{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T14:58:59Z","timestamp":1776178739540,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T00:00:00Z","timestamp":1651536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Faculty of Mining and Geology, VSB\u2014Technical University of Ostrava","award":["SP2020\/48"],"award-info":[{"award-number":["SP2020\/48"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The space\u2013time series carry information on temporal and spatial patterns in observed phenomena. The reported research integrates computational, visual and cartographic methods to support visual analysis of space\u2013time series describing terrain surface movement. The proposed methodology for space\u2013time series visualisation can support their analysts in investigating space\u2013time patterns using transformation, clustering, filtration and visualisation. The presented approach involves spiral graphs for representation time dimension and cartographic visualisation through proportional point symbol map for representation of spatial dimension. The result is an intuitive visualisation of space\u2013time series, conveying the sought-after spatio-temporal information. For practical tests, we used space\u2013time series obtained by permanent scatterers interferometry (PS InSAR) to monitor the Earth\u2019s surface movement above the underground gas storage (UGS) Tvrdonice, the Czech Republic. An UGS is characterised by periodic injection and withdrawal of natural gas, which induces periodic movement of the terrain above it. We have verified that our visualisation method provides the required pattern information and is easy to use.<\/jats:p>","DOI":"10.3390\/rs14092184","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T08:26:35Z","timestamp":1651566395000},"page":"2184","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Spatiotemporal Visualisation of PS InSAR Generated Space\u2013Time Series Describing Large Areal Land Deformations Using Diagram Map with Spiral Graph"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6329-936X","authenticated-orcid":false,"given":"Juraj","family":"Struh\u00e1r","sequence":"first","affiliation":[{"name":"Department of Geoinformatics, Faculty of Mining and Geology, V\u0160B\u2013Technical University of Ostrava, 17. Listopadu 2172\/15, 708 00 Ostrava, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9839-7610","authenticated-orcid":false,"given":"Petr","family":"Rapant","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics, Faculty of Mining and Geology, V\u0160B\u2013Technical University of Ostrava, 17. Listopadu 2172\/15, 708 00 Ostrava, Czech Republic"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hanssen, R.F. (2001). Radar Interferometry: Data Interpretation and Error Analysis, Kluwer Academic Publishers.","DOI":"10.1007\/0-306-47633-9"},{"key":"ref_2","unstructured":"Kerren, A., Stasko, J.T., Fekete, J.D., and North, C. (2008). Visual Analytics: Definition, Process and Challenges. Information Visualisation\u2013Human-Centered Issues and Perspectives, LNCS, Springer. Available online: https:\/\/hal-lirmm.ccsd.cnrs.fr\/lirmm-00272779\/file\/VAChapter_final.pdf."},{"key":"ref_3","unstructured":"M\u00fcller, W., and Schumann, H. (2003, January 5). Visualisation methods for time-dependent data\u2014An overview. Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), Xi\u2019an, China."},{"key":"ref_4","unstructured":"B\u00f6gl, M. (2020). Visual Analysis of Periodic Time Series Data. [Ph.D. Disseration, TU Wien]."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1109\/TVCG.2010.162","article-title":"Graphical Perception of Multiple Time Series","volume":"16","author":"Javed","year":"2010","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lye, J.N., and Hirschberg, J.G. (2020). Time Series Plots: A Compendium of Time Series Plots, Univesity of Melbourne. Available online: https:\/\/www.researchgate.net\/publication\/340326140.","DOI":"10.1111\/1467-8462.12374"},{"key":"ref_7","unstructured":"Wilke, C.O. (2019). Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures, O\u2019Reilly Media. [1st ed.]."},{"key":"ref_8","unstructured":"Clevelend, W.S. (1993). Visualizing Data, Hobart Press. [1st ed.]."},{"key":"ref_9","unstructured":"Huberman, B., Gunn, E., Kindig, C., and Benistant, L. (2022, April 02). Towards Data Science. Available online: https:\/\/towardsdatascience.com\/5-types-of-plots-that-will-help-you-with-time-series-analysis-b63747818705."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"022013","DOI":"10.1088\/1757-899X\/782\/2\/022013","article-title":"A Survey of Time Series Data Visualization Research","volume":"782","author":"Fang","year":"2020","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_11","unstructured":"Van Wijk, J.J., and Van Selow, E.R. (1999, January 24\u201329). Cluster and calendar based visualisation of time series data. Proceedings of the 1999 IEEE Symposium on Information Visualization (InfoVis\u201999), San Francisco, CA, USA."