• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.65-76
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• 2017

The purpose of this study was to present a prediction model that reflects crime risk area analysis, including factors and spatial characteristics, as a precursor to preparing an alternative plan for crime prevention and design. This analysis of criminal cases in high-risk areas revealed clusters in which approximately 25% of the cases within the study area occurred, distributed evenly throughout the region. This means that using a multiple linear regression model might overestimate the crime rate in some regions and underestimate in others. It also suggests that the number of deserted houses in an analyzed region has a negative relationship with the dependent variable, based on the multiple linear regression model results, and can also have different influences depending on the region. These results reveal that closure signs in a study area affect the dependent variable differently, depending on the region, rather than a simple or direct relationship with the dependent variable, as indicated by the results of the multiple linear regression model.

• Korean Security Journal
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• no.48
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• pp.177-206
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• 2016

The purpose of this study is to verify the factors that affect the fear of crime by applying the risk interpretation model. Especially, whereas previous studies have not proven micro individual factor that the risk interpretation model had presented, This study includes micro individual elements such as neighborhood factor, perceived risk of crime, fears of crime as main variables. This study utilized secondary data of the National Crime Victimization Survey 2012, conducted by the Korean Institute of Criminology. In this study, multiple regression analysis of two stages and Sobel Test were conducted for verifying the individual influence of each independent variables and identifying the causal relationship between the variables set out in the risk analysis model. As the result, it appeared that the higher level of perceived risk of crime, neighborhood factor, crime experience, education, income cause the higher degree of the fear of crime. On the other hand, the lower degree of age was found to induce the higher level of the fear of crime. In addition, female showed the higher degree of the fear of crime than man. The causal relationship between the variables set out in the risk interpretation model was presented significantly in all variables, except for education.

• Fire Science and Engineering
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• v.28 no.2
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• pp.69-75
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• 2014

This study derives the factors which affect the occurrence of arson from statistical data (population, economic, and social factors) by multiple regression analysis. Multiple regression analysis applies to 4 forms of functions, linear functions, semi-log functions, inverse log functions, and dual log functions. Also analysis respectively functions by using the stepwise progress which considered selection and deletion of the independent variable factors by each steps. In order to solve a problem of multiple regression analysis, autocorrelation and multicollinearity, Variance Inflation Factor (VIF) and the Durbin-Watson coefficient were considered. Through the analysis, the optimal model was determined by adjusted Rsquared which means statistical significance used determination, Adjusted R-squared of linear function is scored 0.935 (93.5%), the highest of the 4 forms of function, and so linear function is the optimal model in this study. Then interpretation to the optimal model is conducted. As a result of the analysis, the factors affecting the arson were resulted in lines, the incidence of crime (0.829), the general divorce rate (0.151), the financial autonomy rate (0.149), and the consumer price index (0.099).

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1488-1492
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• 2007

현재 도시화로 인한 유출량의 증가 및 도달시간의 단축은 도시 재해의 한 원인이 되고 있으며 그에 따라 수공구조물에 대한 적정 설계가 필요하다. 하지만 계획단계에서부터 설계에 필요한 값을 예측하기는 매우 어려운 실정이다. 더구나 개발로 인해 매개변수가 변화함에 따라 유출 영향 분석이 어려울 뿐 아니라 이에 따른 연구가 미흡한 실정이기 때문에 모형매개변수의 민감도 분석을 통해 유출영향 분석 및 수공구조물 적정설계의 중요 기반자료로 활용하고자 본 연구를 수행하였다. 본 연구에서는 민감도 분석방법 중 절대 및 상대 민감도 분석 방법을 사용하여 각 유역의 지형학적 수문학적 매개변수들의 민감도 분석을 통해 상관관계를 확인하였다. 특히 대표적인 매개변수로서 유출계수 CN의 변화에 따른 유출량 및 유출 용적의 관계를 통해 CN의 증감에 따른 유출량 및 유출용적의 변화량을 산정하고, 또한 각 매개변수들간의 회귀분석을 통해 경험식을 작성, 제안하였다. 한편 현재 국내에서 사용중인 HEC-HMS를 모의하여 매개변수의 민감도 분석을 실시하였다. 본 연구의 결과로 CN값이 개발 전후 5% 증가시 유출량은 약 10%정도 증가한다는 것을 HEC-HMS모의와 자료의 분석을 통해 확인 할 수 있었다. 본 연구의 결과에 대해 검 보정 및 추가적인 자료수집을 통한 분석이 이루어지고, 매개변수 민감도 분석을 통한 국내 실정에 맞는 매개변수도출을 위한 연구가 계속적으로 수행된다면 미계측 유역에 대한 수공구조물의 적정설계에 상당부분 기여할 것으로 판단된다.

