





































Commonwealth: A Journal of Political Science  Volume 15, Number 1  May 2009

Policy Issue on Emergency Management  2009 PPSA/LORL, PA House of Reps. ISSN 0890-2410

Managing Risk in Real Time: Integrating
Information Technology into Disaster Risk

Reduction and Response

Louise K. Comfort, PhD, Professor of Public and Urban Affairs
Daniel Mosse, PhD, Professor of Computer Science
Taieb Znati, PhD, Professor of Computer Science

University of Pittsburgh

Introduction

Emergency managers in Pennsylvania face an extraordinarily
complex set of tasks in reducing risk to communities that vary widely in
size from the metropolitan regions of Philadelphia and Pittsburgh to the
small towns of rural Pennsylvania. These communities, with different
degrees of density in population and interdependence of infrastructure,
are exposed to a wide range of weather conditions, transportation
accidents, hazardous materials spills, technical failures, and potential
security threats. Emergency managers are responsible for identifying
potential threats to the Commonwealth, alerting multiple agencies that
are responsible for different functions to reduce such threats, and
mobilizing an effective inter-organizational system to respond to rapidly
evolving emergency events. Recent events, such as the severe ice storm
of February 14, 2007, illustrate the risks, costs, and losses that occur in
such emergencies on a region-wide basis.

Increasingly, emergency managers are turning to decision support
systems to provide real-time information for the complex tasks of
anticipating, communicating, and responding to emergencies. While
various software programs such as EIS, E-Team, and WebEOC have
been designed, developed, and adopted by emergency response agencies,
none has been particularly successful in providing the type of dynamic,
yet focused information that emergency managers need to mobilize
coordinated action on a regional scale. Emergency managers, operating
at different locations and carrying out different functions simultaneously,
require timely, valid information that can be updated quickly as
conditions change. The challenge is to create a “common operating



28 MANAGING RISK IN REAL TIME: INTEGRATING INFORMATION
TECHNOLOGY INTO DISASTER RISK REDUCTION AND RESPONSE

picture” for the set of emergency managers working at different levels of
responsibility and exposed to different degrees of risk to enable them to
take more informed, effective action in a coordinated response for the
region.

Creating this “common operating picture” is complicated by the
heterogeneity of disciplines, organizations, and jurisdictions that are
involved in the mobilization of an effective response system to an
extreme event. For example, the response system that evolved following
Hurricane Katrina included 535 organizations from city, parish, sub-
region, state, federal, and international jurisdictions, identified in news
reports from the Times Picayune published in New Orleans, Louisiana
(Comfort and Haase 2006). The distribution of organizations crossed
jurisdictional lines, as 76, or 23.9%, were from the municipal level of
operations, while 93, or 29.2%, were federal organizations. In hindsight,
it is clear that few personnel from these two levels of operations had
worked together previously, or had developed a common understanding
of what actions needed to be taken by which agencies at what time to
reduce the threat to the region.

The initial step in disaster reduction lies in recognizing risk, and in
developing a common definition of risk that is shared among the
participating actors. Under these conditions, risk is the probability of
danger, not the hazard itself. It can be defined as the dynamic interaction
of the exposure to hazard, less the capacity of the community to act to
reduce that hazard (Johnson 2005). Many theorists have sought to define
risk, but it is difficult to capture the dynamic characteristics of risk as it
changes both over time and in reference to specific geographic, technical,
organizational, social, economic, and political conditions. Omar Cardona
(2003), an international engineer who has examined risk from multiple
perspectives, refers to risk as the potential loss to the exposed subject or
system, resulting from the convolution of hazard and vulnerability.

