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

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.387-397
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• 2008

Recently many researches on data analysis using spatial statistics have been studied in various field and the studies on small area estimations using spatial statistics are in actively progress. In analysis of lattice data, defining the neighborhood information system is the most crucial procedure because it also determines the result of the analysis. However the used neighborhood informal ion system is generally defined by sharing the common border lines of small areas. In this paper the other neighborhood information systems are introduced and those systems are compared with Moran's I statistic and for the comparisons, Economic Active Population Survey (2001) is used.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.637-645
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• 2003

Small area estimation had been studied using data-based methods such as Direct, Indirect, Synthetic methods. However recently, model-based such as based on regression or time series estimation methods are applied to the study. In this paper we investigate a model-data based small area estimation which takes into account the spatial relation among the areas. The Economic Active Population Survey in 2001 are used for analysis and the results from the model based and model-data based estimation are compared with using MSE(Mean squared error), MAE(Mean absolute error) and MB(Mean bias).

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.627-637
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• 2005

Small area estimation has been studied using various methods such as direct, indirect, synthetic and based on regression or time series model . In this paper we investigate a motel-based small area estimation which takes into account the spare time autoregressive model. The Economic Active Population Surveys in 2001 are used for analysis and the results from space-time autoregressive(STAR) and simultaneous autoregressive(SAR) model are compared with using MSE, MAE and MB.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.2
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• pp.402-419
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• 2014

This study attempts to investigate the spatial characteristics of private tutoring markets for adults which have been expanded rapidly with labor market changes in Korea. In particular, For the purpose, we examine thoroughly various indies of labor markets and private tutoring markets for adults in Korea in first and then analyze the spatial characteristics. We classify private tutoring institutes for adults into two categories by job-statuses and education levels, and analyze the spatial distribution patterns of the attendants of the classes. In order to understand the spatial characteristic of their distributions, we distinguish whether there exist the spatial autocorrelation or not by applying Moran's I values for each categories in first. We also examine the spatial cluster patterns by Hot spots analysis utilizing $G^*$ statistics. Multiple linear regression models are developed for each category to explain the relationships between the spatial distributions of private tutoring institutes and geographical variables.

• Journal of the Korean association of regional geographers
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• v.16 no.5
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• pp.590-609
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• 2010

The purpose of the study is to propose and evaluate Exploratory Spatial Data Analysis(ESDA) methods for examining age-specific population migration characteristics. First, population migration pyramid which is a pyramid-shaped graph designed with in-migration, out-migration, and net migration by age (or age group), was developed as a tool exploring age-specific migration propensities and structures. Second, various spatial statistics techniques based on local indicators of spatial association(LISA) such as Local Moran''s $I_i$, Getis-Ord ${G_i}^*$, and AMOEBA were suggested as ways to detect spatial dusters of age-specific net migration rate. These ESDA techniques were applied to age-specific population migration of Daegu Metropolitan City. Application results demonstrated that suggested ESDA methods can effectively detect new information and patterns such as contribution of age-specific migration propensities to population changes in a given region, relationship among different age groups, hot and cold spot of age-specific net migration rate, and similarity between age-specific spatial clusters.

• Journal of Korean Home Economics Education Association
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• v.17 no.2
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• pp.129-143
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• 2005

The purpose of this study was to verify the validity of Ohio State Teacher Efficacy Scale(OSTES: Tschannen-Moran, 2000) when used with Korean home economics teachers. To achieve this purposes, the surveys with 263 secondary school home economics teachers was conducted by the prime investigator. Statistical procedures included descriptive statistics, factor analysis, internal consistency. Cronbach's a was calculated to test the reliability of items and their internal consistency and Exploratory Factor Analysis(EFA) was conducted to test the structure of each factors with principal analysis and varimax rotation. Confirmatory Factor Analysis(CFA) was conducted to test the factor validity of this scale with AMOS program. The reliability test result of 20 items appeared Cronbach's a=.86. The factor analysis on teacher efficacy with 263 survey of secondary school home economics teachers produced 5 factors including efficacy in classroom management. efficacy in needs assistance, efficacy in instructional strategies, efficacy in environmental assistance and efficacy in teaming assistance. The validity of teacher efficacy scale was moderately verified by the confirmatory factor analyses(GFI=.87, AGFI=.84. CFI=.86. PNFI=.72, RMR=.05. RMSEA=.07). The results lend a support to the teacher efficacy scale as a valid instrument.

• Journal of Ecology and Environment
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• v.37 no.2
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• pp.53-60
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• 2014

The arctic tundra is an important ecosystem in terms of the organic carbon cycle and climate change, and therefore, detailed analysis of vegetation distribution patterns is required to determine their association. We used grid-sampling method and applied geostatistics to analyze spatial variability and patterns of vegetation within a two-dimensional space, and calculated the Moran's I statistics and semivariance to assess the spatial autocorrelation of vegetation. Spatially autocorrelated vegetation consisted of moss, Eriophorum vaginatum, Betula nana, and Rubus chamaemorus. Interpolation maps and cross-correlograms revealed spatial specificity of Carex aquatilis and a strong negative spatial correlation between E. vaginatum and C. aquatilis. These results suggest differences between the species in water requirements for survival in the arctic tundra. Geostatistical methods could offer valuable information for identifying the vegetation spatial distribution.

• 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.