• 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 Digital Convergence
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• v.16 no.11
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• pp.45-55
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• 2018

The purpose of this study was to provide the necessary data to explore the development plans of consumer organizations by looking at the spatial distribution of consumer organizations. This is because community-based consumer organizations can propose concrete measures to solve consumer problems more effectively. In this study, data of 11 consumer organizations and 815 branches were collected and analyzed using local indicators of spatial distribution and spatial lag model. First, it was difficult to find patterns according to the geographical characteristics of the spatial distribution of consumer organizations. Second, consumer organizations were more distributed in areas with large populations and businesses and large areas. Third, there is a discrepancy between the demand and supply of consumer organizations when compared with the number of consumer counseling. Based on this, it is necessary to constantly seek concrete development plans by supplementing the qualitative data on the activities of consumer organizations.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.33-39
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• 2016

With recent advances in sensor technology, CMOS-based semiconductor devices and networking protocol, the areas for application of wireless sensor networks greatly expanded and diversified. Such diversification of uses for wireless sensor networks creates a multitude of beneficial possibilities for several industries. In the application of wireless sensor networks for monitoring systems' data transmission process from the sensor node to the sink node, transmission through multi-hop paths have been used. Also mobile sink techniques have been applied. However, high energy costs, unbalanced energy consumption of nodes and time gaps between the measured data values and the actual value have created a need for advancement. Therefore, this thesis proposes a new model which alleviates these problems. To reduce the communication costs due to frequent data exchange, a State Prediction Model has been developed to predict the situation of the peripheral node using a geographic autocorrelation of sensor nodes constituting the wireless sensor networks. Also, a Risk Analysis Model has developed to quickly alert the monitoring system of any fatal abnormalities when they occur. Simulation results have shown, in the case of applying the State Prediction Model, errors were smaller than otherwise. When the Risk Analysis Model is applied, the data transfer latency was reduced. The results of this study are expected to be utilized in any efficient communication method for wireless sensor network monitoring systems where all nodes are able to identify their geographic location.

• Journal of the Korean association of regional geographers
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• v.21 no.3
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• pp.583-592
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• 2015

As the increase of an environmental disease has become a social problem after industrialization, academic interest in a spatial difference and characteristics of an environmental disease is on rise. The purpose of this study is to analyze the spatial distribution and characteristics of an environmental disease using the data provided by National Health Insurance Corporation in 2009. This research is focusing on atopic dermatitis among a variety of environmental diseases and shows the map that atopic dermatitis patients are distributed. Also, The Local Moran's I show how spatial autocorrelation of atopic dermatitis patients are distributed. First, the distribution of atopic dermatitis patients show the spatial difference. Second, 42 places including the western part of Incheon are hot spots of atopic dermatitis. Third, 39 places including Danyang are cold spot of atopic dermatitis. Forth, Jeju-si and Seogwipo-si are unusually hot spot of atopic dermatitis. These results have important implications that further research need to be done in public health geography.

• Journal of the Economic Geographical Society of Korea
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• v.14 no.3
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• pp.377-393
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• 2011

This study aims to indicate the spatial characteristics of the changes in the Korean farm land. In particular, we analyze the spatial diffusion patterns of organic farms increasing rapidly with the growth in the agricultural product markets as well as the demand for safe food and sustainable growth. For the purpose, we examine the changes in the distribution patterns of organic farms between year 2000 and 2005. We analyze the agglomeration pattern by Location Quotient (LQ) and Local indicator of spatial association (LISA). Organic farms have been spread out from the outscuirts of Seoul, the capital city, to the traditional agriculture spetilized area in the southern parts of the nation. In order to analyze the relationships between organic farm distribution and the geographical variables affecting the organic farming, we develop multivariate regression models. Our findings indicate that organic farming is related with the number of agriculture-based business and information technique adaptation as well as the level of education and farmers age.

