UDC: 
DOI: 
10.22389/0016-7126-2023-996-6-19-29
1 Titov G.S.
2 Kargashin P.E.
Year: 
№: 
996
Pages: 
19-29

Lomonosov Moscow State University (MSU)

1, 
2, 
Abstract:
The authors describe an approach to modeling large volumes of heterogeneous spatial data in the form of a hypercube based on discrete global grid systems. Bibliometric analysis and literature review of academic publications, mapping and explanation of the scientific landscape on the subject of big data and data cubes in Earth sciences are carried out. The mentioned phenomenon is interpreted in Earth sciences in the view of the spatial data life cycle. The results show that its transformative impact on cartography and geoinformatics is mutual, and the resulting methodological problem is their heterogeneity, not volume. To model them, it is proposed to use a data cube in which the spatial dimension is represented using discrete global grid systems with advantages over raster and vector models in application to that phenomenon. The content of the data cube is analysis-ready information.
References: 
1.   Bozhdai A. S. Kontseptsiya kompleksnoi infrastruktury territorii dlya resheniya problemy integratsii mezhotraslevoi statistiki. Vestnik Voronezhskogo gos. tekhn. un-ta, 2011, no. 1, pp. 214–220.
2.   Kosikov A.G., Golubeva E.I., Seliverstov Y.G., Semin V.N., Ushakova L.A., Kharkovets E.G. (2019) Arctic digital model. Geodezia i Kartografia, 80(1), pp. 34-42 . (In Russian). DOI: 10.22389/0016-7126-2019-943-1-34-42.
3.   Kudryavtsev Yu. OLAP-tekhnologii: obzor reshaemykh zadach i issledovanii. Biznes-informatika, 2008, no. 1, pp. 66–70.
4.   Lurie I.K., Samsonov T.E. (2010) Structure and content of spatial database for multi-dimensional mapping. Geodezia i Kartografia, 71(11), pp. 17-23.
5.   Maiorov A.A. (2020) On the issues of ultra-fast processing voluminous spatio-temporal data. Geodezia i Kartografia, 81(12), pp. 50-56. (In Russian). DOI: 10.22389/0016-7126-2020-966-12-50-56.
6.   Maiorov A.A., Materuhin A.V., Kondaurov I.N. (2018) Using computer clusters for processing spatial-temporal data streams in data acquisition systems. Geodezia i Kartografia, 79(5), pp. 54-63. (In Russian). DOI: 10.22389/0016-7126-2018-935-5-54-63.
7.   Nyrtsov M. V., Nyrtsova T. P. Bol'shie dannye v kartografii. Umnoe kartografirovanie: budushchee ili tekhnologicheskoe izmenenie. Izvestia vuzov. Geodesy and Aerophotosurveying, 2016, no. 5, pp. 42–45.
8.   Titov G. S., Prasolova A. I., Kargashin P. E. Veb-kartografirovanie resursov solnechnoi energii Yakutii. InterKarto. InterGIS, 2021, Vol. 27, no. 3, pp. 210–220. DOI: 10.35595/2414-9179-2021-3-27-210-220.
9.   Tsvetkov V.Ya., Oznamets V.V. (2022) Big data in geodetic software. Geodezia i Kartografia, 83(10), pp. 9-19. (In Russian). DOI: 10.22389/0016-7126-2022-988-10-9-19.
10.   Shurygina A. A., Samsonov T. E. Perspektivy ispol'zovaniya diskretnykh global'nykh setochnykh (gridovykh) sistem v geoinformatike. Geoinformatsionnoe kartografirovanie v regionakh Rossii: Materialy KhI Vseross. nauch.-prakt. konf, Voronezh: Tsifrovaya poligrafiya, 2020, pp. 350–358.
11.   Agapito G., Zucco C., Cannataro M. (2020) COVID-warehouse: A data warehouse of Italian COVID-19, pollution, and climate data. International Journal of Environmental Research and Public Health, no. 17 (15), DOI: 10.3390/ijerph17155596.
12.   Baumann P. (2017) Standardizing big earth datacubes. 2017 IEEE International Conference on Big Data (Big Data), IEEE, Boston, MA, pp. 67–73. DOI: 10.1109/BigData.2017.8257912.
13.   Baumann P., Misev D., Merticariu V., Huu B. P. (2019) Datacubes: Towards Space/Time Analysis-Ready Data. Service-Oriented Mapping, ed. J. Döllner, M. Jobst, P. Schmitz. Springer International Publishing, pp. 269–299. DOI: 10.1007/978-3-319-72434-8_14.
14.   Bimonte S., Tchounikine A., Miquel M., Pinet F. (2010) When spatial analysis meets OLAP: Multidimensional model and operators. International Journal of Data Warehousing and Mining, no. 6 (4), pp. 33–60. DOI: 10.4018/jdwm.2010100103.
15.   Chatenoux B., Richard J.-P., Small D., Roeoesli C., Wingate V., Poussin C., Rodila D., Peduzzi P., Steinmeier C., Ginzler C., Psomas A., Schaepman M. E., Giuliani G. (2021) The Swiss data cube, analysis ready data archive using earth observations of Switzerland. Scientific Data, no. 8 (1), DOI: 10.1038/s41597-021-01076-6.
