Abstract
Geovisualization process is the key in map design to extract information from geospatial data set especially on big data. The use of point feature in the geovisualization process of earthquake spatial data in Indonesia caused some problems such as overlapping between symbols, complications due to the large amount of data, and uneven spatial distribution. This research aims to create geovisualization of earthquake spatial data in Indonesia using hexagonal tessellation method, analyze earthquake map in Indonesia based on geovisualization using hexagonal tessellation and interpret earthquake map in Indonesia based on geovisualization using hexagonal tessellation spatiotemporally. The method used in this research is geovisualization of earthquake spatial data in Indonesia using hexagonal tessellation. This research use earthquake epicenter density analysis to discover and illustrate the spatial phenomena pattern of the earthquake epicenter into more easily understood information. The density analysis includes distance matrix analysis to examine the visualization result and proximity analysis to know the proximity of earthquake density represented by centroid hexagon point with the tectonic plate fault line. The result of this research is earthquake map in Indonesia based on geovisualization using hexagonal tessellation in Indonesia 2010 to 2015. The result of this research shows that the map design of earthquake geospatial information using hexagonal tessellation geovisualization method can show the density distribution of earthquake point spatiotemporally. The earthquake epicenter density analysis in Indonesia based on geovisualization using hexagonal tessellation showed that the hexagon centroid point with high density attribute earthquake data of all magnitude classes tended to have a closer distance to the tectonic plate fault lines spatially.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Badan Meteorologi Klimatologi dan Geofisika. Gempabumi (2016). http://inatews.bmkg.go.id/new/tentang_eq.php. Accessed 30 Sep 2016
Birch, C.P., Oom, S.P., Beecham, J.A.: Rectangular and hexagonal grids used for observation, experiment and simulation in ecology. Ecol. Model. 206, 347–359 (2007)
Blinn, C.R., Queen, L.P., Maki, L.W.: Geographic Information Systems: A Glossary. Universitas Minnesota, Minnesota (1993)
Carr, D.B., Olsen, A.R., White, D.: Hexagon mosaic maps for display of univariate and bivariate geographical data. Cartogr. Geog. Inf. Syst. 19(4), 228–236 (1992)
Dewi, R.S.: A GIS-Based Approach to The Selection of Evacuation Shelter Buildings and Routes for Tsunami Risk Reduction. Universitas Gadjah Mada, Yogyakarta (2010)
Dittus, M.: Visualising Structured Data on Geographic Maps Evaluating a Hexagonal Map Visualisation. Centre for Advanced Spatial Analysis, University College London, London (2011)
Kious, W.J., Tilling, R.I.: This Dynamic Earth: The Story of Plate Tectonics, illustrated edn. DIANE Publishing, Washington, DC (1996)
Longley, P.A., Goodchild, M.F., Maguire, D.J., Rhind, D.W.: Geographic Information Science and Systems, 4th edn. Wiley, New Jersey (2016)
Olea, R.: Sampling design optimization for spatial functions. Math. Geol. 16(4), 369–392 (1984)
Poorthuis, A.: Getting Rid of Consumers of Furry Pornography, or How to Find Small Stories With Big Data. North American Cartographic Information Society, Greenville (2013)
Rolf, A., Knippers, R.A., Sun, Y., Kraak, M.: Principles of Geographic Information Systems. ITC Educational Textbook, Enschede (2000)
Sadahiro, Y.: Advanced Urban Analysis: Spatial Analysis using GIS. Associate Professor of the Department of Urban, Tokyo (2006)
Scott, D.W.: Averaged shifted histograms: effective nonparametric density estimators in several dimensions. Ann. Stat. 13(3), 1024–1040 (1985)
Shelton, T., Poorthuis, A., Graham, M., Zook, M.: Mapping the data shadows of hurricane sandy: uncovering the sociospatial dimensions of ‘big data’. Geoforum 52, 167–179 (2014)
Wang, T.: Adaptive tessellation mapping (ATM) for spatial data mining. Int. J. Mach. Learn. Comput. 4, 479–482 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dharmawan, R.D., Suharyadi, Farda, N.M. (2017). Geovisualization Using Hexagonal Tessellation for Spatiotemporal Earthquake Data Analysis in Indonesia. In: Mohamed, A., Berry, M., Yap, B. (eds) Soft Computing in Data Science. SCDS 2017. Communications in Computer and Information Science, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-10-7242-0_15
Download citation
DOI: https://doi.org/10.1007/978-981-10-7242-0_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7241-3
Online ISBN: 978-981-10-7242-0
eBook Packages: Computer ScienceComputer Science (R0)