• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.321-322
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• 2023

본 논문에서는 Scikit-learn 패키지의 LinearRegression 모델과 Keras 딥러닝 모델을 활용하여 수도권 도시 환경 요인에 따른 범죄 발생 건수를 예측 모델을 제안한다. 연구 방법으로 범죄 발생과 유의미한 관계가 있다고 파악되는 수도권의 각 자치구 별 데이터셋을 분석하여, CCTV, 파출소, 가로등의 수가 범죄 발생에 유의미한 영향을 끼치는 것을 확인하였다. 독립 변수들 간에 Scale을 줄이고자 정규화를 진행했고, 종속변수의 정규성 확보를 위해 로그변환을 취했다. 손실 함수는 회귀문제에서 사용되는 'relu'함수를 사용했고 모델의 성능을 확인할 수 있는 지표로 MSE(Mean Squared Error)를 사용해 모델을 구성하였다. 본 논문에서 설계한 이 프로그램은 범죄 발생율이 높은 지역구에 경찰 인력의 추가적 배치, 안전 시설 확충 등 실무적 조치를 취함에 있어 근거를 제공할 수 있을 것으로 사료된다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.382-395
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• 2016

The purpose of this study is to examine how area characteristics influence the health of a population in a particular area by investigating how area deprivation and social capital influence self-rated health. For this study, a multi-level logistic regression was employed to analyze the data collected by Community Health Survey conducted on a target population of 229,186 living at 253 administrative areas of Korea in 2011. First, an analysis was conducted for subjects who have rated their self-health assessment as 'fair', 'poor', and 'very poor' in a 5 -item response survey. Then, a second analysis was conducted for the same subjects by excluding those with a rating of 'fair'. As a result, we found that area deprivation significantly influenced the population's health, according to our second analysis, while it was not significant according to our first analysis. Moreover, social capital was not significant in both analyses. Area deprivation-although the value of it was not so high-seems to explain the differences of individual self-rated health assessment as a contextual effect. In addition, influence of area characteristics is not limited to certain local areas, but to all local areas of Korea. Therefore, it is suggested that efforts to improve area characteristics are necessary to upgrade the individual's health level. A standardized classification system-distinguishing between good and poor self-rated health-is necessary through further comparative studies on self-rated health assessment.