Smartphones, Disasters, and Knowledge Management: An Examination of Field Data Collection, Analysis and Dissemination

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Smartphones, Disasters,
and Knowledge Management:

An Examination of Field Data Collection, Analysis and Dissemination

James F. Ehlert

US Naval Postgraduate School

92
-
1471 Aliinui Drive 33A

Kapolei, Hawaii 96707

USA

jfehlert@nps.edu

ABSTRACT

Global response and relief efforts to the 2011 Japan earthquake and tsunami, and the 2010 Haitian
earthquake have illuminated the need for new collaborative models to facilitate effective response to
international crises and con
flicts. Into the limelight, the relatively new phenomenon of “crowd sourcing”,
defined as the act of outsourcing tasks, traditionally performed by an employee or contractor, to an undefined,
large group of people or community (a crowd), through an open cal
l, has emerged. As witnessed in recent
years, disaster response measures draw a crowd consisting of a wide demographic population, often
composed of a myriad of civilian, military, law enforcement, rescue workers, working side
-
by
-
side with people
represent
ing government and non
-
government organizations spanning the local, state, national, and
international level. This crowd is often equipped with mobile technology (smartphones). This fact, coupled
with the availability of cellular and wireless communication

networks, has increased the speed and quantity at
which information about a disaster site and the resulting human suffering reaches the rest of the globe.

The relief efforts in Japan and Haiti emphasized the need for implementing knowledge management pra
ctices
to more effectively leverage the sheer amount and corresponding proliferation and dissemination of data
attributable to crowd sourcing. The challenges presented by the increased need for knowledge sharing and
collaboration across a non
-
homogenous cr
owd with potentially widely varying culture, language, expertise,
and priorities is readily apparent. Incompatible data often renders disaster site data unusable or requires too
much time to convert for the crowd


which includes first responders
-

to effe
ctively use and share.

The United States sponsors

a variety of disaster management and response conferences, events, and
workshops aimed at fostering a dialogue pertaining to communication interoperability, data sharing,
command and control, knowledge man
agement, and so forth. Many of these venues are slated in the Pacific
Theater and include locales such

as the Philippines
and Nepal. The author of this paper is part of an inter
-
agency team to deploy smartphones into the hands of first responders in order
to collect, analyze, and share
disaster related information in near real
-
time amongst decision
-
makers and other stakeholders at these multi
-
national gatherings.

It is in the bes
t interests of the U.S.

and partner nations to render assistance as quickly as

possible following a
disaster in order to prevent post disaster suffering and to establish civil order before criminals or terrorist
organizations are able to take advantage of the situation. To accomplish this, knowledge management
practices must integra
te crowd source data to inform and enable decision
-
makers to effectively and efficiently
respond to crises. The first vital few days could mean the difference in preventing undue loss of life.

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1.
0

NATURAL DISASTERS, S
MARTPHONES, AND SOCI
AL NETWORKS

“Knowle
dge is experience. Everything else is just information.”
-

Albert Einstein

A natural disa
ster is defined by the United Nations as

“the consequences of events triggered by natural
hazards that overwhelm local response capacity and seriously affect the soc
ial and economic development of a
region.”
1

The 2010 Haitian earthquake relief effort has illuminated the need for new collaborative models to
facilitate effective response to international crises and conflicts. The relatively new phenomenon of “crowd
sour
cing”, defined as the act of outsourcing tasks, traditionally performed by an employee or
contractor
, to an
undefined, large group of people or community (a
crowd
), through an open call. As witnessed in recent years,
disaster response measures generate a crowd potentially with a diverse demographic background such as
civilian, military, law enforcemen
t, rescue workers, along with people representing government and non
-
government organizations spanning the local, state, national, and international level. More often than not, this
crowd is most likely equipped with mobile technology in the form of the m
odern smartphone. These
smartphones are lightweight, affordable, and allow users to collect and transmit data in many formats such as
text, audio, phot
o, and video. These data capturing

savvy crowds, coupled with the availability of cellular and
wireless
communication networks, have increased the speed at which information about a disaster site and the
resulting human suffering reaches the rest of the globe.

While numerous definitions of knowledge management (KM) have been put forth, the Bridgefield Grou
p, Inc.
defined KM as

“a system or framework for managing the organizational processes that create, store and
distribute knowledge, as defined by its collective data, information and body of experience.”