Cardona recognizes, importantly, that the degree of risk to which a
community is exposed may be modified by actions taken to reduce
vulnerability to hazards in the community. Further, he specifies that
vulnerability is increased or decreased by the interaction among
technical, organizational, social, economic, political, and cultural
conditions as the community acts in reference to the hazards to which it
is exposed. That is, if a community acknowledges its exposure to
hurricanes and allocates resources and training to emergency response



Louise K. Comfort, Daniel Mosse, and Taieb Znati 29

agencies to prepare for such extreme events, its vulnerability to
hurricanes will decrease. For example, the state of Florida, based on its
significant experience, has increased its capacity to reduce the likely
consequences of hurricanes for its communities, and has notably
decreased its vulnerability to this hazard, although hurricanes will surely
recur in Florida. Conversely, if a community acknowledges the threat of
severe weather, but takes no action to mitigate the likely consequences
for its population and infrastructure, its vulnerability to severe weather
will increase. Risk is the status of the community at a particular time,
given its exposure to a specific hazard and the actions that it has taken to
reduce that hazard.

Cardona (2003) proposes that the degree of risk for any community
may be expressed in mathematical terms as “the probability of surpassing
a determined level of economic, social, or environmental consequence at
a certain site and during a certain period of time.” This proposition
suggests a practical means of assessing risk from known hazards for
specific communities, and using this information as a basis for informed
decision making to reduce risk from hazards. Determining the degree of
risk for a given community requires the design and development of a
knowledge base and decision support system for the region that
characterizes the economic, social, technical, organizational, and
geophysical characteristics of the region in order to identify accurately
the thresholds of performance that enable a community to manage the
consequences of the hazard and still continue its basic operations.
Current information technologies provide the potential for addressing the
assessment of risk in real time, and providing timely, accurate decision
support to practicing emergency managers in the dynamic environment
of disaster risk reduction and response.

Risk Assessment and Response at a Regional Level

Managing risk for a given region involves measuring the rate of
change between two interacting components in real time: 1) the degree
of exposure to a specific hazard for the region, and conversely, 2) the
capacity of the region to act to reduce the likely consequences of that
hazard when it occurs. The dynamic is reciprocal. As the degree of
exposure to a given hazard increases and the region deploys its existing
capacity to reduce the likely consequences of that hazard, the
vulnerability of the community to danger will increase, unless capacity is



30 MANAGING RISK IN REAL TIME: INTEGRATING INFORMATION
TECHNOLOGY INTO DISASTER RISK REDUCTION AND RESPONSE

augmented. The ratio of the probability of occurrence of the hazard to the
capacity of the region to mitigate that hazard will vary over time.
Achieving the appropriate balance between timely, accurate assessment
of the probability of hazards and allocation of resources and effort to
minimize the potential consequences can be enhanced with the use of
innovative information technologies.1 Designing decision support
systems to enable emergency managers to make more informed, timely
decisions in managing risk represents an innovative approach to the
reduction of recurring risk in metropolitan regions.

A community’s capacity for response to hazards can be considered
as a dynamic, inter-organizational system that is characterized by four
primary decision points: 1) detection of risk; 2) recognition and
interpretation of risk for the immediate context; 3) communication of risk
to multiple organizations in a wider region; and 4) self organization and
mobilization of a collective, community response system to reduce risk
and respond to danger. The decision points move from individual to
organizational to system levels of aggregation and communication of
information that are used as a basis for creating a “common knowledge
base” to support collective action to reduce risk. It is at these four
transition points of escalating requirements for action that human
cognitive, communicative, and coordinating skills frequently fail. Five
propositions present a conceptual framework for building resilience in
communities exposed to recurring risk.

Detection of Risk

In detection of risk, measurement of hazards is conducted by a
network of scientists that review and validate the current status of the
hazard and then forward this information to decision makers in public,
private, and nonprofit organizations. For example, on August 23, 2005,
the meteorologists at the National Hurricane Center (NHC) in Florida
identified a tropical depression forming over the Bahamas. On August
24, they upgraded it to Tropical Storm Katrina. On August 25, the staff at
the NHC tracked the storm as it made landfall in south Florida as a
Category 1 hurricane. On August 26, the storm moved into the Gulf of
Mexico and intensified to become a Category 2 hurricane and the NHC

1The Interactive, Intelligent, Spatial Information System (IISIS) Laboratory at the
University of Pittsburgh has focused on the degree to which the capacity of a region to
respond to shared risk can be enhanced by the appropriate use of innovative information
technologies. For more information, please see http://www.iisis.pitt.edu.



