• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.92-104
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• 2015

In order to analyze the impacts of climate change, a time and space integrated model was developed in this study using system dynamics and GIS. The model built was used to carry out a simulation on the inundation impact on A-gu of Busan Metropolitan city resulting from the sea level rise scenario of IPCC and storm surge, which is the worst case. Through this, the flooded area and population until 2100 were predicted. Also, the result and significance of each alternative was reviewed improving the model by establishing alternative scenarios of protection, accommodation and retreat as plans of reaction to sea level rise. The combination of system dynamics and GIS has advantages of how the diverse variables change until the target year can be traced and, accordingly, not only the results but also the processes of spatial change can be examined by calculating the value of change process at each time step. The synergy of this model presumed to be a foothold for solving problems which are becoming difficult to predict due to increase in uncertainty and complexity such as the support for decision making for urban resilience to natural disasters.

• Journal of the Korean Institute of Landscape Architecture
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• v.38 no.4
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• pp.75-83
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• 2010

College campus landscape plans once focused mainly on campus functionality and aesthetically pleasing buildings. Yet now, after the rise of greater emphasis on afforestation and eco-friendly planning, building spaces for the local culture and community has become the core of the plan. This study analyzed the design strategies and details of the landscape plan that was selected through the contest to select a design plan for the cultural park at Sunchon National University. The key considerations for the landscape Design for the cultural space at Sunchon National University areas follows. First, the design plan sought ways to reach out to the local community, going one step beyond just opening up campus facilities. This means more than just the opening of physical facilities and environments. It was designed to serve as a base to organize diversified programs by generations and groups with an aim to share the history and culture of the college, the local community and the region. Second, shapes and colors were designed to establish a unified image between buildings and outdoor facilities. "Three Books" was selected as the key motif as books were believed to be the most representative symbol of colleges while 6 straight lines, hexagons and circles inspired by the shape of three books were used in the design. In terms of colors, reddish-brown was used for buildings to enhance visibility along with harmony and esthetic appreciation. For facilities, black and blue were used as dominant colors and white and yellow as point colors to promote the image of Sunchon City. Third, with an aim to overcome the limitation of the overall college campus as a closed space, it was designed to be a barrier-free space, remaining open to everyone and encouraging visits and experiences for active communication with the local community.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.576-584
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• 2017

Traffic demand prediction result is a primary factor for decision making such as the traffic planning and operation. The existing traffic demand prediction 4-step model only covers the trip between the origin and the destination, and not the demand followed by the accessibility improvement, due to the characteristic of this model. Therefore, the purpose of this research is to improve the limitations of the existing model by developing the inter-city trip generation and trip distribution model with more accessibility. After calculating of the trip generation and trip distribution model with more accessibility, the sign of the accessibility coefficient was positive. Commuting was the most insensitive indicator, affected by external factors among the other trip purposes. The leisure trip was the most sensitive, affected by the trip fee. According to the result of comparison with each of estimated model and observational data, it was certain that the reliability and assumption of the model have been improved by discovering the reduced weighted average error rate, Root Mean Square Error (RMSE) and total error through the model with more accessibility compared with the existing one.

• Journal of Bio-Environment Control
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• v.28 no.3
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• pp.243-254
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• 2019

With the recent accelerated policy-making and interests in new renewable energy, plans to develop and supply the new renewable energy have been devised across multiple regions in Korea. Solar energy, in particular, is being applied to small-scale power supply in provincial areas, as solar cells are used to convert solar energy into electric energy to produce electric power. Nonetheless, in the case of solar power plants, the need for a large stretch of land and considerable sum of financial support implies that the planning step should take into consideration the most suitable meteorological and geographical factors. In this study, the proxy variables of meteorological and geographical factors associated with solar energy were considered in analyzing the vulnerable areas regarding the photovoltaic power generation facility across the nation. GIS was used in the spatial analysis to develop a map for assessing the optimal location for photovoltaic power generation facility. The final vulnerability map developed in this study did not reveal any areas that exhibit vulnerability level 5 (very high) or 1 (very low). Jeollanam-do showed the largest value of vulnerability level 4 (high), while a large value of vulnerability level 3 (moderate) was shown by several administrative districts including Gwangju metropolitan city, Jeollabuk-do, Chungcheongbuk-do, and Gangwon-do. A value of vulnerability level 2 (low) was shown by the metropolitan cities including Daegu, Ulsan, and Incheon. When the 30 currently operating solar power plants were compared and reviewed, most were found to be in an area of vulnerability level 2 or 3, indicating that the locations were relatively suitable for solar energy. However, the limited data quantity for solar power plants, which is the limitation of this study, prevents the accuracy of the findings to be clearly established. Nevertheless, the significance of this study lies in that an attempt has been made to assess the vulnerability map for photovoltaic power generation facility targeting various regions across the nation, through the use of the GIS-based spatial analysis technique that takes into account the diverse meteorological and geographical factors. Furthermore, by presenting the data obtained for all regions across the nation, the findings of this study are likely to prove useful as the basic data in fields related to the photovoltaic power generation.

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