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Comparison of intensity estimation methods for an earthquake spatial point pattern

Yıl 2020, Cilt: 38 Sayı: 1, 348 - 359, 27.03.2020

Öz

Intensity is described as the number of points per unit area for a spatial point pattern. Intensity estimation of a spatial point pattern is necessary to determine hot spots, cold spots and clusters in a study region. Moreover, intensity may be a determinant for spatial point pattern type. It is also an indicator of risks while the points called events include the locations of the earthquakes through a fault zone, crime incidences in a district and etc. Therefore, determining the intensity provides taking precautions against possible undesirable and unexpected future incidences. In this study, several methods of intensity estimation for a spatial point pattern are given. Advantages and disadvantages of the mentioned methods are discussed. Finally, intensity images that are obtained by using different methods are compared. Adaptive kernel density estimation gave a better result in comparison to other intensity estimation methods.

Kaynakça

  • [1] Diggle P.J., (2013) Statistical analysis of spatial and spatio-temporal point patterns, CRC Press, Boca Raton, USA.
  • [2] Cressie N.A., (1993) Statistics for spatial data, revised edition, John Wiley & Sons, Hoboken NJ, USA.
  • [3] Schabenberger O., Gotway C.A., (2005) Statistical methods for spatial data analysis, CRC press, Boca Raton, USA.
  • [4] Baddeley A., Rubak E., Turner R., (2015) Spatial point patterns: methodology and applications with R, CRC Press, Boca Raton, USA..
  • [5] Gatrell A.C., (1994) Density Estimation and the visualization of point patterns, in: H.J. Hearnshaw, D.J. Unwin, (Eds.), Visualization in geographical information systems, Chicheste, John Wiley, 65-75.
  • [6] Gatrell A.C., Bailey T.C., Diggle P.J., Rowlingson B.S., (1996) Spatial point pattern analysis and its application in geographical epidemiology, Transactions of the Institute of British Geographers, 256-274.
  • [7] Stock C., Smith E.G., (2002) Comparison of seismicity models generated by different kernel estimations , Bulletin of the Seismological Society of America, 92(3), 913-922.
  • [8] Stock C., Smith E.G., (2002) Adaptive kernel estimation and continuous probability representation of historical earthquake catalogs, Bulletin of the Seismological Society of America, 92(3), 904-912.
  • [9] Silverman B.W., (1986) Density estimation for statistics and data analysis, Routledge.
  • [10] Davies T.M., Marshall J.C., Hazelton M.L., (2017) Tutorial on kernel estimation of continuous spatial and spatiotemporal relative risk with accompanying instruction in R. arXiv preprint arXiv:1707.06888.
  • [11] Loader C., (1999) Local regression and Likelihood, Springer ,New York, USA.
  • [12] Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency 1900-20xx earthquake catalog Online Available: https://deprem.afad.gov.tr/depremkatalogu
  • [13] Bohnhoff M., Martínez-Garzón P., Bulut F., Stierle E., Ben-Zion Y., (2016) Maximum earthquake magnitudes along different sections of the North Anatolian fault zone. Tectonophysics 674, 147-165.
Yıl 2020, Cilt: 38 Sayı: 1, 348 - 359, 27.03.2020

Öz

Kaynakça

  • [1] Diggle P.J., (2013) Statistical analysis of spatial and spatio-temporal point patterns, CRC Press, Boca Raton, USA.
  • [2] Cressie N.A., (1993) Statistics for spatial data, revised edition, John Wiley & Sons, Hoboken NJ, USA.
  • [3] Schabenberger O., Gotway C.A., (2005) Statistical methods for spatial data analysis, CRC press, Boca Raton, USA.
  • [4] Baddeley A., Rubak E., Turner R., (2015) Spatial point patterns: methodology and applications with R, CRC Press, Boca Raton, USA..
  • [5] Gatrell A.C., (1994) Density Estimation and the visualization of point patterns, in: H.J. Hearnshaw, D.J. Unwin, (Eds.), Visualization in geographical information systems, Chicheste, John Wiley, 65-75.
  • [6] Gatrell A.C., Bailey T.C., Diggle P.J., Rowlingson B.S., (1996) Spatial point pattern analysis and its application in geographical epidemiology, Transactions of the Institute of British Geographers, 256-274.
  • [7] Stock C., Smith E.G., (2002) Comparison of seismicity models generated by different kernel estimations , Bulletin of the Seismological Society of America, 92(3), 913-922.
  • [8] Stock C., Smith E.G., (2002) Adaptive kernel estimation and continuous probability representation of historical earthquake catalogs, Bulletin of the Seismological Society of America, 92(3), 904-912.
  • [9] Silverman B.W., (1986) Density estimation for statistics and data analysis, Routledge.
  • [10] Davies T.M., Marshall J.C., Hazelton M.L., (2017) Tutorial on kernel estimation of continuous spatial and spatiotemporal relative risk with accompanying instruction in R. arXiv preprint arXiv:1707.06888.
  • [11] Loader C., (1999) Local regression and Likelihood, Springer ,New York, USA.
  • [12] Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency 1900-20xx earthquake catalog Online Available: https://deprem.afad.gov.tr/depremkatalogu
  • [13] Bohnhoff M., Martínez-Garzón P., Bulut F., Stierle E., Ben-Zion Y., (2016) Maximum earthquake magnitudes along different sections of the North Anatolian fault zone. Tectonophysics 674, 147-165.
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Articles
Yazarlar

Cenk İçöz Bu kişi benim 0000-0002-0219-1088

Kadir Özgür Peker Bu kişi benim 0000-0002-9275-0161

Yayımlanma Tarihi 27 Mart 2020
Gönderilme Tarihi 15 Şubat 2019
Yayımlandığı Sayı Yıl 2020 Cilt: 38 Sayı: 1

Kaynak Göster

Vancouver İçöz C, Peker KÖ. Comparison of intensity estimation methods for an earthquake spatial point pattern. SIGMA. 2020;38(1):348-59.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/