},{"key":"ref_12","unstructured":"Larsen, J.E., Cuttone, A., and J\u00f8rgensen, S.L. (2013, January 27\u201328). QS Spiral: Visualising Periodic Quantified Self Data. Proceedings of the CHI 2013 Workshop on Personal Informatics in the Wild: Hacking Habits for Health & Happiness, Paris, France. Available online: https:\/\/backend.orbit.dtu.dk\/ws\/portalfiles\/portal\/73859557\/chi2013_pi.pdf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jabbari, A., Blanch, R., and Dupuy-Chessa, S. (2018, January 10\u201313). Composite Visual Mapping for Time Series Visualization. Proceedings of the 2018 IEEE Pacific Visualization Symposium (PacificVis), Kobe, Japan.","DOI":"10.1109\/PacificVis.2018.00023"},{"key":"ref_14","unstructured":"Weber, M., Alexa, M., and Muller, W. (2001, January 22\u201323). Visualising time-series on spirals. Proceedings of the IEEE Symposium on Information Visualization, 2001, INFOVIS 2001, San Diego, CA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hewagamage, K.P., Hirakawa, M., and Ichikawa, T. (1999, January 13\u201316). Interactive Visualization of Spatiotemporal Patterns Using Spirals on a Geographical Map. Proceedings of the 1999 IEEE Symposium on Visual Languages, Tokyo, Japan.","DOI":"10.1109\/VL.1999.795916"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Struh\u00e1r, J., and Rapant, P. (2022). 3D Visualisation of Periodic Space-Time Series from Radar Interferometry Measurements over Underground Gas Storage, GIS Ostrava.","DOI":"10.31490\/9788024846026-9"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Thakur, S., and Hanson, A.J. (2010, January 26\u201329). A 3D Visualisation of Multiple Time Series on Maps. Proceedings of the 2010 14th International Conference Information Visualisation, London, UK.","DOI":"10.1109\/IV.2010.54"},{"key":"ref_18","unstructured":"Laughlin, R.B. (2004). Energy Information Administration\u2013Basics of Underground Natural Gas Storage, Department of Physics, Stanford University. Available online: http:\/\/large.stanford.edu\/publications\/coal\/references\/gastore\/."},{"key":"ref_19","unstructured":"(2022, April 02). Underground Gas Storage in the World\u2013Part 1: Current Capacity. CEDIGAS. Available online: https:\/\/www.cedigaz.org\/underground-gas-storage-world-part-1-current-capacity\/."},{"key":"ref_20","unstructured":"(2022, April 02). Underground Gas Storage in The World\u20132021 Status. CEDIGAS. Available online: https:\/\/www.cedigaz.org\/shop-with-selector\/?type=publications&search=UNDERGROUND%20GAS%20STORAGE%20IN%20THE%20WORLD%20-%202021%20STATUS%20."},{"key":"ref_21","unstructured":"Amadei, C. (2005). Encyclopaedia of Hydrocarbons, Istituto Della Enciclopedia Italiana. Available online: http:\/\/www.treccani.it\/export\/sites\/default\/Portale\/sito\/altre_aree\/Tecnologia_e_Scienze_applicate\/enciclopedia\/inglese\/inglese_vol_1\/pag879-912ING3.pdf."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rapant, P., Struh\u00e1r, J., and Lazeck\u00fd, M. (2020). Radar Interferometry as a Comprehensive Tool for Monitoring the Fault Activity in the Vicinity of Underground Gas Storage Facilities. Remote Sens., 12.","DOI":"10.3390\/rs12020271"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5170","DOI":"10.1029\/2019GL081916","article-title":"Strain Rate Distribution in South-Central Tibet From Two Decades of InSAR and GPS","volume":"46","author":"Wang","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_24","first-page":"221","article-title":"Monitoring dam structural health from space: Insights from novel InSAR techniques and multi-parametric modeling applied to the Pertusillo dam Basilicata, Italy","volume":"52","author":"Milillo","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2202","DOI":"10.1109\/36.868878","article-title":"Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry","volume":"38","author":"Ferretti","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/36.898661","article-title":"Permanent scatterers in SAR interferometry","volume":"39","author":"Ferretti","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","first-page":"245","article-title":"Sentinel-1 InSAR over Germany: Large-Scale Interferometry, Atmospheric Effects, and Ground Deformation Mapping","volume":"2017","author":"Haghighi","year":"2017","journal-title":"Z. F\u00fcr Geod\u00e4sie Geoinf. Und Landmanag."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Singhroy, V., Li, J., Samsonov, S., Shen, L., and Pearse, J. (2014, January 13\u201318). InSAR monitoring of surface deformation induced by steam injection in the Athabasca oil sands, Canada. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6947567"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Granda, J., Arnaud, A., Pay\u00e0s, B., and Lecampion, B. (2012, January 26\u201327). Case Studies for Monitoring of CO2 Storage Sites, based on Ground Deformation Monitoring with Radar Satellites. Proceedings of the 3rd EAGE CO2 Geological Storage Workshop, Edinburgh, UK.","DOI":"10.3997\/2214-4609.20143801"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1109\/JSTARS.2016.2587778","article-title":"Bridge Displacements Monitoring using Space-Borne SAR Interferometry","volume":"10","author":"Lazecky","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4309","DOI":"10.1109\/TGRS.2010.2051333","article-title":"Geodetically Accurate InSAR Data Processor","volume":"48","author":"Zebker","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","unstructured":"Lazecky, M. (2017, January 28\u201330). System for Automatized Sentinel\u20131 Interferometric Monitoring. Proceedings of the 2017 Conference on Big Data from Space, Toulouse, France."