• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 춘계 학술발표회 논문집
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• pp.269-273
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• 2003

We consider an efficient parametric estimation method of spatial dependence in weak stationary processes. Spatial dependence is modeled through variogram and correlogram. Most of parametric estimation methods of correlogram use two step method; nonparametric estimation and parametric integration. We bind these two steps into one step by using GEE method instead of least squares type optimization. Our one step method is more efficient statistically and gives a clear interpretation of related concepts used in traditional two step methods.

• 응용통계연구
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• 제17권3호
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• pp.519-533
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• 2004

본 연구에서는 베리오그램 추정을 통한 공간 종속성 추정방법에 있어서 의사우도 사용 방법을 설명하고, 모의실험을 통하여 전통적으로 사용되는 다른 방법들과 그 특성을 비교하고자 한다. 의사우도를 이용한 공간 종속 추정방법들은 그 통계적 성질이 우수할 뿐만 아니라, 전통적인 방법들에서 요구되어지는 관측치가 갖는 래그(lag)들을 미리 지정된 래그로 그룹화하는 과정이 필요 없어서 활용상의 우수성도 함께 가지고 있다. 또한, 이 방법에 대한 로버스트 방법을 개발하고 그 특성을 알아보고자 한다.

• 국토계획
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• 제54권7호
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• pp.89-102
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• 2019

The growth of vacant housing has been problematic in both Korea and other countries as it causes various socio-economic problems and negatively affects residential environments. Despite the importance of effectively managing vacant housing, few studies have been undertaken regarding spatial patterns of vacant housing growth. This study aims to examine spatial dependence in vacant housing growth. We used 2005 and 2015 Population and Housing Census and employed spatial modeling. The empirical analysis shows that there is spatial dependence in vacant housing growth. Also, the spatial clusters of growing vacant housing are present in the non-capital region and nearby cities while the spatial clusters of declining vacant housing are present in the capital region. The policy implications of this study are as follows: First, local governments should make collaborate efforts with geographically proximate cities for more effective management of vacant housing. Second, given that vacant housing is more prevalent and growing in the non-capital region, it is necessary to employ differential policies to manage housing vacancy between the capital and non-capital regions.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.180-180
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• 2015

Weather radar has been widely used in measuring precipitation and discharge and predicting flood risks. The radar rainfall estimate has one of the essential problems in terms of uncertainty and accuracy. Previous study analyzed radar errors to reduce its uncertainty or to improve its accuracy. Furthermore, a recent analyzed the effect of radar error on rainfall-runoff using spatial error model (SEM). SEM appropriately reproduced radar error including spatial correlation. Since the SEM does not take the time dependence into account, its time variability was not properly investigated. Therefore, in the current study, we extend the SEM including time dependence as well as spatial dependence, named after Spatial-Temporal Error Model (STEM). Radar rainfall events generated with STEM were tested so that the peak runoff from the response of a basin could be investigated according to dependent error. The Nam River basin, South Korea, was employed to illustrate the effects of STEM on runoff peak flow.

• Journal of Biosystems Engineering
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• 제29권2호
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• pp.109-120
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• 2004

To obtain basic information for precision agriculture, spatial variability of rice growth condition was evaluated in 100m ${\times}$100m paddy field. The rice growth condition of four hundred locations in the field were investigated to analyze the spatial variability of their properties ; SPAD, plant length and tiller number. Geostatistical analysis was carried out to examine within-field spatial variability using semivariograms and kriged maps as well as descriptive statistics. Descriptive statistics showed that the coefficient of variation for SPAD, plant length, and tiller number exceeded 5.70 %, suggesting a relatively high variability. Geostatistical analysis indicated a high spatial dependence for all the properties except for the second tiller number. The range of spatial dependence was about 20 m for SPAD, plant length, and tiller number. Based on the results of spatial dependence, kriged maps were prepared for the properties to analyse their spatial distribution in the field. The results reflected the history of field management. In conclusion, the need for site-specific field management and possibility of precision agriculture were demonstrated even in an almost flat paddy field.

