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

• Journal of Korean Society of Forest Science
• /
• v.106 no.3
• /
• pp.310-319
• /
• 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 the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.1
• /
• pp.29-40
• /
• 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 the Korean Association of Geographic Information Studies
• /
• v.10 no.4
• /
• pp.60-74
• /
• 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 for Geospatial Information Science
• /
• v.23 no.1
• /
• pp.39-45
• /
• 2015

The housing market is transformed by a variety of socio-economic characteristics, also appeared differently according to regional characteristics. This study aims to draw out the change factors influencing on the housing market structure and to analyze the drawn factors' distribution pattern by area. For this purpose, First, targeting 251 areas in the units of city, county and districts nationwide, this study drew out demographic, socio-economic variables influencing on the housing market structure for 5 years ranging 2005 to 2010. For that, the factor analysis was conducted. Second, this study grasped the change factors of the housing market structure's spatial patterns using the kriging method, a spatial statistical method. Third, this study used the Moran I, one of spatial autocorrelation analysis methods in order to grasp whether the factors had statistically significant concentration or dispersion or showed a random distribution pattern.

• Journal of Korea Water Resources Association
• /
• v.57 no.2
• /
• pp.87-97
• /
• 2024

This study analyzed the water shortage hotspot areas in South Korea using spatial clustering analysis for water shortage estimates in 2030 of the Master Plans for National Water Management. To identify the water shortage cluster areas, we used water shortage data from the past maximum drought (about 50-year return period) and performed spatial clustering analysis using Local Moran's I and Getis-Ord Gi*. The areas subject to spatial clusters of water shortage were selected using the cluster map, and the spatial characteristics of water shortage areas were verified based on the p-value and the Moran scatter plot. The results indicated that one cluster (lower Imjin River (#1023) and neighbor) in the Han River basin and two clusters (Daejeongcheon (#2403) and neighbor, Gahwacheon (#2501) and neighbor) in the Nakdong River basin were found to be the hotspot for water shortage, whereas one cluster (lower Namhan River (#1007) and neighbor) in the Han River Basin and one cluster (Byeongseongcheon (#2006) and neighbor) in the Nakdong River basin were found to be the HL area, which means the specific area have high water shortage and neighbor have low water shortage. When analyzing spatial clustering by standard watershed unit, the entire spatial clustering area satisfied 100% of the statistical criteria leading to statistically significant results. The overall results indicated that spatial clustering analysis performed using standard watersheds can resolve the variable spatial unit problem to some extent, which results in the relatively increased accuracy of spatial analysis.

• Health Policy and Management
• /
• v.23 no.4
• /
• pp.387-396
• /
• 2013

Background: The purpose of this study was to analyze the association between areas of Korea Train Express (KTX) region and external medical service use in Korean society using spatial statistical model. Methods: The data which was used in this study was extracted from 2011 regional health care utilization statistics and health insurance key statistics from National Health Insurance Corporation. A total spatial units of 229 districts (si-gun-gu) were included in this study and spatial area was all parts of the country excepted Jeju, Ulleungdo island. We conducted Kruskal-Wallis test, correlation, Moran's I and hot-spot analysis. And after, ordinary linear regression, spatial lag, spatial error analysis was performed in order to find factors which were associated with external medical service use. The data was processed by SAS ver. 9.1 and Geoda095i (windows). Results: Moran's I of health insurance patients' external medical service use was 0.644. Also, population density, Seoul region, doctor factors positively associated with health insurance patients' external medical service. In contrast, average age, health care organization per 100 thousand were negatively associated with health insurance patients' external medical service use. Conclusion: The finding of this study suggested that health insurance patient's external medical service use correlated for seoul region in korea. The study results imply the need for more attention medical needs in the region (si-gun-gu unit) for health insurance patients of seoul region. It is important to adapt strategy to activation of primary health care as well as enhancing public health institution for prevent leakage of patients to other areas.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.32 no.4_1
• /
• pp.327-333
• /
• 2014

To improve thermal environments in urban area, the structural characteristic analysis of thermal environments of the certain area should be preceded to analyze and supplement its problems. With Landsat-8, we measured the centrality estimation, the distribution map, and the spatial statistical analysis of Daegu Metropolitan City in January and August, which of data applied in analyzing the structure of thermal environments following to its spatial property. The thermal infrared band of satellite images has been used to analyze the standard normal deviated scores, which extract the centrality, while the cluster map, based upon Local Local Moran's I, has composed for understanding the autocorrelation of local spatial within environment space structure. Understanding the distribution features as well as the pivot center of thermal environments with satellite images provides principle database for updating urban thermal environments' policies and plans; because those are reference materials that should have precedence over for diverse thermal environment policies.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.25 no.1
• /
• pp.41-55
• /
• 2022

Social interest in the fine particulate matter has increased significantly since the 2010s, and various efforts have been made to reduce it through environmental plans and policies. To support such environmental planning, in this study, spatial cluster characteristics of fine particulate matter (PM2.5) concentrations were analyzed in the metropolitan area to identify high-risk areas spatially, and the correlation with local environmental characteristics was also confirmed. The PM2.5 concentration for the recent 5 years (2016-2020) was targeted, and representative spatial statistical methods Getis-Ord Gi^* and Local Moran's I were applied. As a result of the analysis, the cluster form was different in Getis-Ord Gi^* and Local Moran's I, but they show high similarity in direction, therefore complementary results could be obtained. In the high concentration period, the hotspot concentration of the Getis-Ord Gi* method increased, but in Local Moran's I, the HH region, the high concentration cluster, showed a decreasing trend. Hotspots of the Getis-Ord Gi^* technique were prominent in the Pyeongtaek-Hwaseong and Yeoju-Icheon regions, and the HH cluster of Local Moran's I was located in the southwest, and the LL cluster was located in the northeast. As in the case of the metropolitan area, in the results of Seoul, there was a phenomenon of division between the northeast and southwest regions. The PM2.5 concentration showed a high correlation with the elevation, vegetation greenness and the industrial area ratio. During the high concentration period, the relation with vegetation greenness increased, and the elevation and industrial area ratio increased in the case of the annual average. This suggests that the function of vegetation can be maximized at a high concentration period, and the influence of topography and industrial areas is large on average. This characteristic was also confirmed in the basic statistics for each major cluster. The spatial clustering characteristics of PM2.5 can be considered in the national land and environmental plan at the metropolitan level. In particular, it will be effective to utilize the clustering characteristics based on the annual average concentration, which contributes to domestic emissions.

• Communications for Statistical Applications and Methods
• /
• v.10 no.3
• /
• pp.835-844
• /
• 2003

A difficulty in spatial data analysis is to choose a suitable theoretical variogram. Generally mean squares error(MSE) is used as a criterion of selection. However researchers encounter the case that the values of MSE are almost the same whereas the estimates of parameters are different. In this case, the selection criterion based on MSE should take into account the parameter estimates. In this paper we study on the method of selecting a variogram using spatial correlation.