• ETRI Journal
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• v.44 no.2
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• pp.208-219
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• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.3
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• pp.139-154
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• 1993

The major purpose of this study is to develop a regionalized regression model, which predicts flood peaks from the characteristics of the ungaged catchments, through the regional flood frequency analysis for the selected stage gauging stations located on several natural rivers of Korea. The magnitude and the frequency of flood peaks with specified recurrence intervals were estimated from the flood frequency analysis on the 28 selected stage gauging stations distributed on the five major rivers of Korea. The results of the analysis were compared with the predictions from the two different flood frequency models. From the statistical evaluation of these models, it was revealed that the POT model (Peaks Over a Threshold model), which is based on the partial duration method, is more effective in predicting flood peaks from short period records than the ANNMAX model (ANNual MAXimum model) which is based on the annual maximum series method. A regionalized regression model was developed to facilitate the estimation of design floods for ungaged catchments through the regression analysis between flood peaks and the topographic characteristics of the catchments assumed to be important in runoff processes. In addition to this, the correlation diagrams are presented which show the relationships between flood peaks with specified recurrence intervals and the major characteristics of the catchments.

• Korean Journal of Community Nutrition
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• v.4 no.2
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• pp.157-165
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• 1999

This study was conducted to identify the determinants of regional body fat distribution of obesity(upper body obesity and lower body obesity) for adolescents. The macronutrient consumption pattern utilized the most important variables to test for potential determinants. A total of 726 adolescents living in rural areas in Korea had been observed for four years from 1992 to 1996 about their diet, sexual maturation, serum components and physical growth. The study design was similar to that of a case control study. Logistic regression analysis were used as an analytical method to identify the determinants of upper body obesity and lower body obesity. Odd ratios were estimated from the regression to identify the determinants of upper body obesity and lower body obesity. Odd ratios were estimated from the regression to identify the risk factors. Fat consumption pattern was the most frequent one among the three macronutrient consumption pattern of carbohydrate, fat and protein. Prevalence of obesity for the subjects was 9.5%. Prevalence of upper body obesity was higher in malestudents than in female students. On the other had, prevalence of lower body obesity was higher in females. The results of the logicstic regression analysis showed that the risk factor for upper body obesity was sexual maturity rather than dietary factors. None of the factors included in the analysis for lower body obesity appear to be the risk factor. The result may suggest that to develop a determinant model for obesity of adolescents, the model should include a wider range of variables other than diet, sexual maturity and changes in blood serum.

• IE interfaces
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• v.24 no.4
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• pp.351-361
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• 2011

Forecasting of electricity demand have difficulty in adapting to abrupt weather changes along with a radical shift in major regional and global climates. This has lead to increasing attention to research on the immediate and accurate forecasting model. Technically, this implies that a model requires only a few input variables all of which are easily obtainable, and its predictive performance is comparable with other competing models. To meet the ends, this paper presents an energy demand forecasting model that uses the variable selection or extraction methods of data mining to select only relevant input variables, and employs support vector regression method for accurate prediction. Also, it proposes a novel performance measure for time-series prediction, shift index, followed by description on preprocessing procedure. A comparative evaluation of the proposed method with other representative data mining models such as an auto-regression model, an artificial neural network model, an ordinary support vector regression model was carried out for obtaining the forecast of monthly electricity demand from 2000 to 2008 based on data provided by Korea Energy Economics Institute. Among the models tested, the proposed method was shown promising results than others.

• Korea Trade Review
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• v.47 no.2
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• pp.69-88
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• 2022

This paper aims to predict Busan's regional product and employment using the logistic regression models and machine learning models. The following are the main findings of the empirical analysis. First, the OLS regression model shows that the main industries such as electricity and electronics, machine and transport, and finance and insurance affect the Busan's income positively. Second, the binomial logistic regression models show that the Busan's strategic industries such as the future transport machinery, life-care, and smart marine industries contribute on the Busan's income in large order. Third, the multinomial logistic regression models show that the Korea's main industries such as the precise machinery, transport equipment, and machinery influence the Busan's economy positively. And Korea's exports and the depreciation can affect Busan's economy more positively at the higher employment level. Fourth, the voting ensemble model show the higher predictive power than artificial neural network model and support vector machine models. Furthermore, the gradient boosting model and the random forest show the higher predictive power than the voting model in large order.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.865-877
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• 2009

While characterized initially as an urban-scale pollutant, ozone has increasingly been recognized as a regional and even global-scale phenomenon. The complexity of environmental data dynamics often requires models covering non-linearity. This study deals with modeling ozone with meteorology in Seoul area. The relationships are used to construct a nonlinear regression model relating ozone to meteorology. The model can be used to estimate that part of the trend in ozone levels that cannot be accounted for by trends in meteorology.

