Application And Platform Design Of Geospatial Big Data
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B4-2021XXIV ISPRS Congress (2021 edition)Application and Platform Design of Geospatial Big DataLi Heng1,2,3,*, Huang Wei1, Zha Zhuhua1, Yang Jing11NationalGeomatics Center of China, Beijing, China-(liheng, huangwei, zhazh, yangjing)@ngcc.cnof Information Engineering, Chinese Academy of Sciences, Beijing, China3School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China2InstituteCommission IV, WG IV/7KEY WORDS: Geospatial big data, Geospatial big data application, Platform design, Geographic visualization, Spatiotemporal bigdata.ABSTRACT:With the wide application of Big Data, Artificial Intelligence and Internet of Things in geographic information technology andindustry, geospatial big data arises at the historic moment. In addition to the traditional "5V" characteristics of big data, which areVolume, Velocity, Variety, Veracity and Valuable, geospatial big data also has the characteristics of "Location Attribute". At present,the study of geospatial big data are mainly concentrated in: knowledge mining and discovery of geospatial data, Spatiotemporal bigdata mining, the impact of geospatial big data on visualization, social perception and smart city, geospatial big data services forgovernment decision-making support four aspects. Based on the connotation and extension of geospatial big data, this papercomprehensively defines geospatial big data comprehensively. The application of geospatial big data in location visualization,industrial thematic geographic information comprehensive service and geographic data science and knowledge service is introducedin detail. Furthermore, the key technologies and design indicators of the National Geospatial Big Data Platform are elaborated fromthe perspectives of infrastructure, functional requirements and non-functional requirements, and the design and application of theNational Geospatial Public Service Big Data Platform are illustrated. The challenges and opportunities of geospatial big data arediscussed from the perspectives of open resource sharing, management decision support and data security. Finally, the developmenttrend and direction of geospatial big data are summarized and prospected, so as to build a high-quality geospatial big data platformand play a greater role in social public application services and administrative management decision-making.1. INTRODUCTIONGeographical Information is an important basic and strategicinformation resource for the country, which involves economicand social development, ecological civilization construction,national security and people's life facilitation. It is playing anincreasingly important role in digital economy, governmentdecision-making, industrial development and people's life. Withthe development of Big Data, Artificial Intelligence and othertechnologies, the huge amount of geospatial data generating inthe application of geographic information forms therudimentary form of geospatial big data.Scholars' studies on geospatial big data mainly focus on:Knowledge mining and discovery of geospatial data:academician Li Deren has took the lead in international researchon the nature of geospatial data knowledge mining, pioneeredthe discovery of knowledge from GIS databases, and publishedthe monograph "Spatial Data Mining Theory and Application"in 1995. Harvey proposed "Geographic Data Mining andKnowledge Discovery" for the first time in 2009. There is apaper which further expounds the characteristics of big data GISfrom three aspects of GIS spatial data management, spatial dataanalysis and visualization (Li Qingquan et al., 2014). They putforward the concept of big data generalized GIS, and furtherelaborated the characteristics of big data GIS from the threeaspects of GIS spatial data management, spatial data analysisand visualization. The integration trend of geographiccomputing, urban computing and social computing in the era ofgeneralized GIS is forecasted (Lu Feng et al., 2014; Mccoy etal., Soille et al., 2017).Spatiotemporal big data mining: Li Deren believes thatspatiotemporal data mining is a process of automaticallydiscovering and extracting implicit and non-visible patterns,rules and knowledge from massive and multi-sourcespatiotemporal big data. The techniques are analyzed todiscover knowledge from spatial big data (Praveen et al., 2016),and to further make knowledge change into data intelligence(Wang Shuliang et al., 2013; Shekhar et al., 2015). Songnian Liclassified and summarized the geographic big data miningmethods from the perspective of mining targets in 2015. BianFuling discussed the latest research progress of spatial big datamining from two aspects of platform and algorithm in 2017.Another five attributes except for "5V", including granularity,scope, density, skewness