• Journal of Korea Planning Association
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• v.54 no.7
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• pp.5-23
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• 2019

The central government has implemented policies to strengthen the competitiveness of small and medium sized cities for balanced development at the national scale. However, since it is often difficult to enhance the competitiveness through partial projects of each jurisdiction, many local governments collaborate at the regional scale. This suggests that a regional approach is important for the management of small and medium sized cities. On the one hand, the concept of network city suggests that various functional networks can affect the growth of small and medium sized cities. Given this background, the purposes of this study are to delineate regional boundaries at national scale and identify their relations of growth by using functional network and Moran's I index. The study uses the Markov-chain model and cluster analysis to delineate the regions, and Moran's I is employed to identify the relations of growth. The results show that interactions between jurisdictions through networks could be crucial factors for growth of small and medium sized cities, while the networks based on passenger travel and freight movement have different implications. The results suggest that policy makers should not only consider local level investments, but also take the characteristics of networks between cities into account for achieving balanced development and developing regeneration policies.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.60-74
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• 2007

For quantitative analysis of spatio-temporal distribution pattern on marine environment, spatial autocorrelation statistics on the both global and local aspects was applied to the observed data obtained from Gwangyang Bay in South Sea of Korea. Global indexes such as Moran's I and General G were used for understanding environmental distribution pattern in the whole study area. LISAs (local indicators of spatial association) such as Moran's I ($I_i$) and $G_i{^*}$ were considered to find similarity between a target feature and its neighborhood features and to detect hot spot and/or cold spot. Additionally, the significance test on clustered patterns by Z-scores was carried out. Statistical results showed variations of spatial patterns quantitatively in the whole year. Then all of general water quality, nutrients, chlorophyll-a and phytoplankton had strong clustered pattern in summer. When global indexes showed strong clustered pattern, the front region with a negative $I_i$ which means a strong spatial variation was observed. Also, when global indexes showed random pattern, hot spot and/or cold spot were/was found in the small local region with a local index $G_i{^*}$. Therefore, global indexes were useful for observing the strength and time series variations of clustered patterns in the whole study area, and local indexes were useful for tracing the location of hot spot and/or cold spot. Quantification of both spatial distribution pattern and clustering characteristics may play an important role to understand marine environment in depth and to find the reasons for spatial pattern.

• Journal of Korean Society of Forest Science
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• v.110 no.1
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• pp.106-115
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• 2021

The aim of the study was to analyze the spatial characteristics of the main producing areas of nontimber forest products. We analyzed the spatial aggregations of the main producing area and their changes using the Moran's I index. We found that 45% of nontimber forest products were significanty spatially clustered. Additionally, in five major products, we observed that the main producing area has expanded and the degree of aggregation has also strengthened over the last ten years. The results of this study can be effectively used for forest policies, such as determining the location and size of the distribution centers of specific forest products.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.29-40
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• 2021

Risk factors that threaten public safety such as crime, fire, and traffic accidents have spatial characteristics. Since each region has different dangerous environments, it is necessary to analyze the spatial pattern of risk factors for each sector such as traffic accident, fire, crime, and living safety. The purpose of this study is to analyze the spatial distribution pattern of local safety level index, which act as an index that rates the safety level of each sector (traffic accident, fire, crime, living safety, suicide, and infectious disease) for basic local governments across the nation. The following analysis tools were used to analyze the spatial autocorrelation of local safety level index : Global Moran's I, Local Moran's I, and Getis-Ord's G⁎i. The result of the analysis shows that the distribution of safety level on traffic accidents, fire, and suicide tends to be more clustered spatially compared to the safety level on crime, living safety, and infectious disease. As a result of analyzing significant spatial correlations between different regions, it was found that the Seoul metropolitan areas are relatively safe compared to other cities based on the integrated index of local safety. In addition, hot spot analysis using statistical values from Getis-Ord's G⁎i derived three hot spots(Samchuck, Cheongsong-gun, and Gimje) in which safety-vulnerable areas are clustered and 15 cold spots which are clusters of areas with high safety levels. These research findings can be used as basic data when the government is making policies to improve the safety level by identifying the spatial distribution and the spatial pattern in areas with vulnerable safety levels.

• Journal of Life Science
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• v.18 no.10
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• pp.1325-1330
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• 2008

Salix koriyanagi is a deciduous shrub and native to Korea. The spatial distribution of multilocus allelic frequencies and geographical distances of the natural population in upper swampy area at the Namgang River in Korea were studied. The species showed a significant positive and negative spatial autocorrelation according to geographical distances as measured by Moran's I. Genetic similarity of individuals was found among subpopulations at up to a scale of a 12 m distance, and this was partly due to a combination of allelic frequencies, and therefore, a significant spatial autocorrelation was composed of a scale of 12 m intervals. Within S. koriyanagi in swampy area at the Namgang River, a strong spatial structure was observed for allozyme markers, indicating a migration within subpopulations.

