With the continuous effort for a harmonious coexistence with nature in human settlement, theories for ecological design and urban sustainability are proposed and developed. However, in 2014, a new concept called ‘ecological wisdom’ was proposed with the baseline that knowledge of ecological design should be gained from past sustained examples as ecological design problem spaces are unbounded. How can ecological wisdom from designs be acquired for reuse? The purpose of this research is to propose a conceptual framework for the acquisition of ecological wisdom and develop a detailed externalization methodology (a process of making tacit knowledge into explicit knowledge). Then, to see if it works or not, the methodology is applied to private Korean gardens of the Joseon Dynasty, which are renowned for being ‘naturalistic’. First, references from knowledge management (KM) are reviewed to claim that externalization step is necessary for the knowledge cycle of ecological wisdom. Then, for externalization, four steps are presented: (1) Case definition; (2) case deconstruction into geometric data and environmental data; (3) similarity calculation for each data types; and (4) matrix correlation between similarity matrices from geometric data and similarity matrices from environmental data. When the above externalization method is applied to 35 cases of private Korean gardens, yearly average temperature and proximity to the nearest river were the most correlated natural factors in terms of architectural spatial relationship in Korean gardens.

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The goal of the article is to find the relationships between theme park visitor traffics and service facility location along with their capacities. To do that, we analyzed four Disneylands situated in Paris, Tokyo, and the United States (Florida and California). By analyzing the visual integration of visitor traffics at each Disneyland and calculating the capacities of service facilities such as the attractions, shops, and restaurants, we ran through a linear and a geographically weighted regression analysis. Our results indicate that there is a unique relationship between the service facility placements and the amount of predicted traffic flows for each Disneyland.

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