• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.29-40
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• 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.

• Health Policy and Management
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• v.27 no.4
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• pp.315-323
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• 2017

Background: This study was purposed to analyze the effect of spatial accessibility to the psychiatry department in general hospital on the outpatient visit of mental patients. Methods: Data was provided from the Statistics Korea and Statistical Geographic Information Service, National Health Insurance Service, Health Insurance Review and Assessment Service, and Korea Transport Institute in 2015. The study regions were 103 administrative regions such as Si and Gu. The 103 regions had at least one general hospitals with a psychiatry department. The number of outpatient visit of mental patients in regions was used as the dependent variable. Spatial accessibility to mental general hospital was used as the independent variable. Control variables included such as demographic, economic, and health medical factors. This study used network analysis and multi-variate regression analysis. Network analysis by ArcGIS ver. 10.0 (ESRI, Redlands, CA, USA) was used to evaluate the average travel time and travel distance in Korea. Multi-variate regression analysis was conducted by SAS ver. 9.4 (SAS Institute Inc., Cary, NC, USA). Results: Travel distance and time had significant effects on the number of outpatient visits in mental patients in general hospital. Average travel time and travel distance had negative effects on the number of visits. Variables such as (number of total population, percentage of aged population over 65, and number of mental general hospital) had significant effects on the number of visit in mental patients. Conclusion: Health policy makers will need to consider the spatial accessibility to the mental healthcare organization in conducting regional health planning.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.39-45
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• 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 the Korean Geotechnical Society
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• v.28 no.12
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• pp.65-76
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• 2012

In this study, probabilistic analysis of seepage through a two-layered soil foundation was performed. The hydraulic conductivity of soil shows significant spatial variations in different layers because of stratification; further, it varies on a smaller scale within each individual layer. Therefore, the deterministic seepage analysis method was extended to develop a probabilistic approach that accounts for the uncertainties and spatial variation of the hydraulic conductivity in a layered soil profile. Two-dimensional random fields were generated on the basis of the Karhunen-Lo$\grave{e}$ve expansion in a manner consistent with a specified marginal distribution function and an autocorrelation function for each layer. A Monte Carlo simulation was then used to determine the statistical response based on the random fields. A series of analyses were performed to verify the application potential of the proposed method and to study the effects of uncertainty due to the spatial heterogeneity on the seepage behavior of two-layered soil foundation beneath water retaining structure. The results showed that the probabilistic framework can be used to efficiently consider the various flow patterns caused by the spatial variability of the hydraulic conductivity in seepage assessment for a layered soil foundation.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1225-1239
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• 2016

We investigate pediatric tumor incidence data collected by the Florida Association for Pediatric Tumor program using various models commonly used in disease mapping analysis. Particularly, we consider Poisson normal models with various conditional autoregressive structure for spatial dependence, a zero-in ated component to capture excess zero counts and a spatio-temporal model to capture spatial and temporal dependence, together. We found that intrinsic conditional autoregressive model provides the smallest Deviance Information Criterion (DIC) among the models when only spatial dependence is considered. On the other hand, adding an autoregressive structure over time decreases DIC over the model without time dependence component. We adopt weighted ranks squared error loss to identify high risk regions which provides similar results with other researchers who have worked on the same data set (e.g. Zhang et al., 2014; Wang and Rodriguez, 2014). Our results, thus, provide additional statistical support on those identied high risk regions discovered by the other researchers.

• Journal of the Korean Geotechnical Society
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• v.19 no.2
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• pp.123-133
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• 2003

A practical statistical approach that can be used to model various sources of uncertainty systematically is presented in the context of reliability analysis of slope stability. New expressions for probabilistic characterization of soil properties incorporate sampling and measurement errors, as well as spatial variability and its reduced variance due to spatial averaging. The stochastic nature of seismic loading is studied by generating a large series of hazard-compatible artificial motions, and by using them in subsequent response analyses. The analyses indicate that in a seismically less active region such as the Korean Peninsular, a moderate variability in soil properties has an effect as large as the characterization of earthquake hazard on the computed risk of slope failure and excessive slope deformations.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.730-738
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• 2022