Louise K. Comfort, Daniel Mosse, and Taieb Znati 31

projected landfall in Louisiana and Mississippi. At each step of the
evolving state of the storm, scientists at the NHC notified the public
officials and news media regarding its changing strength and direction.
On August 27, as the storm strengthened over the Gulf of Mexico, the
NHC upgraded it to a Category 3 hurricane; the next day, August 28, the
storm intensified to a Category 4, and then Category 5 storm, with winds
over 175 miles per hour. On Monday morning, August 29, Hurricane
Katrina made landfall just east of New Orleans as a Category 4 storm.
The scientists monitoring the hazard B the evolving hurricane B provided
timely, accurate information to both decision makers and the public.
Ironically, this scientific information did not lead to sufficiently informed
action by the policymakers and emergency managers.

While the validation of weather data is an important stage in this
process, monitoring performance across a wider range of critical
conditions for the community would provide more accurate and timely
detection of emerging danger than separate assessments for particular
conditions. The process of risk detection is vulnerable to the fragilities of
human organization and performance. Responsible decision makers may
be watching separate conditions for indications of vulnerability, but miss
the interaction among these conditions that may intensify the potentially
destructive impact of the hazard on the whole community. The design of
appropriate means to assess the status of a core set of interacting
conditions and operational systems critical to the community would
augment the early detection and validation of risk. These assessments,
reported as thresholds of risk across a set of critical conditions and
functions for the community, would provide a more integrated and
timely assessment of risk to human decision makers responsible for risk
reduction.

In order to achieve such a distributed system of data detection and
analysis, an effective decision support system would require a number of
monitoring systems that would measure change in critical conditions for
maintaining continuity of operations. For example, measuring the
capacity of the levee system to withstand the added pressure of a storm
surge from the Gulf of Mexico moving into the City of New Orleans
through the Mississippi River Gulf Outlet would be a key factor in
determining the vulnerability of the city’s population to the storm. Other
factors, such as monitoring the state of readiness to implement the
evacuation plan for all sectors of the city’s population, or the current
status of alternate communications facilities, are essential measures to



32 MANAGING RISK IN REAL TIME: INTEGRATING INFORMATION
TECHNOLOGY INTO DISASTER RISK REDUCTION AND RESPONSE

calculate the degree of risk for the entire metropolitan region. These
monitoring systems would not only provide redundancy in incident
detection, but also allow measurement of the rate of change in key
factors over time, which is critical to understanding the probable impact
of the hurricane on the community under study. When more details are
needed on a particular event, the set of monitors should be able to deliver
this information in as much detail as the emergency managers require.
Only the needed information should be shown to the emergency
managers during the daily operation of the system.

Proposition 1: Human capacity to perceive risk increases with
the timeliness, accuracy, and validity of information transmitted
in reference to a core set of thresholds of risk to conditions
critical for community resilience.

Recognition and Interpretation of Risk

Prior research has found that an individual’s capacity for problem
solving drops under stress (LaPorte 1975, Miller 1967, Simon 1981).
This drop in capacity is the result of the increased number of risk factors,
the degree of unfamiliarity with new information, and the degree of
uncertainty that characterizes extreme events. In these contexts,
appropriate uses of information technology offer a means of extending
human problem solving capacity in uncertain conditions. A key question
for investigation is the extent to which a socio-technical information
infrastructure, designed to detect and transmit risk information quickly
and accurately, can facilitate the rapid recognition of risk within a
community and lead to more informed, timely action.

With a well-designed socio-technical system, the emergency
managers will receive only the information they select to view. The usual
way of doing this is to centralize all information into a single server, use
database technology to extract relevant views from the database, and
transmit it to the consoles of the emergency managers. This approach
often creates a bottleneck, since data must be collected into a single
server and extracted from the single server under tight timing constraints
(in real-time) to be useful. Most systems that use this method slow down
the information distribution among emergency managers who operate at
different jurisdictional levels of responsibility.

Proposition 2: Human capacity to recognize risk conditions can
be increased by focusing risk data through “views” that are



Louise K. Comfort, Daniel Mosse, and Taieb Znati 33

directly relevant to the responsibilities of each major decision
maker in the system, thus reducing the overload of less relevant
information and time required for information processing and
facilitating rapid absorption of threatening information by
individual decision makers.

