• Journal of the Korean Institute of Landscape Architecture
• /
• v.38 no.2
• /
• pp.109-118
• /
• 2010

This study aims to propose practical strategies for the new urban ideal of regeneration. A book review highlights the emergence of new trends of urbanization in knowledge-information industrial society beyond the new town Ideal of the industrial society. The meaning of ‘landscape’ in landscape urbanism represents not the visual and decorative pictures, but the dynamic process in the context of changes and evolutions. Also, knowledge-information industrial society and landscape have a meaning in the same context of flow and process with changes of velocity. Finally, these key words convey a meaning with the new urban trends of urbanization in knowledge-information industrial society in the context of value-oriented characteristics of dynamics and process. Urban regeneration is emerging as the new urban ideal in the knowledge-information industrial society, beyond the new town ideal of industrial society. It is in the same context as landscape urbanism with respect to green infrastructure buildings and designs for the transformation of urban surfaces covered with concrete and asphalt into the ecological surface, and of the ecological surfaces into the cultural surface that could be communicated with human beings. This research revealed the six strategies for urban regeneration as follows. The First, the strategies for the transformation of urban surfaces into ecological surfaces, the second, the strategies for the transformation of ecological surfaces into cultural surfaces, the third, the introduction of mixed and convergence land use, the forth, the transformation of former sites(e.g. military and factory) into urban parks, the fifth, the introduction of waterfront park zones that have the function of ecological and park-oriented mixed land use and, the sixth, the building and design of green infrastructure in the residential and commercial complex in CBD. These strategies call for the reforms of development laws and regulations to restrict building coverage ratio, building heights, and the introduction of park-oriented mixed zoning regulations. Another method for implementating the above listed strategies was the introduction of a strategic planning system instead of the traditional master plan system. This system uses a value planning approach and brand making by imagery. It is able to construct the meaning of an image and its creativeness directly.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.37 no.1
• /
• pp.18-27
• /
• 2009

The purpose of this study is to understand the scale of temperature change following large-scale urban developments in paddy fields to present possible measures to preserve suburban area paddy fields and to lower the scale of temperature increase after developing paddy fields in urban areas. The study was conducted in Bupyeong and Bucheon of Incheon Metropolitan City. The satellite image($1989{\sim}2000$) before and after the development of old paddy fields were used to analyze the land surface temperature changes according to the land use types. Building coverage, green coverage, non-permeable pavement coverage, and floor area ratio(FAR) were selected as the factors that influence urban temperature changes and the temperature estimation model was constructed by using correlation and regression analyses. The before and after satellite images of Bupyeong and Bucheon were classified into forests, greens and plantations, paddy fields, unused lands, and urban areas. The results indicate that most of the paddy fields that existed in the center of Bupyeong and Bucheon were converted into unused lands which were undergoing construction to become new urban areas. The difference between the surface temperatures of May 17th, 1989 and May 7th, 2000 was analyzed to reveal that most land converted from paddy fields to unused lands or urban areas saw an increase in surface temperature. Han River was used as a comparison to analyze the average surface temperature changes($1989{\sim}2000$) in former paddy fields. The scale of temperature changes were: $+1.6697^{\circ}C$ in urban parks; $+2.5503^{\circ}C$ in residential zones; $+2.9479^{\circ}C$ on public lands, $+3.0385^{\circ}C$ in commercial zones, and $+3.1803^{\circ}C$ in educational zones. The correlation between building coverage, green coverage, non-permeable pavement coverage, or floor area ratio(FAR) and surface temperature increases was also analyzed. The green coverage to temperature increases, but building coverage, non-permeable pavement coverage, and floor area ratio(FAR) had no statistically significant temperature increases. The factors that influence urban temperature changes were set up as independent variables and the surface temperature changes as dependent variables to construct a surface temperature change model for the land use types of former paddy fields. As a result of regression analysis, green coverage was selected as the most significant independent variable. According to regression analysis, if farmland is converted into an urban area, a temperature increase of $+3.889^{\circ}C$ is anticipated with 0% green coverage. The temperature saw a decrease of $-0.43^{\circ}C$ with every 10% increase of green coverage.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.4
• /
• pp.64-80
• /
• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.