Based on visual Thinking theory, VR(virtual reality) game changes the traditional form of memory and maps the content into game elements to realize the immersive spatial memory mode. This paper analyzes the influencing factors of game design and system function construction. This paper proposes a hypothesis: with the help of visual thinking theory, VR game is helpful to improve learners' visual memory, and carries out research. The experiment sets different levels of game through empirical research and case analysis of memory flip game. For example, when judging two random cards. If the pictures are the same, it will be judged as the correct combination; if they are different, the two cards will be restored to the original state. The results are analyzed by descriptive statistical analysis and AMOS data analysis. The results show that game content using the concept of "Memory Palace", which can improve the accuracy of memory. We conclude that the use of spatial localization characteristics in flip games combining visual thinking can improve users' memory by helping users memorize and organize information in a Virtual environment, which means VR games have strong feasibility and effectiveness in improving memory.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.107-114
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• 2011

In this study, when interpreting leakage using the concept of geographical dispersion based on grid, to choose an appropriate spatial resolution, the statistical characteristics of drainage path length and the pattern of change of hydrodynamic parameters have been observed. Drainage path length has been calculated using an 8-direction algorithm from digital elevation model, from which the hydrodynamic parameters of the watershed were estimated. The scales of topographical map for this analysis are 1:5,000 and 1:25,000, appling grid sizes 5, 10, 15, 20 m and 20, 30, 50, 100, 150, 200 m, respectively. As results of this analysis, depending on the scale of stream networks, the statistical characteristics of drainage path length by spatial resolution and hydrodynamic parameters of the watershed have been changed. Based on the above results, when interpreting leakage using the concept of the geographical dispersion based on grid, in the case of 1:5,000 scale topographical map, a spatial resolution of 5 m will be better showing geographical and hydrodynamic characteristics to apply to the well developed stream network in basins, spatial resolution of 5~20 m to the less developed stream network in basins. And in the case of 1:25,000 scale topographical map, spatial resolution below 50 m is more desirable to show above two characteristics to apply to both cases.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.335-344
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• 2010

Reliability of the estimation is usually damaged in the situation where a linear regression model without spatial dependencies is employed to the spatial data analysis. In this study, we considered the conditional autoregressive model in order to construct spatial association structures and estimate the parameters via the Bayesian approaches. Finally, we compared the performances of the models with spatial effects and the ones without spatial effects. We analyzed the yearly total crime count data measured from each of 25 districts in Seoul, South Korea in 2007.

• Journal of Korean Society on Water Environment
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• v.37 no.6
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• pp.520-530
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• 2021

Multivariate statistical analysis and an environmental hydrological model were applied for investigating the causes of water pollution and providing best management practices for water quality improvement in urban and agricultural watersheds. Principal component analysis (PCA) and cluster analysis (CA) for water quality time series data show that chemical oxygen demand (COD), total organic carbon (TOC), suspended solids (SS) and total phosphorus (T-P) are classified as non-point source pollutants that are highly correlated with river discharge. Total nitrogen (T-N), which has no correlation with river discharge and inverse relationship with water temperature, behaves like a point source with slow and consistent release. Biochemical oxygen demand (BOD) shows intermediate characteristics between point and non-point source pollutants. The results of the PCA and CA for the spatial water quality data indicate that the cluster 1 of the watersheds was characterized as upstream watersheds with good water quality and high proportion of forest. The cluster 3 shows however indicates the most polluted watersheds with substantial discharge of BOD and nutrients from urban sewage, agricultural and industrial activities. The cluster 2 shows intermediate characteristics between the clusters 1 and 3. The results of hydrological simulation program-Fortran (HSPF) model simulation indicated that the seasonal patterns of BOD, T-N and T-P are affected substantially by agricultural and livestock farming activities, untreated wastewater, and environmental flow. The spatial analysis on the model results indicates that the highly-populated watersheds are the prior contributors to the water quality degradation of the river.