and precision, are summarizedregarding geospatial big data (Mir et al., 2018; Pei Tao et al.,2019).The impact of geospatial big data on visualization, socialperception, smart city, etc: some people believe that the newtechnical features of geospatial big data will promote the newdevelopment of map synthesis, map visualization and mapprojection (Kramer et al., 2015; Ai Tinghua, 2016). Liu Yubelieve that multi-source geographic big data provides anunprecedented means of social perception for the distributionpattern, interaction and dynamic evolution of geographicalphenomena in 2018. Academicians such as Li Deren and Wang* Corresponding authorThis contribution has been es-XLIII-B4-2021-293-2021 Author(s) 2021. CC BY 4.0 License.293
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B4-2021XXIV ISPRS Congress (2021 edition)Jiayao believed that without geospatial big data, there would beno smart city.other industrial economic data. Historical and cultural dataincludes historical atlases and national maps.Geospatial big data services for government decision-makingsupport: Liu Jiping believes that geospatial big data technologyoriented to e-government provided a new means for governmentinformation management and decision-making in 2014. TheFogGIS (which is a geographic information system based on fogcalculation method) is proposed to government geospatialhealth big data analysis and coastal risk adaptation (Rumson etal., 2017; Barik et al., 2016; Barik et al., 2019).It is worth mentioning that through the collection, fusion andaggregation of a certain volume of applied big data on the basisof geospatial basic big data, a complete geospatial big data isformed, which provides the basic data source for the followinggeospatial big data application, national spatial informationinfrastructure platform, project information system, etc.In brief, this paper is organized as follows: Section 2 gives theconnotation of geospatial big data at first. Section 3 introducesthe application of geospatial big data, while the keytechnologies and design are introduced in Section 4 whichmainly contains the infrastructure, functional and nonfunctional basic requirements and indicators of the NationalGeospatial Big Data Platform, and gives a practical example.Section 5 summaries the opportunities and challenges ofgeospatial big data. We conclude our paper in Section 6.The geospatial big data is widely used in the Internet, MobileInternet, Internet of Things and other scenarios. At present, theapplication fields of geospatial big data are mainly divided intogeospatial location visualization, industrial thematic geographicinformation integrated services, geographic data science andknowledge service research.2. CONCEPT OF GEOSPATIAL BIG DATAGeographical spatial scope of big data: including the geographiclocation of the geographical space (mainly refers to thesurveying and mapping geographic information, withoutgeography, geology) location data and business data, such aseconomy and society not only has the property of common datameasured value (V), but also contains the time (T) and space (X,Y, Z) information, with the characteristics of space, time,attributes, 3d. Geographical spatiotemporal data constitute theera of big data describing various features and phenomena ofspace frame and space benchmark (basic geographicinformation), moreover, contains the surface features andeconomic and social activities of time and space distribution. Asan important data type, geographical spatiotemporal data has"5V" characteristics like as a traditional big data, andgeographical "Location Attributes" (Location). It is mainlydivided into two categories:Geospatial basic big data: basic geospatial big data mainlyincludes vector data, raster data, map tile data and remotesensing image data from mapping and remote sensing. Basicgeographic data as a unified framework of spatial orientationand spatial analysis, describes the measurement of the earth'ssurface control points, drainage, residents and facilities, traffic,pipeline, state and administrative region, landform, vegetationand soil, cadastral management, place names and otherinformation of location, shape and properties on natural andsocial factors. Basic geographic data has become a kind ofimportant geographic space based large data.Geospatial applied big data: on the one hand, geospatialapplied big data comes from industry data of natural resources,environment, water conservancy, public security, statistics andother departments, such as IoT sensor data collected by water,electrical or mining companies. On the other hand, throughorganic integration of basic geographic data with natural,cultural, economic and cultural data, it provides correctguidance and decision-making assistance for various industriesand forms industrial economic data and historical and