Handbook of Big Geospatial Data

633 Pages·2021·19.208 MB·English

by  Martin Werner, Yao-Yi Chiang

Martin Werner
Yao-Yi Chiang  Editors
Handbook
of Big
Geospatial
Data
Handbook of Big Geospatial Data
Martin Werner • Yao-Yi Chiang
Editors
Handbook of Big Geospatial
Data
Editors
MartinWerner Yao-YiChiang
ProfessorshipBigGeospatialData SpatialSciencesInstitute
Management UniversityofSouthernCalifornia
TechnicalUniversityofMunich(TUM) LosAngeles,CA,USA
Munich,Germany
ISBN978-3-030-55461-3 ISBN978-3-030-55462-0 (eBook)
https://doi.org/10.1007/978-3-030-55462-0
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Preface
Spatial data form the backbone of many science and engineering investigations
and applications. For example, spatial data are fundamental to construction and
engineering,socialsciencesandlifesciences,artandvisualization,andmanymore
fields. In spatial data, the geometry component captures the concept of shape,
presence,movement,andsimilarityaboutthingsinspace.Inthisbook,wefocuson
aparticulartypeofspatialdata:datathataresuitabletounderstandvariousaspects
of the physical surroundings, either in a local environment or for the entire Earth.
Suchdataareknownasgeospatialdata,astheyaregeometricdatalinkedtothings
in the physical world. Specifically, we focus on big geospatial data, a subdomain
withinspatialdata,whichconsidersthecomputingtechnologiesforhandlinglarge
datasetsofgeometryrepresentingthingsonEarth(hence“geo”).
Due to the advent of mobile computing and GPS (global positioning system)
technologies,thelocationofmobiledevices,includingsmartphones,cars,andassets
such as ships, containers, and more, can be determined without a high cost. The
implicationofthesemobilelocationdataispotent.Still,itrequirescarefulconsider-
ationsandtechnologiestounderstandandexploitthepositivepotentialandmitigate
therisksofthenegativepotentialofsuchdata.Ontheonehand,suchinformation
can be used tooptimize logistics,increase the efficiency of transportation systems
in cities, and enhance personalized experiences for individual users. On the other
hand,ubiquitouslocationinformationcanproduceundesiredapplications,including
deanonymizingindividuals,trackinguserbehaviorunwillingly,andmicro-targeting
commercials with fake news, for example, micro-targeting fake news that trick
people into spending money they would not have spent or (much worse) deceive
peopletovoteforcertainpoliticiansandpoliticalparties.
In addition to mobile location data, public and private satellites and in situ
measurements are continuously generating geospatial databases of unprecedented
sizes and accuracy. High-quality satellite missions with global coverage at limited
resolutions, like Landsat and Copernicus, provide us a global view of our planet
with pretty short update times (a few days) between individual acquisitions.
High-resolutionsatellitescanprovidecentimeter-levelgeospatialobservationswith
typically either cheaper sensors building some constellation or less frequent visits
v
vi Preface
ofthesameplaceonEarth.Airbornemachines(plane,helicopter,drone)andstreet-
levelmeasurementcampaigns(streetview,urbanmapping)providetargeted,highly
precise data describing selected aspects of the physical and natural environment.
Typically, the amount of collected/sensed data is so large, and we do not yet
understand the impact of the existence of these collections at all. For example,
what is the best use case for such data? Can we solve some of our most pressing
challenges, like feeding the world, fighting climate change, and fighting poverty,
using increased precision and detailed views of the Earth? Or are we going to
experience malicious use of these data first, such as predictive policing affected
bybiasespresentinsuchobservationslimitingcivilrights?
Yet another domain generating huge amounts of big geospatial data that have
provenly affected our democratic elections at least once (though there is a debate
about how much actually) is the domain of social media. Social media datasets
withlocationinformationrepresenttheconnectionofwherepeopleare(accessible
from their smartphones) with what they want to communicate (content) and to
whom (their social network). This domain generates impressive amounts of data
andenablespositiveandnegativeusecases.
The previous paragraphs have shown that our societies are currently facing a
significant challenge: we are collecting huge amounts of location data and do not
yetunderstandtheimpact.Wehopethatthepositiveuseofthesedatawilloutweigh
therisks,butwecannotbesure.Therefore,researchonthetechnologiesformaking
use of spatial data is crucial, especially in the context of big data where the sheer
amount of data allows for advanced processing, including machine learning and
datamining.However,spatialdataarehighlyheterogeneous,and,therefore,many
interdisciplinary islands do exist, each of which is having their ideals, languages,
problems,andcultures.Thevariousfundamentaltheoriesofspatialcomputingcome
from both computer science and spatial science, which presents a challenge for
workingwithspatialdataastherearenotmanytrulyinterdisciplinarycollectionsof
knowledgethatcanserveasastartingpoint.Veryoften,thesefundamentaltheories
can be hidden from the applications, since they are not at all easy to access or
understand for the users who approach spatial computing from an applied setting.