Within this
definition knowledge can take on one of

two forms, either tacit or explicit. If the knowledge is tacit it is
knowledge residing inside the human brain in the form of experience. That knowledge residing outside the
human brain in databases, books, and so forth is explicit knowledge. Relief ef
forts in Haiti, along with the
less catastrophic flooding in Australia and Thailand, have evidenced that existing KM practices do not
integrate nor account fully for the sheer amount of data that crowd sourcing activities will create nor the
corresponding
proliferation and dissemination of that data. The challenge presented by the increased need for
knowledge sharing and collaboration across a non
-
homogenous crowd with widely varying culture, language,
expertise, and priorities is readily apparent. Incomp
atible data often renders disaster site data unusable or
requires too much time to convert for the crowd, i.e. the various responding nations and organizations, to
effectively use and share.

It is in the best interests

of the United States (US),

Partner

Nations and participating organizations to render
assistance as quickly as possible following a disaster in order to prevent post disaster suffering and to establish
civil order before criminals or terrorist organizations are able to take advantage of the

situation. To achieve
this, knowledge management practices must integrate, aggregate and disseminate crowd source data in order
to inform and enable decision
-
makers to effectively and efficiently respond to crisis and to maximum the
impact of available r
esources to the situation. The first vital few days could mean the difference in preventing
undue loss of life.

The discovery and quick adaption of new crowd source and social network technology is a popular method of
keeping in touch with friends, family,

relatives, and co
-
workers. Concomitantly, this same functionality to
“push” personal information (ubiquitous) quickly to pre
-
determined and undetermined subscribers, as well as
to other social networks, is potentially useful in reporting on current event
s, such as a disaster. The ensuing
paragraphs

highl
ight a social network technology (called FIST) being developed by the US and partners,



1


InterAgency Standing Comm
ittee,
Operational Guidelines on Human Rights and Natural Disasters
.

Washington: Brookings
-
Bern
Project on Internal Displacement, June 2006.

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currently in use around the world
. In this context of this article, the FIST

capability

portend
s

significant
implicat
ions respective to knowledge managemen
t and disaster response efforts.

1.1

Field Information Support Tool

(FIST)

Overview

The concept of FIST originated with Captain Carrick Longley, US Marine Corp, and Chief Warrant Officer
Chad Machiela, US

A
rmy
, while both
were students at the US Naval Postgraduate School (NPS). FIST is a
field based collection system using commercial
-
off
-
the
-
shelf mobile electronic devices (laptops, tablets, smart
phones, etc.) customized software, and a robust information management backe
nd known as
FusionPortal
.
FusionPortal
enables information to flow from the point of capture to an analyst in near real
-
time regardless
of location or physical proximity. As cited above, FIST was designed to operate in a variety of environments
and suppo
rts a variety of mission sets such as counterinsurgency operations, counter
-
narcotics missions,
humanitarian assistance/disaster response (
HA/DR
)
, and other inter
-
agency environments. The overarching
principle of FIST is the development of a user
-
friendly
data collection tool that utilizes automated information
systems to enable unstructured data to be collected, processed, and structured for analysis and visualization in
a variety of analytic packages.
FusionPortal

is under development to enable real
-
time

integration of disparate
sensor systems (such as facial recognition) that provides a powerful common operating picture critical for
today's decision makers.
FusionPortal
allows for data to be exported and analysed using geospatial, geo
-
statistical, tempo
ral, link, and social network analysis in addition to enabling the exchange of information with
external databases

used by partners in the relief effort
.

2.0

KNOWLEDGE MANAGEMENT

AND SOCIAL NETWORKS

“If sufficient number of management layers are superimposed on top of each other,

it can be assured that disaster is not left to chance.



Norman R. Augustine

2.1

Knowledge

M
anagement (KM)

The research and

study of KM

has been moving forward at a significant pace for at least the last two decades.
Indeed some scholars opine that the KM will eventually become a discipline of its own complete with
“theories, jargon, practices,
tools, skills, and other accoutrements of an independent discipline.”
2

Salisbury
(2003) defined a KM “cycle” as shown in Figu
re 1

below:



Figure 1
: Knowledge Management Cycle
3
.




2

Thomas, J.C.; Kellogg, W.A.; Eric
kson, T. “The knowledge management puzzle: Human and social factors in knowledge
management”.
IBM Systems Journal
. Vol. 40 No. 4, 2001, pp. 863
-
884.

