This study shows that the SWOT is efficient to select a factor which is valuable as worth consideration when a decision making is necessary. Such processing of information is able to present the policy for the development of bio-industry in northern area of Kyonggi province, and create the pragmatic value and effect in carrying out the policy. The object of this study is to survey present conditions, to analyze the development of bio-industry in northern area of Kyonggi province by the decision making method of the SWOT model, to suggest a plan for the prospect of continued development field and the location of industry, and to extract fundamental data for establishment of annual action and investment plan which can develop bio-industry.

레저용 플로팅 건축물은 구조물 안전성, 건축물 용도, 건설 및 관리 경제성, 주변 지역이나 도시와 개발 연계성, 해양환경에의 영향 등을 고려할 때 입지선정에 대한 체계적 지침과 합리적 기준이 반드시 필요하다. 그러나 현재 우리나라에는 레저용 플로팅 건축물이 많이 계획되고 설치되는 시점에 있음에도 불구하고 입지선정에 관한 연구나 지침이 전혀 없는 실정이다. 따라서 본 연구는 레저용 플로팅 건축물의 입지선정을 위해 체계적 입지선정프로세스. 합리적 입지선정기준 및 입지평가지침을 제시하는 것이 목적이다. 본 연구에서는 지자체나 민간 기업에서 레저용 플로팅 건축물을 계획할 경우 체계적이고 합리적 입지선정에 활용할 수 있도록 입지선정과정, 입지선정기준, 입지평가항목 및 요인을 그림과 표로 알기 쉽게 제시하였다.

This study aims to build a model dealing with the location decision of new manufacturing firms and their land demand. The model is composed with 1) the binary logit model structure identifying a future probability of manufacturing firms to locate in a city and their land demand; and 2) the land use suitability of the land demand. The model was empirically tested in the case of Anseong City. We used establishment-level data for the manufacturing industry from the Report on Mining and Manufacturing Survey. 48 industry groups were scrutinized to find the location probability in the city and their land demand via logit model with the dependent variables: number of employment, land capital, building capital, total products, and value-added for a new industry since 2001. It is forecasted that the future land areas (to 2025) for the manufacturing industries in the city are 5.94km2 and additional land demand for clustering the existing industries scattered over the city is 2.lkm2. Five industrial complex locations were identified through the land use suitability analysis.

For efficient development of rural facilities, choice of their optimum locations would be an important issue, however, existing research works concentrated much more an allocation policy of urban industrial complex and public facilities than rural ones. In this study, because agricultural-cum-industrial complex has been the most widely developed representative one of rural facilities, it was selected as a case study facility. As a pre-study to system development, existing governmental location-decision system was checked and interviewing survey carried out to find out on-spot problems. And, being based on literature review and survey analysis results, 4-step optimum locational decision model was developed , formulation of locational goal system, ranking tabulation on components, determination of significance values of components, calculation of component scores. Finally, through the case study works on 3 sites, system applicability was checked, Considering together the simplicity problem of existing guidelines and the interviewing survey results favoring the diversified viewpoints, it would be necessary to develop multifaceted support system for locational decision making. 3-tier classification steps from the higher, middle to lower one were used and their underpinning viewpoints were sorted as on regional development, entrepreneurship, spatial rationality, from which a tentative locational goal system was formulated. Through the expert group checking, final locational goal system was determined having 3 of the higher classification items, 7 of the middle ones, 23 of the lower ogles. For ranking tabulation, 3 types of ranking criteria were arranged which were based on statistical analysis using mean and standard deviation(Type I ), its existence or not 1 good or not(Type E ), and the others(Type E ). From the significance evaluation results, regional development and entrepreneurship aspects were valued much higher than spatial rationality aspect. And, in the middle step, items as spread effects of regional economy, accessibility and social potentialities were highly valued while infrastructural development level and natural condition being low. The application results of the system to 3 case study total. However, the detailed ones differed among study the influencing effects on regional economy, and contrast greater the infrastructural development level. Conclusively, final evaluation values well represented the characteristics of each area. If this system be complemented and applied comprehensively by the successive studies, it would be developed to a general model of locational decision supporting system for rural facilities.