• Journal of The Geomorphological Association of Korea
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• v.21 no.4
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• pp.149-164
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• 2014

The representative landscapes in Jeju Island called as Ten sceneries of Yeongju originates with Ten sceneries of Tamra which were organized in the late seventeenth century by Jeju governor Lee Iktae. In order to compose ten representative sceneries in Jeju Island, he selected four strongholds such as Jocheon, Byeolbang, Seogwi and Myeongwol and six places of scenic beauty such as Seongsan, Baekrokdam, Yeonggok, Cheonjiyeon, Sanbang and Chwibyeongdam, and made a painting named as Ten sceneries of Tamra. Since the scenes of strongholds also exhibit a lot of geomorphic landscapes, the painting could be considered a collection of representative landforms in Jeju Island. The painting depicts several types of landform such as volcano, crater, river, waterfall, pool, wetland, cavern, tor and rocky coast. The volcanic edifice appears most frequently in the painting, reflecting the particular emotion of Jeju people on Mt. Halla and oreums. However, another volcanic landform, a lava tube, was not highly evaluated due to its negative images. Three famous waterfalls in Jeju Island were included in Ten sceneries of Tamra, and it suggests that they are essentially outstanding landforms, and besides, there are few sites related with fresh water in Jeju Island. The ten sceneries were entirely organized in spite of the limitation of selecting places at that time. The landscape images of Jeju Island, which had been shared as collective representation by Jeju people, were firstly externalized through Lee Iktae's painting, and have long been passed down and established a kind of scenic stereotype.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.923-932
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• 2014

The Hurdle model can to analyze zero-inflated count data. This model is a mixed model of the logit model for a binary component and a truncated Poisson model of a truncated count component. We propose a new hurdle model with a general heterogeneous random effects covariance matrix to analyze longitudinal zero-inflated count data using modified Cholesky decomposition. This decomposition factors the random effects covariance matrix into generalized autoregressive parameters and innovation variance. The parameters are modeled using (generalized) linear models and estimated with a Bayesian method. We use these methods to carefully analyze a real dataset.