• Journal of Korean Society of Forest Science
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• v.97 no.2
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• pp.165-170
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• 2008

Litterfall has been recognized an important part of the forest ecosystem production, playing a major pathway in energy flow and nutrient cycling through the ecosystem. This study was carried out to examine the quantity and components, temporal variation, and spatial heterogeneity of the litterfall in the mixed broadleaved-Korean pine forest. The data were collected from the 9ha permanent experimental plot, of which on the center area, i.e. $150m{\times}150m$, the total number of 319 circular litterfall traps with the size of $0.5m^2$ were established to collect falling litterfall. The results showed that the annual amount of litterfall was totalized 3,033.7 kg/ha, occupying broad-leaves of 39.3%, conifer-leaves of 29.5%, others of 18.5%, branches of 10.4%, and seeds of 2.3%. The peak point of the litterfall production was made at the end of September, proportionating 32.2% of total amount. The analysis of semivariogram revealed the existence of high spatial heterogeneity, calculated the scale of spatial heterogeneity ranged from 11.6 m to 29.1 m. The result of proportion (C/[Co+C]) showed that spatial heterogeneity of autocorrelation in total spatial heterogeneity were from 97.0% to 100%. The relatively heavy branches and others had significant differences in litterfall production between the areas of canopy gap and closed canopy in the 95% probability level, but the other components did not show statistical differences.

• Spatial Information Research
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• v.21 no.5
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• pp.15-22
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• 2013

The purpose of this work is to investigate correlation between $CO_2$ concentration and NDVI (Normalized Difference Vegetation Index) in North East Asia. Geographically weighted regression techniques were used to evaluate the spatial relationships between GOSAT (Greenhouse Observing SATellite) $CO_2$ measurement and MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation index. The results reveals that $CO_2$ concentration to be negatively associated with NDVI. The analysis of Global Morans' I index and Anselin Local Morasn's I showed spatial autocorrelation between the overall spatial pattern of $CO_2$ and NDVI. Ultimately, there were clustered patterns in both data sets. The results show that carbon dioxide concentration shows non-random distribution patterns in relation to NDVI clusters, which proves that intense development activities such as deforestation are influencing carbon dioxide emission across the area of analysis. However, as the concentration of carbon dioxide varies depending on a variety of factors such as artificial sources, plant respiration, and the absorption and discharge of the ocean, follow-up studies are required to evaluate the correlations among more related variables.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.873-885
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• 2016

Onions are grown in a few specific regions of Korea that depend on the climate and the regional characteristic of the production area. Therefore, when onion yields are to be estimated, it is reasonable to use a statistical model in which both the climate and the region are considered simultaneously. In this paper, using a spatial panel regression model, we predicted onion yields with the different weather conditions of the regions. We used the spatial auto regressive (SAR) model that reflects the spatial lag, and panel data of several climate variables for 13 main onion production areas from 2006 to 2015. The spatial weight matrix was considered for the model by the threshold value method and the nearest neighbor method, respectively. Autocorrelation was detected to be significant for the best fitted model using the nearest neighbor method. The random effects model was chosen by the Hausman test, and the significant climate variables of the model were the cumulative duration time of sunshine (January), the average relative humidity (April), the average minimum temperature (June), and the cumulative precipitation (November).

• Journal of Cadastre & Land InformatiX
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• v.45 no.1
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• pp.241-256
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• 2015

The purpose of this paper is to verify the spatial distribution difference between formation and dissolution of start-up firms related to life-cycle. For this purpose, a Korean business directories and information on business closures were used, and location information, starting time, and closure time of start-up firms in the Seoul metropolitan region from 2007 to 2009 were generated in this study. It applied The spatial distribution analysis methods on the formation and dissolution of the start-up firms included Barcki measurements and global spatial autocorrelation. The total number of start-up firms was 5,810, and their five-year survival rate was 77.25%. The dissolution pattern of the start-up firms was dispersed more than their formation pattern in each of the Seoul metropolitan regions, the city of Seoul, and the city of Incheon-Gyeonggi province. In addition, differences between the formation and dissolution patterns according to size and industry category of the firms were confirmed.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.119-131
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• 2018

The increase of high-resolution spatial data and methodological developments in recent years has enabled a detailed analysis of individual experiences in space and over time. However, despite the increasing availability of data and technological advances, such individual-level analysis is not always possible in practice because of its computing requirements. To overcome this limitation, there has been a considerable amount of research on the use of high-performance, public cloud computing platforms for spatial analysis and simulation. The purpose of this paper is to empirically evaluate the efficiency and effectiveness of spatial analysis in cloud computing platforms. We compare the computing speed for calculating the measure of spatial autocorrelation and performing geographically weighted regression analysis between a local machine and spot instances on clouds. The results indicate that there could be significant improvements in terms of computing time when the analysis is performed parallel on clouds.