culturaldata. For example, industrial economic data includes not onlyGIS, satellite positioning and navigation, aviation and aerospaceremote sensing industrial economic data, but also LBS(Location-based Services), geographic information service and3. APPLICATIONS OF GEOSPATIAL BIG DATA3.1 Visualization of geospatial locationsThe visualization of geospatial position mainly includes mapvisualization, GIS visualization and graph visualization.3.1.1 Map visualization: map means describing spatialelements with symbols, and using cartographic theory toexpress the representation of the earth on the plane. Mapvisualization is to express geospatial phenomena and laws bytransforming geospatial location data into visible static twodimensional symbols. Figure 1 shows the global COVID-19epidemic distribution map.Figure 1. The global COVID-19 epidemic distribution map.3.1.2 GIS visualization: Geographic Information System(GIS) is a technical system that collects, stores, manages,calculates, analyses, displays and describes the geographicdistribution data in the whole or part of the earth's surface(including the atmosphere) with the support of computerhardware and software systems. GIS visualization focuses ongeoseological data model and structure design, dynamic multidimensional data display, cultural and economic spatial regionaldata visualization, etc., and its visualization analysis results arealso expressed in the form of maps.3.1.3 Visualization of charts and graphs: graphvisualization includes line graph, histogram, bar graph, piegraph, line graph, rectangular tree graph, curved surface graph,scatter graph, parallel coordinate graph and radar graph. Graphvisualization includes density map, heat map, vector rectangulargrid map, vector hexagonal grid map, linkage map and vectorpolygon thematic map.This contribution has been es-XLIII-B4-2021-293-2021 Author(s) 2021. CC BY 4.0 License.294
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B4-2021XXIV ISPRS Congress (2021 edition)3.2 Comprehensive servicesgeographic informationofindustrialthematicComprehensive services of industrial thematic geographicinformation integrates information sharing, data publishing andfunctional service. It also realizes the efficient and accuratecollection of geographic information data from non-surveyingand mapping geographic information industry sectors. Besides,it realizes the integrated application display of industrialthematic data and geospatial big data. For a long time,geographic information has been deeply applied in the fields ofnatural resources, water conservancy, transportation, publicsecurity, safety and emergency response, ensuring efficient andhigh-quality integrated industrial thematic geographicinformation services. Here are four examples:Emergency industry: the National Emergency Early WarningInformation Publishing Network comprehensively displays theearly warning information of 76 emergencies in four categoriesof natural disasters, accidents and disasters, public health eventsand social security events. The Network provides accurate earlywarning information services for the state and the public, asshown in Figure 2.disaster survey. Figure 4 shows the reserves and distribution ofrare earth minerals on a scale of 1:500,000.Figure 4. The reserves and distribution of rare earth mineralson a scale of 1:500,000 map.Livelihood Economy: China Land Price Information ServicePlatform presents real-time data of land auction prices, landprice monitoring indicators and transaction facts across thecountry, as shown in Figure 5.Figure 2. The National Emergency Early Warning InformationPublishing Network map.Meteorological industry: China Central MeteorologicalObservatory provides real-time weather information andservices for 60,000 cities, towns, scenic spots, airports, islands,ski resorts and golf courses at home and abroad. Figure 3 showsthe generation track and real-time location of typhoon No. 2102SURIGAE on the typhoon website.Figure 3. Generation track and real-time location of typhoonNo. 2102 SURIGAE on the typhoon website.Natural Resources industry: the geological cloud systemportal which developed by the China Geological Survey of theMinistry of Natural Resources, integrates the massivegeological survey data formed by the national geological worksince the founding of the People's Republic of China—oil andgas, minerals, energy resources, mineral resources, as well asthe scientific data of geological environment and geologicalFigure 5. The National land price monitoring and tradingreality map.3.3 Research on geographic data science and knowledgeservicesAs an important part of Earth big data, geospatial big data hasalways been highly concerned by researchers. The Earth BigData Science Project led by academician Guo Huadong is basedon Earth big data analysis, including geospatial big data, tostudy