Communication of Risk to Wider Arenas of Response Organizations
and Resources

The prevailing method of communicating risk relies largely on
command and control processes through a carefully defined hierarchical
order. For example, during Hurricane Katrina, the official policies
included the National Response Plan (FEMA 2004) and the National
Incident Management System (FEMA 2005) that were adopted by the
Federal Emergency Management Agency and the Department of
Homeland Security. These policies follow a serial format for
communication of risk and requests for assistance from lower to upper
jurisdictional levels. These plans have recently been replaced by the
National Response Framework (January 2008), which includes minor
adjustments, but still follows the prevailing pattern of established
hierarchical control. A sobering analysis of communication patterns
among emergency response agencies in the hours and days leading up to
and following Hurricane Katrina illustrates vividly the breakdown of this
formal design in practice (Comfort 2005, 2006). Building the awareness
of risk to support collective action is a cumulative process. If the first
two steps of risk detection and communication have not been carried out
successfully, the effort to engage organizations from a wider arena into
the emergency response system is likely to flounder or fail.

Our model for achieving this task of communicating critical
information to focused audiences is the “bowtie” architecture for
decision support (Csete and Doyle 2004, Comfort 2005). This
architecture offers a method of presenting relevant views to different
emergency managers, while preserving not only the meaning of the
incoming data, but also preserving the integrity of the collected data. As
shown in Figure 1 on the following page, a “bowtie” design identifies
key sources of data that “fan in” simultaneously to a central processing
unit (or “knot”) where the data are integrated, analyzed and interpreted
from the perspective and performance of the entire system. New
information is then “fanned out” to relevant actors or operating units that



34 MANAGING RISK IN REAL TIME: INTEGRATING INFORMATION
TECHNOLOGY INTO DISASTER RISK REDUCTION AND RESPONSE

use the information to make adjustments in their specific operations
informed by the global perspective.

Figure 1

This design fits well with an Emergency Operations Center (EOC).
In an EOC, status reports from multiple agencies are transmitted to the
service chiefs who collectively integrate, analyze and interpret the data in
reference to the performance of the whole response system. They then
transmit the relevant information to the respective agency personnel, who
adjust the performance of their units informed by the operations
perspective for the entire system. The capacity for reciprocal adjustment
of performance among multiple organizations based on timely, valid
information represents self organization in emergency response, guided
by the shared goal of protecting lives, property and maintaining
continuity of operations for the whole community (Comfort 1994,
Axelrod and Cohen 1999).

State Level

Integration

Aggregation

Visualization

Interpretation

Redirection

Data
Collection

Data
Analysis

Organizational
Action

Fire, Police

Medical Facilities

Public Works

Search & Rescue

Utilities

Technical

Environmental

Resources

Organizational

Social

Bowtie Architecture for the Iterative Flow of
Information within a Disaster Management System

(2) (3) (4)

(5)(6) (9)(8)(12) (7)(12) (11)(10)

Initial
Conditions

http://ww

(1)

Engineering District Level

County Level

Municipal Level



Louise K. Comfort, Daniel Mosse, and Taieb Znati 35

This theoretical framework acknowledges the importance of both
design and self organizing action in guiding coordinated action in a
complex, dynamic environment. It can be modeled as a set of networks
that facilitate the exchange of incoming and outgoing information
through a series of analytical activities that support systemic decision
making. The information flow is multi-way, but gains efficiency through
integrated analysis and coordinated action toward a clearly articulated
goal for the whole system. It operates by identifying the key sources of
information, the key processes of analysis and interpretation for the
whole system, and the key routes of transmission. It maintains self
organizing functions in that personnel, with informed knowledge, adjust
their own performance to achieve the best performance for the whole
system. Design, self organization, and feedback are central to effective
performance of distinct organizational units within the global system.