},{"key":"ref_33","unstructured":"Dodge, Y. (2008). The Concise Encyclopedia of Statistics, Springer. (CES 2008)."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1111\/cgf.13237","article-title":"Data Abstraction for Visualizing Large Time Series","volume":"37","author":"Shurkhovetskyy","year":"2017","journal-title":"Comput. Graph. Forum"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Frank, A.U. (1998). Different types of \u201ctimes\u201d in GIS. Spatial and Temporal Reasoning in Geographic Information Systems, Oxford University Press.","DOI":"10.1093\/oso\/9780195103427.003.0003"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Carlis, J.V., and Konstan, J.A. (1998, January 1\u20134). Interactive visualisation of serial periodic data. Proceedings of the 1998 11th Annual ACM Symposium on User Interface Software and Technology, UIST-98, San Francisco, CA, USA.","DOI":"10.1145\/288392.288399"},{"key":"ref_37","unstructured":"Tominsky, C., and Schmann, H. (2008, January 27\u201328). Enhanced Interactive Spiral Display. Proceedings of the annual SIGRAD Conference, Special Theme, Interaction, Stockholm, Sweden. Available online: https:\/\/www.researchgate.net\/publication\/230887318_Enhanced_Interactive_Spiral_Display."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kirch, W. (2008). Pearson\u2019s Correlation Coefficient. Encyclopedia of Public Health, Springer.","DOI":"10.1007\/978-1-4020-5614-7"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/BF00648343","article-title":"Least-squares frequency analysis of unequally spaced data","volume":"39","author":"Lomb","year":"1976","journal-title":"Astrophys. Space Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1086\/160554","article-title":"Studies in astronomical time series analysis. II\u2013Statistical aspects of spectral analysis of unevenly spaced data","volume":"263","author":"Scargle","year":"1982","journal-title":"Astrophys. J."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1086\/167197","article-title":"Fast algorithm for spectral analysis of unevenly sampled data","volume":"338","author":"Press","year":"1989","journal-title":"Astrophys. J."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"103857","DOI":"10.1016\/j.engappai.2020.103857","article-title":"Space\u2013time series clustering: Algorithms, taxonomy, and case study on urban smart cities","volume":"95","author":"Belhadi","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_43","unstructured":"Simoudis, E., Han, J., and Fayyad, U.M. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, Oregon. Available online: https:\/\/www.aaai.org\/Papers\/KDD\/1996\/KDD96-037.pdf?source=post_page."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Antunes, M., Gomes, D., and Aguiar, R.L. (2018, January 26\u201329). Knee\/Elbow Estimation Based on First Derivative Threshold. Proceedings of the 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService), Bamberg, Germany.","DOI":"10.1109\/BigDataService.2018.00042"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Satopaa, V., Albrecht, J., Irwin, D., and Raghavan, B. (2011, January 20\u201324). Finding a \u201cKneedle\u201d in a Haystack: Detecting Knee Points in System Behavior. Proceedings of the 2011 31st International Conference on Distributed Computing Systems Workshops, Minneapolis, MN, USA.","DOI":"10.1109\/ICDCSW.2011.20"},{"key":"ref_46","unstructured":"Card, S.K., Mackinlay, J.D., and Shneiderman, B. (1999). Readings in Information Visualisation\u2013Using Vision to Think, Morgan Kaufmann."},{"key":"ref_47","unstructured":"Thom, D.B. (2015). Visual Analytics of Social Media for Situation Awareness. [Ph.D. Dissertation, Universit\u00e4t Stuttgart]. Available online: https:\/\/www.researchgate.net\/publication\/279397560_Visual_analytics_of_social_media_for_situation_awareness."},{"key":"ref_48","unstructured":"AGSI+ (2022). Aggregated Gas Storage Inventory\u2014The Czech Republic (2022), Gas Infrastructure Europe (GIE) AGSI+ Storage Transparency Platform. Available online: https:\/\/agsi.gie.eu\/#\/graphs\/CZ."},{"key":"ref_49","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"JMLR"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1109\/TVCG.2006.84","article-title":"A Visualisation System for Space-Time and Multivariate Patterns (VIS-STAMP)","volume":"12","author":"Guo","year":"2006","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_51","unstructured":"Andrienko, G., and Andrienko, N. (2022, April 02). Applying Visual Analytics Methods to Space-Time Series Data: Forest Fires, Phone Calls, May 2010, presentations at ESPON and GeoVA(t). Available online: https:\/\/www.espon.eu\/sites\/default\/files\/attachments\/SOM-gennady_andrienko.pdf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2184\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:05:37Z","timestamp":1760137537000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2184"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,3]]},"references-count":51,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092184"],"URL":"https:\/\/doi.org\/10.3390\/rs14092184","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,3]]}}}