• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.203-211
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• 2019

This study is to examine the impact of spatial characteristics of cities on homicide based on spatial econometric model. It selects housing types, racial heterogeneity, residential instability, overcrowding, commercial area, rate of 15 to 29 ages, and rate of the elderly as variables for spatial characteristics of cities. This study employs spatial regression analysis applying the spatial error model to analyze the data from 229 locals collected from Korean Statistical Information Service and Statistical Year Book of local governments. As a result, it shows that homicide has close relationships with apartment and multi-housing as housing types, racial heterogeneity, residential instability, and overcrowding, but not with the commercial area, rate of 15 to 29 ages, and rate of the elderly. The study contributes to expanding understanding and explanation on the causes of homicide focusing on social-structure approach for criminology by analyzing a more advanced model in applying variables than one of existing literature. This study suggests follow-up research on homicide based on both social-behavior approach and social-structure approach in the near future for the development of criminological theory.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.89-99
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• 2018

The purpose of this study is to analyse the determinants of the change in the welfare expenditure of local governments in 2015. This study analyzed the spatial correlation of welfare expenditure among neighboring local governments and determined the factors affecting the welfare expenditures. According to the results of the study, spatial correlation of welfare expenditure among local governments appears. Determinants, such as socio-economic factors, administrative factors, public financial factors are affecting the amount of the welfare expenditures, but local political factors, and local tax, last year's budgets are not correlated with the amount of local welfare expenditures. In this study, it is significant to found out that the spatial correlation of welfare expenditure among the local governments and to examine the determinants. If possible, it is necessary to analyze the time-series analysis using the multi-year welfare expenditure data, expecially self-welfare expenditures.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.302-305
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• 2004

The aim of this study is to apply and crossvalidate a spatial logistic multiple-regression model at Boun, Korea, using a Geographic Information System (GIS). Landslide locations in the Boun area were identified by interpretation of aerial photographs and field surveys. Maps of the topography, soil type, forest cover, geology, and land-use were constructed from a spatial database. The factors that influence landslide occurrence, such as slope, aspect, and curvature of topography, were calculated from the topographic database. Texture, material, drainage, and effective soil thickness were extracted from the soil database, and type, diameter, and density of forest were extracted from the forest database. Lithology was extracted from the geological database and land-use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using landslide-occurrence factors by logistic multiple-regression methods. For validation and cross-validation, the result of the analysis was applied both to the study area, Boun, and another area, Youngin, Korea. The validation and cross-validation results showed satisfactory agreement between the susceptibility map and the existing data with respect to landslide locations. The GIS was used to analyze the vast amount of data efficiently, and statistical programs were used to maintain specificity and accuracy.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.315-327
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• 2021

The Spatial Autoregressive (SAR) models have drawn considerable attention in recent econometrics literature because of their capability to model the spatial spill overs in a feasible way. While considering the Bayesian analysis of these models, one may face the problem of lack of robustness with respect to underlying prior assumptions. The generalized Bayes estimators provide a viable alternative to incorporate prior belief and are more robust with respect to underlying prior assumptions. The present paper considers the SAR model with a set of linear restrictions binding the regression coefficients and derives restricted generalized Bayes estimator for the coefficients vector. The minimaxity of the restricted generalized Bayes estimator has been established. Using a simulation study, it has been demonstrated that the estimator dominates the restricted least squares as well as restricted Stein rule estimators.

• Spatial Information Research
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• v.10 no.4
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• pp.501-513
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• 2002

Eurasian otter (Lutra lutra) is one of endangered wildlife species whose population size is declining in Korea. To manage and conserve habitat for Eurasian otter, it is crucial to understand which habitat components affect otter habitat qualities. The objectives of this study were to develop a habitat suitability model of Eurasian otter in Soraksan National Park, to validate the model in Odaesan National Park. The research methods of this study were as follows. First, trace data and characters of Eurasian otter habitat were collected with Geographic Information System (GIS) data and Global Positioning System (GPS) receivers between 2000 and 2002. Second, the habitat use factors were identified as habitat characteristics of Eurasian otter and classified with habitat use and availability analyses. Third, significant factors of habitat model were extracted by Chi-square test. The last, Eurasian Otter Habitat Suitability Model (EOHSM) was employed by logistic regression method. Otter habitat use was positively associated with the reeds and shrubs areas adjacent to streams, the size of boulders, and low human disturbance in Soraksan National Park by EOHSM. This model had a classification accuracy of 74.4% at cutoff value of 0.5. Model validation showed a classification accuracy of 86.6 % at cut off value of 0.5 for otter habitat in Odaesan National Park.

• Management & Information Systems Review
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• v.37 no.1
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• pp.155-175
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• 2018

Lots of previous researches on determinants of apartment prices in Korea consider spatial dependence while few studies regard endogeneity of spatial lag by adding a spatial lag to an OLS regression. Thus, this study intends to include this spatial lag in its analysis of determinants of apartment price in Busan by using a two-stage quantile regression. The empirical results are : the coefficient of spatial lag variable is more than 0.5 and is statistically significant at 1% level. From this result we can confirm that the effect of the price of nearby apartment on that of another apartment is very big. We also find that apartment buyers prefer larger size, height in both the total floors and living floor, south-facing living room with a ocean view, and proximity to metros, high school and coast. Unlike our expectation, however, mountain view is less favored than building view, which we can guess is because apartments with mountain views are mostly located in the low-priced apartment area where some of their living rooms face north. Quantile regression also explains the effect of hedonic characteristics on apartment price better than OLS estimation. For instance, the effect of south facing living room variable on the price is twice larger in high-price apartments than in low-price counterparts. And the effect of vicinity to the coast or the ocean is ten times bigger in high priced apartments.

