• Proceedings of the Korea Database Society Conference
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• 2008.05a
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• pp.477-486
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• 2008

This paper examines the evolution issues of regional tourism resources in complicated and networked industry the perspective of co-evolution types and dualism. Regional tourism structure has been changing more and faster according to various attractions and internal and external environment; natural resources, facilities, festivals and events, drama and movies, and public resources, etc. This paper approaches Olikowski's dualism perspective as a theoretical view about the alliance network between region's attractions and tourism industry in Korea. Exploratory analysis was explained the dualism cases performed on the matrix between resource characteristics and alliance complexity on human resources based on regional tourism industry.

• 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.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.43-48
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• 2021

The article proposes a method of modeling a comprehensive indicator for evaluating the effectiveness of regional management of innovation activity. This will make it possible to assess the effectiveness of personnel, financial and credit and foreign economic activity of the regions from the standpoint of an integrated approach. The modeling technique is proposed to be carried out using the tools of taxonomic analysis and the calculation of a complex indicator of the effectiveness of the innovation activity management.

• Journal of the Korean Geographical Society
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• v.46 no.3
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• pp.351-365
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• 2011

The purpose of this study is to investigate how regional resource-based industries affect regional economic space. To achieve this goal, a new framework has been proposed, which integrates traditional firm network models in economic geography with the concept of value chain. Firms' networks are elaborated and classified by firm activities along with global value chain. Firm-activities are composed of primary activities and supportive activities. Primary activities include, production, sales, and marketing. Support activities are conceptualized as networks of R&D and of firm support services. On the basis of this framework, fermented soy product industry, representative industry of regional resource-based industries in Korea, is analyzed. Finally, this study shows the dynamics of regional resource-based industry.

• Journal of the Korean Regional Science Association
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• v.15 no.2
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• pp.75-102
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• 1999

This paper attempts at analyzing the urban dynamics in Northeast Asia by looking at major forces transforming the regional structure of Northeast Asia. Trade and foreign direct invest-ment are identified as two principal channels of increasing economic interdependence in the region. In addition, macro development strategy and infrastructure policy are another set of determining factors for changes in the regional structure of Northeast Asia. To examine the role of cities and inter-city linkages, the paper first tries to identify major urban centers and urban hierarchy in Northeast Asia. Secondly, it examines the prospects for inter-city network formation. Against these anticipated changes in the regional structure and inter-city networks in Northeast Asia, the paper discusses about the future of Korea as well as the role of Koran cities in the regional economy of Northeast Asia.

• Journal of the Korean Geographical Society
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• v.36 no.4
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• pp.402-417
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• 2001

This study empahsized the nature of spatial patterns, characteristics and embeddeness of foreign business service firms in Korea utilizing questionnaire survey and interview data. Foreign business services firms are active in forming interfirm networks with clients and supply firms in Korea for widening the market share in Korea. But a low proportion of foreign firms is engaged in academies-industry linkages, government organizations, research institutes, and trade associations. Knowledge transfer and interaction also shows low level of network and the regional development of foreign firms is still in the process of developing, not quite embedded yet. Policy guidances and instituional supports are very essential to strenthen interfirm network and collective learning process of foreing firms in Korea lather than mechanical accumulation of investments. Thus, regional foreign direct investment policy should be targeted towards the incrementation of the potential of foreign firms as a knowledge-intensive industry.

• Atmosphere
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• v.16 no.4
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• pp.259-267
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• 2006

In the present study, the neural network (NN) model with cluster analysis method was developed to predict storm surge in the whole Korean coastal regions with special focuses on the regional extension. The model used in this study is NN model for each cluster (CL-NN) with the cluster analysis. In order to find the optimal clustering of the stations, agglomerative method among hierarchical clustering methods was used. Various stations were clustered each other according to the centroid-linkage criterion and the cluster analysis should stop when the distances between merged groups exceed any criterion. Finally the CL-NN can be constructed for predicting storm surge in the cluster regions. To validate model results, predicted sea level value from CL-NN model was compared with that of conventional harmonic analysis (HA) and of the NN model in each region. The forecast values from NN and CL-NN models show more accuracy with observed data than that of HA. Especially the statistics analysis such as RMSE and correlation coefficient shows little differences between CL-NN and NN model results. These results show that cluster analysis and CL-NN model can be applied in the regional storm surge prediction and developed forecast system.

• Proceedings of the Korea Water Resources Association Conference
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• 1998.05b
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• pp.7-13
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• 1998

A methodology to analyze and quantify regional meteorological drough based on annual precipitation data has been introduced in this paper In this study, based on posterior probability estimator and Bayesian classifier in Spatial Analysis Neural Network ISANN), point drought probabilities categorized as extreme, severe, mild, and non drought events has been defined, and a Bayesian Drought Severity Index (BPSI) has been introduced to classify the region of interest into four drought serverities. For example, the proposed methodology has been applied to analyze the regional drought of South Korea. This is a new method to classify and quantify the spatial or regional drought based on neural network pattern recognition technique and the results show that it may be apprepriate and valuable to analyze the spatial drought.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.170-170
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• 2020

Due to the development of technologies, complex computation of huge data set is possible with a prevalent personal computer. Therefore, machine learning methods have been widely applied in the hydrologic field such as regression-based regional frequency analysis (RFA). The main purpose of this study is to compare two frameworks of RFA based on the artificial neural network (ANN) models: quantile regression technique (QRT-ANN) and parameter regression technique (PRT-ANN). As an output layer of the ANN model, the QRT-ANN predicts quantiles for various return periods whereas the PRT-ANN provides prediction of three parameters for the generalized extreme value distribution. Rainfall gauging sites where record length is more than 20 years were selected and their annual maximum rainfalls and various hydro-meteorological variables were used as an input layer of the ANN model. While employing the ANN model, 70% and 30% of gauging sites were used as training set and testing set, respectively. For each technique, ANN model structure such as number of hidden layers and nodes was determined by a leave-one-out validation with calculating root mean square error (RMSE). To assess the performances of two frameworks, RMSEs of quantile predicted by the QRT-ANN are compared to those of the PRT-ANN.

• Journal of Korea Planning Association
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• v.53 no.7
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• pp.5-22
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• 2018

While the competitiveness of small and medium sized cities has become important for balanced development at the national scale, they have experienced continuous decline in population and employment, particularly those in non-capital regions. In addition, some of small and medium sized cities have been classified into shrinking cities that have declined due to their long-term structural reasons. To address these issues, a regional approach, by which a hub city and its surrounding small and medium sized cities can collaborate has been suggested. Given this background, the purpose of this study is to identify and delineate hub cities and their impact areas by using travel data as a functional network index. This study uses a centrality index to identify the hub cities of small and medium sized cities and Markov-chain model and cluster analysis to delineate regional boundaries. The mean first passage time (MFPT) generated from the Markov-chain model can be interpreted as functional distance of each region. The study suggests a methodological approach delineating the boundaries of regions incorporating functional relationships of hub cities and their impact areas, and provides 59 hub cities and their impact areas. The results also provide policy implications for regional spatial planning that addresses appropriate planning boundaries of regions for enhancing the economic competitiveness of small and medium sized cities and ensuring services for shrinking cities.