• Journal of the Korean association of regional geographers
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• v.17 no.4
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• pp.432-442
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• 2011

Climatic variables such as temperature and precipitation tend to vary both in space and in time simultaneously. Thus, it is necessary to include space-time autocorrelation into conventional spatial interpolation methods for reliable time-series mapping. This paper introduces and applies space-time variogram modeling and space-time kriging to generate time-series temperature maps using hourly Automatic Weather System(AWS) temperature observation data for a one-month period. First, temperature observation data are decomposed into deterministic trend and stochastic residual components. For trend component modeling, elevation data which have reasonable correlation with temperature are used as secondary information to generate trend component with topographic effects. Then, space-time variograms of residual components are estimated and modelled by using a product-sum space-time variogram model to account for not only autocorrelation both in space and in time, but also their interactions. From a case study, space-time kriging outperforms both conventional space only ordinary kriging and regression-kriging, which indicates the importance of using space-time autocorrelation information as well as elevation data. It is expected that space-time kriging would be a useful tool when a space-poor but time-rich dataset is analyzed.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.256-266
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• 2020

Spatial distribution analysis of industrial locations plays a very important role in the establishment of relevant spatial policies and plans. The first thing to consider in this analysis is what analysis indicators and spatial units are used, because the interpretation of the analysis results may vary depending on the analysis indicators and the spatial units. Therefore, this study first examines various industrial integration indicators considering spatial autocorrelation and suggests the classification of regional types of industrial aggregation through the combination of related indicators. And then, this paper aims to empirically analyze the root industry by presenting a methodology for analyzing industrial integration by various spatial units such as individual locations, grids, and administrative districts. The results of the empirical analysis show that the grid in the spatial unit can be analyzed in more detail than the administrative unit. In addition, it is expected to overcome the limitations such as differences in interpretation that may occur due to the setting of spatial units. In the classification of regional types, the south-eastern region of Ulsan, Busan, and Changwon, and the western region of the SMA of Incheon, Hwaseong, and Ansan were analyzed as the industrial cluster type.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.3
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• pp.253-262
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• 2016

As a basic research to investigate geographical variations of diseases, this study analyzes and compares spatial patterns of 24 different diseases in South Korea using prevalence rate data provided by Community Health Survey in 2012. Descriptive statistical analysis, global Moran’s I computation, and disease mapping were conducted to examine spatial associations and patterns of each disease. After the unique spatial patterns and distinctive spatial associations of each disease were observed, we concluded that 12 diseases displayed statistically significant spatial autocorrelation while the other 12 showed no spatial associations. This study suggests that diseases are caused by different risk factors and possess different etiological mechanisms. Furthermore, the study may lay foundation for future studies of geographical variations of disease prevalence in South Korea.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.335-344
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• 2010

Reliability of the estimation is usually damaged in the situation where a linear regression model without spatial dependencies is employed to the spatial data analysis. In this study, we considered the conditional autoregressive model in order to construct spatial association structures and estimate the parameters via the Bayesian approaches. Finally, we compared the performances of the models with spatial effects and the ones without spatial effects. We analyzed the yearly total crime count data measured from each of 25 districts in Seoul, South Korea in 2007.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.2
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• pp.73-85
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• 2016

Systematic and efficient forestry management is required because of the long-term low profitability of timber production and forest products. In this situation, non-timber forest products can be the solution to secure stable sources of income for workers in the forestry field. However, the existing studies for non-timber forest products focus on effective production and economic analysis and provide plans for increasing the income and improving the marketing system. Therefore, this research intends to analyze the spatial distribution as well as quantitative concentration of non-timber forest production. To achieve this goal, this study examined the regional concentration and dispersion of non-timber forest production in 2001, 2007, and 2014 using the coefficient of localization(CL) and location quotient(LQ) and investigated the change in spatial distribution using spatial statistics. The production of chestnuts generally showed a concentrated pattern in 2014 based on the outputs of the CL and LQ, but the result of spatial autocorrelation indicated a decrease in the spatial concentration. In addition, astringent persimmon showed more concentration from the output of CL than oak mushroom, but Moran's I suggests the opposite. Therefore, it is necessary to examine the spatial distribution to understand and improve the marketing system and intensify the production of forest products.

