• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.173-183
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• 2017

We investigate the factors affecting the price of apartments using the spatial and temporal data of private real estate prices. The factors affecting the price of apartment were analyzed using geographical and temporal weighted regression (GTWR) model which incorporates the temporal and spatial variation. In contrast to the OLS, a general approach used in previous studies, and GWR method which is most widely used for analyzing spatial data, GTWR considers both temporal and spatial characteristics of the house price, and leads to better description of the house price determination. Year of construction and floor area are selected as the significant factors from the analysis, and the house price are affected by them temporally and geographically.

• Journal of the Korean Geographical Society
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• v.49 no.6
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• pp.935-948
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• 2014

As chronic diseases have become more prevalent and problematic, effective cares for major chronic diseases have been a locus of the healthcare policy. In this regard, this study examines how region-specific characteristics affect the prevalence of hypertension in South Korea. To analyze, we combined a unique multi-year data set including key indicators of health conditions and health behaviors at the 237 small administrative districts. The data are collected from the Annual Community Health Survey between 2009 and 2011 by Korea Centers for Disease Control and Prevention and other government organizations. For the purpose of investigating regional variations, we estimated using Geographically Weighted Regression (GWR) and decision tree model. Our finding first suggests that using the multi-year data is more legitimate than using the single-year data for the geographical analysis of chronic diseases, because the significant annual differences are observed in most variables. We also find that the prevalence of hypertension is more likely to be positively associated with the prevalence of diabetes and obesity but to be negatively associated with population density. More importantly, noticeable geographical variations in these factors are observed according to the results from the GWR. In line with this result, additional findings from the decision tree model suggest that primary influential factors that affect the hypertension prevalence are indeed heterogeneous across regional groups. Taken as a whole, accounting for geographical variations of health conditions, health behaviors and other socioeconomic factors is very important when the regionally customized healthcare policy is implemented to mitigate the hypertension prevalence. In short, our study sheds light on possible ways to manage the chronic diseases for policy makers in the local government.

• 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.

• 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.

• Land and Housing Review
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• v.11 no.1
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• pp.79-86
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• 2020

The number of business closure is one of key indicators diagnosing the status of local economy. The increases in closure are attributed to various endogenous/exogenous factors such as decreases in sales of stores, decline of local market, deterioration of global financial condition, but it is not trivial task to figure out the cause and effect mechanism among variables. The effects of those factors are expected to show geographical variations, which the empirical analysis results in this study presented. As such, the objective of this study is to estimate the effects of variables on increase in the number of business closure and examine the distributional properties of the geographic variations of the effects among spatial units of analysis. To this end, GWR (Geographically Weighted Regression) model was utilized to draw empirical analysis outcomes. It is expected that the outcomes of the sort in this research may be useful in aiding decision-making process of drafting locality-specific policies and/or deciding where to prioritize the limited public resources available.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.469-482
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• 2022

The purpose of this study is to analyze regional variation factors of hypertension treatment rate in COVID-19 based on the analysis results based on ecological methodology. To this end, data suitable for ecological analysis were collected from the Korea Centers for Disease Control and Prevention's regional health statistics, local government COVID-19 confirmed cases, National Health Insurance Corporation, Health Insurance Review and Assessment Service's welfare statistics, and Korea Transport Institute's traffic access index. Descriptive statistics and correlation analysis were conducted using SPSS Statistics 23 for regional variation and related factors in hypertension treatment rate, and geographical weighted regression analysis was conducted using Arc GIS for regional variation factors. As a result of the study, the overall explanatory power of the calculated geo-weighted regression model was 27.6%, distributed from 23.1% to 33.4% by region. As factors affecting the treatment rate of hypertension, the higher the rate of basic living security medical benefits, diabetes treatment rate, and health institutions per 100,000 population, the higher the rate of hypertension treatment, the lower the number of COVID-19 confirmed patients, the lower the rate of physical activity, and the alcohol consumption. Percentage of alcohol consumption decreased due to COVID-19 pandemic. It was analyzed that the lower the ratio, the higher the treatment rate for hypertension. Based on these results, the analysis of regional variables in the treatment rate of hypertension in COVID-19 can be expected to be effective in managing the treatment rate of hypertension, and furthermore, it is expected to be used to establish community-centered health promotion policies.

• Journal of the Korean Geographical Society
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• v.39 no.2
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• pp.212-228
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• 2004

This paper is concerned with an important aspect of regional income convergence, ${\beta}$-convergence, which refers to the negative relationship between initial income levels and income growth rates of regions over a period of time. The common research framework on ${\beta}$-convergence which is based on OLS regression models has two drawbacks. First, it ignores spatially autocorrelated residuals. Second, it does not provide any way of exploring spatial heterogeneity across regions in terms of ${\beta}$-convergence. Given that empirical studies on ${\beta}$-convergence need to be edified by spatial data analysis, this paper aims to: (1) provide a critical review of empirical studies on ${\beta}$-convergence from a spatial perspective; (2) investigate spatio-temporal income dynamics across the U.S. labor market areas for the last 30 years (1969-1999) by fitting spatial regression models and applying bivariate ESDA techniques. The major findings are as follows. First, the hypothesis of ${\beta}$-convergence was only partially evidenced, and the trend substantively varied across sub-periods. Second, a SAR model indicated that ${\beta}$-coefficient for the entire period was not significant at the 99％ confidence level, which may lead to a conclusion that there is no statistical evidence of regional income convergence in the US over the last three decades. Third, the results from bivariate ESDA techniques and a GWR model report that there was a substantive level of spatial heterogeneity in the catch-up process, and suggested possible spatial regimes. It was also observed that the sub-periods showed a substantial level of spatio-temporal heterogeneity in ${\beta}$-convergence: the catch-up scenario in a spatial sense was least pronounced during the 1980s.

• Journal of Korean Society of Transportation
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• v.31 no.4
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• pp.47-57
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• 2013

This study aims at analyzing transportation equity between geographical areas of Gyonggi Province, by taking both the transportation infrastructure and travel behavior into account. Indicators of transportation infrastructure include the indices of road infrastructure, transit infrastructure and regional characteristics. Travel behavior concerns information from bus card data on a survey day. The hot-spot analysis conducted included spatial cluster analysis and global/local regression analyses. The analysis results identified geographical areas of four different classes of transportation equity, from the area with high level infrastructure surrounded by the areas with high level infrastructure (HH) to the area with low level surrounded by the areas with low level (LL). The area of HH type showed big numbers of passengers, trips and transfers, whereas the area of LL type shows big figures of internal trip frequency, travel time, travel distance, travel speed and transit fare. Global regression analysis showed that number of passengers, number of transfers, number of internal trips and mean travel speed are important to the level of transportation infrastructure. GWR with these four significant variables significantly improved the AICs and ANOVA results, which implies that the infrastructure is likely explained by travel characteristics differently between geographical areas in Gyonggi Province.

• 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.

• 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.