• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.65-76
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• 2017

The purpose of this study was to present a prediction model that reflects crime risk area analysis, including factors and spatial characteristics, as a precursor to preparing an alternative plan for crime prevention and design. This analysis of criminal cases in high-risk areas revealed clusters in which approximately 25% of the cases within the study area occurred, distributed evenly throughout the region. This means that using a multiple linear regression model might overestimate the crime rate in some regions and underestimate in others. It also suggests that the number of deserted houses in an analyzed region has a negative relationship with the dependent variable, based on the multiple linear regression model results, and can also have different influences depending on the region. These results reveal that closure signs in a study area affect the dependent variable differently, depending on the region, rather than a simple or direct relationship with the dependent variable, as indicated by the results of the multiple linear regression model.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.271-280
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• 2005

Kriging methods as traditional spatial data analysis methods and geographical weighted regression models as statistical analysis methods are compared. In this paper, we apply data from the Ministry of Environment to spatial analysis for practical study. We compare these methods to performance with monthly carbon monoxide observations taken at 116 measuring area of air pollution in 1999.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.638-650
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• 2014

The objective of this study is to identify the causes of pedestrian volume path to the destination by investigating the influential levels of regional and planning features in the central area of Seoul. Regional characteristics can be classified from the result of the analysis and through the spatial characteristics of pedestrian volume. For global scale analysis, Ordinary Least Squares (OLS) regression is used for the degree of influence of each characteristics to pedestrian volume. For the local scale, Geographically Weighted Regression (GWR) is used to identify regional influential factors with consideration for spatial differences. The results of OLS indicate that boroughs with transportation facilities, commercial business districts, universities, and planning features with education research facilities and planning facilities have a positive effect on pedestrian volume path to the destination. Correspondingly, transportation hubs and congested areas, commercial and business centers, and university towns and research facilities in the Seoul central area can be identified through the results of GWR. The results of this study can provide information with relevance to existing plans and policies about the importance of regional characteristics and spatial heterogeneity effects on pedestrian volume, as well as significance in the establishment of regional development plans.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.3
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• pp.77-84
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• 2015

The key objective of this research is to review the effectiveness of spatial regression to identify the influencing factors of spatial distribution patterns of floating population. To this end, global and local spatial autocorrelation test were performed using seoul floating population survey(2014) data. The result of Moran's I and Getis-Ord $Gi^*$ as used in the analysis derived spatial heterogeneity and spatial similarities of floating population patterns in a statistically significant range. Accordingly, Geographically Weighted Regression was applied to identify the relationship between land use attributes and population floating. Urbanization area, green tract of land of micro land cover data were aggregated in to $400m{\times}400m$ grid boundary of Seoul. Additionally public transportation variables such as intersection density transit accessibility, road density and pedestrian passage density were adopted as transit environmental factors. As a result, the GWR model derived more improved results than Ordinary Least Square(OLS) regression model. Furthermore, the spatial variation of applied local effect of independent variables for the floating population distributions.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.6
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• pp.43-51
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• 2013

This study aims to examine the presence of non-stationary relationship between water quality and land use in watersheds. In investigating the relationships between land use and water quality, most previous studies adopted OLS method which is assumed stationarity. However, this approach is difficult to capture the local variation of the relationships. We used 146 sampling data and land cover data of Korean Ministry of Environment to build conventional regressions and GWR models for BOD, TN and TP. Regression model and GWR models of BOD, TN, TP were compared with $R^2$, AICc and Moran's I. The results of comparisons and descriptive statistics of GWR models strongly indicated the presence of Non-Stationarity between water quality and land use.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.68-79
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• 2021

The recent occurrence of vacant houses in urban areas is a remarkable social problem. One of the physical declines, the occurrence of vacant houses, accelerates various social and economic declines, such as a decline in population and a slump in the commercial district. Vacant houses have regional characteristics and spatial influence, and it is necessary to approach them locally in order to grasp the exact status of vacant houses. Therefore, in this study, the effect of urban decline on the occurrence of vacant homes was examined by region using global Moran's I and Geographic Weighted Regression(GWR) model. As a result of the analysis, there were spatial autocorrelation and heterogeneity in the occurrence of vacant houses in each eup·myeon·dong, Busan metropolitan city. In addition, there is a difference in the influence of each variable of urban decline on the occurrence of vacant houses, and even the same variable of urban decline has different effects on the occurrence of vacant houses in different regions. Therefore, it is expected that a more efficient vacant home management plan can be presented if the GWR model is used to analyze the coefficient values differentiated by region and categorize the occurrence of vacant houses.

• 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 the Korean Geographical Society
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• v.49 no.6
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• pp.917-934
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• 2014

Geographically weighted regression(GWR) model has been widely used to estimate spatially heterogeneous real estate prices. The GWR model, however, has some limitations of the selection of different price determinants over space and the restricted number of observations for local estimation. Alternatively, the geographically weighted LASSO(GWL) model has been recently introduced and received a growing interest. In this paper, we attempt to explore various local price determinants for the real estate by utilizing the GWL and its applicability to forecasting the real estate price. To do this, we developed the three hedonic models of OLS, GWR, and GWL focusing on the sales price of apartments in Seoul and compared those models in terms of model fit, prediction, and multicollinearity. As a result, local models appeared to be better than the global OLS on the whole, and in particular, the GWL appeared to be more explanatory and predictable than other models. Moreover, the GWL enabled to provide spatially different sets of price determinants which no multicollinearity exists. The GWL helps select the significant sets of independent variables from a high dimensional dataset, and hence will be a useful technique for large and complex spatial big data.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.223-235
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• 2011

Although ordinary least squares (OLS) regression analysis can be used to examine the spatial relationships between environmental equity and urban quality of life, this global method may mask the local variations in the relationships between them. These geographical variations can not be captured without using local methods. In this context, this paper explores the spatially varying relationships between environmental equity and urban quality of life across the Atlanta metropolitan area by geographically weighted regression (GWR), a local method. Environmental equity and urban quality of life were quantified with an integrated approach of GIS and remote sensing. Results show that generally, there is a negatively significant relationship between them over the Atlanta metropolitan area. The results also suggest that the relationships between environmental equity and urban quality of life vary significantly over space and the GWR (local) model is a significant improvement on the OLS (global) model for the Atlanta metropolitan area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.343-351
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• 2014

The correlation between regional Walkability Index and their physical socio-economic characteristics has evaluated by the spatial statistical analysis to understand the urban pedestrian environments, where has been emerging the significance, recently. Following to the study, the Walkability Indexes were calculated quantitatively from two administrative districts of Busan and measured Global Local spatial autocorrelation indices. Additionally, the Geographically Weighted Regression model was applied to define the correlation between Walkability Indexes and urban environmental variables. The spatial autocorrelation values and clusters on the Walkability Indexes were derived in statistically significant level. Furthermore, the Geographically Weighted Regression model has been derived more improved inference than the OLS regression model, so as the influence of local level pedestrian environment was identified. The results of this study suggest that the spatial statistical approach can be effective on quantitative assessing the pedestrian environment and navigating their associated factors.