Table Of ContentRajendra Akerkar Editor
Big Data in
Emergency
Management:
Exploitation
Techniques for Social
and Mobile Data
Big Data in Emergency Management: Exploitation
Techniques for Social and Mobile Data
Rajendra Akerkar
Editor
Big Data in Emergency
Management: Exploitation
Techniques for Social and
Mobile Data
Editor
RajendraAkerkar
BigDataResearchGroup
WesternNorwayResearchInstitute
Sogndal,Norway
ISBN978-3-030-48098-1 ISBN978-3-030-48099-8 (eBook)
https://doi.org/10.1007/978-3-030-48099-8
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Preface
The term emergency management can be defined as the organization and manage-
mentofresources andresponsibilitiesfordealing withallhumanitarian aspects of
emergenciesordisasters,inparticularpreparedness,response,andrecoveryinorder
tolessentheimpactofdisasters.Earthquakes,floods,terroristacts,andcatastrophic
infrastructure failures are events that cause huge physical destruction, loss of life
andpropertyaroundtheworld.Thefrequency,intensity,andimpactofcatastrophic
eventshavesignificantlyincreasedinrecentdecades.Facedwithsuchevents,public
authoritieshaverecognizedemergencyresponseanddisastermanagementasmajor
concernsinneedofconcertedefforts,incollaborationwithbusinessandacademia.
The emergence of extremely large and complex datasets (i.e., big data) made it
possibletoutilizeadvancedtechniquestorevealpatterns,trends,andassociations.
Data-drivenemergencyrescueandresponsehavebeenefficientlyappliedinseveral
recenthazardousevents.
Big Data provides valuable insights into all main phases of emergency man-
agement: prevention, preparedness, response, and recovery. There are two major
sourcesofbigdata,sensornetworks(e.g.,earthquakedetectionusingseismometers)
and multipurpose sensornetworks (e.g.,socialmediasuch asTwitterusingsmart-
phones),bothhaveprovedtheireffectivenessinemergenciessuchasthehurricane
Sandy, coronavirus outbreak, and so on. However, significant big data research
challenges arise because of emergency management requirements for quality of
service (e.g., highly available real-time response) and quality of information (e.g.,
reliablecommunicationsonresourceavailabilityforthevictim).
The Transnational Partnership for Excellent Research and Education in Big
Data and Emergency Management (BDEM) is a research and education network
coordinated by Western Norway Research Institute. The BDEM aims through a
cooperationbetweenWesternNorwayResearchInstitute,UniversityofBergen,and
six world-class universities from Hong Kong, Japan, and the USA, to establish a
long-termpartnershipwhereexcellenteducationistobeembeddedinhigh-quality
researchinbigdataforemergencymanagement.Thefieldofdata-drivenemergency
managementinvolvescross-domainterminologyandmethodologiesandshouldbe
carried forward by multidisciplinary and international efforts. This edited book is
vii
viii Preface
based on a specially designed topic on big data in emergency management by the
BDEM project team. The master level course also launched at the University of
Bergen,Norwayfromautumn2017.
Thiseditedtextbookhasbeendesignedtomeettheneedsofindividualswishing
to pursue a research and development career in emergency management and big
data,specificallytechniquesforsocialandmobiledata.
Overviewofthe Book
Particularly, every chapter is based on graduate lectures or research seminars
delivered by authors over the last several years for a wide variety of courses
in various universities and research venues. The feedback from participants and
colleaguesatthesevenueshashelpedthemtoimprovethetextsignificantly.
A brief description of the contents found within each chapter of the book is as
follows:
• In the chapter“Introduction to Emergency Management,” we examine basic
concepts of emergency management and provide its brief history. In addition,
the concept of data-driven emergencies is described by considering the unique
characteristicsofbigdata.
• Thechapter“BigData”reviewsthesourcesofbigdataandtheircharacteristics.
Further,itdiscussesthepotentialbenefitsofbigdataforemergencymanagement
along with the technological and societal challenges it poses. Also, central
technologiesforbig-datastorageandprocessingingeneralarereviewed,before
presentingtheSparkbig-dataengineinmoredetail.Finally,thechapterexplains
theethicalandsocietalthreatsthatbigdatapose.
• Next,thechapter“LearningAlgorithmsforEmergencyManagement”dealswith
the fundamental machine learning techniques to support the decision-making
processes for emergency management. Then the practices and exercises of the
learningtechniqueswithrealtweetdatasetsinrealemergenciesarediscussed.
• Emergency-relevantdatacomesinmanyvarieties.Itcanbehighvolumeandhigh
velocity, and reaction times are critical, calling for efficient and powerful tech-
niquesfordataanalysisandmanagement.Knowledgegraphsrepresentdataina
rich,flexible,anduniformwaythatiswell-matchedwiththeneedsofemergency
management. The chapter“Knowledge Graphs and Natural-Language Process-
ing” explains the most important semantic technologies and how they support
knowledge graphs. The chapter discusses their benefits and challenges and
givesexamplesofrelevantsemanticdatasourcesandvocabularies.Thechapter
concludeswithanoverviewoftechniquesforprocessingnatural-languagetexts.