• Journal of the Korean Data and Information Science Society
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• 제27권5호
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• pp.1225-1239
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• 2016

We investigate pediatric tumor incidence data collected by the Florida Association for Pediatric Tumor program using various models commonly used in disease mapping analysis. Particularly, we consider Poisson normal models with various conditional autoregressive structure for spatial dependence, a zero-in ated component to capture excess zero counts and a spatio-temporal model to capture spatial and temporal dependence, together. We found that intrinsic conditional autoregressive model provides the smallest Deviance Information Criterion (DIC) among the models when only spatial dependence is considered. On the other hand, adding an autoregressive structure over time decreases DIC over the model without time dependence component. We adopt weighted ranks squared error loss to identify high risk regions which provides similar results with other researchers who have worked on the same data set (e.g. Zhang et al., 2014; Wang and Rodriguez, 2014). Our results, thus, provide additional statistical support on those identied high risk regions discovered by the other researchers.

• 아태비즈니스연구
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• 제13권1호
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• pp.277-288
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• 2022

Purpose - This study analyzed the determinants of local unemployment rate in Korea using panel data from 16 metropolitan cities and provinces from 2000 to 2018. Design/methodology/approach - We use a dynamic spatial panel model that considers characteristics of the regional unemployment rate such as the common factors effect, spatial dependence, and serial correlations. Findings - The local unemployment rate is affected by the past and present values of the national unemployment rate. And it is significantly affected by the past local unemployment rate and the past neighboring unemployment rate because spatial dependence and serial correlations are clearly present. In addition, when the industrial structure diversity and labor productivity were high, the regional unemployment rate decreased, and when the education level was high, the regional unemployment rate increased. Research implications or Originality - In order to reduce regional unemployment rate, it is necessary to plan and establish regional customized industrial structure policies under the stance of diversification rather than specializing the regional industrial structure and accompany improvement of the quality of education with the number of years of education. In addition, the redistribution of labor from low labor productivity sectors to high labor productivity sectors through technology development will help to reduce the local unemployment rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.1909-1928
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• 2020

Predicting human mobility has always been an important task in Location-based Social Network. Previous efforts fail to capture spatial dependence effectively, mainly reflected in weakening the location topology information. In this paper, we propose a neural network-based method which can capture spatial-temporal dependence to predict the next location of a person. Specifically, we involve a graph convolutional network (GCN) based on a seq2seq framework to capture the location topology information and temporal dependence, respectively. The encoder of the seq2seq framework first generates the hidden state and cell state of the historical trajectories. The GCN is then used to generate graph embeddings of the location topology graph. Finally, we predict future trajectories by aggregated temporal dependence and graph embeddings in the decoder. For evaluation, we leverage two real-world datasets, Foursquare and Gowalla. The experimental results demonstrate that our model has a better performance than the compared models.

• 응용통계연구
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• 제30권2호
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• pp.215-231
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• 2017

미국 대통령 예선은 선거인단이 시차를 두고 여러 회에 걸쳐 진행되는 특징이 있음에도 많은 연구가 진행되지 않았다. 본 연구에서는 다양한 자기로지스틱 모형을 통해 미국 대통령 예비선거 결과와 사회경제적 변수간의 시공간 의존성의 관계를 파악하고자 한다. 2016년 데이터에 적용한 분석결과 각 카운티의 노년층, 흑인, 여성 그리고 히스패닉 인구 비율이 높은 지역일수록 힐러리 클린턴을 지지할 확률이 높은 것으로 나타났다. 또한, 주변 카운티에서 많은 지지를 받은 후보가 이웃 지역에서도 많이 지지를 받을 확률이 높고 이전 선거에서 많은 지지를 받는 것과 다음 선거 지역의 결과 간의 상관관계도 확인되었다. 시공간 의존성을 알아보기 위한 모형 중에서 슈퍼화요일의 선거 결과가 이후 선거와 관련이 있다고 가정한 모형의 설명력이 가장 높은 것으로 판명되었다.

• 대한건축학회논문집:계획계
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• 제36권5호
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• pp.23-32
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• 2020

Recently, women's fear of crime continues to increase in space of everyday. By the way, the fear of crime has the spatial properties as crime. Therefore, The purpose of this study is to evaluate the spatial dependence of fear of crime and to suggest the physical environmental factors influencing fear of crime. For this, a spatial regression analysis using spatial weights was conducted based on the location data of the fear of crime measured through a survey. The results of this study are as follows; First, the fear of crime felt by women in their twenties who are vulnerable to crime has spatial dependence. Therefore, it is necessary to consider the spatial characteristics in analyzing the environmental factors affecting this. Second, in order to reduce the fear of crime, it is necessary to improve the environments of old housing and entertainment facilities. There is also a need for ongoing management. Third, careful consideration is needed in the installation of CCTV and street lights, which are factors influencing the fear of crime. It is necessary to establish a reasonable arrangement standard for CCTV and to analyze the street lighting in detail.