The system will use multicasting networks to support the
simultaneous transmission of information to multiple actors under
conditions of escalating risk. Because such conditions require the
aggregation of information at successive levels of responsibility, the
proposed network will provide such aggregation functions within the
network itself, guided by the views that emergency managers specify.
Further, the presentation of fused and selected information (readily
understandable to personnel with different levels of training and
experience) will be done to avoid information overload. Lastly, the views
requested by emergency managers may (and will) change based on
temporal conditions and event information. For example, in the early
stages, vague, scattered, incoherent indicators of risk will necessitate
little information and aggregation, allowing the network to self-organize.
Then, during emerging risk, clear indicators trending toward significant
danger will require more information to be provided, while still avoiding
information overload. During rescue and recovery, data will be
distributed in the reverse direction and transformed into action to protect
lives and property.

Proposition 3: The capacity of a set of organizational managers,
each with specific responsibilities and operating at different
locations, to coordinate their actions can be increased by the
simultaneous transmission of relevant risk information to each
manager, creating a “common operating picture” of risk to the
region for all managers.



36 MANAGING RISK IN REAL TIME: INTEGRATING INFORMATION
TECHNOLOGY INTO DISASTER RISK REDUCTION AND RESPONSE

Self Organization and Mobilization of Collective Action to Reduce Risk

The collective capacity of a community to take informed, coherent
action in the face of danger is a measure of that community’s resilience.
This capacity depends upon the cumulative set of cognitive,
communicative, and adaptive processes outlined above. If any one of the
preceding steps fails, the capacity of the community for collective action
is weakened. If all of the preceding steps are performed effectively, the
capacity for collective action is strengthened. Further, instances of
negative feedback can have the reverse effect of weakening the whole
system’s performance in response to danger.

Disaster management involves multiple governmental, nonprofit, and
private entities with different structures and organizational models. The
interest of each organization in gathering information and data regarding
exposure to hazards derives directly from its own mission. In current
disaster management systems, these organizations are much more
vulnerable to information overload caused by the transmission of a large
amount of irrelevant information. As the number and variety of sources
of information and sensors continue to grow, so does the volume of data
generated by these socio-technical sources of information.

One method of facilitating the timely exchange of valid information
to multiple managers simultaneously is the design of an “executive
dashboard,” in which data from different sources are represented visually
to provide a “common operating picture” of the status of the region at
risk. This design translates the concept of the “bowtie” architecture for
information processes into a working decision support system for
emergency managers operating at graduated levels of responsibility and
authority. The capacity for multiple managers at different levels of
responsibility to view the relevant information for their specific arenas of
action simultaneously enhances their ability to adapt and adjust their
performance to the emerging threat more quickly, efficiently, and
effectively.

Setting the thresholds of risk for participating agencies that have
different levels of resources but are exposed to threats of different
degrees of severity requires the judgment of experienced emergency
managers as well as timely, valid information. In planning sessions for
simulated operations exercises, selected practicing emergency managers
would be invited to participate in the definition of thresholds that are
relevant to their specific responsibilities. The model of an executive



Louise K. Comfort, Daniel Mosse, and Taieb Znati 37

dashboard offers a mechanism for building a “common operating
picture” among responsible actors in a complex disaster management
system for a community at risk.

Proposition 4. The collective capacity of a community to act in
coherent ways to reduce risk can be increased through
information search, exchange, focused views, and feedback
processes to create an inter-organizational learning system that
adapts its behavior to fit available resources to changing
conditions of risk more appropriately.

Vulnerability to Systemic Failure in Communities Exposed to Risk

At each of the four decision points identified above, human capacity
for informed action is enhanced by access to appropriately designed and
functioning information technology. The interaction between
organizational performance in coordinating action and the availability
and access to a functioning information infrastructure has a fundamental
effect upon a community’s capacity to manage the risk to which it is
exposed. Without access to such a technical information infrastructure,
the organizational capacity to mobilize collective action in a region will
likely fail. The collapse of the emergency response system in New
Orleans after the city lost its communications illustrates this argument
vividly.

Proposition 5: Without a well-defined, functioning information
infrastructure supported by appropriate technology, the
collective response of a community exposed to serious threat
will fail.

The five propositions, taken together, constitute a conceptual
framework regarding the evolution of capacity for collective action in
communities exposed to recurring risk. The basic argument is that human
capacity to act collectively and constructively in risky, uncertain
environments can be significantly enhanced through appropriate uses of
information technology.