• Survey Research
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• v.10 no.3
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• pp.1-19
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• 2009

This study investigates the utilities of spatial analysis in the context of survey research using Geographical Information System(GIS) and Geographically Weighted Regression (GWR) which take account of spatial heterogeneity. Many social phenomena involve spatial dimension, and with the development of GIS, GPS receiver, and online location-based services, spatial information can be collected and utilized more easily, and thus application of spatial analysis in the survey research is getting easier. The traditional OLS regression models which assume independence of observations and homoscedasticity of errors cannot handle spatial dependence problem. GWR is a spatial analysis technique which utilizes spatial information as well as attribute information, and estimated using geographically weighted function under the assumption that spatially close cases are more related than distant cases. Residential survey data from a Primary Autonomous District are used to estimate a model of public service satisfaction. The findings show that GWR handles the problem of spatial auto-correlation and increases goodness-of-fit of model. Visualization of spatial variance of effects of the independent variables using GIS allows us to investigate effects and relationships of those variables more closely and extensively. Furthermore, GIS and GWR analyses provide us a more effective way of identifying locations where the effect of variable is exceptionally low or high, and thus finding policy implications for social development.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.345-356
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• 2010

In this paper, we introduce the linear regression model taking the parametric spatial association structure into account and employ it to five-year averaged wind speed data measured at 460 meteorological monitoring stations in South Korea. From the prediction map obtained by the model with spatial association parameters, we can see that inland area has smaller wind speed than coastal regions. When comparing the spatial linear regression model with classical one by using one-leave-out cross-validation, the former outperforms the latter in terms of similarity between the observations and the corresponding predictions and coverage rate of 95% prediction intervals.

• Environmental Analysis Health and Toxicology
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• v.29
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• pp.5.1-5.7
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• 2014

Objectives Numerous studies have revealed the adverse health effects of acute and chronic exposure to particulate matter less than $10{\mu}m$ in aerodynamic diameter ($PM_{10}$). The aim of the present study was to examine the spatial distribution of $PM_{10}$ concentrations and cardiovascular mortality and to investigate the spatial correlation between $PM_{10}$ and cardiovascular mortality using spatial scan statistic (SaTScan) and a regression model. Methods From 2008 to 2010, the spatial distribution of $PM_{10}$ in the Seoul metropolitan area was examined via kriging. In addition, a group of cardiovascular mortality cases was analyzed using SaTScan-based cluster exploration. Geographically weighted regression (GWR) was applied to investigate the correlation between $PM_{10}$ concentrations and cardiovascular mortality. Results An examination of the regional distribution of the cardiovascular mortality was higher in provincial districts (gu) belonging to Incheon and the northern part of Gyeonggi-do than in other regions. In a comparison of $PM_{10}$ concentrations and mortality cluster (MC) regions, all those belonging to MC 1 and MC 2 were found to belong to particulate matter (PM) 1 and PM 2 with high concentrations of air pollutants. In addition, the GWR showed that $PM_{10}$ has a statistically significant relation to cardiovascular mortality. Conclusions To investigate the relation between air pollution and health impact, spatial analyses can be utilized based on kriging, cluster exploration, and GWR for a more systematic and quantitative analysis. It has been proven that cardiovascular mortality is spatially related to the concentration of $PM_{10}$.

• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.121-135
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• 2022

The purpose of this study is to explore the convergence sector of the spatial information industry based on the business transaction data of spatial information companies and to predict the market size of the industry using the Seemingly Unrelated Regression(SUR) model. The convergence part of spatial information industry, which cannot be identified in the Spatial Data Industry Survey, was analyzed by exploring keywords related to spatial information using the business DB of Korea Enterprise Data (2010-2019). The convergence of spatial information businesses mainly appeared in the business relationship between the value chain between Seoul and Gyeonggi Province. The convergence business has the largest sales in the value chain 2 (utilization, service) & 3 (convergence), and also the convergence in the value chain 1 (production, construction) & 2, 2 & 3 stages has doubled in 2019 compared to 2010. In 2019, the total sales of the spatial information industry based on the Statistical Korea were announced at about 8 trillion won, but in this study, the total sales of the spatial information industry were estimated at 28 trillion won considering convergence activities. Finally, when scenario 1 (0.38% population growth, 2020-2024) and 0.07% (2026-2030) were applied using the SUR model to predict the expected market size of the industry, sales decreased by -0.37% to 0.069% in 2025 and 2030 by respectively. When scenario 2 (average wage growth 1.2%) was applied during the same period, sales in the industry increased by 2.326% to 12.185%. In other words, the sales in the spatial information industry depends on Labor, Total Factor Productivity, and Capital Productivity so it is necessary to additional research on policy development and alternatives of enhancing each productivity.