• 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.

• Korean Journal of Legislative Studies
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• v.26 no.2
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• pp.113-140
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• 2020

This study is meaningful in that it is the first analysis of Korean elections using the concept of spatial autocorrelation. Spatial autocorrelation means that an event occurring in one location in space has a high correlation with an event occurring in the surrounding area. The voter turnout rate in the 21^st general election of Seoul area was divided into the early-voting turnout and voting-day turnout, and the spatial pattern of the turnout was examined. Most of the previous studies were based on the unit of the precinct and personal data, but this study analyzed on the basis of the lower unit, Eup-myeon-dong, and analyzed using spatial data and aggregate data. Moran I index showed a fairly high spatial autocorrelation of 0.261 in the voting-day turnout, while the index of the early-voting turnout was low at 0.095, indicating that there was little spatial autocorrelation despite statistical significance. The voting-day turnout, which showed strong spatial autocorrelation, was compared and analyzed using the OLS regression model and the spatial statistics model. In the general regression model, the coefficient of determination R² rose from 0.585261 to 0.656631 in the spatial error model, showing an increase in explanatory power of about 7 percentage points. This means that the spatial statistical model has high explanatory power. The most interesting result is the relationship between the early-voting turnout and the voting-day turnout. The higher the early-voting turnout is, the lower the voting-day turnout is. When the early-voing turnout increases by about 2%, the voting-day turnout drops by about 1%. In this study, the variables affecting the early-voting turnout and the voting-day turnout are very different. This finding is different from the previous researches.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.835-847
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• 2016

To analyze lattice or areal data, a conditionally autoregressive (CAR) model has been widely used in the eld of spatial analysis. The spatial neighborhoods within CAR model are generally formed using only inter-distance or boundaries between regions. Kyung and Ghosh (2010) proposed a new class of models to accommodate spatial variations that may depend on directions. The proposed model, a directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Properties of maximum likelihood estimators of a Gaussian DCAR are discussed. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.3
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• pp.40-53
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• 2013

Most of the existing domestic studies to identify the distribution of longevity population and influencing factors oriented confirmatory approach. Furthermore, most of the studies in this research topic simply have used their own definition of spatial unit of analysis or employed arbitrary spatial units of analysis according to data availability. These research approaches can not sufficiently reflect the spatial characteristic of longevity phenomenon and exposed to the Modifiable Aerial Unit Problem(MAUP). This research performed the Exploratory Spatial Data Analysis(ESDA) to identify the spatial autocorrelation of the distribution of longevity population and investigated whether the modifiable areal unit problem in the aspect of scale effect using spatial population data in Korea. We used Si_Gun_Gu and Eup_Myeon_Dong as two different spatial units of regional longevity indicators measured. Then, we applied Getis-Ord Gi* to investigate the existence of spatial hot spots and cold spots. The results from our analysis show that there exist statistically significant spatial autocorrelation and spatial hot spots and cold spots of regional longevity at both Si_Gun_Gu and Eup_Myeon_Dong levels. This result implies that the modifiable areal unit problem does exist in the studies of spatial patterns of longevity population distribution. The demand for longevity researches would be increased inevitably. In addition, there were apparent differences for the global spatial autocorrelation and local spatial cluster which calculated different spatial units such as Si_Gun_Gu and Eup_Myeon_Dong and this can be seen as scale effect of MAUP. The findings from our analysis show that any study in this topic can mislead results when the modifiable areal unit problem and spatial autocorrelation are not explicitly considered.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.4
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• pp.564-579
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• 2008

It is often challenging to evaluate local spatial association among onedimensional vectors generally representing paired-location events where two points are physically or functionally connected. This is largely because of complex process of such geographic phenomena itself and partially representational complexity. This paper addresses an alternative way to identify spatially autocorrelated paired-location events (or vectors) at a local scale. In doing so, we propose a statistical algorithm combining univariate point pattern analysis for evaluating local clustering of origin-points and similarity measure of corresponding vectors. For practical use of the suggested method, we present an empirical application using transactions data in a local housing market, particularly recorded from 2004 to 2006 in Franklin County, Ohio in the United States. As a result, several locally characterized similar transactions are identified among a set of vectors showing various local moves associated with communities defined.

• 한국지구과학회:학술대회논문집
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• 2005.09a
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• pp.112-123
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• 2005

자기지전류 탐사의 적용에 있어 인공잡음의 영향은 탐사의 승패를 좌우하는 중요한 요소이며 인공잡음의 영향을 최소화할 수 있는 탐사의 설계와 자료처리가 요구되고 있다. 본 연구에서는 수치공간자료를 이용한 공간모델링을 통해 MT 주파수 대역에서의 잡음을 예측하고 실제 탐사 자료와 비교분석하여 MT 잡음 모델링을 가능성을 살펴보았다. 수치지도로부터 추출된 잡음원일 가능성이 높은 건물, 도로, 고압 송전선에 의해 발생하는 전자기장의 강도를 지하매질의 전기전도도에 따른 전자기파의 전파 특성을 고려하여 예측하는 잡음모델을 제안하였다. 제안된 잡음모델로부터 예측된 잡음 파워와 실제 탐사를 통해 측정된 MT 자료와의 상관도 분석을 수행한 결과, 전반적으로 전기장에서는 넓은 주파수 대역에서 높은 상관관계를 보이는 반면 자기장은 60 Hz 부근의 대역에서만 상관관계를 가진다. 본 연구에서 제안된 공간모델링을 통한 잡음 예측은 특히 고도로 산업화되어가는 도시 주변지역에서의 MT 탐사를 수행하는데 있어 유용한 정보를 제공할 수 있을 것이다.