The Mon Valley Project

In order to test the conceptual framework of the “bowtie”
architecture for decision support, the IISIS Laboratory research team
engaged in a trial demonstration project that involved 27 Pennsylvania



38 MANAGING RISK IN REAL TIME: INTEGRATING INFORMATION
TECHNOLOGY INTO DISASTER RISK REDUCTION AND RESPONSE

municipalities2 that border the Monongahela River. Pittsburgh=s signature
three rivers create a recurring risk of flooding for the metropolitan
region. Within the last 23 years, seven damaging floods have ravaged
riverfront communities, each causing substantial loss of property,
disruption of community economic activity, and significant hardship for
families, businesses, and communities. These floods include the
following events:

1985: Monongahela Flood, Pittsburgh, November 14.

1986: North Hills Flash Flood, May 31. Eight dead.

1996: River Floods, January, Pittsburgh area.

2001: Southern West Virginia Floods, June-July.

2004: Tropical Storm Ivan, September 17.

2005: January Floods, Allegheny River.

2007: July Floods, Allegheny River.

Given past experience, exposure to flooding is a known hazard for
the Pittsburgh metropolitan region which compels the communities to
seek more effective means of managing this risk. The reconstruction of
the storm water drainage systems is, of course, an overriding need, but as
shown by recent studies sponsored by the Allegheny Conference, the
price tag for these infrastructural improvements runs well into the
billions of dollars. New methods for analyzing risk to human life and
property from natural disasters can help public managers, businesses, and
households take preventive action in a timely fashion and at manageable
cost. Several of these methods have been incorporated into an innovative
decision support system under development at the University of
Pittsburgh.

The Interactive, Intelligent, Spatial Information System (IISIS)
Laboratory of the University of Pittsburgh has developed a prototype
decision support system (DSS) to enable communities to make decisions
regarding the vulnerability of existing infrastructure, the threat to local

2Braddock, Braddock Hills, Chalfont, Churchill, Dravosburg, Duquesne, East
McKeesport, East Pittsburgh, Edgewood, Forest Hills, Homestead, McKeesport,
Munhall, North Braddock, North Versailles, Rankin, South Versailles, Swissvale, Turtle
Creek, Versailles, Wall, West Mifflin, Whitaker, White Oak, Wilkins, Wilkinsburg, and
Wilmerding.



Louise K. Comfort, Daniel Mosse, and Taieb Znati 39

populations, and the capacity of local resources to respond effectively to
flooding in the Pittsburgh metro region. This prototype is currently being
tested in a real world setting. The Allegheny County Emergency
Management Agency and 27 municipalities along the Monongahela
River are collaborating with the IISIS Laboratory to test the model.

The primary objectives of this project are:

1. To implement an innovative decision support system (JIISIS)3

for assessing the vulnerability to flooding in 27 municipalities
exposed to risk in the Monongahela Valley.

2. To collect and validate the data needed to establish a regional
knowledge base for the 27 participating communities.

3. To evaluate the performance of the JIISIS prototype in a
demonstration exercise with practicing emergency managers.

4. To communicate the results of the exercise to the partner
agencies that supported the project: Allegheny County
Emergency Management Agency, Southwestern Pennsylvania
Chapter of the American Red Cross, the 27 municipalities, the
Pittsburgh Foundation, the Buhl Foundation, and an anonymous
local foundation.

Interactive, Intelligent, Spatial Information System (IISIS)

The JIISIS is a computational decision support system that integrates
data from key sources into a visual framework to assist managers in
making an effective assessment of risk to their communities. It helps
managers to interpret this information through color-coded graphics that
indicate varying levels of risk as the conditions change. While initially
developed for flooding incidents, the basic techniques are applicable to
any type of hazard. The JIISIS includes three principal components, all
of which are integrated into a dynamic information processing system
that provides updates on the status of key functions in real time. The
components include four types of components: 1) a dynamic bridge to
the local 911 system in which incidents are reported in real time, along
with the current availability of emergency response units and personnel;

3The prototype Interactive, Intelligent, Spatial Information System (IISIS) has now been
converted into JAVA, hence the acronym, JIISIS.



