• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1215-1224
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• 2016

Climate information of the high resolution grid units is an important factor to explain the phenomenon in a variety of research field. Statistical linear interpolation models are computationally inexpensive and applicable to any climate data compared to the dynamic simulation method at regional scales. In this paper, we considered four different linear-based statistical interpolation models: general linear model, generalized additive model, spatial linear regression model, and Bayesian spatial linear regression model. The climate variable of interest was the daily mean temperature, where the spatial variability was explained using geographic terrain information: latitude, longitude, elevation. The data were collected by weather stations in January from 2003 and 2012. In the sense of RMSE and correlation coefficient, Bayesian spatial linear regression model showed better performance in reflecting the spatial pattern compared to the other models.

• Spatial Information Research
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• v.18 no.5
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• pp.107-117
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• 2010

Taking the East-Hwasung area as the case, this study first builds gridded population data based on the municipal population survey raw data, and then measures, by way of GIS tools, the major urban spatial variables that are thought to influence the composition of the regional population. For the purpose of comparison, the urban models based on the Genetic Algorithm technique and the regression technique are constructed using the same input variables. The findings indicate that the GA output performed better in differentiating the effective variables among the pilot model variables, and predicted as much consistent and meaningful coefficient estimates for the explanatory variables as the regression models. The study results indicate that GA technique could be a very useful and supplementary research tool in understanding the urban phenomena.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.217-228
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• 2018

Since the real estate reporting system was first introduced, about 2 million real estate transaction per year have been reported over the last 10 years with an increasing demand for real estate price estimates. This study looks at the applicability and superiority of the regression-kriging method to derive effective real transaction prices estimation on the location where information about real transaction is unavailable. Several issues on predicting the real estate price are discussed and illustrated using the real transaction reports of Jinju, Gyeongsangnam-do. Results have been compared with a simple regression model in terms of the mean absolute error and root square error. It turns out that the regression-kriging model provides a more effective estimation of land price compared to the simple regression model. The regression-kriging method adequately reflects the spatial structure of the term that is not explained by other characteristic variables.

• Environmental and Resource Economics Review
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• v.33 no.1
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• pp.59-86
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• 2024

Recently, research on city gas demand is increasing by reflecting the characteristics of each region. The similarity of the social structure of the adjacent region and the density of the supply infrastructure induce spatial correlation with the clustering that has a microscopic relationship between regions. Accordingly, as a result of analyzing the spatial correlation after dividing the demand for city gas for civilian use into a total of 54 regions based on the jurisdiction of 34 city gas companies, it was confirmed that there was a positive spatial correlation from a global and local perspective. In this study, the demand for city gas for civilian use for 54 regions from January 2014 to December 2022 was composed of panel data, and the spatial panel regression analysis and the general panel regression analysis were compared, and it was found that the spatial error model (SEM) was the most suitable model. This presents policy and practical implications by confirming that the demand for city gas for civilian use in one region has a significant relationship with the adjacent region.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.5
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• pp.23-32
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• 2020

Recently, women's fear of crime continues to increase in space of everyday. By the way, the fear of crime has the spatial properties as crime. Therefore, The purpose of this study is to evaluate the spatial dependence of fear of crime and to suggest the physical environmental factors influencing fear of crime. For this, a spatial regression analysis using spatial weights was conducted based on the location data of the fear of crime measured through a survey. The results of this study are as follows; First, the fear of crime felt by women in their twenties who are vulnerable to crime has spatial dependence. Therefore, it is necessary to consider the spatial characteristics in analyzing the environmental factors affecting this. Second, in order to reduce the fear of crime, it is necessary to improve the environments of old housing and entertainment facilities. There is also a need for ongoing management. Third, careful consideration is needed in the installation of CCTV and street lights, which are factors influencing the fear of crime. It is necessary to establish a reasonable arrangement standard for CCTV and to analyze the street lighting in detail.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.1-18
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• 2019

