Geovisual Analytics of Spatio-Temporal Earthquake Data in Indonesia

Febrian Fitryanik Susanta; Cecep Pratama; Trias Aditya; Alian Fathira Khomaini; Hadi Wijaya Kusuma Abdillah

doi:10.22146/jgise.51131

Geovisual Analytics of Spatio-Temporal Earthquake Data in Indonesia

https://doi.org/10.22146/jgise.51131

Febrian Fitryanik Susanta^(1*), Cecep Pratama⁽²⁾, Trias Aditya⁽³⁾, Alian Fathira Khomaini⁽⁴⁾, Hadi Wijaya Kusuma Abdillah⁽⁵⁾

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(4) Universitas Gadjah Mada
(5) Universitas Gadjah Mada
(*) Corresponding Author

Abstract

Indonesia is one of the nations located in the Ring of Fire. Indonesia has a high level of geodynamic activities so that it's often earthquake tectonics. The earthquakes are caused by Indonesia position located in the confluence of four main plates. At present, the history of earthquake data in Indonesia has been accessible by the public. However, general visualization which can present history earthquake in the form maps and summary statistics have not been available. Therefore, this research aims to visualize the history of earthquake data interactively combining spatial data and temporal data. The data used for this research was obtained from BMKG website. The data variables used in this research include CORS stations and history of earthquake phenomenons between 2004 and 2019. The earthquake phenomenon consists of occurrence time, coordinate position, depth and magnitude. The data are processed using Ms Excel and ArcGIS Online Map then are visualized by Web AppBuilder for ArcGIS. The results of the data processing are maps presented in a dashboard with time-series animation and widgets features. We performed maps, graphics and time-series animation as interactive visual interfaces and matched the tasks to visual analytics techniques that are capable to support them. In this paper, we introduce the relationship between variables and present the visual analytics techniques using several example scenarios of Spatio-temporal earthquake data.

Keywords

Dashboard, geovisualization, spatiotemporal analysis, disaster

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References

Andrienko, G., Andrienko, N., Demsar, U., Dransch, D., Dykes, J., Fabrikant, S.I., Jern, M., Kraak, M.-J., Schumann, H., Tominski, C., 2010. Space, Time and Visual Analytics. International Journal of Geographical Information Science 24, 1577–1600. doi:10.1080/13658816.2010.508043

Andrienko, N. and Andrienko, G., 2013. A visual analytics framework for spatio-temporal analysis and modelling. Data Mining and Knowledge Discovery, 27(1), pp.55-83.Bock, Y. (2003). Crustal motion in Indonesia from Global Positioning System measurements. Journal of Geophysical Research, 108(B8), 3–17. https://doi.org/10.1029/2001JB000324

Dharmawan, R.D., Suharyadi and Farda, N.M., 2017, November. Geovisualization using hexagonal tessellation for spatiotemporal earthquake data analysis in Indonesia. In International Conference on Soft Computing in Data Science (pp. 177-187). Springer, Singapore.

Hamilton, W. (1979). Tectonics of the Indonesian Region. Geological Society of Malaysia, Bulletin, 6, 3–10. https://doi.org/10.1016/0003-6870(73)90259-7

Heer, J., Mackinlay, J., Stolte, C. and Agrawala, M., 2008. Graphical histories for visualization: Supporting analysis, communication, and evaluation. IEEE transactions on visualization and computer graphics, 14(6), pp.1189-1196.

Hernández-Castro, F. and Monge-Fallas, J., 2019. Plinius: A Visualization System of Costa Rica's Tectonic Plates. Scientific Visualization, 11(2).

Ismailova, R., 2017. Web site accessibility, usability and security: a survey of government web sites in Kyrgyz Republic. Universal Access in the Information Society, 16(1), pp.257-264.

Jern, M. and Franzen, J., 2006, July. " GeoAnalytics"-Exploring spatio-temporal and multivariate data. In Tenth International Conference on Information Visualisation (IV'06) (pp. 25-31). IEEE.

Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G., 2008. Visual analytics: Definition, process, and challenges, in: Information Visualization. Springer, pp. 154–175.

Maciejewski, R., Rudolph, S., Hafen, R., Abusalah, A., Yakout, M., Ouzzani, M., Cleveland, W.S., Grannis, S.J. and Ebert, D.S., 2009. A visual analytics approach to understanding spatiotemporal hotspots. IEEE Transactions on Visualization and Computer Graphics, 16(2), pp.205-220.

Meilano, I., Abidin, H. Z., Andreas, H., Gumilar, I., Sarsito, D., Rahma, H., … Fukuda. (2012). Slip rate estimation of the lembang fault west java from geodetic observation. Journal of Disaster Research. https://doi.org/10.20965/jdr.2012.p0012

Metsalu, T. and Vilo, J., 2015. ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic acids research, 43(W1), pp.W566-W570.

Popelka, S., Herman, L., Řezník, T., Pařilová, M., Jedlička, K., Bouchal, J., Kepka, M. and Charvát, K., 2019. User evaluation of map-based visual analytic tools. ISPRS International Journal of Geo-Information, 8(8), p.363.

Pusat Studi Gempa Nasional. (2017). Peta Sumber dan Bahaya Gempa Indonesia Tahun 2017. Bandung: Pusat Penelitian dan Pengembangan Perumahan dan Pemukiman.

Simons, W. J. F., Socquet, A., Vigny, C., Ambrosius, B. A. C., Abu, S. H., Promthong, C., … Spakman, W. (2007). A decade of GPS in Southeast Asia: Resolving Sundaland motion and boundaries. Journal of Geophysical Research: Solid Earth, 112(6). https://doi.org/10.1029/2005JB003868

Von Landesberger, T., Bremm, S., Andrienko, N., Andrienko, G. and Tekušová, M., 2012, October. Visual analytics methods for categoric spatio-temporal data. In 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) (pp. 183-192). IEEE.

DOI: https://doi.org/10.22146/jgise.51131