• Journal of agricultural medicine and community health
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• v.36 no.1
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• pp.25-35
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• 2011

Objectives: This study aimed to compare depression and social support according to general characteristics and to investigate the influence of social support on depression. Methods: This study analyzed raw data from a project funded by Jeju Province. Data were collected through home visit interview with 750 households selected by using a randomized cluster sampling method. CES-D was used to measure depression, and the Medical Outcomes Study Social Support Survey was used to measure social support. The data of 1,155 subjects were analyzed using t-test, ANOVA, and regression. Results: The mean was 11.35 for depression and 75.53 for social support. Women showed a higher depression score and a lower social support score than men did. Older people; the divorced or the bereaved; and those in groups comprising people with lower education, lower social class, poor health, or high stress presented higher depression and lower social support. The result of stepwise regression showed that social support was one of the predictive variables of depression, and 22% of variance was explained by social support in this study. Conclusions: Social support was a powerful predictive variable of depression, and it was suggested that to prevent and manage depression, strategies that enhance social support should be developed and evaluated.

• Journal of the Korean Society for information Management
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• v.22 no.3 s.57
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• pp.213-232
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• 2005

The purpose of this study is to determine factors affecting text difficulty and to model objective formulas which measure readability scores. Some readability-related factors such as total number of letters, total number of syllables, total number of unique syllables, total number of sentences and total number of paragraphs were found through correlation analysis. Some regression equations with these factors as their variables were produced through regression analysis. A model estimating readability score from total number of unique syllables was a good formula, while a model with two factors, total number of unique syllables and new syllable occurrence ratio, was a better enhanced one. The readability score represents detailed level so we can recommend students read texts corresponding to their reading levels.

• Journal of Korea Technology Innovation Society
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• v.19 no.4
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• pp.687-710
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• 2016

This study aims to empirically estimate distributed lag effects of expenditure by type of R&D on scientific publication in the national R&D program. To analyze the lag structure between them, we used a dataset comprised of panel data from 104 technologies categorized by 6T (IT, BT, NT, ST, ET, CT) from 2007 to 2014, and employed multiple regression analysis based on the polynomial distributed lag model. This is because it is highly likely to emerge multicollinearity, if a distributed lag model without special restrictions is applied to multiple regression analysis. The main results are as follows. In the case of basic research, its lag effects are relatively evenly distributed during four years. On the other hand, the applied research and experimental development have distributed lag effects for three years and two years respectively. Therefore, when it comes to analyzing performance of scientific publication, it is necessary to be performed with characteristics of the time lag by type of R&D.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.419-429
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• 2015

We investigate how the regional prevalence of hyperlipidemia is affected by health-related and socioeconomic factors with a special emphasis on geographic variations. We focus on the likelihood of hyperlipidemia as function of various region-specific attributes. We analysis a data set at the level of 249 small administrative districts collected from 2012 Korean Community Health Survey by Korea Centers for Disease Control and Prevention. To estimate, we use several methods including correlation analysis, multiple regression and decision tree model. We find that the average prevalence of hyperlipidemia in 249 small districts is 9.6% and its coefficient of variation is 28.3%. Prevalence of hyperlipidemia in continental and capital regions is higher than in southeast coastal regions. Further findings using decision tree model suggest that variations of hyperlipidemia prevalence between regions is more likely to be associated with rate of employee, level of stress, prevalence of hypertension, angina pectoris, and osteoarthritis in their regions.