• Journal of Life Science
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• v.18 no.11
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• pp.1465-1470
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• 2008

Salix koriyanagi is a deciduous shrub and native to Korea. The spatial distribution of multilocus allelic frequencies and geographical distances of the natural population in upper swampy area at the Namgang River in Korea were studied. The species showed a significant positive and negative spatial autocorrelation according to geographical distances as measured by Moran's I. Genetic similarity of individuals was found among subpopulations at up to a scale of a 12 m distance, and this was partly due to a combination of allelic frequencies, and therefore, a significant spatial autocorrelation was composed of a scale of 12 m intervals. Within S. koriyanagi in swampy area at the Namgang River, a strong spatial structure was observed for allozyme markers, indicating a migration within subpopulations.

• Journal of Korea Multimedia Society
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• v.18 no.2
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• pp.233-243
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• 2015

A hotspot is a spatial pattern that properties or events of spaces are densely revealed in a particular area. Whereas location information is easily captured with increasing use of mobile devices, so is not our emotion unless asking directly through a survey. Tweet provides a good way of analyzing such spatial sentiment, but relevant research is hard to find. Therefore, we analyzed hotspots of emotion in the twitter using spatial autocorrelation. 10,142 tweets and related GPS data were extracted. Sentiment of tweets was classified into good or bad with a support vector machine algorithm. We used Moran's I and Getis-Ord $G_i^*$ for global and local spatial autocorrelation. Some hotspots were found significant and drawn on Seoul metropolitan area map. These results were found very similar to an earlier conducted official survey of happiness index.

• Journal of Korean Society of Forest Science
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• v.106 no.3
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• pp.310-319
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• 2017

This study categorized the damaged trees by Supervised Classification using time-series-aerial photographs of Bukhan, Cheonggae and Suri mountains because oak wilt disease seemed to be concentrated in the metropolitan regions. In order to analyze the spatial characteristics of the damaged areas, the geographical characteristics such as elevation and slope were statistically analyzed to confirm their strong correlation. Based on the results from the statistical analysis of Moran's I, we have retrieved the following: (i) the value of Moran's I in Bukhan mountain is estimated to be 0.25, 0.32, and 0.24 in 2009, 2010 and 2012, respectively. (ii) the value of Moran's I in Cheonggye mountain estimated to be 0.26, 0.32 and 0.22 in 2010, 2012 and 2014, respectively and (iii) the value of Moran's I in Suri mountain estimated to be 0.42 and 0.42 in 2012 and 2014. respectively. These numbers suggest that the damaged trees are distributed in clusters. In addition, we conducted hotspot analysis to identify how the damaged tree clusters shift over time and we were able to verify that hotspots move in time series. According to our research outcome from the analysis of the entire hotspot areas (z-score>1.65), there were 80 percent probability of oak wilt disease occurring in the broadleaf or mixed-stand forests with elevation of 200~400 m and slope of 20~40 degrees. This result indicates that oak wilt disease hotspots can occur or shift into areas with the above geographical features or forest conditions. Therefore, this research outcome can be used as a basic resource when predicting the oak wilt disease spread-patterns, and it can also prevent disease and insect pest related harms to assist the policy makers to better implement the necessary solutions.

• Journal of Fisheries and Marine Sciences Education
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• v.23 no.3
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• pp.477-485
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• 2011

For the identifying of spatial distribution pattern, Moran's Index(I) which has the range of values from -1 to +1 is common method for the spatial autocorrelation measurement. When I is close to 1, all neighboring features have close to the same value, indicating clustered pattern. Conversely, if the spatial pattern is dispersed, I is close to -1. And I closing to 0 means spatially random pattern. However, this index equation is influenced by how defining the neighboring features for target feature. To compare and understand the difference of neighborhood definition methods, fixed distance neighboring method and Gabriel Network method were used for I. In this study, these two methods were applied to two marine environments with water quality data. One is Gwangyang Bay which has complex geometric coastal structure located in South Sea of Korea. Another is Uljin area adjacent to open sea located in east coast of Korea. The distances between water quality observed locations were relatively regular in Gwangyang Bay, however, irregular in Uljin area. And for the fixed distance method popular Arc GIS tool was used, but, for the Gabriel Network, Visual Basic program was developed to produce Gabriel Network and calculate Moran's I and its Z-score automatically. According to this experimental results, different spatial pattern was showed differently for some data with using of neighboring definition methods. Therefore there is need to choose neighboring definition method carefully for spatial pattern analysis.

• Ocean and Polar Research
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• v.44 no.2
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• pp.179-190
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• 2022

Data missing in optical satellite images caused by natural variations have been a crucial barrier in observing the status of marine surfaces. Although there have been many attempts to fill the gaps of non-observation, there is little research to analyze the ratio of missing grids to overall sea grids and their seasonal patterns. This report introduces the method of quantifying the distribution of missing points and then shows how the missing points have spatial correlation and seasonal trends. Both temporal and spatial integration methods are compared to assess the effectiveness of reducing missing data. The temporal integration shows more outstanding performance than the spatial integration. Moran's I and K-function with statistical hypothesis testing show that missing grids are clustered and there is a non-random distribution from daily integration. The result of the seasonality test for Moran's I through a periodogram shows dependency on full-year, half-year, and quarter-year periods respectively. These analysis results can be used to deduce appropriate integration periods with permissible estimation errors.