3


Salisbury, M. W. “Putting theory into practice to build knowledge management
Systems”.
Journal of Knowl
edge Management
.
Vol. 7, No. 2, 2003, pp. 128
-
141

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Thomas, Kellogg and Erickson (2001) conducted a case study review of KM and
contend that KM is more
than enhanced data mining, i.e. collecting, storing, processing, sharing, and retrieving information.
“Knowledge is inextricably bound up with human cognition, and the management of knowledge occurs within
an intricately structured

social context.”
4

In their extensive case study review it became evident that
knowledge was being created through loose social networks and communities and that the understanding of
this creation required an understanding of the relationships and social
contexts for a given environment. The
social context or network, they argue, is the appropriate level of study and is more conducive than studying
the individual. Through this process Thomas, Kellogg, and Erickson found several attributes, or phenomena
a
s coined in their article, that are important to KM within social networks, notably relationships, awareness,
common ground, incentives, and motivation. Extending this concept further they proposed that the ideal KM
system would not only make people and i
nformation visible but also the interaction between them visible.
They contend that “it should be possible to see people interacting with explicitly expressed knowledge (e.g.
reading), and it should be possible to see people conversing with one another (b
oth as a means of explicating
tacit knowledge and as a means of building and maintaining the social factors such as trust and relationships
that are important in knowledge management).”
5

In an early study, Churchill and Bly (1999) examined a text
-
based con
versation environment and found that social ties and physical proximity had an effect on what
conversations occurred between people and also the frequency of those conversations. In Putnam’s research
of regions and local governments in Italy he identified

communication and trust, both facilitated through
participation in groups, clubs, and other associations as good predictor of future economic growth.

Nonaka and Takeuchi (1995) defined four different phases of the knowledge life
-
cycle
-

socialization,
i
nternalization, externalization and com
bination


and shown in Figure 2

below. Under Nonaka and Takeuchi
“knowledge management” is defined as the management of the environment that makes knowledge flow
through all the different phases of its life
-
cycle.




To




Tacit

Explicit

From

Tacit

Socialization

Externalization


Explicit

Internalization


Combination


Figure 2
:

Knowledge Conversion as proposed by Nonaka and Takeuchi (1995)
.




4

Thomas, J.C.; Kellogg, W.A.; Erickson, T. “The knowledge management puzzle: Human and social factors in knowledge
management”.
IBM Systems Journal
. Vol. 40 No. 4, 2001, pp. 863
-
884.

5

Ibid
.

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2.2


Crowd Sourcing and Social Networks

The concept

of crowd sourcing is perhaps best defined by Howe (2006) as the “…the act of a company or
institution taking a function once performed by employees and outsourcing it to an undefined (and generally
large) network of people in the form of an open call. Thi
s can take the form of peer
-
production (when the job
is performed collaboratively), but is also often undertaken by sole individuals.”
6

The emergence of crowd sourcing, and the social networks they enable, impact HADR decision
-
maker ability
and effectivene
ss through the positive or negative effects on KM. New information technology tools
increase connectivity, the availability to collect and share information, while the capability of KM practices to
benefit from this remains indeterminate. Based on rece
nt disaster events and the impact created by crowd
sourcing, disaster response decision
-
makers and leadership appear to recognize a shift in effective
communication among collaborating assets separated by professional and personal relationships, trust,
phy
sical distance, and other social contexts.

“Tactical employment of social networking in the midst of operations can be very successful as
well. Use of chat and shared blogs delivers a richer situational awareness, especially when
combined with a common ope
rating picture developed from fused information sources”.

-

Rear Admiral J. Hamby, U.S. Navy

Crowd sourcing represents a powerful information gathering and communication medium, conversely it offers
the potential to enhance many of the limitations to effecti
ve KM. Through crowd sourcing, aided by ever
more powerful mobile electronic devices, the amount of data collected increases, thus testing the ability of
KM practices to process this data into actionable information and to disseminate it to consumers. Ac
cording
to U.S. Navy Lieutenant (LT) Jeffrey Bennington and LT Ryan King (2010), social networking may be the
key to making that fundamental shift and engendering a mindset of information sharing.
7

Similar to the
discussion of KM above, LT Bennington and
LT King stated that “Trust, in particular, influences what kind of
information is shared among nodes in a network, how often it is shared, and whether it is shared with all or
select members of that network based on levels of confidence for an organization
”.

Recently Vuorikari, Ochoa, and Duval (2007) found that social tagging, i.e. allowing individuals to apply free
text keywords to digital objects, potentially offers advantages in terms of personal knowledge management,
serendipitous access to objects thr
ough tags, and enhanced possibilities to share content with emerging social
networks.

As

illuminated in the earlier

section of this submission, information technology and the Internet
have had a profound effect in the creation, rise, and proliferation of c
rowd sourcing technology. Stenmark
(2002) developed a model to depict where the KM perspective is captured by three concepts relating to
Internet accessibility: information, awareness, and communication respectively (please refer to Figure

3

below). The
three concepts pertain directly to crowd sourcing as the cellular network and the modern
smartphone provides Internet accessibility.




6

Howe, J
. “The Rise of Crowd Sourcing”.
Wired Magazine
. Vol. 14 No. 06, June 2006.

7

Ibid.

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Figure 3
:

A Multi
-
Perspective View for the Internet
.

