• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.109-123
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• 2008

Many small area estimation methods have been suggested. Also for the comparison of the estimation methods, model diagnostic checking techniques have been studied. Almost all of the small area estimators were developed by minimizing MSE(Mean square error) and so the MSE is the well-known comparison criterion for superiority. In this paper we suggested a new small area estimator based on minimizing MSPE(Mean square percentage error) which is recently re-highlighted. Also we compared the new suggested estimator with the estimators explained in Shin et al. (2007) using MSE, MSPE and other diagnostic checking criteria.

• The Korean Journal of Ecology
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• v.19 no.5
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• pp.465-476
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• 1996

There are several environmental variables that may be influential to the spatial distribution of forest vegetation. To create a map of forest vegetation zone over southern part of Korean Peninsula, digital map layers were produced for each of environmental variables that include topography, geographic locations, and climate. In addition, an extensive set of field survey data was collected at relatively undisturbed forests and they were introduced into the GIS database with exact coordinates of survey sites. Preliminary statistical analysis on the survey data showed that the environmental variables were significantly different among the previously defined five forest vegetation zones. Classification of the six layers of digital map representing environmental variables was carried out by a supervised classifier using the training statistics from field survey data and by a clustering algorithm. Although the maps from two classifiers were somewhat different due to the classification procedure applied, they showed overall patterns of vertical and horizontal distribution of forest zones. considering the spatial contents of many ecological studies, GIS can be used as an important tool to manage and analyze spatial data. This study discusses more about the generation of digital map and the analysis procedure rather than the outcome map of forest vegetation zone.

• Journal of Fashion Business
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• v.10 no.1
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• pp.148-163
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• 2006

The purpose of this study is to examine differences in university students' spatial behavior and time for purchasing weekdays or weekends according to trading areas they use to purchase casual wears. Theoretical background examined trading areas, in Busan, consumers' spatial behavior. An empirical research developed a questionnaire as a measuring tool to conduct a preliminary survey and a main survey. Data collection was implemented with 507 students from four universities in Busan; and for data analysis, descriptive statistics, cross-tabulation analysis, correspondence analysis, and McNemar test were carried out by using the SPSS for Windows 12.0K program. This study obtained the main results as follows: The characteristics of university students' spatial behavior according to trading areas show significant difference in reasons of trading area selection, time slots for visiting. University students who visited the Seomyeon trading area were found to consider comparison-based purchasing and prominence of the trading area, regardless of the time for purchasing weekdays or weekends. As for trading areas around Busan National University, visits were mainly due to accessibility. Students visited trading areas in Nampo-Gwangbok-dong regardless of the time for purchasing in diverse reasons of trading area selection, time slots. As for trading areas around Kyungsung University, students were visited due to accessibility.

• Journal of Power System Engineering
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• v.4 no.2
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• pp.40-45
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• 2000

Some engineering materials are often known to have considerable spatial variation in their resisting strength and other properties. The objective of this study is to investigate the averaging effect and the applicability of extremal statistic for the statistical size effect. In the present study, it is assumed that the material property is a stationary random process in space. The theoretical autocorrelation function of the material strength are discussed for several correlation lengths. And, in order to investigate the statistical size effect, the material properties was simulated by using the non-Gaussian random process method. The material properties were plotted on the Weibull probability papers. The main results are summarized as follows: The autocorrelation function of the material properties are almost independent of the averaging length. The variance decreases with increasing the averaging length. As correlation length is smaller, the slope is larger. And also, it was found that Weibull statistics based on the weakest-link model could not explain the spatial variation of material properties with respect to the size effect satisfactory.

