• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.403-418
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• 2004

Recently space-time model in spatial data analysis is widly used. In this paper we applied this model to analysis of greenhouse whitefly. For handling time component, we used ARMA model and autoregressive error model and for outliers, we adapted Mugglestone's method. We compared space-time models and geostatistic model with MSE and MAPE.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.91-102
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• 2007

The major purpose of this article is to formulate a class of Space Time Autoregressive Moving Average(STARMA) model and Space Time Bilinear model(STBL), to discuss some of the their statistical properties such as model, identification approaches, some procedure for estimation and the predictions, and to compare the STARMA model with the STBL model. For illustration, The Mumps data reported from eight city & provinces monthly over the years 2001-2006 are used and the result from STARMA and STBL model are compared with using SSF(Sum of Square Prediction Error).

• Journal of Korean Society of Transportation
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• v.15 no.1
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• pp.5-15
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• 1997

공간통계분석은 공간적으로 연계된 변수들간의 관계를 분석하는 통계분야이다. 일 반적으로 공간적으로 연계된 변수들간의 관계는 각 변수간의 공간적 분포정도에 따라서 영 향을 받는다. 전통적인 통계 분석의 방법은 동질의 자료발생과정에 의하여 확률적으로 축출 된 표본자료를 가정하고 있으나, 공간적인 자료는 이와 같은 동질의 자료발생과정의 가정을 부정한다. 교통류 및 교통사고 등과 같은 교통분야의 자료는 대부분 공간적인 상관관계에 의하여 축출된 이질적인 표본자료이며 따라서 공간상관관계를 동질적으로 가정한 전통적인 통계적 분석 방법은 오류를 범할 수 있다. 본 논문은 공간적인 관계를 고려한 공간자기상관 분석기법을 이용하여 고속도로상의 교통사고에 관하여 분석하였다. 분석의 결과에 의하면 4 개 고속도로 중 경인고속도로를 제외한 3개의 고속도로상의 교통사고건수는 통계적으로 현 저한 양의 공간적 상관관계가 있음을 알 수 있었다. 이에 따라 공간적 상관관계를 고려한 교통사고분석을 위하여 종속변수로 단위구간별 교통사고건수를 그리고 설명변수로서는 단위 구간별 교통량, I.C. 유무 및 화물차량비율을 이용하여 공간 자기회귀분석을 시도하였다. 분 석의 분석에서는 구간별 교통량과 화물차량의 비율이 호남/남해 고속도로의 경우에는 구간 별 교통량과 I.C. 유무가 통계적으로 유의한 것으로 분석되었다.

• Journal of the Korean Society for Railway
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• v.12 no.5
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• pp.656-662
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• 2009

In general, the analysis of travelers' mode choice behavior is accomplished by developing the utility functions which reflect individual's preference of mode choice according to their demographic and travel characteristics. In this paper, we propose a methodology that takes the spatial effects of individuals' departure locations into account in the mode choice model. The statistical models considered here are spatial logistic regression model and conditional autoregressive model taking a spatial association parameter into account. We employed the Bayesian approach in order to obtain more reliable parameter estimates. The proposed methodology allows us to estimate mode shares by departure places even though the survey does not cover all areas.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.873-885
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• 2016

Onions are grown in a few specific regions of Korea that depend on the climate and the regional characteristic of the production area. Therefore, when onion yields are to be estimated, it is reasonable to use a statistical model in which both the climate and the region are considered simultaneously. In this paper, using a spatial panel regression model, we predicted onion yields with the different weather conditions of the regions. We used the spatial auto regressive (SAR) model that reflects the spatial lag, and panel data of several climate variables for 13 main onion production areas from 2006 to 2015. The spatial weight matrix was considered for the model by the threshold value method and the nearest neighbor method, respectively. Autocorrelation was detected to be significant for the best fitted model using the nearest neighbor method. The random effects model was chosen by the Hausman test, and the significant climate variables of the model were the cumulative duration time of sunshine (January), the average relative humidity (April), the average minimum temperature (June), and the cumulative precipitation (November).

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.335-344
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• 2010

Reliability of the estimation is usually damaged in the situation where a linear regression model without spatial dependencies is employed to the spatial data analysis. In this study, we considered the conditional autoregressive model in order to construct spatial association structures and estimate the parameters via the Bayesian approaches. Finally, we compared the performances of the models with spatial effects and the ones without spatial effects. We analyzed the yearly total crime count data measured from each of 25 districts in Seoul, South Korea in 2007.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1155-1168
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• 2016

This paper considers a probability density estimation problem of climate values. In particular, we focus on estimating probability densities of summer extreme temperature over South Korea. It is known that the probability density of climate values at one location is similar to those at near by locations and one doesn't follow well known parametric distributions. To accommodate these properties, we use a mixture of conditional autoregressive species sampling model, which is a nonparametric Bayesian model with a spatial dependency. We apply the model to a dataset consisting of summer maximum temperature and minimum temperature over South Korea. The dataset is obtained from University of East Anglia.

• 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.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.413-421
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• 2010

In this study, we considered bivariate conditional auto-regressive model taking into account spatial association as well as correlation between the two dependent variables, which are the counts of murder and burglary. We conducted likelihood ratio test for checking over-dispersion issues prior to applying spatial poisson models. For the real application, we used the annual counts of violent crimes at 25 districts of Seoul in 2007. The statistical results are visually illustrated by geographical information system.

• 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.