As noted by Crowley (2010) the cell phone

has become ubiquitous, reaching even into the hands of the
poorest people and deep into the world's worst slums. As a result, citizens of countries prone to disasters
-

either manmade or natural
-

can now share information about ongoing operations and coo
rdinate their own
actions. Some have taken to using these capabilities to support insurgent campaigns. Others are trying to use
this network to route around barriers to markets. Regardless, there is a new sense of the edge of truth being in
the hands of ci
tizens: truth as immediate information, shared between friends and family, in near real time. In
contrast, large bureaucracies tend to trust information only after it has traversed a long chain of vetting,
delaying its best understanding it days or weeks b
ehind the edge of truth.

Today's crowd sourcing applications
enable a crowd to submit and view it
s
own messages, with each contributor being an equal peer. Applications
like Ushahidi and Frontline SMS provide software platforms for aggregating SMS messages

into channels,
parsing SMS into structured data, and then provide means to visualize the data. That said, today's applications
are all scoped to enable a single crowd in a given place to aggregate equal repots, not to enable exchange of
data within a ecos
ystem of multiple crowds and other information resources (like structured linked data and
information feeds from sensors).

3.0

THE
FIST

SYSTEM & CAPABILITIE
S

“If sufficient number of management layers are superimposed on top of each other,

it can be assu
red that disaster is not left to chance.”


Norman R. Augustine


FIST is divided into two separate components that comprise the system. The field collection tool is a
smartphone application known simply as
Gather
, the web
-
based information management port
al and the
analysis, sensor fusion and visualization system is known as
FusionPortal
.

3.1

The
Gather

Application

FIST
Gather

is an Android based application that enables the collection of data in a structured, form based
menu interface to be transferred to

the remote
FusionPortal
server.
Gather

is composed of the smart phone,
the mobile operating system, the software application running on the phone, and the collection modules for
the specific mission type.

The
Gather

handheld software contains a number of

capabilities and features which allow for a dynamic,
flexible approach to field data collection. The primary focus of the application is to allow customized forms
to be created and loaded into the device which support a variety of data types, multimedia
formats, and
intelligent auto
-
suggestions for commonly used words and names. The software application can be broken
into three major components: the forms processor, local database, and server interaction functionality.

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Figure

4
:

Gather
s
martphone screen shot visualizing link analysis from collected notional data
.


Figure
5
:

Gather

multimedia report showing detailed

infrastructure

(bridge)

informat
ion
.


A primary focus of FIST is capturing field data in a structured yet flexible manner.
Gather

is designed as a
form based processing engine to this end. Forms are loaded via a network connection to
FusionPortal
, the
forms manager, and can be updated
in real
-
time at any future point. The forms support multiple language
packs and will render the questions in the appropriate target language, provide a dynamic environment for
data validation and streamlined entry, and enable the operator to capture a var
iety of multimedia formats for
attachment to the collection report, such as geo
-
coordinates, voice, video, and photographs. Although not
implemented in the original FIST specification, the ability to attach external Bluetooth devices such as a
fingerprint

scanner is being considered for future iterations. The local database on the phone provides the
ability to cache the collection reports while awaiting transmittal, as well storing collection reports previously
submitted to
FusionPortal
. This construct a
llows for
Gather

to work in an entirely disconnected state while
still providing the collector an information rich environment to operate in. The
Gather



FusionPortal

interaction allows for the transferring of information to and from the smart phones, th
e updating of forms and
collection reports, and access control based on account specifications at the organization's account in
FusionPortal
.

Can be video &
audio or images

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3.2

FusionPortal

FusionPortal

provides visualization (geospatial and report based), analysis, consolidating and in
formation
sharing via streamlined and intuitive user displays. These four capabilities form the foundation of a powerful
and flexible information management strategy. This application receives data input from the field and then
processes the data into a
fused view that can be shared and analyzed. The resulting transformation of raw data
enables knowledge creation that is much more useful to a consumer, be that an analyst or decision maker.


Figure 6
:

FusionPortal

screenshot, each icon represents a
Gath
er

report and the “lines”

depict the GPS tracks of the operator as reported through the
Gather

application
.

This architecture results in a capable and practical application. One that is multifunctional, real
-
time, and
easily accessed in the field from sma
rtphones as well as central command centers and mobile laptops. The
web architecture is highly scalable, so growth of the operation benefits from economies of scale. The
centralized web architecture also provides versatility to add alarms, data quality a
ssurance, mathematical tools
and advanced systems data, such as unmanned vehicles and sensors such as radars, cameras, etc., as well as
straightforward data interchange with other applications and databases.

As FIST
Gather

is designed to improve data colle
ction processes,
FusionPortal
supports a number of
processes and external tools for enhancing and improving data visualization, sharing, consolidation and
analysis.