• Korea journal of population studies
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• v.32 no.3
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• pp.1-20
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• 2009

본 연구는 지리정보시스템(GIS)과 지리적 가중 회귀(GWR)를 이용하여 건강 관련 삶의 질(HRQoL)의 사회인구학적 상관요인에 대한 공간분석을 시도한다. 관찰의 독립성과 오차의 동분산성을 가정하는 전통적 회귀분석과 달리, 지리적 가중 회귀분석은 속성정보뿐만 아니라 공간정보를 활용하는 공간분석 기법이다. 분석모형은 건강 관련 삶의 질을 종합적으로 측정하는 EQ-5D를 종속변수로 하고 지역의 사회인구학적 특성인 노령인구비율, 조이혼율, 병상수, 재정자주도를 독립변수로 하여 구성하였다. 종속변수는 질병관리본부에서 실시한 <지역사회건강조사>의 자료를 이용하였고, 독립변수는 통계청 온라인 DB에 수록된 지역별 자료를 이용하였다. 모형을 추정해 본 결과 전반적으로 사회적 특성보다는 노령인구비율이나 조이혼율과 같은 인구학적 특성이 건강 관련 삶의 질에 더 많은 영향을 미치는 것으로 나타났다. 공간적 변이를 고려하는 지역모형은 전역모형에서 드러나지 않았던 중요한 유형을 보여주는데, 노령인구비율 변수와 조이혼율 변수의 지역별 추정치를 지도상으로 살펴본 결과 변수들의 효과가 공간적 위치에 따라 차이를 보인다는 점이 확인되었다. 분석 결과는 또한 지리적 가중 회귀분석이 전통적 회귀분석에 비해 공간적 자기상관의 문제를 극복하고 모형의 부합도를 증가시킨다는 것을 보여준다.

• Journal of Korean Society of Forest Science
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• v.101 no.2
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• pp.203-212
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• 2012

This study aims to test presence of spatial autocorrelation of burn severity in Uljin and Youngduk areas burned in 2011. SPOT satellite images were used to compute the NDVI representing burn severity, and NDVI values were sampled for 5,000 randomly dispersed points for each site. Spatial autocorrelations of sampled NDVI values were analyzed with Moran's I and Variogram models. Moran's I values of burn severity in Uljin and Youngduk areas were 0.7745 and 0.7968, respectively, indicating presence of strong spatial autocorrelations. On the basis of Variogram and changes of Moran's I values by lag class, ideal sampling distance were proposed, which were 566-2,151 m for Uljin and 272-402 m for Youngduk. It was recommended to apply these ranges of sampling distance in flexible corresponding to Anisotropic characteristics of burned areas.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.1
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• pp.1-16
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• 2013

This study investigates the spatial distribution characteristics of Korean fashion industries during the last decade, in which the economic geography of fashion industries has changed dynamically with economic globalization and "thus resulted in increased" demand "of" diversification. In particular, this study examines the spatial distribution patterns of fashion industries in the Seoul metropolitan area where fashion industries are highly agglomerated. For the purpose, this study applies Moran's I Index of spatial autocorrelation analysis for seven functional sectors of fashion industries related to fashion production. The global and local agglomeration patterns are examined for each functional sector. The results clarify the distinction in the spatial agglomeration patterns among the seven functional sectors of fashion industries in the Seoul Metropolitan area. Logit models are developed to examine the interrelationships among functional sectors in their spatial agglomeration distribution patterns. By conducting binary logistic regression analysis, we find out how the spatial agglomeration of each functional sector is related to the others.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.54-69
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• 2021

Policy improvement such as the revision of the Road Traffic Act are proposed for personal mobility(especially e-scooter) usage. However, there is not enough discussion to solve the problem of using shared e-scooter. In this study, we analyze the influencing factors that amount of pick-up and drop-off of shared e-scooter by dividing the Seoul into a 200m grid. we develop spatial auotcorrelation model such as spatial lag model, spatial error model, spatial durbin model, and spatial durbin error model in order to consider the characteristics of the aggregated data based on a specific space, and the spatial durbin error model is selected as the final model. As a result, demographic factor, land use factor, and transport facility factors have statistically significant impacts on usage of shared e-scooter. The result of this study will be used as basic data for suggesting efficient operation strategies considering the characteristics of weekday and weekend.