• Journal of the Korean Geographical Society
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• v.43 no.6
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• pp.873-893
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• 2008

This article focuses on reviewing place branding theories as a major toolkit of 'soft regional development'. Place branding provides sophisticate methodologies to strengthen city and regional image and identity. For effective place marketing practices, four of useful place branding tools are suggested. They include the methodology of place identity development, brand leadership system in local governance, construction of city brand architecture, and evaluation model for city brand equity. A process model is suggested for systematically organizing the diverse tools of place marketing and branding. The model is expected to be used as a framework of strategical soft regional development and planning. The place branding process model is to be an important theoretical and methodological basement of postmodern urban and regional development on which creativity, symbol and signs are more emphasized. More in-depth theoretical and empirical studies are needed. Geographical tradition and knowledge can play important roles in this inter-disciplinary process.

• Journal of Korean Society on Water Environment
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• v.28 no.4
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• pp.495-504
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• 2012

In recent years, urbanization has been a hot issues in watershed management due to increased pollutant loads from impervious urban areas. The Soil and Water Assessment Tool (SWAT) model has been widely used in hydrology and water quality studies at watershed scale. However, the SWAT has limitations in simulating water flows between HRUs and hydrological effects of LID practices. The Storm Water Management Model (SWMM) has LID capabilities, but it does not simulate non-urban areas, especially agricultural areas. In this study, a SWAT-SWMM coupled model was developed to evaluate effects of LID practices on hydrology and water quality at mixed-landuse watersheds. This coupled SWAT-SWMM was evaluated by comparing calibrated flow with and without coupled SWAT-SWMM. As a result of this study, the $R^2$ and NSE values with SWAT are 0.951 and 0.937 for calibration period, and 0.882 and 0.875 for validation period, respectively. the $R^2$ and NSE values with SWAT-SWMM are 0.877 and 0.880 for validation period. Out of four LID scenarios simulated by SWAT-SWMM model, the green roof scenario was found to be most effective which reduces about 25% of rainfall-runoff flows.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3196-3203
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• 2010

The regional strategic industry support has been increasingly developed due to the change of environmental and political conditions for the last 10 years. As the paradigm changed from the central government-sponsored system to regional government-sponsored system, the policy has been diversified into various support systems such as regional strategic industry, specialized industry, supporting technology, supporting business, etc. It is crucial to establish the overall development plans and project strategies for the regional industry in terms of effectiveness and efficiency which will generate an integrated strategic frame to adjust overlapped supporting policies and offer a consumer-oriented service. Not only does the study focus on performance-based outcome regarding to a regional business (supporting policy) portfolio, but it also analyzes and proves an empirical decision-making model to draw out the priority and relative importance according to the regional strategic industry and support cases by analyzing the case of Chungnam area's strategic industry support in 2009. Following a priority list of 20 support cases selected from this research model by the Analytic Hierarchy Process (AHP), the result shows that the diverse support plans are needed according to the different strategic industries or the size of enterprise due to the fact that priority of project type varies upon the characteristics of strategic industry. Thus, the support project needs to generate various strategic industries and develop differentiated support policy to consolidate competitiveness of regional enterprises and revitalize the regional economy.

• Journal of Korea Water Resources Association
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• v.46 no.1
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• pp.13-24
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• 2013

The main objective of this study was to develop a new regional frequency analysis model based on hierarchical Bayesian model that allows us to better estimate and quantify model parameters as well as their associated uncertainties. A Monte-carlo experiment procedure has been set up to verify the proposed regional frequency analysis. It was found that the proposed hierarchical Bayesian model based regional frequency analysis outperformed the existing L-moment based regional frequency analysis in terms of reducing biases associated with the model parameters. Especially, the bias is remarkably decreased with increasing return period. The proposed model was applied to six weather stations in Jeollabuk-do, and compared with the existing L-moment approach. This study also provided shrinkage process of the model parameters that is a typical behavior in hierarchical Bayes models. The results of case study show that the proposed model has the potential to obtain reliable estimates of the parameters and quantitatively provide their uncertainties.

• 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.

• The Korean Journal of Health Service Management
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• v.10 no.2
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• pp.71-82
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• 2016

Objectives : This study purposed to analyze the productivity trends of regional health care resource uses in South Korea. Methods : Data was provided from the regional health care statistics by the National Health Insurance Service(NHIS) and collected from 2011 to 2014 at the 226 administrative regions such as Si(city in Korean), Gun(county in Korean), Gu(district in Korean). Productivity trend was analyzed with Malmquist Productivity Index(MPI). Input variables were the number of medical personnels, facilities, and major medical equipments. Output variables were the number of inpatient and outpatients in model A, and the amount of inpatient and outpatient reimbursements in model B. Results : In model A, the productivity of 62 regions were increased but it was decreased in 164 regions. In model B, the productivity of 123 regions were increased but it was decreased in 123 regions. Conclusions : If these trends were continued, there will be problems with the efficiency of national regional healthcare resource utilization. Health policy makers will require to focus in solving this phenomenon.

