• Journal of the Korean Institute of Landscape Architecture
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• v.39 no.4
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• pp.83-97
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• 2011

In this study, space allocation with an assessment system for improving the function of the ecological use and function of each space was undertaken to suggest appropriate goals and directions. Ogeum Park, used as the study site, is a stronghold green zone located in the fan-shaped area of the Songpa-gu green zone on a 22ha area site. The assessment system for this study consisted of a total of 3 steps: Step 1 includes the division of the spatial block, Step 2 assesses the proper functioning of each spatial block, while Step 3 includes space allocation and presents improvements for function by space. This study performed a basin analysis with the consideration that Ogeum Park is a forest area and divided the site into a total of 8 areas according to how the sections of land are used. The function of wildlife habitat included an analysis of plant ecology(vegetation type, vegetation layer, potential vegetation), animal ecology(wild birds), and waterways. The function of leisure and use included an analysis of gradients, noise, paths, status of use, and status of facilities. The evaluation of the function of habitat sorted items into native vegetation, vegetation diversification, vegetation potential, animal diversification and potential of animal habitats. The results of grading the evaluation scores by space in the function of habitat showed that the Areas IV and VII, which were 90% of the maximum point, were Grade A, Areas II and V were Grade B at 70% and Area I and VI were Grade C at 50%. Areas III and VIII, which were not found to be beyond the standard of 50%, were excluded. The evaluation of the functions of leisure and use classified items into use potential, use preference, use concentration, use diversification and use convenience. The results of the graded evaluation scores by space in use function, showed areas V and VI as Grade A, or 90% of the maximum score. Grade B, 70% of the maximum score, was given to Areas I and VII. Grade C, 50% of the maximum score, included Areas II, IV and VIII. Area III, graded lower the standard of 50%, was excluded. The study evaluated areas according to a common standard, classified spaces by proper functions into ecological spaces, environmentally-friendly use spaces and use spaces according to the standard of spatial distribution on the basis of the above results through a synthesis of grades of habitat function, leisure function and use. This offers ideas for the improvement of wildlife habitat and environment-friendly use functions by space.

• Land and Housing Review
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• v.4 no.1
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• pp.77-87
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• 2013

About 1,800 villages have released from Greenbelt since Greenbelt-reform-policy for readjustment of the area was promoted after 1997. Even though the government intended to attract planned development & improvement of these lifted villages through District Unit Plan and designating the lifted area as low-rise and low-density zoning considering the characteristics of the Greenbelt region, there are still many problems to be solved: a lack of funds, insufficient capability for self-improvement and unexecuted SOCs in long-term etc. It seems that these problems are caused by focusing on the lifting areas itself instead of researching deeply the condition and characteristics of the villages and searching proper direction/plans of improvement before lifting Greenbelt In addition, the existing plan of village improvement and management was not considering physical and spacial characteristics of the areas, social and economic situation of residents and relationship between the villages and surrounding cities, though these conditions are different among each villages, and the related regulations are applied uniformly across all the villages and those have been causing many civil appeals and environmental problems. In these respects, this study aims to consider the problems of the lifted villages using the existing researches on them and to make typology by characteristics-data of the villages and to establish improvement strategies of each types. In this study, the villages were classified into 5 types as a result of cluster analysis on 424 villages among all 1,800 through variables of locational potentiality : location, accessibility, size and form of village, condition of regulations etc. According to function of the villages, they were divided into 4 types: urban-type, rural-type, industrial-type and neighborhood-centered-type. This study also drew 4 improvement-strategy-types by combination of locational potentiality and village-function : type of improving life-environment, type of improving production-infra, type of inducing-planned-improvement and type of constructing center-of living-circle. Finally, this study suggested the directions of the each 4 types to desirable improvement and management which could be used to make and complement plans for village improvement.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.89-101
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• 2017

Crime occurs differently based on not only place locations and building uses but also the characteristics of the people who use the place and the spatial structures of the buildings and locations. Therefore, if spatial big data, which contain spatial and regional properties, can be utilized, proper crime prevention measures can be enacted. Recently, with the advent of big data and the revolutionary intelligent information era, predictive policing has emerged as a new paradigm for police activities. Based on 7420 actual crime incidents occurring over three years in a typical provincial city, "J city," this study identified the areas in which crimes occurred and predicted risky areas. Spatial regression analysis was performed using spatial big data about only physical and environmental variables. Based on the results, using the street width, average number of building floors, building coverage ratio, the type of use of the first floor (Type II neighborhood living facility, commercial facility, pleasure use, or residential use), this study established a Crime Incident Prediction Model (CIPM) based on Bayesian probability theory. As a result, it was found that the model was suitable for crime prediction because the overlap analysis with the actual crime areas and the receiver operating characteristic curve (Roc curve), which evaluated the accuracy of the model, showed an area under the curve (AUC) value of 0.8. It was also found that a block where the commercial and entertainment facilities were concentrated, a block where the number of building floors is high, and a block where the commercial, entertainment, residential facilities are mixed are high-risk areas. This study provides a meaningful step forward to the development of a crime prediction model, unlike previous studies that explored the spatial distribution of crime and the factors influencing crime occurrence.