16.   Dutton G. (1989) Modeling locational uncertainty via hierarchical tesselation. The Accuracy of spatial databases, ed. M. F. Goodchild, S. Gopal. Taylor and Francis, London; New York, pp. 81–91.
17.   Van Eck N. J., Waltman L. (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, no. 84 (2), pp. 523–538. DOI: 10.1007/s11192-009-0146-3.
18.   Gandomi A., Haider M. (2015) Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, no. 35 (2), pp. 137–144. DOI: 10.1016/j.ijinfomgt.2014.10.007.
19.   Giuliani G., Camara G., Killough B., Minchin S. (2019) Earth observation open science: enhancing reproducible science using data cubes. Data, no. 4 (4), DOI: 10.3390/data4040147.
20.   Giuliani G., Masó J., Mazzetti P., Nativi S., Zabala A. (2019) Paving the way to increased interoperability of Earth observations data cubes. Data, no. 4 (3), DOI: 10.3390/data4030113.
21.   Goodchild M. F. (2018) Reimagining the history of GIS. Annals of GIS, no. 24 (1), pp. 1–8. DOI: 10.1080/19475683.2018.1424737.
22.   Gorgoglione A., Castro A., Chreties C., Etcheverry L. (2020) Overcoming data scarcity in Earth science. Data, no. 5 (1), DOI: 10.3390/data5010005.
23.   Guo H., Liu Z., Jiang H., Wang C., Liu J., Liang D. (2017) Big Earth data: a new challenge and opportunity for Digital EarthТs development. International Journal of Digital Earth, no. 10 (1), pp. 1–12. DOI: 10.1080/17538947.2016.1264490.
24.   Hahmann S., Burghardt D. (2013) How much information is geospatially referenced? Networks and cognition. International Journal of Geographical Information Science, no. 27 (6), pp. 1171–1189. DOI: 10.1080/13658816.2012.743664.
25.   Han J. (2017) OLAP, Spatial. Encyclopedia of GIS, ed. S. Shekhar, H. Xiong, X. Zhou. Springer International Publishing, Cham, pp. 1479–1482. DOI: 10.1007/978-3-319-17885-1_910.
26.   Jagadish H. V. (2015) Big data and science: myths and reality. Big Data Research, no. 2 (2), pp. 49–52. DOI: 10.1016/j.bdr.2015.01.005.
27.   Jamison A. (2011) Knowledge making in transition: On the changing contexts of science and technology. Science transformed?: Debating Claims of an Epochal Break, ed. A. Nordmann, H. Radder, G. Schielmann. University of Pittsburgh Press, Pittsburgh, pp. 93–105. DOI: 10.2307/j.ctt5hjssc.11.
28.   Kasprzyk J.-P., Devillet G. (2021) A data cube metamodel for geographic analysis involving heterogeneous dimensions. ISPRS International Journal of Geo-Information, no. 10 (2), DOI: 10.3390/ijgi10020087.
29.   Kasprzyk J.-P., Donnay J.-P. (2016) A Raster SOLAP for the visualization of crime data fields. GEOProcessing 2016, pp. 109–117.
30.   Kim A. M. (2015) Critical cartography 2.0: From Уparticipatory mappingФ to authored visualizations of power and people. Landscape and Urban Planning, no. 142, pp. 215–225. DOI: 10.1016/j.landurbplan.2015.07.012.
31.   Kitchin R. (2014) Big data, new epistemologies and paradigm shifts. Big Data and Society, no. 1 (1), pp. 1–12. DOI: 10.1177/2053951714528481.
32.   Kmoch A., Vasilyev I., Virro H., Uuemaa E. (2022) Area and shape distortions in open-source discrete global grid systems. Big Earth Data, no. 6 (3), pp. 256–275. DOI: 10.1080/20964471.2022.2094926.
33.   Kuznetsov S. D., Kudryavtsev Yu. A. (2009) A mathematical model of the OLAP cubes. Programming and Computer Software, no. 35 (5), pp. 257–265. DOI: 10.1134/S0361768809050028.
34.   Laney D. (2001) 3D data management: Controlling data volume, velocity and variety. META group research note, Stanford, no. 6 (70), pp. 1–4.
35.   Lee J.-G., Kang M. (2015) Geospatial big data: Challenges and Opportunities. Big Data Research, no. 2 (2), pp. 74–81. DOI: 10.1016/j.bdr.2015.01.003.
36.   Li M., McGrath H., Stefanakis E. (2022) Multi-resolution topographic analysis in hexagonal Discrete Global Grid Systems. International Journal of Applied Earth Observation and Geoinformation, Volume 113, no. 102985, DOI: 10.1016/j.jag.2022.102985.
37.   Li M., Stefanakis E. (2020) Geospatial operations of discrete global grid systems – a comparison with traditional GIS. Journal of Geovisualization and Spatial Analysis, no. 4 (2), DOI: 10.1007/s41651-020-00066-3.