• Next, the chapter“Social Media Mining for Disaster Management and Com-
munity Resilience” describes the role of social media during disasters as how
platforms like Twitter and Facebook facilitate communication for both the
public and response agencies during the time-critical events. Specifically, it
Preface ix
introduces the concepts of social media mining for disaster events, provides
the use-cases to mine social media for helping public and emergency services,
and describes different methods of user, content, network, context, and visual
analytics to process and analyze social media data for emergency management
andcommunityresilience.
• Human mobility modeling for emergency management plays a critical role in
guaranteeing people safety and saving people’s life. However, many traditional
techniquesforregularhumanmobilitymodelingfailonemergencymanagement,
because human mobility differs significantly from routines. The chapter“Big
Data-DrivenCitywideHumanMobilityModelingforEmergencyManagement”
elaborates the challenges and reviews the state-of-the-art technologies to cope
with the three fundamental tasks of human mobility modeling for emergency
management.
• Emergency communication networks (ECNs) are designed to provide reli-
able communications under emergent scenarios. Recently, smartphone-based
networks have attracted remarkable attention on the management of ECNs.
Thechapter“SmartphonebasedEmergencyCommunication”presentsthestate-
of-the-art research efforts devoted to the establishment and management of
smartphone-based ECNs. The related key techniques and their significant roles
fordisasterreliefinECNsarediscussed.Further,thechapteralsopresentsseveral
real-world applications and case studies. Finally, it summarizes the open issues
andfutureresearchdirections.
• Finally, the chapter“Emergency Information Visualisation” is the final puzzle
piece to complete a big picture of big data in emergency management. A good
visualizationcantransferbigemergencydataintoawaythatiseasierandmore
comprehensivetodiscoverunderlyingpatternsandvaluableinsights,whichare
usefulforeitherotheranalyzingtasksorendusers.
Throughout the book, cases and exercises are given tohighlight certain aspects
ofthecoveredmaterialandtostimulatethought.TheBDEMwebsite(https://www.
bigdata.vestforsk.no/resources) also has a dedicated webpage containing various
pointersaboutemergencydatasets,tools,andsoftware.
Intended Audience
Thebookcanbeusedatthegraduateoradvancedundergraduatelevelasatextbook
ormajorreferenceforcoursesonBigDataandDisasterManagement.
• Advanced undergraduate and PhD students and other early-career researchers
whoseektoconductresearchondata-drivenemergencymanagement.
• Researchers,engineers,dataanalysts,anddatamanagerswhoneedtodealwith
largeandcomplexsetsofdata.
• Besides,thegoalistohelppolicymakersandotherindividualsnavigatethenew
data-drivenemergencyresponselandscape.
x Preface
Prerequisites
Toappreciatefullythematerialinthisbook,werecommendthefollowingprereq-
uisites:
• AbasiccourseinRprogrammingandrelateddatabasesystems.
• Asophomore-levelcourseindatastructures,algorithms,anddiscretemathemat-
ics.
The organization and the contents of this edited book have benefited from
our outstanding contributors, members of the BDEM network. I am very proud
and happy that outstanding researchers agreed to join this project and prepared a
chapter for this book. I am also very pleased to see it materialize in a way as we
originally envisioned. I hope that this book will be a source of inspiration to the
readers.Iespeciallywishtoexpressmysinceregratitudetoalltheauthorsfortheir
contributiontothisbook.
I am grateful to the Research Council of Norway (RCN) and the Norwegian
Agency forInternational Cooperation and QualityEnhancement inHigher Educa-
tion(Diku)forsupportingtheBDEMinitiativethroughINTPARTprogramme.
SpringerVerlag,especiallySusanLagerstrom-Fife,productioneditor,production
staff,andreviewersofthisbookinbringingoutinanorderlymanner.
Iwishallreadersafruitfultimereadingthisbookandwishtheyexperiencethe
sameexcitementasIdid—andstilldo—whendealingwithBigData.
Sogndal,Norway RajendraAkerkar
March2020
Contents
IntroductiontoEmergencyManagement ..................................... 1
RajendraAkerkarandMinsungHong
BigData........................................................................... 15
AndreasL.OpdahlandVimalaNunavath
LearningAlgorithmsforEmergencyManagement.......................... 31
MinsungHongandRajendraAkerkar
KnowledgeGraphsandNatural-LanguageProcessing...................... 75
AndreasL.Opdahl
SocialMediaMiningforDisasterManagementandCommunity
Resilience.......................................................................... 93
HemantPurohitandStevePeterson
Big Data-Driven Citywide Human Mobility Modeling for
EmergencyManagement........................................................ 109
ZipeiFan,XuanSong,andRyosukeShibasaki
SmartphoneBasedEmergencyCommunication ............................. 131
HuaweiHuangandSongGuo
EmergencyInformationVisualisation......................................... 149
HoangLongNguyenandRajendraAkerkar
xi