the association and coupling of the earth system,understand the complex interaction and evolution processbetween the earth natural system and human social system, andrealize the Sustainable Development Goals (SDGs) (GuoHuadong et al., 2018).Based on the background of spatio-temporal big data,Academician Chen Jun, a member of the team, discussed theconnotation, times characteristics, structure and researchdirection of geographic knowledge engineering (Chen Jun et al.,2019a). Based on massive basic geographic data, they putsforward the general idea of basic geographic knowledge servicewith important features of structuralization and correlation, andtakes GlobeLand30 (which is a set of high-resolution globalland cover data products, covering the land range of 80 degreesnorth and south latitude, including 10 categories of land coverThis contribution has been es-XLIII-B4-2021-293-2021 Author(s) 2021. CC BY 4.0 License.295
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B4-2021XXIV ISPRS Congress (2021 edition)4.1 Infrastructure requirementsThe geospatial big data platform infrastructure mainly includeshardware and software systems, networks and security systemsthat provide basic computing support for spatiotemporal bigdata storage, processing, analysis and application.Hardware System: mainly including cell, unit of computingspeed, storage unit, high-speed Internet and administrativesupervision unit, power unit and structure unit and the coolingunit.Software System: mainly including the operating system,virtualization software, resource management software andmanagement software of communication.System Management ModuleData Access ModuleData VisualizationModuleData AnalysisModuleData ProcessingModuleData StorageModuleData ConsumerAs an important component of the National Spatial DataInfrastructure, the geospatial big data platform should primarilymeet the universality requirements of big data systemconstruction, and at the same time meet the characteristicsrequirements of geospatial big data. At present, there arerelevant Chinese National Standards (GB/T 38675-2020 andGB/T 38673-2020) that provide detailed provisions onhardware, software, network, security and other infrastructure,as well as basic functional and non-functional requirements ofbig data system. Secondly, a big data platform with geospatialapplication and public service attributes should be designed.The overall framework of geospatial big data platform functionis shown in Figure 6. It mainly includes distributed storage andprocessing system, analysis system, data collection, preprocessing and visualization system and access managementsystem.Data Management ModuleWith the further growth of geographic information engineeringrequirement, each project information system is independentfrom each other, leading to information silo and barrier, whichseriously hinders the application of geospatial big data. Theapplication of geospatial big data needs to be highly dependenton information system and integrated service platform. Bybuilding a one-stop geospatial big data platform, it caneffectively break the information island and realize the highquality application of geospatial big data.4.2 Basic functional requirementsData PRe-processing Module4. DESIGN OF GEOSPATIAL BIG DATA PLATFORMSecurity System: should not be below the levels of networksecurity protection level 3 requirements (GB/T 21028 and GB/T37973-2019).Data Collection ModuleAll in all, geospatial big data (including basic big data andapplied big data) can obtain more useful value through datamining, knowledge discovery and visualization.Network System: have the ability to high availability and highconsistency, scalability, and isolation.Data Providerinformation, such as cultivated land, forest, grassland and shrubland) knowledge service as an example to introduce thepreliminary research progress (Chen Jun et al., 2014). They alsodeveloped and launched geographic information professionalknowledge service system. The system gathers the geographicalinformation metadata professional literature, public version ofbasic geographic information data, basic geographicinformation resources directory, global surface coverage data,statistical analysis and structured, the spatial knowledgemodelling and other data resources. The system also providesthe latest public version of the geographic information data,thematic maps, free download service. A number of knowledgeapplications have been developed and put online to provideservices of spatial association, mining, analysis andvisualization of geographic information expertise. Chen Junproposed a new method of monitoring and evaluating SDGsbased on geographical data and spatial evidence, which is acomprehensive assessment of the progress of 2030 SDGS basedon statistics and geographic information, and completed theworld's first