40 MANAGING RISK IN REAL TIME: INTEGRATING INFORMATION
TECHNOLOGY INTO DISASTER RISK REDUCTION AND RESPONSE

2) a GIS system with data stored on physical characteristics, engineered
infrastructure, and social and economic characteristics of the region; 3) a
Documents Library that stores current policies, procedures, emergency
plans for participating jurisdictions in a regional response system, and
professional assessments of risk; and 4) a series of computational
modules that estimate the vulnerability/capacity of communities to
manage their own risk.

911 Bridge

A major challenge for emergency managers is to keep abreast of the
status of a dynamically evolving emergency situation. The JIISIS
prototype has developed a bridge that transmits data directly from the
local 911 system to the central IISIS processing server in real time to
provide a continually updated profile of incidents as they occur. This
feature includes dynamically tracked data on resources so that
emergency managers know what resources are available at any given
time and which have already been committed to action.

Incident Action Plans

As required to be compliant with the National Incident Response
System, emergency managers now must file a series of Incident Action
Plans. The forms for these plans have been replicated in electronic
format, with the identifying information for each form carrying over to
subsequent forms. This format facilitates the required recordkeeping
tasks for emergency managers, saving time and focusing their attention
on the critical information required for coordinated action.

Active Geographic Information System

A key feature of the JIISIS is its capacity to generate GIS thumbnail
maps as part of the information that is registered for a particular incident.
That is, as an incident is reported, with location, type of hazard, and
severity, this information generates a small GIS map that locates the
incident in the region. This function has the advantage of building a
geographic profile of incidents in the region, and emergency managers
can refer to this larger pattern of incidents in making decisions regarding
the routing of emergency units and allocation of resources.



Louise K. Comfort, Daniel Mosse, and Taieb Znati 41

Documents Library

The Documents Library stores policies, procedures, plans, and other
relevant information for easy reference by emergency managers as
complex incidents unfold. These documents are especially useful in
inter-jurisdictional events with multiple agencies from many jurisdictions
involved in response operations.

Dynamic Assessment Module (DAM)

This module represents a decision-support tool that measures change
in a set of significant parameters in infrastructure, threat level, changing
environmental conditions, and availability of the emergency response
units and personnel. It indicates the vulnerability, or its inverse B
capacity – of a particular community or region to respond to disaster. As
incidents occur and response assets are assigned, their availability for
subsequent incidents lowers the resiliency of the community, and other
communities to whom they provide mutual aid. The inverse of the
response organization capability is the changing vulnerability of the
community based on varying states of infrastructure, environment, and
weather conditions.

The equations upon which the model is built include 36 different
variables to determine initial vulnerability. These variables are combined
into three components of vulnerability: geophysical attributes,
engineered structures, and social environments. A total of 10 variables
are used to determine the response capacity of public safety and
emergency management officials. One variable is used to measure the
resiliency of public safety response organizations.

The capability of the public safety response organizations is
subtracted from the initial vulnerability of the region to show the change
in risk conditions as local resources are deployed to bring the threat
under control. This adjusted vulnerability varies as response assets
fluctuate between available and assigned. The results have been
translated into color-coded graphics that allow the emergency managers
to gauge the community=s present status at a glance. In practice, a
connection to a live 911 data stream would allow managers to assess
real-time information on the status of their community and provide
additional warning time to minimize the hazard to a community. This
unique decision support tool gives emergency managers the
technological edge necessary to gain insight into how disasters evolve



42 MANAGING RISK IN REAL TIME: INTEGRATING INFORMATION
TECHNOLOGY INTO DISASTER RISK REDUCTION AND RESPONSE

and to alert them to potential problems in time to intervene and minimize
the damaging impact to their communities.

Risk Assessment Analytical Tools

Several tools for identifying hazardous materials or locating existing
quantities in storage are also available, as well as a module for Patient
Tracking. Other modules for Traffic Monitoring and Routing are in the
early planning stages. These modules represent continuing development
by the IISIS Lab research team, with informed guidance and review by
practicing managers and interdisciplinary experts.

