• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.877-888
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• 2021

This study used three types of temporary shelters, tents, and prefabricated houses provided by the Chinese government for victims after the Wenchuan earthquake in 2008 as case study objects. Through literature review, 12 evaluation items were selected from the social, economic, and environmental elements of the sustainability of residential space design to analyze and evaluate three types of temporary shelters, and derive their respective characteristics and problems. The analysis results show that the temporary centralized settlements and tents had problems such as imperfect infrastructure, poor sanitation, narrow living space, no personal space, and inconvenience in life. Prefabricated houses had problems such as high construction costs, non-environmentally friendly building materials, occupation of arable land, low recycling rate of materials, and environmental pollution by waste. The common problem of the three types of shelters was that the government took the lead in the construction and distribution of shelters, and the disaster victims passively accept government support. Therefore, disaster victims were not actively involved in the construction and management of temporary communities. Secondly, the designs of all three types of temporary shelters did not fully consider the psychological needs of the victims, especially the need for safe and hygienic personal space. Finally, this research proposes improvement plans for the problems in the sustainable design of three temporary shelters and the construction and management of temporary communities.

• 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.