• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.19 no.1
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• pp.1-11
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• 1983

Data on the trawl operation was compiled from the Korea stern trawlers operated in the sea off the Sahara coast of Africa from May 1975 to April 1976. The distribution of some important demersal fishes were investigated by calculating the catch per haul in every fishing ground sections divided by every fishing ground sections divided by every 30' of latitude and longitude. The results obtained are as follows; 1. The mean catch per haul calculated as: squid 14.9kg, large cuttlefish 29.9kg, small cuttlefish 37.8kg, octopus 44.1kg, sole 8.0kg, seabream 9.3kg and miscellaneous fishes 63.1kg. Where cuttlefish is divided into two sizes, large or small by if it weighs over 300kg or not. 2. Squid were caught mostly from August to November in the northern part of 24$^{\circ}$30'N and southern part of 23$^{\circ}$30'N. 3. Large cuttlefish were caught mostly from December to May of the next year, in the coast from 23$^{\circ}$00'N to 25$^{\circ}$00'N, where as small cuttlefish were caught mostly from April to June and from November to January of next year, in the coast from 23$^{\circ}$30'N to 25$^{\circ}$00'N. 4. Octopus were caught mostly from September to January of the next year and from March to April. in the coast from 23$^{\circ}$00'N to 25$^{\circ}$00'N rather in the offshore than in the near coast. 5. Miscellaneous fishes including seabream were caught from May to November, sole from June to November and the others from May to October.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.95-118
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• 2017

Recent centrally the downtown area, the transaction between the row housing and multiplex housing is activated and platform services such as Zigbang and Dabang are growing. The row housing and multiplex housing is a blind spot for real estate information. Because there is a social problem, due to the change in market size and information asymmetry due to changes in demand. Also, the 5 or 25 districts used by the Seoul Metropolitan Government or the Korean Appraisal Board(hereafter, KAB) were established within the administrative boundaries and used in existing real estate studies. This is not a district classification for real estate researches because it is zoned urban planning. Based on the existing study, this study found that the city needs to reset the Seoul Metropolitan Government's spatial structure in estimating future housing prices. So, This study attempted to classify the area without spatial heterogeneity by the reflected the property price characteristics of row housing and Multiplex housing. In other words, There has been a problem that an inefficient side has arisen due to the simple division by the existing administrative district. Therefore, this study aims to cluster Seoul as a new area for more efficient real estate analysis. This study was applied to the hedonic model based on the real transactions price data of row housing and multiplex housing. And the K-Means Clustering algorithm was used to cluster the spatial structure of Seoul. In this study, data onto real transactions price of the Seoul Row housing and Multiplex Housing from January 2014 to December 2016, and the official land value of 2016 was used and it provided by Ministry of Land, Infrastructure and Transport(hereafter, MOLIT). Data preprocessing was followed by the following processing procedures: Removal of underground transaction, Price standardization per area, Removal of Real transaction case(above 5 and below -5). In this study, we analyzed data from 132,707 cases to 126,759 data through data preprocessing. The data analysis tool used the R program. After data preprocessing, data model was constructed. Priority, the K-means Clustering was performed. In addition, a regression analysis was conducted using Hedonic model and it was conducted a cosine similarity analysis. Based on the constructed data model, we clustered on the basis of the longitude and latitude of Seoul and conducted comparative analysis of existing area. The results of this study indicated that the goodness of fit of the model was above 75 % and the variables used for the Hedonic model were significant. In other words, 5 or 25 districts that is the area of the existing administrative area are divided into 16 districts. So, this study derived a clustering method of row housing and multiplex housing in Seoul using K-Means Clustering algorithm and hedonic model by the reflected the property price characteristics. Moreover, they presented academic and practical implications and presented the limitations of this study and the direction of future research. Academic implication has clustered by reflecting the property price characteristics in order to improve the problems of the areas used in the Seoul Metropolitan Government, KAB, and Existing Real Estate Research. Another academic implications are that apartments were the main study of existing real estate research, and has proposed a method of classifying area in Seoul using public information(i.e., real-data of MOLIT) of government 3.0. Practical implication is that it can be used as a basic data for real estate related research on row housing and multiplex housing. Another practical implications are that is expected the activation of row housing and multiplex housing research and, that is expected to increase the accuracy of the model of the actual transaction. The future research direction of this study involves conducting various analyses to overcome the limitations of the threshold and indicates the need for deeper research.

• Journal of Fisheries and Marine Sciences Education
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• v.7 no.2
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• pp.182-200
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• 1995

Thirty two vessels of the Korean purse seiner had been operated in the Western Pacific Ocean for mainly skipjack tuna, Katsuwonus pelmis LINNAEUS and yellowfin tuna, Thunnus albacares BONNATERRE from January to December in 1991. Among them, fourteen vessels were chosen for this research. During the year their daily operated vessels totalled 4,153 vessels, their total casting net were 2,982 times, in caught 1,798 times, and their total catch was 106,300 M/T. We investigate the distribution of their catch by species, by body size, and by surfance water temperature, and also investigate the distribution of their catch by month and section of the sea, where the sections are separated by 30' of longitude and latitude from the monthly operated sea. We summarize these as follows : 1. The rate of catch by species is 75r/o skipjack tunas, 22.3% yellowfin tunas, and 2.7% bigeye and other tunas. 2. Of the caught skipjack tunas, those of weight 2.0~10kg are most and 68%, those of 1.5~8kg are 11.6%, and those of 3.0~8kg are 9.9%. Of the caught yellowfin tunas, those of weight 5~50kg and 10~50kg are most and 23.1%, and 28.3% respectively, those of 20~50kg are 15.8%, weight 30~50kg are 12.5%, and weight 2~50kg are 9.7%. 3. On the distribution of catch by surface water temperature, 49% of catch are taken between $29.0^{\circ}C$ and $29.4^{\circ}C$, 37% are taken between $29.5^{\circ}C$ and $29.9^{\circ}C$, and about 6% are taken between $28.5^{\circ}C$ and $28.9^{\circ}C$, but very little, only about 1% are taken below $28.4^{\circ}C$ and above $30.5^{\circ}C$. 4. On the distribution of catch by month and section of sea, skipjack tunas are most caught 10,618M/T in August and 10,412M/T in September in the section of Lat. $3^{\circ}{\sim}6^{\circ}S$ and Long. $174^{\circ}E{\sim}176^{\circ}W$, caught much 8,825M/I' in June and 8,057M/T in January in section of Lat. $1^{\circ}S{\sim}3^{\circ}N$ and Long. $142^{\circ}{\sim}151^{\circ}$E, but caught very little in May, November and December in the costal area of New Guinea. Yellowfin tunas are mostly caught 4,070M/T in June in the section of Lat. $0^{\circ}{\sim}4^{\circ}$N and Long. $142^{\circ}{\sim}151^{\circ}$E, and caught much over 2,000M/T in February~April and October~December in the section of coastal area and near islands, but caught very little in distant water area.