This research measured and visualized the spatial dependency and the spatial heterogeneity of the small business in Cheonan-si, Asan-si with $100m{\times}100m$ grids based on global and local spatial autocorrelation. First, we confirmed positive spatial autocorrelation of small business in the research area using Moran's I Index, which is ESDA(Exploratory Spatial Data Analysis). And then, through Getis-Ord $GI{\ast}$, one kind of LISA(Local Indicators of Spatial Association), local patterns of spatial autocorrelation were visualized. These verified that Spatial Regression Model is valid for the location factor analysis on small business commercial buildings. Next, GWR(Geographically Weighted Regression) was used to analyze the spatial relations between the distribution of small business, hourly mobile traffic-based floating population, land use attributes index, residence, commercial building, road networks, and the node of traffic networks. Final six variables were applied and the accessibility to bus stops, afternoon time floating population, and evening time floating population were excluded due to multicollinearity. By this, we demonstrated that GWR is statistically improved compared to OLS. We visualized the spatial influence of the individual variables using the regression coefficients and local coefficients of determinant of the six variables. This research applied the measured population information in a practical way. Reflecting the dynamic information of the urban people using the commercial area. It is different from other studies that performed commercial analysis. Finally, this research has a differentiated advantage over the existing commercial area analysis in that it employed hourly changing commercial service population data and it applied spatial statistical models to micro spatial units. This research proposed new framework for the commercial analysis area analysis.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.741-746
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• 2016

Albedo is one of the climate variables that modulate absorption of solar energy, and its retrieval is important process for climate change study. High spatial resolution and long-term consistent periods are important considerations in order to efficiently use the retrieved albedo data. This study retrieved surface broadband albedo based on Landsat 8 as high resolution which is consistent with Landsat 7. First of all, we analyzed consistency of Landsat 7 channel and Landsat 8 channel. As a result, correlation coefficient(R) on all channels is average 0.96. Based on this analysis, we used multiple linear regression model using Landsat 7 albedo, which is being used in many studies, and Landsat 8 reflectance channel data. The regression coefficients of each channel calculated by regression analysis were used to derive a formula for converting the Landsat 8 reflectance channel data to broadband albedo. After Landsat 8 albedo calculated using the derived formula is compared with Landsat 7 albedo data, we confirmed consistency of two satellite using Root Mean Square Error (RMSE), R-square ($R^2$) and bias. As a result, $R^2$ is 0.89 and RMSE is 0.003 between Landsat 7 albedo and Landsat 8 albedo.

• Journal of Distribution Science
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• v.16 no.7
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• pp.47-56
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• 2018

Purpose - This study aims to not only investigate spatial pattern of immigrants' residence and crime occurrences in South Korea, but shed light on how geographic distribution of immigrants and immigrant segregation affect crime rates. Research design, data, and methodology - Th unit of analysis is Si-Gun-Gu municipal level entities of South Korea. The crime data was obtained by Korea National Police Agency and two major types(violence and property) of crime were measured. Most demographic, social, and economic variables were derived from Korean Census Data in 2015. In order to examine spatial patterns of immigrants' distribution and crime rates in South Korea, the present study utilized GIS mapping technique and Exploratory Spatial Data Analysis(ESDA) tools. The causal linkage was investigated by a series of regression models using STATA. Results - Spatial inequality between urban metropolitan vs rural areas was visualized by mapping. Assuming large Moran's I value, spatial autocorrelation appeared to be quite strong. Several neighborhood characteristics such as residential stability and economic prosperity were found to be important factors leading to crime rate change. Residential distribution and segregation for immigrants were negatively significant in the regression models. Conclusions - Unlike the traditional arguments of social disorganization theory, immigrant segregation appeared to reduce violent crime rate and the high proportion of immigrants also turned out to be a crime prevention factor.

• Journal of Korea Planning Association
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• v.54 no.2
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• pp.5-16
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• 2019

The purpose of this study is to analyze the spatial characteristics of business-type-changed parcel in the Hongik-University commercial area, from the view point of commercial gentrification. A commercial gentrification occurs through a business-type-change in a spatial basic unit (microscopic spatial unit such as parcel) of an area which has not been considered in relavent policies and research. So, this study analyzed the spatial characteristics of business-type-changed parcels using the Cox's proportional hazard regression model. The main results of this study are as follows. First, as new developments in the adjacent area occur, the business-type-change probability increases. Second, by the commercial area division, the business-type-change probability is different. Finally, the accessibility is better, the probability is higher. These results could suggest that a consideration of the spatial characteristics form microscopic viewpoint is necessary to understand the commercial gentrification. And these could be used as basic data for a gentrification diagnostic and management system, which can predict gentrification from the view point of business-type-change on the basis of a parcel.

• 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.