A density-based clustering algorithm for earthquake zoning

Scitovski, Sanja

doi:10.1016/j.cageo.2017.08.014

Cited by 38 publications

(13 citation statements)

References 20 publications

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“…Similar to the findings reported by studies in other countries, the extreme events of earthquakes studied here exhibited spatial variations within the study area [37]. These spatial variations have also been found in the results of various types of studies on seismogenic zoning [38]. The analysis of the extreme values of earthquakes using the extreme distribution of the value in the non-stationary approach, in addition to allowing the calculation of the probabilities of extreme events, helps to solve the problem of seismogenic zoning in a limited area, by establishing suitably chosen thresholds in the isolines of the estimated smoothed function of the location parameter.…”

Section: Resultssupporting

confidence: 90%

Facing Missing Observations in Data—A New Approach for Estimating Strength of Earthquakes on the Pacific Coast of Southern Mexico Using Random Censoring

et al. 2019

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We introduced a novel spatial model based on the distribution of generalized extreme values (GEV) to analyze the maximum intensity levels of earthquakes with incomplete data (randomly censored) on the Pacific coast of southern Mexico using a random censorship approach. Spatiotemporal trends were modeled through a non-stationary GEV model. We used a multivariate smoothing function as a linear predictor of GEV parameters to approximate nonlinear trends. The model was fitted using a flexible semi-parametric Bayesian approach and the parameters are estimated via Markov chain Monte-Carlo (MCMC). Through a rigorous simulation study, we showed the robustness of both the model and the estimation method used. Maps of the location parameter on the spatial plane for different periods of time show the existence of local variations in the extreme values of seismicity in the study area. The results indicate strong evidence of an increase in the magnitude of earthquakes over time. A spatial map of risk with maximum intensity of earthquakes in a period of 25 years was elaborated.

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Section: Resultssupporting

confidence: 90%

Facing Missing Observations in Data—A New Approach for Estimating Strength of Earthquakes on the Pacific Coast of Southern Mexico Using Random Censoring

et al. 2019

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“…A density-based algorithm (MDBSCAN) (Schoier & Borruso, 2015) is used to discover the clusters of units in large spatial sets. Earthquake zoning can be seen using density-based clustering in the case of big data (Scitovski, 2018). Spatial interpolation (Yao, Zhu, Ye, Zhang, & Li, 2014) is adopted to compute the spatial distribution of unknown points from a sampling data-set.…”

Section: Spatial Analysismentioning

confidence: 99%

Big spatial vector data management: a review

Yao

2018

Big Earth Data

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Spatial vector data with high-precision and wide-coverage has exploded globally, such as land cover, social media, and other datasets, which provides a good opportunity to enhance the national macroscopic decision-making, social supervision, public services, and emergency capabilities. Simultaneously, it also brings great challenges in management technology for big spatial vector data (BSVD). In recent years, a large number of new concepts, parallel algorithms, processing tools, platforms, and applications have been proposed and developed to improve the value of BSVD from both academia and industry. To better understand BSVD and take advantage of its value effectively, this paper presents a review that surveys recent studies and research work in the data management field for BSVD. In this paper, we discuss and itemize this topic from three aspects according to different information technical levels of big spatial vector data management. It aims to help interested readers to learn about the latest research advances and choose the most suitable big data technologies and approaches depending on their system architectures. To support them more fully, firstly, we identify new concepts and ideas from numerous scholars about geographic information system to focus on BSVD scope in the big data era. Then, we conclude systematically not only the most recent published literatures but also a global view of main spatial technologies of BSVD, including data storage and organization, spatial index, processing methods, and spatial analysis. Finally, based on the above commentary and related work, several opportunities and challenges are listed as the future research interests and directions for reference.

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“…Among them are density-based clustering [4,5,6,7], which calculates density according to neighboring data, and graph-based clustering [8,9,10], which extracts corresponding points using a mathematical model through an affinity matrix. These techniques show excellent performance in detecting not only clusters with uniform data distribution and ideal shapes, but also clusters with some nonlinearity, and have been applied to various fields such as traffic accident analysis [11], seismic analysis [12,13], image segmentation [14], and three-dimensional object modeling [15] based on sensor information.…”

Section: Introductionmentioning

confidence: 99%

Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds

Kim

Cho

2019

Sensors

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In spatial data with complexity, different clusters can be very contiguous, and the density of each cluster can be arbitrary and uneven. In addition, background noise that does not belong to any clusters in the data, or chain noise that connects multiple clusters may be included. This makes it difficult to separate clusters in contact with adjacent clusters, so a new approach is required to solve the nonlinear shape, irregular density, and touching problems of adjacent clusters that are common in complex spatial data clustering, as well as to improve robustness against various types of noise in spatial clusters. Accordingly, we proposed an efficient graph-based spatial clustering technique that employs Delaunay triangulation and the mechanism of DBSCAN (density-based spatial clustering of applications with noise). In the performance evaluation using simulated synthetic data as well as real 3D point clouds, the proposed method maintained better clustering and separability of neighboring clusters compared to other clustering techniques, and is expected to be of practical use in the field of spatial data mining.

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A density-based clustering algorithm for earthquake zoning

Cited by 38 publications

References 20 publications

Facing Missing Observations in Data—A New Approach for Estimating Strength of Earthquakes on the Pacific Coast of Southern Mexico Using Random Censoring

Facing Missing Observations in Data—A New Approach for Estimating Strength of Earthquakes on the Pacific Coast of Southern Mexico Using Random Censoring

Big spatial vector data management: a review

Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds

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