• Journal of Environmental Impact Assessment
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• v.21 no.1
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• pp.105-117
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• 2012

Future climate according to land-use change was simulated by regional climate model. The goal of study was to predict the distribution of meteorological elements using the Weather Research & Forecasting Model (WRF). The KME (Korea Ministry of Environment) medium-category land-use classification was used as dominant vegetation types. Meteorological modeling requires higher and more sophisticated land-use and initialization data. The WRF model simulations with HyTAG land-use indicated certain change in potential vegetation distribution in the future (2086-2088). Compared to the past (1986-1988) distribution, coniferous forest area was decreased in metropolitan and areas with complex terrain. The research shows a possibility to simulate regional climate with high resolution. As a result, the future climate was predicted to $4.5^{\circ}$ which was $0.5^{\circ}$ higher than prediction by Meteorological Administration. To improve future prediction of regional area, regional climate model with HyTAG as well as high resolution initial values such as urban growth and CO2 flux simulation would be desirable.

• Journal of Korean Society of Rural Planning
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• v.26 no.3
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• pp.25-38
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• 2020

The purpose of this study is constructing a regional-level crop acreage choice model incorporating the impacts of producer risk aversion, and applying the constructed model to the Korean policy that promotes rice paddy conversion into non-rice crop fields. The study adopts the approach of Paris (2018) which estimates the absolute risk aversion coefficient inside of a positive mathematical programming model. A panel data set of 143 cities/counties is used for the empirical study where agricultural land in each region is allocated to 8 crops. Our estimated absolute risk aversion coefficients are smaller than those of Paris (2018), but are a little bit larger than those of the existing Korea studies based on survey or econometric methods. We found that there are close relationships among the estimated risk aversion, regional characteristics, and farming patterns. We also found that incorporating the estimated risk attitudes results in substantial differences in the impacts of the rice paddy conversion policy.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.6
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• pp.2895-2899
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• 2016

Background: Increasing breast cancer (BC) incidence rates have been reported from India; causal factors for this increased incidence are not understood and diagnosis is mostly in advanced stages. Trivandrum exhibits the highest BC incidence rates in India. This study aimed to estimate trends in incidence by age from 2005-2014, to predict rates through 2020 and to assess the stage at diagnosis of BC in Trivandrum. Materials and Methods: BC cases were obtained from the Population Based Cancer Registry, Trivandrum. Distribution of stage at diagnosis and incidence rates of BC [Age-specific (ASpR), crude (CR) and age-standardized (ASR)] are described and employed with a joinpoint regression model to estimate average annual percent changes (AAPC) and a Bayesian model to estimate predictive rates. Results: BC accounts for 31% (2681/8737) of all female cancers in Trivandrum. Thirty-five percent (944/2681) are <50 years of age and only 9% present with stage I disease. Average age increased from 53 to 56.4 years (p=0.0001), CR (per $10^5$ women) increased from 39 (ASR: 35.2) to 55.4 (ASR: 43.4), AAPC for CR was 5.0 (p=0.001) and ASR was 3.1 (p=0.001). Rates increased from 50 years. Predicted ASpR is 174 in 50-59 years, 231 in > 60 years and overall CR is 80 (ASR: 57) for 2019-20. Conclusions: BC, mostly diagnosed in advanced stages, is rising rapidly in South India with large increases likely in the future; particularly among post-menopausal women. This increase might be due to aging and/or changes in lifestyle factors. Reasons for the increased incidence and late stage diagnosis need to be studied.

• Journal of Climate Change Research
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• v.2 no.4
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• pp.269-281
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• 2011

In this review, numerical model results from global and regional climate models are introduced to regional detailed climate changes over East Asia and Korea. In particular, regional climate change scenarios in this region, which are created by several research groups in Korea based on Special Report on Emissions Scenarios (SRES) of IPCC 4th assessment report are introduced and characteristics of the scenarios are investigated. Despite slight differences in intensity, all scenarios reveal prominent warming over the Korean peninsula in future climate. Changes in precipitation amount vary with given scenarios and periods, but the frequency and intensity of heavy precipitation generally tend to increase in all scenarios. South Korea except for mountainous regions is expected to change into subtropical climate in future, which accompanies distinct changes in ecosystems and seasons.