• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.979-986
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• 2011

A linear regression method is commonly used to analyze data because of its simplicity and applicability; however, it is well known that data may contain some outliers and influential cases that may have a harmful effect on a statistical analysis. Thus detection and examination of outliers or influential cases are important parts of data analysis. In detecting multiple outliers, masking effects usually occur and make it difficult to identify the true outliers. We propose to use dynamic plots as a method resistant to masking effect. The procedure using dynamic plots is useful to find appropriate basic sets with which a dependent outliers detection method start and detect a true outliers set. Examples are given to demonstrate the effectiveness of the suggested idea.

• Journal of the Korean Geographical Society
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• v.30 no.4
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• pp.333-351
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• 1995

Trend surface analysis (TSA) was selected to estimate a natural trend in precipitation and to examine urban influences on precipitation over five urban areas (Houston, Dallas, and San Antonio, TX; New Orleans, LA; and Memphis, TN) in the southern United States. TSA was applied to monthly, seasonal and annual normal precipitation data for the period of 1961-1990. Winter and spring have more trends than summer and fall and the period of November through March have more marked trends than the period of April through October in all study areas except the Houston area. Residual maps for Houston, Dallas and San Antonio have positive residuals in the city and downwind during summer indicating that urban effects on precipitation enhancement in these areas do exist during these seasons after eliminating the natural precipitation variations. Summer residual maps for New Orleans and Memphis have no distinct precipitation increases due to urban effects. The June residual map in New Orleans and the July residual map in Memphis have positive values in the city, but the magnitude of values is smaller than other cities.

• Journal of the Korean Geographical Society
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• v.39 no.6 s.105
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• pp.907-921
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• 2004

Lots of characteristics such as dwelling, neighborhood, and accessibility characteristics affect to the housevalue. Many researches have been done to identify values of each characteristic using hedonic technique. However, there is a limit to identify interaction of each characteristic and variation of each characteristic among the accessibility context. This paper has implemented the Expansion Method research paradigm to model the housevalue determination process in the city of Seoul. The findings of this paper have revealed the presence of contextual variations in the housevalue determination process. The initial model for housevalue reveals that as $F_1$ increases (i.e., larger the number of rooms/bathrooms, larger parking space) and/or $F_2$ increases (i.e., higher owner occupied housing units, higher apartment housing units) and/or $F_3$ increases, (i.e., higher the ratio of higher than college graduated households, 8 school zone, older housing units) the estimated housevalue increases. However, the above relationships drift across their respective contexts. The houses which have negative $F_1$ value, the housevalue does not fluctuate according to the distance to the city center or subcenters. However, the houses which have positive $F_1$ value, the closer to the subcenters or shorter to the river, the higher the estimated housevalues. On the other hand, in areas far from the subcenters, the estimated housevalues does not fluctuate much according to the corresponding $F_2$ level. In areas close to the subcenters, the estimated housevalues vary tremendously according to the $F_2$ value. In the residual analysis, it is revealed that large apartment which are located in Kangnam, IchongDong, MokDong are underestimated. This paper has contributed to our understanding of the housevalue determination process by providing an alternative conceptualization to the traditional approach.

• Journal of the Korean Geographical Society
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• v.33 no.3
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• pp.395-409
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• 1998

The purpose of this paper is to determine the atmospheric conditions in whih urban areas affect the precipitation processes and to evaluate whether certain weather types show more apparent urban effect on precipitation modification over five cities in the southem United States. Each heavy rainstorm is classified into one of three synoptic weather types (frontal storm, airmass storm or tropical disturbance storm). Heavy rainstorm day is defined as day producing rainfall totals that equal o exceed 2 inches (50.08 mm). Houston, Dallass and San Antonio show possible urban effects on rainfall totals and frequencies of heavy rainstorms by airmass storm type while New Orleans and Memphis do not reveal any distinct precipitation enhancements through the synoptic analysis. The results of TSA (Trend Surface Analysis) show that frontal and tropical disturbance storm types have stronger climatic gradients than airmass types and the patterns of rainfall totals have stronger trends than those of rainfall frequencies for the five cities. The results suggest that airmass type events may well reveal possible precipitation enhancements due to urban effects since they are less influenced by a strong climate gradient and they provide favorable conditions for development of urban heat islands. Residual analysis confirms that rainfall totals and frequencies of heavy rainstorms by airmass storm type have positive residuals over the city or the major effect area.