Risks and Risk Management Strategies

Introducing new concepts and strategies for action into any set of
communities poses the risk of rejection by existing managers in favor of
maintaining a more familiar status quo. In the case of flooding risk,
recent events have increased the awareness of these riverfront
communities to the dangers they face and the economic and physical
losses they are likely to sustain. Introducing a new approach to managing
well-known flooding risk is likely to be clearly understood and well-
received by the communities that have recently experienced losses from
Hurricane Ivan and watched the devastation created by the flooding of
New Orleans in Hurricane Katrina. The timing for introducing a method
of computational modeling of flooding risk is appropriate, given the
heightened level of public awareness of risk. The trial demonstration
project for communities in the Mon Valley included meetings with local
communities to explain the need for data collection, the methods, and
benefits of the project=s operation. Project staff worked directly with
emergency service personnel in each community to ensure that they
understand the workings of the prototype and would be able to
implement it in their respective communities.

Current Status

The one-year pilot project to implement the JIISIS decision support
tool with the collaboration of 27 riverfront communities that are most at-
risk from flooding was the first demonstration project designed primarily
for a study region of municipalities in Allegheny County. These
communities also participated in a companion project, the Monongahela
River Community Shelter Project that is being conducted by the
Southwestern Pennsylvania Chapter of the American Red Cross. The
Community Shelter Project, supported by a grant from the U.S. Steel



Louise K. Comfort, Daniel Mosse, and Taieb Znati 43

Foundation, is both strengthened by, and complementary to, the
University of Pittsburgh project to assess vulnerability to flooding in the
Mon Valley.

These two related projects integrated existing databases as well as
added new data to create a regional model for managing risk in the Mon
Valley. IISIS staff members are working to develop the partnerships
essential for continuing this project on a regular basis. Organizations
central to the coalition include Allegheny County Emergency
Management Agency, Southwestern Pennsylvania Chapter of the
American Red Cross, and the 27 municipalities participating in the
Community Shelter Project. Additional organizations may be included as
the effort builds a region-wide coalition of informed organizations
committed to reduce the risk and losses from flooding.

The Mon Valley Project began on October 1, 2006, and formally
concluded on October 8, 2007, with a demonstration of the working
design for the selected communities in Allegheny County that are
exposed to flooding risk. The prototype is currently being checked and
validated for use by the municipalities, and a trial version was released to
the participant communities in Fall 2008 for continued testing and
feedback. This demonstration served as the major method of evaluating
the performance of the project and the efficacy of using information
technologies to support decision processes in managing recurring risk.
Practicing emergency managers from the 27 municipalities were invited
to assess the performance of the prototype and its contribution to
increasing the regional capacity to reduce damage and losses from
flooding.

Conclusions

The Mon Valley trial demonstration project offered a means of
testing a decision support system based on the “bowtie” architecture
prior to making a major investment in hardware and software in the
Pittsburgh Metropolitan Region. Experience to date demonstrates that
information technology facilitates information search and exchange
processes among organizations participating in disaster risk reduction
and response, but that demonstration and training are essential to
encourage adoption and use. Unless such processes are recognized and
accepted as part of continuing operations in communities, they are not



44 MANAGING RISK IN REAL TIME: INTEGRATING INFORMATION
TECHNOLOGY INTO DISASTER RISK REDUCTION AND RESPONSE

likely to produce the desired results. If information technologies are
introduced as part of a sociotechnical system that enables human
managers to make more accurate, timely decisions under stress, they are
more likely to be incorporated quickly into daily operations as a means to
improve performance within and among organizations.

Acknowledgments

We acknowledge, with thanks and appreciation, the members of the
IISIS staff who have participated in the design and implementation of the
JIISIS model: Mark Dunn, Adam Zagorecki, David Johnson, Robert
Skertich, Leonard Huggins, Clayton Wukich, David Evans, and Elliott
Levenson. We also acknowledge Thomas W. Haase for his formatting of
the “bowtie” model in Figure 1. This research was supported in part by
National Science Foundation grant, ITR #0325353, and grants from local
Pittsburgh foundations, including The Buhl Foundation and The
Pittsburgh Foundation.

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