• Korea Real Estate Review
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• v.28 no.4
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• pp.37-51
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• 2018

The purpose of this paper is to analyze the effects of location factors on sales by restaurant type in the six districts of Seoul (Jongno-gu, Jung-gu, Yeongdeungpo-gu, Gangnam-gu, Seocho-gu, and Songpa-gu). Ordinary least squares (OLS) regression model is selected for four restaurant types whose spatial autocorrelation is not identified, spatial lag model (SLM) is only selected for seafood restaurant, and spatial error model (SEM) is selected for nine other restaurant types. The floating population and the workers of surrounding businesses have generally positive effects on the sales of restaurants. The floating population elasticity of the sales of restaurants are found to be in the descending order of Oriental food, pub, Western food, and traditional food restaurant, and the elasticity of the workers of surrounding businesses are in the descending order of bakery, Oriental food, and Western food restaurant. The spatial multiplier effects are in the descending order of Oriental food, pub, and Western food restaurant. There is a statistically significant sales gap between roast meat, pub, and bakery in Gangnam-gu and those in five other districts. The results of this research can help in starting a restaurant in that they can provide information on the suitability of location by restaurant type.

• Journal of Korea Technology Innovation Society
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• v.21 no.3
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• pp.1077-1100
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• 2018

The present study analyzed the effect of firms' external research and development (R&D) on corporate productivity, while investigating the moderating effect of firm size on the external R&D-productivity nexus. In the empirical analysis, we estimated South Korean manufacturing firms' total factor productivity (TFP) using the firm level data drawn from the Survey of Business Activities (Korea National Statistical Office) for the years 2006-2015. Thereafter, focusing on the role of external R&D and its interaction with the firm size in determining firms' TFP, the productivity function was estimated as well. To this end, we used ordinary least squares (OLS) and quantile regression to highlight the heterogeneous impacts of external R&D by companies' productivity level. Empirical results confirmed that firms' external R&D significantly enhanced corporate productivity in all manufacturing industries, from high-tech to low-tech. The moderating effect of firm size in determining the productivity effect of external R&D was not as prominent as in the case for internal R&D, which exhibited some degree of the size premium in the productivity-enhancing effect. These results suggest that regardless of the firm size, external R&D can be an important channel for corporate productivity improvement, and can be a particularly effective strategy for SMEs with relatively limited internal R&D capacities.

• Journal of the Korean association of regional geographers
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• v.22 no.1
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• pp.251-266
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• 2016

Intelligent interpolation methods such as dasymetric mapping are considered to be the best way to disaggregate zone-based population data by observing and utilizing the internal variation within each source zone. This research reviews the advantages and problems of the dasymetric mapping method, and presents a geographically weighted regression (GWR) based method to take into consideration the spatial heterogeneity of population density - land cover relationship. The locally adaptive intelligent interpolation method is able to make use of readily available ancillary information in the public domain without the need for additional data processing. In the case study, we use the preclassified National Land Cover Dataset 2011 to test the performance of the proposed method (i.e. the GWR-based multi-class dasymetric method) compared to four other popular population estimation methods (i.e. areal weighting interpolation, pycnophylactic interpolation, binary dasymetric method, and globally fitted ordinary least squares (OLS) based multi-class dasymetric method). The GWR-based multi-class dasymetric method outperforms all other methods. It is attributed to the fact that spatial heterogeneity is accounted for in the process of determining density parameters for land cover classes.

• 한국노년학
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• v.38 no.3
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• pp.481-499
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• 2018

The purpose of this study is to examine the impacts of smartphone use on structured social network types in the Korean elderly population. Data was derived from the 2014 survey of living conditions and welfare needs of Korean older persons collected by Korea Institute for Health and Social Affairs. A total of 4,180 participants were selected for the Propensity Score Matching (PSM) analysis. Based on propensity score estimates, the 491 smartphone users (treatment group) and 491 featurephone users (control group) were matched. Ordinary Least Squares (OLS) regression analysis was conducted to examine the relationship between smartphone use and structural social network types. The results showed that among retired older adults, people using smartphone had significantly better structured social networks than those using featurephone even after controlling for covariates. Smartphone users had the higher levels of social contact and social activity. These findings suggested empirical evidence that using smartphone positively affects structured social networks, which might be used as the basis for designing intervention programs to promote social networks and social engagement of retired older adults.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.203-217
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• 2024

The Land Use, Land-Use Change and Forestry (LULUCF) sector of the National Greenhouse Gas Inventory is crucial for obtaining data on carbon sinks, necessitating accurate estimations. This study analyzes cases of countries applying the LULUCF sector at the Tier 3 level to propose enhanced methodologies for carbon sink estimation. In nations like Japan and Western Europe, satellite spatial information such as SPOT, Landsat, and Light Detection and Ranging (LiDAR)is used alongside national statistical data to estimate LULUCF. However, in Korea, the lack of land use change data and the absence of integrated management by category, measurement is predominantly conducted at the Tier 1 level, except for certain forest areas. In this study, Space-borne LiDAR Global Ecosystem Dynamics Investigation (GEDI) was used to calculate forest canopy heights based on Relative Height 100 (RH100) in the cities of Icheon, Gwangju, and Yeoju in Gyeonggi Province, Korea. These canopy heights were compared with the 1:5,000 scale forest maps used for the National Inventory Report in Korea. The GEDI data showed a maximum canopy height of 29.44 meters (m) in Gwangju, contrasting with the forest type maps that reported heights up to 34 m in Gwangju and parts of Icheon, and a minimum of 2 m in Icheon. Additionally, this study utilized Ordinary Least Squares(OLS)regression analysis to compare GEDI RH100 data with forest stand heights at the eup-myeon-dong level using ArcGIS, revealing Standard Deviations (SDs)ranging from -1.4 to 2.5, indicating significant regional variability. Areas where forest stand heights were higher than GEDI measurements showed greater variability, whereas locations with lower tree heights from forest type maps demonstrated lower SDs. The discrepancies between GEDI and actual measurements suggest the potential for improving height estimations through the application of high-resolution remote sensing techniques. To enhance future assessments of forest biomass and carbon storage at the Tier 3 level, high-resolution, reliable data are essential. These findings underscore the urgent need for integrating high-resolution, spatially explicit LiDAR data to enhance the accuracy of carbon sink calculations in Korea.