• Journal of the Korean association of regional geographers
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• v.16 no.5
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• pp.610-622
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• 2010

The proportion of overweight and obese individuals in the United States has been continuously increasing up to recently. Many studies related to obesity have concentrated on jurisdictional levels of aggregation, making it very difficult to dearly illustrate at risk regions. In other words, little research has been conducted in relation to spatial patterns considering spatial dependency and heterogeneity by spatial autocorrelation models over space. In response, this research analyzes spatial patterns between overweight/obesity and risk factors, such as high blood pressure and diabetes, over space. Specifically, the Moran''s I and Geary''s C will be conducted for global and local measures. What is more, the Ordinary Least Square (OLS) linear regression and Geographically Weighted Regression methods will be applied to identify spatial dependency and spatial heterogeneity. Data provided by the Behavioral Risk Factor Surveillance System (BRFSS) have Body-Mass Index (BMI) rates, containing 4 rates of under, healthy, overweight, and obesity. In addition, high blood pressure and diabetes rates in the United States will be used as independent variables. Lastly, we are confident that this research will be beneficial for a decision maker to make a prevention plan for obesity.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1055-1067
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• 2017

Most previous works to study for the depression of the disabilities in Korea have analyzed the repeated measured data of each individual under the mutually independent assumption. In this study, Korea Welfare Panel data of the disabilities surveyed additionally every three years are analyzed to detect the significant exploratory variables by the linear mixed models. A suitable correlation matrix is considered for the dependency of repeated measurement of each individual. The random effect to reflect the characteristics of the individuals as well as the fixed effect is included in the fitted linear mixed model. By the residual plot of the fixed effect model, the problem that the averages of residuals of each individual do not seem to be around zero is described. Further, the residual plot and the Q-Q plot coming from the selected final model are shown that the problem is modified well.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.630-643
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• 2022

This study was conducted to derive the fresh weight and dry weight estimation formulas of Pinus densiflora and prepare a weight table using them. Aone-variable formula using only the diameter at breast height (DBH) and a two-variable formula using DBH and height were used to calculate the fresh and dry weight. Each equation was verified using statistics, such as fit index, standard error, and residuals. Theoptimal equation was evaluated for applicability by calculating the weight as a coefficient derived from a statistical verification. W = bD+cD² was selected for the one-variable equation, while W = aD^bH^c was selected for the two-variable equation. The fit index of the former was 0.87-0.92, while that of the latter was 0.94-0.98, both of which showed a good fit. A new weight table was prepared using the optimal estimation formula, and it was compared and analyzed with a previous weight table. Analysis results showed that Gangwon pine had higher values in the previous weight table, while pines in the central region had higher values in the newly created weight table.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.125-140
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• 2017

Until recently, regression based spatial interpolation methods and Kriging based spatial interpolation methods have been largely used to estimate land price or housing price, but less attention has been paid on comparing the performance of these spatial interpolation methods. In this regard, this research applied regression based spatial interpolators and Kriging based spatial interpolators for estimating the land prices in Dalseo-gu, Daegu metropolitan city and evaluated the accuracy of eight spatial interpolators. OLS, SLM, SEM, and GWR were used as regression based spatial interpolators while SK, OK, UK, and CK were employed as Kriging based spatial interpolators. The global accuracy was statistically evaluated by RMSE, adjusted RMSE, and COD. The relative accuracy was visually compared by three-dimensional residual error map and scatterplot. Results from statistical and visual analyses indicate that GWR reflecting the spatial non-stationarity was a relatively more accurate spatial predictor to estimate land prices in the study area than SAR and Kriging based spatial interpolators considering the spatial dependence. The findings from this research will contribute to the secondary research into analyzing the urban spatial structure with land prices.

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.648-657
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• 2021

This study was conducted to estimate the volume growth by forest type and major species using the national forest resource inventory and to predict the final age of maturity by deriving the mean annual increment (MAI) and the current annual increment (CAI). We estimated the volume growth using the Chapman-Richards model. In the volume estimation equations by forest type, coniferous forests exhibited the highest growth. According to the estimation formula for each major species, Larix kaempferi will grow the highest among coniferous tree species and Quercus mongolica among broad-leaved tree species. And these estimation formulas showed that the fitness index was generally low, such as 0.32 for L. kaempferi and 0.21 for Quercus variabilis. In the analysis of residual amount, which indicates the applicability of the volume estimation formula, the estimates of the estimation formula tended to be underestimated in about 30 years or more, but most of the residuals were evenly distributed around zero. Therefore, these estimation formulas have no difficulty estimating the volume of actual forest species in Korea. The maximum age attained by calculating MAI was 34 years for P. densiflora, 35 years for L. kaempferi, and 31 years for P. rigida among coniferous tree species. In broad-leaved tree species, we discovered that the maximum age was 32 years for Q. variabilis, 30 years for Q. acutissima, and 29 years for Q. mongolica. We calculated MAI and CAI to detect the point at which these two curves intersected. This point was defined by the maximum volume harvesting age. These results revealed no significant difference between the current standard cutting age in public and private forests recommended by the Korea Forest Service, supporting the reliability of forestry policy data.