• Journal of the Korean Geographical Society
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• v.39 no.2
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• pp.212-228
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• 2004

This paper is concerned with an important aspect of regional income convergence, ${\beta}$-convergence, which refers to the negative relationship between initial income levels and income growth rates of regions over a period of time. The common research framework on ${\beta}$-convergence which is based on OLS regression models has two drawbacks. First, it ignores spatially autocorrelated residuals. Second, it does not provide any way of exploring spatial heterogeneity across regions in terms of ${\beta}$-convergence. Given that empirical studies on ${\beta}$-convergence need to be edified by spatial data analysis, this paper aims to: (1) provide a critical review of empirical studies on ${\beta}$-convergence from a spatial perspective; (2) investigate spatio-temporal income dynamics across the U.S. labor market areas for the last 30 years (1969-1999) by fitting spatial regression models and applying bivariate ESDA techniques. The major findings are as follows. First, the hypothesis of ${\beta}$-convergence was only partially evidenced, and the trend substantively varied across sub-periods. Second, a SAR model indicated that ${\beta}$-coefficient for the entire period was not significant at the 99％ confidence level, which may lead to a conclusion that there is no statistical evidence of regional income convergence in the US over the last three decades. Third, the results from bivariate ESDA techniques and a GWR model report that there was a substantive level of spatial heterogeneity in the catch-up process, and suggested possible spatial regimes. It was also observed that the sub-periods showed a substantial level of spatio-temporal heterogeneity in ${\beta}$-convergence: the catch-up scenario in a spatial sense was least pronounced during the 1980s.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.129-141
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• 2019

In recent years, fast growing cities in Korea are showing signs of being vulnerable to more disasters as their population and facilities increase and intensify. In particular, fire is one of the most common disasters in Korea's cities, along with traffic accidents. Therefore, in this study, we analyze what type of factors affect the fire that threatens urban people. Fire data were acquired for 10 years, from 2007 to 2017, in Jinju, Korea. Spatial distribution pattern of fire occurrence in Jinju was assessed through the spatial autocorrelation analysis. First, spatial autocorrelation analysis was carried out to grasp the spatial distribution pattern of fire occurrence in Jinju city. In addition, correlation and multiple regression analysis were used to confirm spatial dependency and abnormality among factors. Based on this, OLS (Ordinary Least Square) regression analysis was performed using space weighting considering fire location and spatial location of each facility. As a result, First, LISA (Local Indicator of Spatial Association) analysis of the occurrence of fire in Jinju shows that the most central commercial area are fire department, industrial area, and residential area. Second, the OLS regression model was analyzed by applying spatial weighting, focusing on the most derived factors of multiple regression analysis, by integrating population and social variables and physical variables. As a result, the second kind of neighborhood living facility showed the highest correlation with the fire occurrence, followed by the following in the order of single house, sales facility, first type of neighborhood living facility, and number of households. The results of this study are expected to be useful for analyzing the fire occurrence factors of each facility in urban areas and establishing fire safety measures.

• International Area Studies Review
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• v.16 no.1
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• pp.275-301
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• 2012

This study analyzes the regional disparity in India between 24 states over the period 1980 to 2009. The traditional regressive and spatial autoregressive models are used that includes measures of spatial effects. The results provide no evidence that convergence is valid in India. However, the results indicate that spatial interaction is an important element of state growth in India. The result of spatial analysis excluded two outliner states reveals more strong relationship between the weighted spatial income level and the state growth rates. Moreover, the results find that the coefficients of spatial lag of initial per capital and error terms are significantly negative. The coefficient of variation measures that the distribution of state income level has diverged over time. Therefore, this study concludes that the growth of regional state income does not have a tendency to converge rater than diverge. The results is rational because as the Indian economy is growing rapidly, some states grow faster than the others while initial poor states become the poorest ones, which increases regional disparity in India.

• Journal of the Economic Geographical Society of Korea
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• v.8 no.1
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• pp.17-29
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• 2005

This study has attempted to analyze the spatial structure of industrial agglomerations with elaborated spatial econometric techniques. First of all, spatial patterns and structures of industrial agglomerations in Korea show a multi-polar spatial pattern of industrial agglomeration, Major industries from industrial agglomerations in the Seoul Metropolitan Area, part of the Chungcheong Area and Dongnam Area. Second, as some industrial agglomerations show an agglomerative pattern beyond a regionally based-administrative jurisdiction, the effects of agglomeration seem to be produced across regionally based-administrative jurisdictions. Finally, it can be considered that industrial agglomerations have generally been produced by spatial divisions of labor in which the functions of conception and execution are separated from each other. According to this results, in designing regional innovation systems, their spatial coverage should draw upon an extended region with a few adjacent provinces, and there is a need to form networked clusters in order to sufficiently capitalize upon the spatial spillovers of agglomerations.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.1 s.39
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• pp.23-29
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• 2007

Spatial outliers in remotely sensed imagery represent observed quantities showing unusual values compared to their neighbor pixel values. There have been various methods to detect the spatial outliers based on spatial autocorrelations in statistics and data mining. These methods may be applied in detecting forest fire pixels in the MODIS imageries from NASA's AQUA satellite. This is because the forest fire detection can be referred to as finding spatial outliers using spatial variation of brightness temperature. In this paper, we propose a new forest fire detection algorithm which is based on local spatial association analysis, and test the proposed algorithm to evaluate its applicability. In order to evaluate the proposed algorithm, the results were compared with the MODIS fire product provided by the NASA MODIS Science Team, which showed the possibility of the proposed algorithm in detecting the fire pixels.