• 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.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.4
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• pp.438-444
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• 2023

Italian ryegrass (Lolium multiflorum Lam.; IRG) sowing season is delayed due to the autumn rainy season. Therefore, to address this problem, transplanting date and plant density were investigated. Transplant times investigated were October 20th, October 30th, and November 10th and planting densities were 50, 70, and 80 hills per 3.3 m². The plant height, leaf area index, and plant coverage rate were high in the following order: October 20th, October 30th, and November 10th. There was no significant difference among planting densities. In addition, the number of tillers and dry weight before and after wintering were high on October 20th. In terms of yield components, the number of tillers, dry weight, and seed yield per unit area were higher with the transplanting date of October 20th than with transplanting on November 10th. There was no difference in seed yield between the planting densities of 80 and 70 hills per 3.3 m². However, seed yield was low at 50 hills per 3.3 m². In conclusion, the transplanting time for stable seed production is late October, and optimal plant density is 70 and 80 hills per 3.3 m². A stable interplanting number before wintering will contribute to the seed yield.