• 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.

• Journal of the Economic Geographical Society of Korea
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• v.19 no.2
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• pp.286-299
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• 2016

The increase of elderly population is inevitable a universal trend in developed countries. The Korea also followed this tendency. Especially the elderly population in Korea has been rapidly increasing since the 1990s. The rural population is aging more rapidly than the urban one because younger generations move into the urban area, due to better jobs and children education's opportunities. However, the majority of studies on population ageing are focused on the urban area rather than the rural ones. Rural areas also have been excluded as a priority of the national welfare policy for the elderly people. This paper tries to analyze the population ageing among regions and to identify the regional disparity between the elderly people and the level of public services in the rural area. Based upon these results, this study notes some policy alternatives for the improvement of quality of life of the rural elderly. It also suggests on the suitable location of public service facilities in the rural areas.

• Journal of Korea Water Resources Association
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• v.49 no.9
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• pp.761-771
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• 2016

The GloSea5 (Global Seasonal forecasting system version 5) is provided and operated by the KMA (Korea Meteorological Administration). GloSea5 provides Forecast (FCST) and Hindcast (HCST) data and its horizontal resolution is about 60km ($0.83^{\circ}{\times}0.56^{\circ}$) in the mid-latitudes. In order to use this data in watershed-scale water management, GloSea5 needs spatial-temporal downscaling. As such, statistical downscaling was used to correct for systematic biases of variables and to improve data reliability. HCST data is provided in ensemble format, and the highest statistical correlation ($R^2=0.60$, RMSE = 88.92, NSE = 0.57) of ensemble precipitation was reported for the Yongdam Dam watershed on the #6 grid. Additionally, the original GloSea5 (600.1 mm) showed the greatest difference (-26.5%) compared to observations (816.1 mm) during the summer flood season. However, downscaled GloSea5 was shown to have only a -3.1% error rate. Most of the underestimated results corresponded to precipitation levels during the flood season and the downscaled GloSea5 showed important results of restoration in precipitation levels. Per the analysis results of spatial autocorrelation using seasonal Moran's I, the spatial distribution was shown to be statistically significant. These results can improve the uncertainty of original GloSea5 and substantiate its spatial-temporal accuracy and validity. The spatial-temporal reproducibility assessment will play a very important role as basic data for watershed-scale water management.

• Journal of Gifted/Talented Education
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• v.23 no.5
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• pp.697-713
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• 2013

The research aimed to investigate characteristics of middle school students enrolled in a science gifted education center affiliated with university in terms of multiple intelligence, self-regulated learning and personality traits. The 89 subjects in the study responded to questionnaires of multiple intelligence, self-regulated learning ability and a personality trait in October, 2011. It was found that both science and math gifted students presented intrapersonal intelligence as strength and logical-mathematical intelligence as weakness. While physics and earth science gifted ones showed spatial intelligence as strength, chemistry and biology gifted ones did intrapersonal intelligence. For self-regulated learning ability, both science and mathematics gifted students tend to show higher levels than general students, in particular, cognitive and motivation strategies comparatively higher than meta-cognition and environment condition strategies. Characteristics of personal traits widely distributed across science and mathematics gifted students, showing that each gifted student presented distinct characteristics individually. Those gifted students showing certain intelligence such as spatial, intrapersonal, or natural intelligences as strength also showed different characteristics of self-regulated learning ability and personal traits among students showing same intelligence as strength. It was concluded that science and mathematics gifted students showed various characteristics of multiple intelligences, self-regulated learning ability, and personal traits across science and mathematics areas.

• Journal of Environmental Impact Assessment
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• v.28 no.6
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• pp.580-591
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• 2019

This study analyzed roadkill hotspots in Yeongju, Mungyeong-si Andong-si and Cheongsong-gun to compare the method of searching the area of the spatial cluster for selecting the roadkill hotspots. The local spatial autocorrelation index Getis-Ord Gi* statistics were calculated by different units of analysis, drawing hotspot areas of 9% from 300 m and 14% from 1 km on the basis of the total road area. The rating of Z-score in the 1km hotspot area showed the highest Z-score in the 28th National Road section on the border between Yecheon-gun and Yeongj-si. The kernel density method performed general kernel density estimation and network kernel density estimation analysis, both of which made it easier to visualize roadkill hotspots than district unit analysis, but there were limitations that it was difficult to determine statistically significant priority. As a result, local hotspot areas were found to be different according to the cluster analysis method, and areas that are in common need of reduction measures were found to be the hotspot of 28th National Road through Yeongju-si and Yecheon-gun. It is deemed that the results of this study can be used as basic data when identifying roadkill hotspots and establishing measures to reduce roadkill.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.93-103
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• 2018

The purpose of this study is to analyze the spatial cluster characteristics of fire using long-term fire data. For this, fire data which were broke out in the last 40 years were converted into GIS data and spatial analysis was performed at Gangwon-do province's minimum administrative district level. In order to grasp the spatial distribution of the fire, Moran's I, Geary's Ci and Getis-Ord's Gi*, which are methods that analyze the local indicators of spatial association(LISA), were used. By integrating the characteristics of the spatial distribution of fire by integrating the results obtained from each analysis, the advantages of the individual analysis methods were reflected in the study results. As a result of the study, hotspot areas of fire in Gangwon-do was derived out. Among the hot spot areas, some areas, where the fire frequency is higher than the adjacent areas, have been identified. The results of this study can be used as information for predicting the fire hazard area and relocating of fire-fighting facilities in the study area.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.413-421
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• 2010

In this study, we considered bivariate conditional auto-regressive model taking into account spatial association as well as correlation between the two dependent variables, which are the counts of murder and burglary. We conducted likelihood ratio test for checking over-dispersion issues prior to applying spatial poisson models. For the real application, we used the annual counts of violent crimes at 25 districts of Seoul in 2007. The statistical results are visually illustrated by geographical information system.

• 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 Geographical Society
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• v.51 no.2
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• pp.219-233
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• 2016

In social science fields, statistical models are used almost exclusively for causal explanation, and explanatory modeling has been a mainstream until now. In contrast, predictive modeling has been rare in the fields. Hence, we focus on constructing the predictive non-parametric model, instead of the explanatory model. Gangnam-gu, Seoul was chosen as a study area and we collected single-family house sales data sold between 2011 and 2014. We applied non-parametric models proposed in machine learning area including generalized additive model(GAM), random forest, multivariate adaptive regression splines(MARS) and support vector machines(SVM). Models developed recently such as MARS and SVM were found to be superior in predictive power for house price estimation. Finally, spatial autocorrelation was accounted for in the non-parametric models additionally, and the result showed that their predictive power was enhanced further. We hope that this study will prompt methodology for property price estimation to be extended from traditional parametric models into non-parametric ones.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.479-485
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• 2011

A number of case studies that find distribution of longevity population and influencing factors through the spatial data fusion using GIS techniques are growing. The majority cases of these studies are adopt census administrative boundary data for the spatial analysis. However, these methods cannot fully explain the phenomenon of longevity because there are a variety of spatial characteristics within the census administrative boundaries. Therefore, studies of spatial unit are required that realistically reflect the phenomenon of human longevity. The dasymetric mapping method enables to product of spatial unit more realistic than census administrative boundary map and statistic estimates of small area utilizing diversity spatial information. In this study, elderly population of small area has been estimated within statistically significant level that applied the vector based dasymetric mapping method. Also, the cluster analysis confirmed that the variation of local spatial relationship within census administrative boundary. The result of this study implied that the need for local-level studies of the human longevity and the validity of the dashmetric mapping techniques.