• Journal of the Korean Regional Science Association
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• v.34 no.1
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• pp.3-17
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• 2018

This paper empirically investigates time-varying regression parameter of hedonic price model for Seoul office rental market in distinct periods of a market cycle. Office rental index is constructed and the index indicates that the global financial crisis differentiates the analysis period into decline stage and recovery stage. Pre-crisis period is classified into decline stage and post-crisis is classified into recovery stage. Structural break-point test suggests structural change of hedonic model of rent determinants occurred in 2008. Evidence indicates that individual regression parameters of hedonic price model for decline stage are significantly different from those for recovery stage. Changes in the regression parameters of land price, distance to metro, building size, building age, and conversion rate are consistent. In recovery stage, the effect of locational advantage on office rent decreases whereas the effect of building characteristics on the rent increases.

• Smart Media Journal
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• v.11 no.5
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• pp.38-47
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• 2022

Honey is one of the most significant ingredients in conventional food production in different regions of the world. Honey is commonly used as an ingredient in ethnic food. Beekeeping is performed in various locations as part of the local food culture and an occupation related to pollinator production. It is important to conduct beekeeping so that it generates food culture and helps regulate the regional environment in an integrated manner in preserving and improving local food culture. This study analyzes different types of environmental factors of a smart bee farm. The major goal of this study is to determine the best prediction model between the linear regression model (LM) and the support vector regression model (SVR) based on the environmental factors of a smart bee farm. The performance of prediction models is measured by R² value, root mean squared error (RMSE), and mean absolute error (MAE). From all analysis reports, the best prediction model is the support vector regression model (SVR) with a low coefficient of variation, and the R² values for Farm inside temperature, bee box inside temperature, and Farm inside humidity are 0.97, 0.96, and 0.44.

• Journal of Korean Society of Rural Planning
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• v.10 no.2 s.23
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• pp.1-6
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• 2004

This study tried to construct a direction in regional planning concerning the structural relationship between the ratio of aged population and the industrial characteristics. We investigated this structural relationship incorporating the aged population index and the number of classified companies. We applied diverse statistical analyses to understand the relationship. We classified the number of companies to reflect regional industrial characteristics using the principal component analysis. We applied a multiple regression model to understand the relationship between these two indices. The aged population index represents the degree of being old divided by the ratio of juvenile population and aged population. We found that such industries as manufacturing, service, and conveyance increase the ratio of juvenile population. However, industries such as tourism, waterworks, forestry, agriculture and etc. have a positive effect on the aged population index. In addition to these findings, we believe that the efficacy of this study is the possibility that can be used as the basic data when central or local autonomous entities need to adopt rural development planning.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.400-407
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• 2022

Innovations significantly affect the efficiency of the socioeconomic systems of the regions, acting as a system-forming element of their development. Modern models of economic development also consider innovation activity, intellectual potential, knowledge as the basic factors for stimulating the economic growth of the region. The purpose of the study is to develop methodological foundations for evaluating the effectiveness of a regional innovation system based on a multidimensional analysis of its effects. To further study the effectiveness of RIS, we have used one of the methods of multidimensional statistical analysis - canonical analysis. The next approach allows adding another important requirement to the methodological provision of evaluation of the level of innovation development of industries and regions, namely - the time factor, the formalization of which is realized in autoregressive dynamic economic and mathematical models and can be used in our research. Multidimensional Statistical Analysis for RIS effectiveness estimation was used to model RIS by typological regression. Based on it, multiple regression models were built in groups of regions with low and relatively high innovation potential. To solve the methodological problem of RIS research, we can also use the approach to the system as a "box" with inputs and outputs.