comprehensive assessment demonstration for theUnited Nations 2030 Agenda for Sustainable Development atcounty level in 2017 (Chen Jun et al., 2019b).Figure 6. Overall functional architecture of geospatial big dataplatform.4.2.1 Storage and Processing System (SPS): thespatiotemporal big data storage and processing system shouldinclude storage subsystem and processing subsystem. Commonspatiotemporal big data storage and processing systems includeHDFS, HBase and Postgres-XL. HDFS is the file system, thequery ability is the weakest; HBase is built on HDFS, usingcolumn storage, for unstructured data and structured data has astrong query ability. Postgres-XL which is the most querycapable supports complex SQL queries.Storage Subsystem: providing data storage function, supportstructured and unstructured and semi-structured data storage;provides the ability to exchange data or files with relationaldatabases and other file systems; supports distributed filestorage, distributed column data storage, distributed structureddata storage and distributed graph data storage.Processing Subsystem: supporting batch, stream processing,graph calculation framework; support memory computing, batchstream fusion computing framework; support for automaticscheduling of tasks based on dependencies between tasks.4.2.2 Analysis System (AS): the temporal and spatial bigdata analysis system should support data query, machinelearning, statistical analysis, offline data analysis, stream dataanalysis and spatial analysis; support interactive on-line analysis;support for visualize large screen display. This is shown inTable 1.FunctionsDate QueryContentstrack, attributes, mesh, and area of theThis contribution has been es-XLIII-B4-2021-293-2021 Author(s) 2021. CC BY 4.0 License.296
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B4-2021XXIV ISPRS Congress (2021 earningFlowDataAnalysisquery and summaryspatial location, spatial distribution,spatial relationships and spatial behaviourand processOD, hot and density analysis, etc.schedule, status of real-time tracking and reporting, and otherfunctions.Usability: including system installation configuration toolproviding a graphical interface, and complete productdocumentation.anomaly detection, filtering and similarposition elements connectedconvection data processing4.4 An example of geospatial big data platform designTable 1. Functions of the AS.4.2.3 Data collection, Pre-processing and Visualizationsystem (DPV): data collection, pre-processing and visualizationsystem are important parts of big data system. This is shown inTable alizationAt present, the National Platform for Common GeospatialInformation Services as sharing and service portal of geographicinformation network, has integrated geographic informationpublic service resources of the relevant government departments,enterprises, institutions, social organizations and public, whichare from surveying and mapping geographic informationdepartments at various levels (the national, provincial,municipal (county)). The National Platform provides varioustypes of user authority, standard, unified online geographicinformation integrated service, as Figure 7 red/offline data importdata extraction, data cleaning, linestructureddataconversion,transformation and table conversion, dataloading, etc.tables, histograms, pie charts, line charts,heat map, etc.Table 2. Functions of the DPV.4.2.4 Access Management System (AMS): big data accessmanagement system includes data access, resource managementand system management. This is shown in Table 3.FunctionsData cess centralizedmanagement of global resources, staticresource allocation strategy and dynamicresource allocation strategy, resourceflexibility and grab, oringalarmmanagement, service management, etc.Table 3. Functions of the AMS.4.3 Non-functional basic requirementsThe non-functional requirements of geospatial big data platformmainly include reliability, compatibility, security, scalability,maintainability and ease of use.Reliability: including support for high availability, dataredundant storage and distribution of the data backup, recoveryand migration, etc.Compatibility: supporting compatible with different brandoperating system.Security: including support for user management, rightsmanagement, log management, data security, etc.Scalability: including cluster expansion, online and offlinecapacity, capacity reduction function.Maintenance: including providing installation deploymentmanagement, information system version, online upgradesystem, fault diagnosis, all kinds of computing tasks runningFigure 7. The portal of National Platform for CommonGeospatial Information Services.The value of geospatial big data lies in the application service,and the promotion of geospatial big data application and theprovision of geospatial big data service are the importantsignificance of the construction of geospatial big data platform.Therefore, the basic indicators of infrastructure, function andnon-function of the big data platform for geographicinformation public service is shown in Table areSystemSoftwareSystemDesign IndexEachvirtualserverspecifications: CPU 24cores, memory 96GBmemory, system disk 200GB SSD storage ofultra-highperformancecomputing services. Amongthem, CPU frequency 3.0GHz, Intranet bandwidth12Gbps,Intranet receivingandsendingpacket 2 million PPS,disk IO read and write delay 1msSupport the virtual server tomount 20 data disks, andThis contribution has been es-XLIII-B4-2021-293-2021 Author(s) 2021. CC BY 4.0 License.297
