• 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.