Figure
7
:

FusionPortal

map of Nepal depicting notional areas affected by heavy flooding
.

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3.3

FusionPortal

Analysi
s Tools Interface Module (ATIM)

The ATIM allows non
-
analysts to create products included temporal data presentations (animated video),
geospatial views, data space views, network diagrams and traditional PowerPoint slides. The ani
mated
geospatial format equates to a survey of many data sets in time and allows for rapid assessment of relational
patterns by analysts and planners. The following screenshots (social network analysis, geospatial movie, motion
charts, heat maps, etc.) ar
e automated outputs from the ATIM based on data submitted from
Gather

reports.


Figure 8
:

ATIM Social Network Analysis visualization of fused data from
FusionPortal.
Red nodes represent
persons while other nodes represent disaster
-
related events and or
ganizations to which they are linked
.


Figure
9
:

FusionPortal

data converted and exported to Google Earth, where

events may be played out in time and space (Geospatial Movie).

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Figure 10
:

FusionPortal educational data displayed in a “Motion Chart” to vi
sualize correlation
between various variables. The graph shows correlation between Total Elementary

Enrollment (X
-
Axis) and Pupil to Teacher Ratio (Y
-
Axis) over time.




Figure 11
:

Notional FusionPortal data was used to create a “heat map” showing th
e

density of points (red indicates higher density). Blue and red points represent

displaced populations and locations of hospitals respectively.

When
Gather
reports and corresponding data are added to the portal, and through subsequent use of the
automa
ted output from the ATI, events gain context and perspective through visualization. Data and events
can now be included in other analytical processes (such as related events) that might lead to planning and
causality determinations. This ultimately helps

decision makers understand their overall effect on the
environment and how to best achieve their goals.

The
FusionPortal /
ATIM system illuminates the possibilities of fusing multi
-
source and dissimilar mission
data sets. As more data is aggregated, the
range of analysis products that can be used to visualize patterns and
infer relationships increases.

4
.0

FIST

USE CASE IN THE PACI
FIC THEATER

“All models are wrong; some models are useful.”
-

George Box, Stu Hunter, & Bill Hunter

The US
support
ed a field
data collection proof
-
of
-
concept in both the Philippines and in Nepal with the full
cooperation of the US Embassy and the Partner Nation team.
The project goal wa
s to collaborate with the
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host nation personnel as well as Disaster Readiness teams to optimi
ze a process to collect, aggregate, and
disseminate field data from smart phones, i.e. pictures/video of infrastructure both social
and physical. The
process
include
d

routing the data to an analysis center to enable better understanding of the environme
nt for
security, development and response to disaster incidents.

FIST team r
egional

know how


from this

deployment
synergistically benefit
ted both

the
Philippines and
Nepal effort with Geographic Information System capability (GIS), knowledge management p
rocesses,
analytic workflows, visualization and system architectures for global access, field operations and command
both mobile and fixed command centers. The analytic processes include
d

fusing field, historical and
public/social media data into a tempor
al, network and geospatial formats to enable knowledge creation and
enhance decision making.

4.1


Philippines

In lat
e August 2011, US organizations

agreed to share a corpus of education data with the FIST Team in order
to evaluate FIST knowledge manageme
nt and visualization capabilities. Data was systematically geocoded
(processed to apply geographic attribute data to educational training events), uploaded to a private
compartment on
FusionPortal
, and visualized as both a geospatial movie and a social ne
twork diagram.
FusionPortal
allowed the management and visualization of data spatially, while export to a Geospatial Movie
through the FIST Analysi
s Tools Interface Module
enabled the analysis of data in both space and time.
Educational training event da
ta was also overlain with negative e
vent data gleaned from other databases (such
as a counter
-
narcotics d
atabase
)

in order to derive insights from the resulting multi
-
source fusion product.

Data was also processed and visualized as a social network using

the
Carnegie Mellon University
Organizational Risk Analyzer (ORA), in order to evaluate relationships between persons, events,
organizations, and locations. Sample analysis was carried out for educationa
l training events in a southern
p
rovince and
presen
ted
in order to show the potential for Social Network Analysis to reveal trends and
insights. The goal of this exchange was to show the
“art of the possible” with the

data, and illustrate ways that
improved data gathering, visualization, and analysis can
promote efficiency, highlight trends, and enhance
decision
-
making power. Future collaboration will likely focus on methods for completing the data ingest
process of the educational data corpus, innovating ways for FIST to directly populate this optimized
data
structure, and illuminating actionable analysis.











Figure 12
:

The complete network of administrators and training events

shown left (red nodes=attendee
s, green nodes=training events)
.



Extracted cluster
colored by religion
(pink=Christian, red=Muslim).