Also, there are many domains in which spatial data are generated and processed
for specific applications. This leads to the fact that significant developments in
spatialcomputingarebeingpublishedinseparatedomains,notdirectlyaccessibleor
transferrabletootherdomains.Asaresult,thesametechniquesoftenaredeveloped
multipletimes.
InthecontextoftheACMSIGSPATIALGISconferencein2017,weconcluded
that a collection of a diverse set of articles from the various subfields of our com-
munitywithstrongtiestospatialcomputingwouldbebeneficialformanyresearch
communities that use spatial data. This collection will provide a forum in which
ourresearchtopicscanbediscussedandmadeaccessibleacrossboundaries.Inthis
setting, we have asked our colleagues to provide book chapters in which various
topics of spatial computing are explained in a didactic setting while still allowing
forpresentingnewresearchresults.Theselectedchaptersareorganizedintoseveral
sections to provide an overview of big geospatial data. Each chapter represents a
Preface vii
uniquecontributionfromexpertresearcherswithintheirowntraditionallyisolated
disciplines and provides a medium for them to write from their view of the topic.
Thesedisciplinesincludemathematics,computationalgeometry,machinelearning,
statistics, remote sensing, smart cities, indoor navigation, politics, big data, and
socialmediaanalytics,tonameafew.Wehopethatthebookwillfurtherfacilitate
interdisciplinary dialogue so that all disciplines working with big geospatial data
canunderstandeachotherabitbetter.
In summary, this book is intended to be an initial contribution to shed light on
howspatialdatacouldimproveourlivesand,atthesametime,howtheycouldharm
our societies. The book consists of contributions of more than 70 authors writing
a total of more than 600 pages aiming at helping to increase the interdisciplinary
understanding of big geospatial data for future collaboration and evolution of the
field.
Overviewofthe Book
Weorganizedthebookintofivetopicareasaimingtodifferentiatethenatureofthe
chaptersandthemindsetoftheauthors.ThefirstPart,SpatialComputingSystems
andApplications,introducesthetopicofbiggeospatialdatabypresentingexample
systems and domains from the wide area of big geospatial data. This includes
processing systems like IBM PAIRS and GeoSpark, as well as domain-dependent
applications like Indoor Mapping or Disaster Response using satellite data. The
secondPart,Trajectories,Event,andMovementData,collectsvariouschapterson
thisinterestingandchallengingtypeofspatiotemporaldata.Thisincludeschapters
on two lately introduced extensions of geospatial trajectories with semantics and,
more generally, toward multi-attribute trajectories and application examples from
urbanroadmapextraction.Itconcludeswithahands-onchapterontheexplorative
analysisofbigtrajectorydatasets.ThethirdPart,Statistics,Uncertainty,andData
Quality,focusesonissuesarisingfromuncertainty,methodstocombatuncertainty
from a database research perspective, and an introduction to statistical models for
spatial datasets. The fourth Part, Information Retrieval from Multimedia Spatial
Datasets,providesabroadrangeofchaptersrelatedtoinformationretrieval,either
fromtextandsocialmedia,bridgingthedomainsofbiggeospatialdataandclassical
information retrieval, or from spatial datasets, such as historic map archives. The
fifth Part, Governance, Infrastructures, and Society, collects chapters on more
societalandsystemicquestionsrelatedtobiggeospatialdata.Twochaptersexplore
thestateoftheartofthegovernmentalbiggeospatialdatainfrastructuresinEurope
(INSPIRE)andtheUnitedStates(theNationalSpatialDataInfrastructure).Theyare
augmentedwithadiscussionofhowdecision-makerscanbeintegratedintospatial
data warehouse architectures and how semantic graphs can be used to deal with
spatialevolution,forexample,changingpolygonalsubdivisionofurbanregionsdue
togrowth.
viii Preface
In summary, this book provides a comprehensive view of the field of big
geospatial data. In addition to covering a wide range of topics, three clusters of
chaptersstandout:multimediainformationretrieval,trajectories,andmovingdata,
aswellassocietalandorganizationalaspectsofinfrastructures.Thisisinlinewith
thecurrentresearchtrendsandneeds.