• Korean Institute of Interior Design Journal
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• v.17 no.6
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• pp.188-196
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• 2008

The study aims to find out the changing aspects of contemporary Korean libraries so called public in terms of their spatial configuration. In so doing, it analysed 14 recently built libraries by investigation of user's behavior. Interview and observation were conducted as a method of investigation. The method of analysis was based on statistics by mapping spatial data. As the result, it could be said that most users have a clear goal in Korea. In some libraries, the distinct characteristics of behavior show that users visit other functional rooms including functional rooms for the purpose. It could be said that the configuration of space in recently public libraries become more public as a place. In view of domestic affairs, in terms of users need, the reading room for personal study have features of the most private room and a static activity. so that the reading room separate from the other functional rooms in floor, but it adjoin other functional rooms in the horizontal configuration. so its location make offset a role of functional rooms can be interactive with each other, except reading room.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.13-26
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• 2004

According to economic growth theory, location configuration of business enterprises engaged in specific industries has spatial affinity. In this research we defined industrial concentration index to measure industry agglomeration using the characteristics of dispersion parameter of negative binomial distribution, and used the industrial concentration index to examine aspect of spatial configuration change. We utilized Census on Basic Characteristics of Establishments of 1995 and 2000 to deduce industrral concentration indices of 7 knowledge-based industries and 9 strategy-based industries of Choongbuk Province and analyzed the aspect of spatial configuration change.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.254-261
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• 2000

Soil chemical properties, relief of field surface, SPAD values and grain yield were investigated in a 0.5ha paddy field in 1999 to obtain basic field information for precision agriculture. Descriptive statistics of field information showed that the coefficient of variation ranged from 1.63% to 38.7%. Field information showed a high spatial dependence for within paddy field. The ranges of spatial dependence were from 15m to 60m, respectively. Kriged maps enable the visualization and comparison the spatial variability of field information. The causes of spatial variability of the field information could be explained rationally by a field management map. Grain yield was negatively correlated with pH, relief values, whereas, was positively correlated with total C, total N, C/N ratio, mineralizable N, available P and exchangeable K, Ca at the significant level of 1 %.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1133-1146
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• 2016

Counts or averages over arbitrary regions are often analyzed using conditionally autoregressive (CAR) models. The spatial neighborhoods within CAR model are generally formed using only the inter-distance or boundaries between the sub-regions. Kyung and Ghosh (2009) proposed a new class of models to accommodate spatial variations that may depend on directions, using different weights given to neighbors in different directions. The proposed model, directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Bayesian inference method is discussed based on efficient Markov chain Monte Carlo (MCMC) sampling of the posterior distributions of the parameters. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.499-507
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• 2018

Agricultural meteorological information is an important resource that affects farmers' income, food security, and agricultural conditions. Thus, such data are used in various fields that are responsible for planning, enforcing, and evaluating agricultural policies. The meteorological information obtained from automatic weather observation systems operated by rural development agencies contains missing values owing to temporary mechanical or communication deficiencies. It is known that missing values lead to reduction in the reliability and validity of the model. In this study, the hierarchical Bayesian spatio-temporal model suggests replacements for missing values because the meteorological information includes spatio-temporal correlation. The prior distribution is very important in the Bayesian approach. However, we found a problem where the spatial decay parameter was not converged through the trace plot. A suitable spatial decay parameter, estimated on the bias of root-mean-square error (RMSE), which was determined to be the difference between the predicted and observed values. The latitude, longitude, and altitude were considered as covariates. The estimated spatial decay parameters were 0.041 and 0.039, for the spatio-temporal model with latitude and longitude and for latitude, longitude, and altitude, respectively. The posterior distributions were stable after the spatial decay parameter was fixed. root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were calculated for model validation. Finally, the missing values were generated using the independent Gaussian process model.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.39-47
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• 2017

Precipitation is an important component for hydrological and water control study. In general, AWS data provides more accurate but low dense information for precipitation while radar data gives less accurate but high dense information. The objective of this study is to construct adjusted precipitation field based on hierarchical spatial model combining radar data and AWS data. Here, we consider a Bayesian hierarchical model with spatial structure for hourly accumulated precipitation. In addition, we also consider a redistribution of hourly precipitation to 2.5 minute precipitation. Through real data analysis, it has been shown that the proposed approach provides more reasonable precipitation field.