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From the data gathered, the
primary social cluster
(
the yellow circle in Figure 12 above) was

extracted,
processed, and colored to show religious diversity of administrators at the clustered events.

This is but one
example analysis to demonstrate the ability to stimulate further queries which in turn drive

the collection and
analysis process.



Figure 13
:

Social Networks: Out of School Youth (OSY) training and key
training
events
.

In Figure 13, student enrolment data is analysed to identify those students with the highest degree
of centrality
amongst Out of School Youths (OSY). The students, for example those like Agent A220248, with high
degrees of centrality may be useful in a social context for recruiting OSY students to future training events.

4.2


Nepal

The US
support
ed a s
ubject matter expert

exchange
(SMEE)
for Social Network Analysis (SNA).

This effort
was designed to b
uild on previous training with the Nepalese police in the scie
nce of SNA culminating

with
an applied inter
-
operability exchange to document critical infr
astructure in Kathmandu
, Nepal

using the FIST
capability

The project goal was

to
collaborate with Nepalese
Disaster Readiness
teams
to familiarize and equip selected
m
embers
with newly developed cellular capabilities that
enable the FIST
-
equipped first res
ponder

to gather
critical infrastructure and social cultural information. This information is then analyzed by specialists to
collate and build a common picture that will aid the commander or appointed leadership in making highly
informed decisions in tim
es of disaster.

Specific

objectives were as follows
:



SNA exchange focused on methodology, collection,
analysis and visualizations tools



Completion of the exchange with a qualitative understanding of an effecting field collection and
analysis program



In
corporation of lessons learned from SNA technology deployment in the Southern Philippines

The
Nepalese
analysi
s team was provided
intensive preparation on the analytical tools and the methods
required to systematically input incoming
FIST
Gather

reports fr
om the surveying

team
s in the Kathmandu
Valley

into
the ORA software. Subsequently these same teams prepared

detailed link and other analysis that
form
ed

a graphical and analytical tool for analysts and respective decision makers to anticipate and prepare

detailed plans for emergency operations.

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The analysis was dependent
on the number and quality of reports on a plethora of infrastructure and human
conditions and

activities.

Surveyors

were given prepared and spontan
eous report requirements which we
re s
pecifically designed to
allow the analysts to continually build more cogent and actionable courses of action.
As
the number of
submitted
Gather

reports increased
, the g
ranularity of the analysis beca
me more refined and allow
ed

pinpoint
knowledge of problem

areas and people of interest, who in

time of catastrophic events, could

influence the
outcome in a positive or negative mann
er. Well
-
informed analysis could
also produce accurate predictions of
trouble spots such as flood areas, earthquake zones, probabl
e fire areas and predicted choke points on
evacuation routes.

Sample analytical products are shown below in Figures 14
-
16 below:


Figure 14
:

FIST generated overlay depicting disaster management and response information
.



Figure 15
:
FusionPortal
overlay

visualizing the
cellular c
overage
for Nepal
.

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Figure 1
6
:

SNA products developed by the Nepalese team
.

The SMEE project goals were met


in particular, to colla
borate with Nepalese
Valley Division Army Disaster
Readiness
person
nel
to optimize a process to collect, aggregate, and disseminate field data such as pictures
and videos of infrastructure both social and physical from smart phones.

The following Findings and Recommendations
were jointly developed by the Nepalese

and US
team:



It was felt that the FIST system would significantly transform t
he ability of the Nepalese
to conduct
planning, consolidation, analysis, production, sharing, and planning in support of multi
-
mission
requirements.



It was apparent that the FIST system
would significantly increase the speed and efficiency of the
Nepal
ese Disaster Management Information

Fusion Cycle specifically the disco
very and
understanding of inter
-
agency, first responder,

and other networks.

It was

observed that one of the consistent

objectives of the ongoing tra
nsformation of the Nepalese first
responder team

has been to explore and embrace technology to overcome hurdles in the context of modern
challenges.
As stated by the Nepalese

“Working with the
FIST devices in
the Humanitarian
Assistance /
Disaster Response mission space has been eye opening. Some of the best benefits have come from immersion
into the analytical processes including the incorporation and merging of field, historical and public/social
media data into network and g
eospatial formats to enhance knowledge and assist decision making.”

5.0

FINAL THOUGHTS

“You can do anything, but not everything”.
-

David Allen

Emerging evidence suggests that science and technology will continue to propel advances in the military and
in
ternational development spheres. Civilian
-
military coordination and cooperation are like to benefit. With
close to 12 billion (?) people having smart phone access today worldwide, and cost dropping, data collection
and information sharing will likely exp
and exponentially. New tools for peace will soon emerge that will
render our simplified notional framework fast out of date. At the same time, science and technology is not the
panacea. Recent cognitive findings suggest that we have excessive and unjust
ified confidence in what we
think we know and we are often unable to acknowledge even the most simple situations
8
…not to mention



8

Thinking, Fast and Slow, Daniel Kaheman, 2011.