• Proceedings of the Technology Innovation Conference
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• 2005.02a
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• pp.268-286
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• 2005

본 연구에서는 OECD 주요 10개국을 대상으로 국가별 정보통신산업의 성장 추이를 각각 분석하고 국별 특성을 비교하는데 목적이 있다. 이를 바탕으로 각국의 정보통신산업이 경기순환 또는 단계별 발전 속성을 지니고 있는지를 파악하고 국가별 공통점과 특이점을 분석하고자 하였다. 방법론적으로 OECD 국가들의 정보통신산업 GDP 추이 및 성장률의 움직임을 국면전환 (regime change) 확산과정으로 묘사함으로써 각 국가별 정보통신산업 발전 양상의 특징 및 국면전환 시점 등을 포착해 내고자 하였다 추세를 갖는 대표적 확산과정인 GBM 모형과 평균회귀 성향을 갖는 대표적 확산과정인 Vasicek 모형에 각각 마코프 국면전환을 도입하여 국가별 정보통신산업 GDP 및 GDP 성장률의 추이에 있어 국면 전환 여부와 독특한 발전 특성을 비교 분석하였다. 실증분석 결과 정보통신산업 GDP의 성장률과 변동성 사이에는 높은 상관관계가 있었으며, 한국, 멕시코 등은 고성장, 고변동성을, 미국, 프랑스, 일본 등은 저성장, 저변동성의 특성을 보이는 것으로 나타났다 또한 한국의 경우 유일하게 성장률과 변동성 모두 국면전환이 일어나는 국가로 나타났다. 장기평균 성장률의 특성에 따라 분류한 결과, 한국, 일본, 미국, 멕시코, 뉴질랜드는 고성장에서 저성장으로의 국면전환, 핀란드와 덴마크는 경기 순환적 국면전환, 노르웨이, 프랑스, 캐나다는 단일 국면으로 분류할 수 있었다. 특히 한국의 경우 평균회귀 속도와 변동성이 타 국가에 비해 높은 특성을 보여주었다. 본 연구는 정보통신산업을 미시적 분석이나 세부 항목별 정량적 분석을 통해서가 아니라 산업의 발전 속성 및 경기 순환 등의 관점에서 분석함으로써 정보통신산업 정책의 수립 및 집행을 거시적 안목 하에 정립할 수 있게 한다는 데 의의를 가진다. 또한 경제변수를 묘사하는데 있어 국면전환 확산과정을 사용함으로써 향후 실물옵션 등을 통한 기술 및 무형자산의 가치평가에 있어 기초자산의 움직임을 보다 정확히 포착해 낼 수 있는 프로세스를 제공하였다는데 또 다른 의의를 갖는다고 하겠다.

• Journal of Korean Society of Transportation
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• v.21 no.3
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• pp.15-22
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• 2003

In general, the linear regression model has been used to estimate trip generation in the travel demand forecasting procedure. However, the model suffers from several methodological limitations. First, trips as a dependent variable with non-negative integer show discrete distribution but the model assumes that the dependent variable is continuously distributed between -$\infty$ and +$\infty$. Second, the model may produce negative estimates. Third, even if estimated trips are within the valid range, the model offers only forecasted trips without discrete probability distribution of them. To overcome these limitations, a poisson model with a assumption of equidispersion has frequently been used to analyze count data such as trip frequencies. However, if the variance of data is greater than the mean. the poisson model tends to underestimate errors, resulting in unreliable estimates. Using overdispersion test, this study proved that the poisson model is not appropriate and by using Vuong test, zero inflated negative binomial model is optimal. Model reliability was checked by likelihood test and the accuracy of model by Theil inequality coefficient as well. Finally, marginal effect of the change of socio-demographic characteristics of households on trips was analyzed.