• Journal of Korean Society of Rural Planning
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• v.11 no.3 s.28
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• pp.11-17
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• 2005

Land prices reflect not only the uses of land, but the potential uses as well(Plantinga, 2002) so land values can be applied to very effective indices for deciding regional status and growing potential. The purpose of this study is to deduce determinant factors of regional land prices. Principal determinants of regional land prices are analyzed with a hedonic technique and spatial econometric models based on 2001 statistic data of Korea except large cities. The results provide the followings. 1. The spatial effect of rural regions are very little with adjacent regions. 2. The common index of land price is population density and other determinant factors are different depending on land uses.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.43-51
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• 2012

The OLS(ordinary least squares) method is widely used in hedonic housing models. One of the assumptions of the OLS is an independent and uniform distribution of the disturbance term. This assumption can be violated when the spatial autocorrelation exists, which in turn leads to undesirable estimate results. An alterative to this, spatial econometric models have been introduced in housing price studies. This paper describes the comparisons between OLS and spatial econometric models using housing transaction prices of Busan, Korea. Owing to the approaches reflecting spatial autocorrelation, the spatial econometric models showed some superiority to the traditional OLS in terms of log likelihood and sigma square(${\sigma}^2$). Among the spatial models, the SAR(Spatial Autoregressive Models) seemed more appropriate than the SAC(General Spatial Models) and the SEM(Spatial Errors Models) for Busan housing markets. We can make sure the spatial effects on housing prices, and the reconstruction plans have strong impacts on the transaction prices. Selecting a suitable spatial model will play an important role in the housing policy of the government.

• Asian Journal of Atmospheric Environment
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• v.11 no.3
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• pp.194-201
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• 2017

This study investigates the factors that affect China's air pollution using city-level panel data and spatial econometric models. We address three air pollutants ($PM_{10}$, $SO_2$, and $NO_2$) present in 30 cities in China between 2004-2012 using global OLS and spatial models. To develop the spatial econometric analysis, we create a spatial weights matrix to define spatial patterns based on two neighborhood criteria - the queen contiguity and k nearest neighbors. The results show that the estimated coefficients are relatively consistent across different spatial weight criteria. The OLS models indicate that the effect of green spaces is statistically significant in decreasing the concentrations of all air pollutants. In the $PM_{10}$ and $SO_2$ analyses, the OLS models find that the number of buses and population density are also positively related to a reduction in the concentration of air pollutants. In addition, an increase in the temperature and the presence of secondary industries increase $SO_2$ and $NO_2$ concentrations, respectively. All spatial models capture a positive and significant effect of green spaces on reducing the concentration of each air pollutant. Our results suggest that green spaces in cities should receive priority consideration in local planning aimed at sustainable development. Furthermore, policymakers need to be able to discern the differences among pollutants when establishing environmental policies.

• Journal of Korea Planning Association
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• v.54 no.3
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• pp.63-74
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• 2019

Korean government has implemented policies to strengthen the competitiveness of small and medium sized cities. However, since it is often difficult to enhance the competitiveness through individual projects, many local governments in metropolitan areas are working together to pursue local growth. On the other hand, small and medium sized cities that are not included in metropolitan areas due to their spatial limitations have difficulties in implementing effective growth policies. Given this background, the purpose of this study is to identify the functional correlation based on urban interactions and develop functional econometric model for the economic growth of small and medium sized cities. This study uses spatial econometrics models and functional weight matrix to identify the effects of functional networks on small and medium sized cities. The results show the effect of functional networks on the growth of small and medium sized cities and provide policy implications for regional spatial planning that addresses effective management of small and medium sized cities.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.373-391
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• 2022

The distribution of observations in most econometric studies with spatial heterogeneity is skewed. Usually, a single transformation of the data is used to approximate normality and to model the transformed data with a normal assumption. This assumption is however not always appropriate due to the fact that panel data often exhibit non-normal characteristics. In this work, the normality assumption is relaxed in spatial mixed models, allowing for spatial heterogeneity. An inference procedure based on Bayesian mixed modeling is carried out with a multivariate skew-elliptical distribution, which includes the skew-t, skew-normal, student-t, and normal distributions as special cases. The methodology is illustrated through a simulation study and according to the empirical literature, we fit our models to non-life insurance consumption observed between 1998 and 2002 across a spatial panel of 103 Italian provinces in order to determine its determinants. Analyzing the posterior distribution of some parameters and comparing various model comparison criteria indicate the proposed model to be superior to conventional ones.

• East Asian Economic Review
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• v.25 no.1
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• pp.3-31
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• 2021

This paper estimates the effects of transport (road and rail) & energy and ICT infrastructure (telephone, mobile, and broadband) on GDP growths in neighboring countries as well as own countries. We confirm positive direct contributions of infrastructure, access to Internet, and human capital on economic growth. The spatial panel regression models indicate that there exist positive externalities of the broadband infrastructure and human capital, and these results are robust regardless of the choice of spatial weight matrices. Our findings on spillover effects of infrastructure suggest the key role of neighboring countries' infrastructure on own country's economic growth.