system may be able to accurately dictate
a rescue team of responders to spots where helps are urgently
needed.
To integrate sensor data with information visualization
technologies could make information sharing more efficient
between organizations, and also make decision makers save
time in managing resources [2]. For example, in most of
counties, emergency services deploy their own sensor
systems respectively. An integrated information visualization
of sensor data can let decision makers know which
ambulances are near by the disaster area and help decision
makers realize the situation of the disaster. In this paper, we
advocate the needs to develop an integrated sensor
information visualization service for large-scale disasters,
and we present a preliminary web service framework that
allows for visualizing information of sensor data. This
framework provides an efficient way to establish an
information visualization platform for the information
exchange and rescue plan setting time reduction. It may
bring an information visualization of rescue staffs or a global
view of a disaster to decision makers. The proposed web
service framework may help decision makers analyzing the
current situation and scheduling resource during disaster
rescue. We present our prototype system by visualizing the
Taipei road status with sensor data on the Google map [3].
As the sensor technology advances, we believe that the
proposed web service framework could enable a host of new
applications areas such personal or public visualization of
sensor data for disaster management.
This paper is organized by follows: Section 2 depicts our
system architecture. Section 3 shows our demonstration.
Section 4 concludes the paper.
II. SYSTEM ARCHITECTURE
The goal of our web framework is to provide an
information visualization platform to help decision makers
analyzing and understanding the current situation in a
disaster. A high-level view of the proposed system
architecture is provided in Figure 1. There are three parts in
our architecture. The first part is sensors. Sensors are
dedicated to the selection of multiple data-sources. The most
important data we would like to know from sensors is the
location of the sensors. The second part is data centers of
each institute. The sensor raw data will be stored in the
institutes’ data center. The main framework will be
presented in the third part. There are six components in the
main framework.
Each of them will be described clearly below. 1) Raw
Data processor: the raw data processor acquires all kinds of
raw data, e.g. XML [4], XMPP [5], from institutes and
parses them to each column, such as longitude, latitude,
sensor speed, rainfall, wind speed, etc. 2) Database: the
database is used to store the parsed sensor data. PostgreSQL
[6] is utilized for this prototype because they are both
extremely robust as well as support geometric data types. 3)

2. Map-matching processor and 4) Drawing: It places sensor
information on the Google map. Because PostgreSQL
enables the geometric data types, we query the data to show
the specific area in a map. The detail sensor information will
be showed on the area where users request. 5) Image: we
apply Google Map API [7] to show the sensor information in
Google Map. Google map API has proven its worth by being
one of the leading web map development tools. The image
will show the road network information including the road
trend, accumulated precipitation, traffic, etc. 6) Web server:
we visualize the location of the vehicle in order to track the
road condition after the disaster occurs. The web will refresh
every thirty seconds. The system we have can collect data
and provide the real-time trend.
Figure 1: System Architecture and Information Flow.
III. DEMONSTRATION
The framework has worked out a prototype of vehicle
movement visualization. We present the visualization of the
Taipei bus location and average maximum speed. The raw
data is fetched from the Taipei e-bus system. The raw data
are acquired by our data processor once in every thirty
seconds. The data processor categorizes these raw data and
restores them into our database. By matching the location
data, i.e., longitude and latitude information, with the
location of map, the bus can be showed on the map about its
location and information we have. Therefore, when the user
requests to see the road information after the disaster, the
movement of the bus may indicate the condition of the road.
The average speed of the buses is presented in Figure 2 and
the heat map of buses is in Figure 3. In the future, we will try
to expand the scalability of our system and reduce the data
refresh time with large amount of data. Moreover, there is a
big challenge about how to deal with private issues. People
would probably care for their privacy about showing their
real-time location. Also, the drawing performance will be a
big challenge because of the reduce time of the data refresh.
Figure 2: Average Speed Demonstration of System
Framework in Taipei City.
Figure 3: Heat Map Demonstration of System Framework in
Taipei City.
IV. CONCLUSIONS
When we are facing the large range disaster, it is crucial
for different district command centers to reduce the response
time and acquire the information with symmetry. This paper
has presented a web service framework to visualize the real-
time vehicle sensor data on the Google map as a prototype of
sensor information visualization. When the disaster occurs,
the vehicle movement information showed on the web
browser could indicate the road condition. It is not only a
way for command centers to coordinate their resources but
also a way for civilians to monitor the area that they care
about in the future.
REFERENCES
[1] http://en.wikipedia.org/wiki/Hurricane_Sandy
[2] Baharin, S.S.K., Shibghatullah, A.S., and Othman, Z, “Disaster
Management in Malaysia: An Application Framework of Integrated
Routing Application for Emergency Response Management System,”
Soft Computing and Pattern Recognition, 2009. SOCPAR '09.
International Conference of, pp. 716- 719, 4-7 Dec 2009.
[3] https://maps.google.com.tw/
[4] http://en.wikipedia.org/wiki/XML
[5] http://en.wikipedia.org/wiki/Extensible_Messaging_and_Presence_Pr
otocol
[6] http://www.postgresql.org/
[7] https://developers.google.com/maps/