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B4-2021XXIV ISPRS Congress (2021 ntsReliabilitysupports the anti-affinityconfiguration of the virtualserverBGP shared bandwidth 600Mbps, flexible publicnetworkIP 50,enterprise-class VPC publicnetwork gateway, maximumnumberofNATconnections 50,000,provide NAT agent (SNAT,DNAT), 10Gbps forwardingcapacityEnterpriseorflagshipDDOS high protection,Web application firewall,host security, situationalawareness,databasesecurity, fortress machine,vulnerability scanning, etc.,throughthenetworksecurity level protectionlevel urationSpecifications 16 CoreMemory 64GB 300GBSolid State Storage Redis4.0 high availability cachecluster service, each set ofRedis memory 16GB;MongoDB 3.4 storageservice, 6 shards, eachshard provides 4TB SSDavailable storage, eachshard specifications CPU 32 core memory 128GB,total storage space 24TBSupport full text retrievalcapability; Support spatialobject, spatial index andspatial operation functionfor geospatial big dataProvide one-click functionof computing service imagecreation, including systemdisk image, machine imageand data disk imageenterprise-level data largescreen display servicesProvidesefficientcollection, transmission tible with Kafkanative Producer/ConsumerAPI. Log storage and realtime analysis of 500 millionto 700 million visits per dayProvidethebackupcapability of the wholemachine, you can choosethe backup time, sabilitycycle, retention rulesDistributed OS/database arecompatiblewiththeOS/database kernel engineand clientSupport IP/Cookie/Refererbasedmulti-dimensionalCC protectionPeak number of concurrentusers 10,0007* 24-hour cloud platformmonitoring and emergencyhandling servicesProvide training serviceprogramTable 4. The basic indicators of the big data platform forgeographic information public service.5. CHALLENGES AND OPPORTUNITIES OFGEOSPATIAL BIG DATA5.1 Open sharing of geospatial big dataDue to historical reasons for China surveying and mappinggeographic information institutions, surveying and mappingachievements of geographic information data scattered in localunits which leads to big obstacles to the sharing of data andintegration. Therefore, the opening and sharing idea of big datashould be followed. We need to set up open geospatial largedata sharing mechanism, and establish the geospatial dataplatform by means of distributed cloud computing technology.On the geospatial data platform, the massive, multi-source andheterogeneous geospatial data can realize collection, integration,processing and application.The reference framework, specific requirements, opennessdegree evaluation system and evaluation indicators ofgovernment data openness and sharing are stipulated (GB/T38664-2020). Its core is to build a unified metadata standard,including title, single identifier, category, description, datapreview, revision history, license items, label, API authorizationitems, subsidiary items, etc. (Qian Xiaohong, 2014)5.2 Decision support for geospatial big data managementsIn September 2015, the UN Development Summit adopted the2030 Agenda for Sustainable Development, which set out 17Sustainable Development Goals (SDGs) and 169 specifictargets covering economic, social and environmental fields. Itcalls on all countries to achieve coordinated development ofeconomic growth, social inclusion and a better environment by2030. The Ministry of Natural Resources cooperated with thePeople's Government of Deqing County in Zhejiang Province tocarry out comprehensive monitoring and assessment based onstatistics and geographic information in Deqing County as apilot area, and finally formed the assessment results with"spatio-temporal coordinates" to help the government makeaccurate decisions and optimize the layout of public facilities(Chen Jun, 2019).
of geospatial basic big data, a complete geospatial big data is formed, which provides the basic data source for the following geospatial big data application, national spatial information infrastructure platform, projectinformation system, etc. 3. APPLICATIONS OF GEOSPATIAL BIG DATA The geospatial big data is widely used in the Internet, obile M
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