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complex HA/DR situations which are usually fast
-
paced, highly complex, and require a cascade of decision
-
making. Smart phone te
chnology and advanced portals to store, analyze, and display data, hold the promise of
putting real
-
time information in the hands of planners and decision makers in ways that no graphs, charts or
spreadsheets ever could. We must never forget that tools can
’t substitute or replace dedicated professionals.

However,
FIST can be an enabling tool to enhance disaster management and response activities. Its multi
-
mission capability and appeal for interagency and international partners is in process of creating a
n
aggregation of shared information. This aggregated information body can over time provide the volume, type
and scope of information to enable effective analysis using novel methods. The system can promote
knowledge creation and sharing, support critica
l decisions and provide a new capability aspect for irregular
warfare and nation partner support.
FIST can be the key.

6.0

REFERENCES

Alavi, M. & Kane, G. C. “Knowledge Management: An Evolutionary View”. Advances in Management
Information System. Vol. 12,
2008, pp. 63

85.

Alavi, M., & Leidner, D. E. “Knowledge manageme
nt systems: Emerging views and
practices from the field”.
Proceedings of the 32nd Hawaii International Confer
ence on Systems Sciences. 1999.

Alavi, M., & Leidner, D. E. “Knowledge manag
ement
and knowledge management
systems: Conceptual
foundations and research issues”. MIS Quarterly. Vol. 25, No. 1, 2001, pp. 107
-
136.

Carlsson, S. A.; El Sawy, O. A.; Erickson, I.; & Raven, A. “Gaining competitive advantage through shared
knowledge creation: In

search of a new design theory for strategic information systems”. Proceedings of the
Fourth European Conference on Information Systems. J. Dias Coelho, T. Jelassi, W. Konig, H. Krcmar, R.
O’Callaghan, and M. Saaksjarvi (Eds.), Lisbon, 2006.

Churchill,
E.F. and Bly S. “Virtual Environments at Work: Ongoing Use of MUDs in the Worskpace”.
Proceedings of International Joint Conference on Work Activities Coordination and Collaboration. San
Francisco, CA., ACM Press, New York, 1999.

Bennington, Jeffrey G. and

King, Ryan H. “Perceptions on Social Networking: A study on Their Operational
Relevance for the Navy”. U.S. Naval Postgraduate School thesis, March 2010.

Borghoff, Uwe M. and Pareschi, Remo. “Information Technology for Knowledge Management “. Journal of

Universal Computer Science. Vol. 3, No. 8, 1997, pp. 835
-
842 submitted: 1/6/97, accepted: 10/8/97, appeared:
28/8/97 ã Springer Pub. Co.

Chen, Chung
-
Jen and Huang, Jing
-
Wen. “How organizational climate and structure affect knowledge
management

The social

interaction perspective”. International Journal of Information Management. Vol.
27, 2007, pp. 104

118.

De Long, David W. and Fahey, Liam Fahey. “Diagnosing Cultural Barriers to Knowledge Management”.
Academy of Management Executive. Vol 14, No. 4, 2000.

Donner, J. “Framing M4D: The Utility of Continuity and the Dual Heritage of Mobiles and Development”.
The Electronic Journal on Information Systems in Developing Countries. Vol. 44, No. 3, pp 1
-
16.

Smartphones, Disasters, and Knowledge Management:

An Examination of Field Data Collection, Analysis and Dissemination

19

-

16

RTO
-
MP
-
HFM
-
201



Geffre, Jennifer L. (Captain, USAF).
“A Layered Social an
d Operational Network Analysis”. Dept. of the Air
Force, Air Force Institute of Technology. Working paper, March 2007.

Giovannini, E. “The role of communication in transforming statistics into knowledge”. Working Papers.
2008.

Golbeck, Jennifer and Hendl
er, James. “Inferring Trust Relationships

in Web
-
based Social Networks”
,

University of Maryland, College Park Computer Science Department. Working paper, 2006.

Howe, J. “The Rise of Crowd Sourcing”. Wired Magazine. Vol. 14 No. 06, June 2006.

Kaklauskas, A
.; Amaratunga, D.; and Haigh.. “Knowledge Management for Post
-
Disaster
-
Management”.
International Journal of Strategic Property Management. Vol. 13, 2009 , pp. 117

128.

Klien, E., Lutz, M., and Kuhn, W. “Ontology
-
based Discovery of Geographic Information S
ervices


An
Application in Disaster Management”. Computers, Environment and Urban Systems. 2005.