For the next generation of big geospatial data systems, we need to overcome
the traditional approach of collecting data for local analysis. Instead, we need to
envision strong infrastructures for bringing the analysis code directly to the data
savingenergyandcost.However,thiscontainschallengesrelatedtoreproducibility
in science and to the business models of both spatial data companies and national
agenciesthathavebeenusedtoearnmoneyonrequestandtoprovide“dumb”data
instead of services. In addition, urbanization and mobility are tightly interlinked
societal challenges: how can we better understand and optimize our urban spaces,
and how can we sustainably fulfill the mobility demand of people and goods?
Trajectorycomputingisaveryhardsubfieldofspatialcomputingthatisessentialto
answeringthesequestionsinadata-drivenway.
At this point, we want to thank all authors of papers and all colleagues who
helpedwiththisbookprojectinonewayoranother.Wehopethatthebookwillbe
usefultogrowourcommunityofspatialcomputing.Now,enjoythebook!
Munich,Germany MartinWerner
LosAngeles,USA Yao-YiChiang
Contents
PartI SpatialComputingSystemsandApplications
1 IBM PAIRS: Scalable Big Geospatial-Temporal Data
andAnalyticsAs-a-Service................................................ 3
SiyuanLuandHendrikF.Hamann
2 BigGeospatialDataProcessingMadeEasy:AWorkingGuide
toGeoSpark................................................................. 35
JiaYuandMohamedSarwat
3 Indoor3D:OverviewonScanningandReconstructionMethods..... 55
VilleV.Lehtola,ShayanNikoohemat,andAndreasNüchter
4 BigEarthObservationDataProcessingforDisasterDamage
Mapping ..................................................................... 99
BrunoAdriano,NaotoYokoya,JunshiXia,andGeraldBaier
5 SpatialDataReductionThroughElement-of-Interest(EOI)
Extraction ................................................................... 119
SamanthaT.ArundelandE.LynnUsery
6 SemanticGraphstoReflecttheEvolutionofGeographic
Divisions ..................................................................... 135
C.Bernard,C.Plumejeaud-Perreau,M.Villanova-Oliver,J.Gensel,
andH.Dao
PartII Trajectories,EventandMovementData
7 BigSpatialFlowDataAnalytics .......................................... 163
RanTao
8 SemanticTrajectoriesDataModels ...................................... 185
MariaLuisaDamiani
ix
x Contents
9 Multi-attributeTrajectoryDataManagement.......................... 199
JianqiuXu
10 MiningColocationfromBigGeo-SpatialEventDataonGPU........ 241
ArpanManSainjuandZheJiang
11 AutomaticUrbanRoadNetworkExtractionFromMassive
GPSTrajectoriesofTaxis.................................................. 261
Song Gao, Mingxiao Li, Jinmeng Rao, Gengchen Mai,
TimothyPrestby,JosephMarks,andYingjieHu
12 ExploratoryAnalysisofMassiveMovementData...................... 285
AnitaGraser,MelittaDragaschnig,andHannesKoller
PartIII Statistics,UncertaintyandDataQuality
13 Spatio-TemporalDataQuality:ExperiencefromProvision
ofDOTTravelerInformation ............................................. 323
DouglasGalarus,IanTurnbull,SeanCampbell,JeremiahPearce,
andLeannKoon
14 UncertainSpatialDataManagement:AnOverview ................... 355
AndreasZüfle
15 SpatialStatistics,orHowtoExtractKnowledgefromData........... 399
AnnaAntoniuk,MiryamS.Merk,andPhilippOtto
PartIV InformationRetrievalfromMultimediaSpatialDatasets
16 ASurveyofTextualData&GeospatialTechnology.................... 429
JochenL.Leidner
17 HarnessingHeterogeneousBigGeospatialData........................ 459
BoYan,GengchenMai,YingjieHu,andKrzysztofJanowicz
18 BigHistoricalGeodataforUrbanandEnvironmentalResearch ..... 475
HendrikHerold
19 HarvestingBigGeospatialDatafromNaturalLanguageTexts....... 487
YingjieHuandBenjaminAdams
20 AutomatingInformationExtractionfromLargeHistorical
TopographicMapArchives:NewOpportunitiesandChallenges..... 509
JohannesH.UhlandWeiweiDuan
PartV Governance,InfrastructuresandSociety
21 TheIntegrationofDecisionMaker’sRequirementstoDevelop
aSpatialDataWarehouse ................................................. 525
SanaEzzedine,SamiYassineTurki,andSamiFaiz