38.   Li S., Dragicevic S., Castro F. A., Sester M., Winter S., Coltekin A., Pettit C., Jiang B., Haworth J., Stein A., Cheng T. (2016) Geospatial big data handling theory and methods: A review and research challenges. ISPRS Journal of Photogrammetry and Remote Sensing, no. 115, pp. 119–133. DOI: 10.1016/j.isprsjprs.2015.10.012.
39.   Li Z., Tang W., Huang Q., Shook E., Guan Q. (2020) Introduction to big data computing for geospatial applications. ISPRS International Journal of Geo-Information, no. 9 (8), DOI: 10.3390/ijgi9080487.
40.   Lukin V., Vasilyeva I., Krivenko S., Li F., Abramov S., Rubel O., Vozel B., Chehdi K., Egiazarian K. (2020) Lossy compression of multichannel remote sensing images with quality control. Remote Sensing, no. 12 (22), DOI: 10.3390/rs12223840.
41.   Lynch C. (2009) Jim Gray's Fourth Paradigm and the Construction of the Scientific Record. The fourth paradigm: data-intensive scientific discovery, ed. T. Hey, S. Tansley, K. Tolle. Microsoft Research, Redmond, Washington, pp. 177–184.
42.   Miller H. J. (2010) The data avalanche is here. ShouldnТt we be digging?. Journal of Regional Science, no. 50 (1), pp. 181–201. DOI: 10.1111/j.1467-9787.2009.00641.x.
43.   Miller H. J., Goodchild M. F. (2015) Data-driven geography. GeoJournal, no. 80 (4), pp. 449–461. DOI: 10.1007/s10708-014-9602-6.
44.   Purss M. B. J., Peterson P. R., Strobl P., Dow C., Sabeur Z. A., Gibb R. G., Ben J. (2019) Datacubes: A discrete global grid systems perspective. Cartographica: The International Journal for Geographic Information and Geovisualization, no. 54 (1), pp. 63–71. DOI: 10.3138/cart.54.1.2018-0017.
45.   Robinson A. C., Demšar U., Moore A. B., Buckley A., Jiang B., Field K., Kraak M.-J., Camboim S. P., Sluter C. R. (2017) Geospatial big data and cartography: research challenges and opportunities for making maps that matter. International Journal of Cartography, no. 3 (sup1), pp. 32–60. DOI: 10.1080/23729333.2016.1278151.
46.   Shurygina A., Titov G. (2022) Building Datacube for Maritime Applications on Discrete Global Grid System. IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium Proceedings, Kuala Lumpur, Malaysia: IEEE, pp. 2446–2449. DOI: 10.1109/IGARSS46834.2022.9884498.
47.   Sudmanns M., Tiede D., Lang S., Bergstedt H., Trost G., Augustin H., Baraldi A., Blaschke T. (2020) Big Earth data: disruptive changes in Earth observation data management and analysis?. International Journal of Digital Earth, no. 13 (7), pp. 832–850. DOI: 10.1080/17538947.2019.1585976.
48.   Sui D., Goodchild M., Elwood S. (2013) Volunteered Geographic Information, the Exaflood, and the Growing Digital Divide. Crowdsourcing geographic knowledge: volunteered geographic information (VGI) in theory and practice, ed. D. Sui, M. Goodchild, S. Elwood. Springer, Dordrecht; New York, pp. 1–14. DOI: 10.1007/978-94-007-4587-2_1.
49.   Vatsavai R. R., Ganguly A., Chandola V., Stefanidis A., Klasky S., Shekhar S. (2012) Spatiotemporal data mining in the era of big spatial data: algorithms and applications. Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data – BigSpatialТ12. ACM Press, Redondo Beach, California, pp. 1–10. DOI: 10.1145/2447481.2447482.
50.   Waltman L., van Eck N. J., Noyons E. C. M. (2010) A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, no. 4 (4), pp. 629–635. DOI: 10.1016/j.joi.2010.07.002.
51.   Wong D. W. S. (2004) The Modifiable Areal Unit Problem (MAUP). WorldMinds: Geographical Perspectives on 100 Problems, ed. D. G. Janelle, B. Warf, K. Hansen. Springer Netherlands, Dordrecht, pp. 571–575. DOI: 10.1007/978-1-4020-2352-1_93.
52.   Yao X., Li G., Xia J., Ben J., Cao Q., Zhao L., Ma Y., Zhang L., Zhu D. (2019) Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sensing, no. 12 (1), DOI: 10.3390/rs12010062.
Citation:
Titov G.S., 
Kargashin P.E., 
(2023) Big spatial data modeling using data cube based on discrete global grid system. Geodesy and cartography = Geodezia i Kartografia, 84(6), pp. 19-29. (In Russian). DOI: 10.22389/0016-7126-2023-996-6-19-29
Publication History
Received: 20.12.2022
Accepted: 15.06.2023
Published: 20.07.2023

Content

2023 June DOI:
10.22389/0016-7126-2023-996-6