• 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.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.475-485
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• 2014

A stochastic weather generator based on a generalized linear model (GLM) approach is a commonly used tools to simulate a time series of daily weather. In this paper, we propose a multi-site weather generator with applications to historical data in South Korea. The proposed method extends the approach of Kim et al. (2012) by considering spatial dependence in the model. To reduce this phenomenon, we also incorporate a time series of seasonal mean precipitations of South Korea in the GLM weather generator as a covariate. Spatial dependence was incorporated into the model through a latent Gaussian process. We apply the proposed model to precipitation data provided by 62 stations in Korea from 1973{2011.

• Spatial Information Research
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• v.10 no.2
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• pp.233-246
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• 2002

The purpose of this paper is to quantitatively measure the effect of location in evaluating the land value through the implementation of GIS coupled with spatial statistical analysis. We assumed that the hedonic price model, which was commonly used in modelling the land value, could not explain the spatial factor effectively. In order to add the spatial factor, the analysis of the spatial autocorrelation was used. The present project used 54 standard land price samples from 1421 parcel land values and applied Kriging to predict stochastically the unsampled values on the basis of spatial autocorrelation between location of vector data. This study confirms that the spatial variogram analysis has an advantage of predicting spatial dependence process and revealing the positive premium and the negative penality on location factor objectively.

• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.335-352
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• 2024

Purpose - Low fertility rate is a serious problem, and this study analyzes factors affecting total fertility rate using panel data from 16 metropolitan cities and provinces in Korea from 2000 to 2022. Design/methodology/approach - Estimating the SAR model considering the weak cross-sectional dependence that exists in variables related to the regional total fertility rate, and using the DKSE estimation method considering the strong cross-sectional dependence. Findings - Estimation results considering weak and strong cross-sectional dependence were similar, confirming the robustness of the results. Female labor force participation rate has a positive effect on total fertility rate, and employment rate has no effect. However, the interaction term is a negative (-) sign. Crude marriage rate has a positive effect on total fertility rate, and apartment price has a slightly positive effect. Environmental factor has no effect, and policy factor has a negative effect. Research implications or Originality - In order for an increase in the female labor force participation rate to lead to an increase in the total fertility rate, qualitative improvements in female employment must be made. Financial investment policies for childbirth must increase their effectiveness. The problem of low fertility rate requires not only population policy but also social, economic, cultural, environmental, and policy conditions to be considered.

• Journal of the Economic Geographical Society of Korea
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• v.12 no.2
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• pp.179-194
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• 2009

The spatial distribution of crime incidences in urban neighborhoods is a reflection of their socio-economic environment and spatial inter-relations. Spatial interactions between offenders and victims lead to spatial autocorrelation of the crime incidences. The spatial autocorrelation among the incidences biases the interpretation of the ecological model in OLS framework. This research investigates residential crimes using residential burglaries and robberies occurred in the city of Columbus, Ohio, for 2000. In particular, the spatial distribution of incidence rates of residential crimes are accounted in OLS framework using eigenvectors, which reflect spatial dependence in crime patterns. Result presents that handling spatial autocorrelation enhanced model estimation, and both economic deprivation and crime opportunity are turned out significant in estimating residential crime rates.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.499-505
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• 2001

Kim and Baek (2000) tested the spatial randomness for he earthquake occurrence in the Korean Peninsula by using the nearest-neighbor test statistics and empirical distribution functions. The K-function, however, has obvious advantages over the methods used in Kim and Baek (2000), such as it does not depend on the shape of the study region and is an effective summary of spatial dependence over a wide range of scales. We applied the K-function method for testing the randomness to both of the historical and the instrumental seismicity data. It was found that he earthquake occurrences for historical and instrumental seismicity data are not random and clustered rather than scattered.

• Journal of the Korean Statistical Society
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• v.32 no.4
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• pp.313-335
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• 2003

Hourly ozone data are available for 73 stations in South Korea from January, 1988 to August, 1998. We are interested in detecting trends in both the mean levels and the extremes of ozone, and in determining how these trends vary over the country. The latter aspect means that we also have to understand the spatial dependence of ozone. In this connection, therefore, we examine in this paper the following features: determining trends in mean ozone levels at individual stations and combination across stations; determining trends in extreme ozone levels at individual stations and combination across stations; spatial modeling of trends in mean and extreme ozone levels.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.539-544
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• 2005

In this paper, we propose the one-step least squares method based on the squared differences to estimate the parameters of the variogram used for spatial data modelling, and discuss its asymptotic efficiency. The proposed method does not require to specify lags of interest and partition lags, so that we can delete the subjectiveness and ambiguity originated from the lag selection in estimating spatial dependence.

• Journal of Biosystems Engineering
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• v.29 no.3
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• pp.267-274
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• 2004

Using geo-statistical method, yield data of different fields were analyzed to examine their field variability according to examining year, analysis method. Semivariogram and Kriged maps of geo-statistical analysis were used to examine their spatial dependence within a filed. The results obtained were as follows. 1) Descriptive statistical results of the yield showed that the yield and the difference of yield ranged from 100 to 946kg/10a and from 272 to 653kg/10a, respectively within a field. The coefficient of variation also ranged from 5.9 to 22.4 %. 2) More than 90% of yield data were placed between 350 to 850kg/10a. e results indicated that the gram mass flow sensor should have the measuring range from 0.34 to 0.82kg/s considering the yields when 4 rows head-feeding combine with 0.8 m/s of working speed was utilized. 3) A high spatial dependence was found within paddy field. The Q values ranged from 0.20 to 0.97, and the range of spatial dependence was from 6.9 to 53.3m. From this result, the rational sampling interval for yield investigation was estimated 6.9m. 4) Yields within a field between observation years showed considerable variability even if the field was evenly cultivated and managed. To apply precision agriculture in a paddy field, the field test should be continued to build a solid data-base including meteorological data, blight damage and insect damage.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.547-568
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• 2020

Modeling the statistical autocorrelations in spatial data is often achieved through the estimation of the variograms, where the selection of the appropriate valid variogram model, especially for small samples, is crucial for achieving precise spatial prediction results from kriging interpolations. To estimate such a variogram, we traditionally start by computing the empirical variogram (traditional Matheron or robust Cressie-Hawkins or kernel-based nonparametric approaches). In this article, we conduct numerical studies comparing the performance of these empirical variograms. In most situations, the nonparametric empirical variable nearest-neighbor (VNN) showed better performance than its competitors (Matheron, Cressie-Hawkins, and Nadaraya-Watson). The analysis of the spatial groundwater dataset used in this article suggests that the wave variogram model, with hole effect structure, fitted to the empirical VNN variogram is the most appropriate choice. This selected variogram is used with the ordinary kriging model to produce the predicted pollution map of the nitrate concentrations in groundwater dataset.