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.
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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
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DOI: https://doi.org/10.1007/978-981-10-7242-0_15
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