• Land and Housing Review
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• v.14 no.3
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• pp.37-53
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• 2023

This study conducted an empirical analysis of how different types of living retail businesses affected housing prices during the COVID-19 pandemic, with a particular focus on both global and local time series effects. The main findings are three folds: First, from a global perspective, the study discovered that the presence of living retail businesses had a significant impact on prices of nearby apartment, varying according to their type. Secondly, the impact of COVID-19 on the retail industry varied depending on the type of business. Thirdly, when viewed from a local standpoint, the impact of the retail business sector on apartment prices due to COVID-19 pandemic was substantial, varying across regions and business types. This implies that external shocks like COVID-19 have the potential to alter the role and perception of living retail businesses. In light of this, the study has put forth policy implications aimed at mitigating the adverse effects of living retail businesses and enhancing residential quality.

• Journal of the Korean Geographical Society
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• v.46 no.2
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• pp.197-211
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• 2011

Due to the complexity of spatial interaction and the necessity of spatial representation and modeling, aggregation of spatial interaction data is indispensible. Given this, the purpose of this paper is to evaluate the effects of modifiable areal unit problem (MAUP) on a spatial interaction model. Four aggregation schemes are utilized at eight different scales: 1) randomly select seeds of district and then allocate basic spatial units to them, 2) minimize the sum of population weighted distance within a district, 3) maximize the proportion of flow within a district, and 4) minimize the proportion of flow within a district. A simple Poisson regression model with origin and destination constraints is utilized. Analysis results demonstrate that spatial characteristics of residuals, parameter values, and goodness-of-fit of the model were influenced by aggregation scale and schemes. Overall, the model responded more sensitively to aggregation scale than aggregation schemes and the scale effect on the model was varied according to aggregation schemes.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.120-132
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• 2022

Recently, the rapid increase in vacant houses in urban areas has caused various problems such as worsening urban landscape, causing safety accidents, crime accidents, and hygiene problems. According to the Statistics Korea Future Population Estimation results, the growth rate of Korean population and households is expected to continue to decrease, which is likely to lead to an increase in the occurrence of vacant houses. If the problem caused by the occurrence of vacant houses is neglected, it causes not only a physical decline such as a deterioration of the residential environment but also a social and economic decline. In order to solve this problem, it is necessary to grasp the spatial distribution characteristics of vacant houses at the local level considering the existence of regional characteristics and spatial influence. Therefore, in this study, in order to measure global spatial autocorrelation, the analysis was conducted centering on the old downtown area of Busan, where there are many vacant houses through Moran's I and Geographically Weighted Regression(GWR). In addition, the distribution of vacant houses in different spatial units in Eup_Myeon_Dong and Census was analyzed to evaluate the possibility of Modifiable Areal Unit Problem(MAUP), which differ in the results of spatial analysis as the spatial analysis units change. As a result of the analysis, the occurrence of vacant houses by Eup_Myeon_Dong in the old downtown area of Busan had spatial heterogeneity, and the spatial analysis results of vacant houses were different as the spatial analysis units were different. Accordingly, in order to understand the exact distribution characteristics of vacant house occurrence, spatial dimensions using the GWR model should be considered, and it is suggested that consideration of the MAUP is necessary.

• Journal of Environmental Impact Assessment
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• v.23 no.1
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• pp.39-50
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• 2014

In recent years, the frequency and scale of the natural disasters are growing rapidly due to the global climate change. In case of the urban flooding, high-density of population and infrastructure has caused the more intensive damages. In this study, we analyzed the spatial characteristics of urban flooding damage factors using GWR(Geographically Weighted Regression) for effective disaster prevention and then, classified the causes of the flood damage by spatial characteristics. The damage factors applied consists of natural variables such as the poor drainage area, the distance from the river, elevation and slope, and anthropogenic variables such as the impervious surface area, urbanized area, and infrastructure area, which are selected by literature review. This study carried out the comparative analysis between OLS(Ordinary Least Square) and GWR model for identifying spatial non-stationarity and spatial autocorrelation, and in the results, GWR model has higher explanation power than OLS model. As a result, it appears that there are some differences between each of the flood damage areas depending on the variables. We conclude that the establishment of disaster prevention plan for urban flooding area should reflect the spatial characteristics of the damaged areas. This study provides an improved understandings of the causes of urban flood damages, which can be diverse according to their own spatial characteristics.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.73-81
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• 2013

Ordinary least squares (OLS) regression is the primary statistical method in previous studies for vegetation distribution patterns in relation to landform. However, this global regression lacks the ability to uncover some local-specific relationships and spatial autocorrelation in model residuals. This study employed geographically weighted regression (GWR) to examine the spatially varying relationships between NDVI (Normalized Difference Vegetation Index) patterns and changing trends of landform (elevation, slope) and solar intensity (insolation and duration of sunshine) in Mt Geum-gang of North-Korea. Results denoted that GWR was more powerful than OLS in interpreting relationships between NDVI patterns and landform/solar intensity, since GWR was characterized by higher adjusted R2, and reduced spatial autocorrelations in model residuals. Unlike OLS regression, GWR allowed the coefficients of explanatory variables to differ by locality by giving relatively more weight to NDVI patterns which are affected by local landform and solar factors. The strength of the regression relationships in the GWR increased significantly, by showing regression coefficient of higher than 70% (0.744) in the southern ridge of the experimental area. It is anticipated that this research output will serve to increase the scientific and objective vegetation monitoring in relation to landform and solar intensity by overcoming serious constraints suffered from the past non-GWR-based approach.