Larsson, Rikard. “Case Survey Methodology: Qunatitative Analysis of Patterns Across Case Studies”.
Academy of Management Journal. Vol.
36, No. 6, 1993, pp.
1515
-
1546.

Madey, Gregory R.; Barabási, Albert
-
László ; Chawla, Nitesh V.; Gonzalez, Marta; Hachen, David; Lantz,
Brett; Pawling, Alec; Schoenharl, Timothy; Szabó, Gábor; Wang, Pu; and Yan, Ping. “Enhanced Situational
Awareness: Application of DDDAS Concep
ts to Emergency and Disaster Management”. Working Papers
supported by the National Science Foundation, the DDDAS Program, under Grant No. CNS
-
050312.

Mansourian A.; Rajabifard A.; Valadan Zoej M.J.; Williamson I.; and Toosi K.N. “Using SDI and Web
-
Based
S
ystem to Facilitate Disaster Management”. Center for Spatial Data Infrastructure and Land Administration,
Department of Geomatics, University of Melbourne. Working paper, 2006.

McDermott, Richard. “Why Information Technology Inspired But Cannot Deliver Kno
wledge Management”.
California Management Review. Vol. 41 No. 4, Summer 1999, pp. 103
-
117.

Polania, William G. “Leveraging Social Networking Technologies: An Analysis of the Knowledge Flows
Facilitated By Social Media and the Potential Improvements in Si
tuational Awareness, readiness, and
Productivity”. U.S. Naval Postgraduate School thesis, September 2010.

Putnam, R.D.; Leonardi, R,; Nanetti, R. “Making Democracy Work: Civic Traditions in Modern Italy”.
Princeton University Press. Princeton, NJ, 1993.

Sa
lisbury, M. W. “Putting theory into practice to build knowledge management

Systems”. Journal of Knowledge Management. Vol. 7, No. 2, 2003, pp. 128
-
141.

Sobel, Russell and Leeson, Peter. “The Use of Knowledge in Natural
-
Disaster Relief Management”. The
I
ndependent Review. Vol. XI, No. 3, Winter 2007, pp. 519
-
532.

Stenmark, D. “Information vs. knowledge: the role of intranets in knowledge management”. Proceedings of
the 35th Hawaii International Conference on Sy
stems Sciences, (HICSS, 2002).

Smartphones, Disasters, and Knowledge Management:

An Examination of Field Data Collection, Analysis and Dissemination

RTO
-
MP
-
HFM
-
201

19

-

17



Stenmark, D.
, & Lindgren. “Integrating knowledge management systems with everyday work: Design
principles leveraging user practices”. In Proceedings of 37th International Conference in Systems Sciences
(HICSS), IEEE Computer Society, 2004.

Thomas, J.C.; Kellogg, W.A.;

Erickson, T. “The knowledge management puzzle: Human and social factors in
knowledge management”. IBM Systems Journal. Vo
l. 40 No. 4, 2001, pp. 863
-
884.

Trompette, P.; Chanal, V.; and Pelissier, C. “Crowdsourcing as a way to access external knowledge fo
r
innovation”.
http://hal.archives
-
ouvertes.fr/halshs
-
00367373/.

Retrieved 2008.

Vaast, Emmanuelle; Boland Jr, Richard; Davidson, Elizabeth; Pawlowski, Suzanne D.; and Schultze, Ulrike.
"Investigating the "Knowledge" in Knowledge Management: A Social Rep
resentations Perspective”.
Communications of the Association for Information Systems. Vol. 17, Article 15, 2006.

Vuorikari, R.; Ochoa, X.; and Duval, E. “Analysis of User Behavior on Multilingual Tagging of Learning
Resources”. Proceedings of the 1st Works
hop on Social Information Retrieval for Technology
-
Enhanced
Learning & Exchange. 2007.

"There's a List for That". blog.twitter.com. October 30, 2009.
http://blog.twitter.com/2009/10/theres
-
list
-
for
-
that.html.

Retrieved December 31, 2010.

"Using Twitter Wit
h Your Phone". Twitter Support.
http://help.twitter.com/entries/14226
-
how
-
to
-
find
-
your
-
twitter
-
short
-
long
-
code.

Retrieved December 31, 2010.

Zuckerberg,
Mark. "
500 Million Stories". Facebook.
http://blog.facebook.com/blog.php?post=409753352130
.
Retrieved

December 28, 2010.

Kazeniac, Andy. "Social Networks: Facebook Takes Over Top Spot, Twitter Climbs". Compete.com.
http://blog.compete.com/2009/02/09/facebook
-
myspace
-
twitter
-
social
-
network/
. Retrieved February 17, 2009.






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An Examination of Field Data Collection, Analysis and Dissemination

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