• Journal of Life Science
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• v.15 no.3 s.70
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• pp.359-364
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• 2005

Spatial autocorrelation was applied to microgeographic variations of Sasa borealis populations in Korea. Separate counts of each type of join (combination of genotypes at a single locus) for each allele, and for each distance class of separation, were tested for significant deviation from random expectations by calculating the Standard Normal Deviation. Moran's I was significantly different from the expected value in 25 of 150 cases $(16.7\%)$. Seven of these values $(4.7\%)$ were negative, indicating genetic dissimilarity among pairs of individuals in the ten distance classes. Populations of S. borealis are small in Korea, and are distributed with occasional cutting of seed-bearing stems used for sieves. Thus, artificial disturbance may contribute to the fact that the S. borealis population of Jirisan is unusual in lacking spatial genetic structure.

• 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.

• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.6-11
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• 2015

In this paper, we analyzed current states and spatial characteristics of crime occurring in A city of Colombia using big data of crime. The analysis draws on the crime statistics of Colombia National Police Agency from 2013 January to September. We also investigated spatial autocorrelation of crime using global and local Moran's Index. Spatial autocorrelation analysis shows significant spatial autocorrelation in the high frequency of crime. Global Moran's I analysis indicates that there are statistically significant value of crime area. Using local Moran's Index analysis, we also implement Local Indicators of Spatial Association(LISA) map and hot spot analysis helps us identify crime distribution.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.57-67
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• 2006

In sampling survey, nonresponse tend to occur inevitably. If we use information from respondents only, the estimates will be baised. To overcome this, various non-response imputation methods have been studied. If there are few auxiliary variables for replacing missing imputation or spatial autocorrelation exists between respondents and nonrespondents, spatial autocorrelation can be used for missing imputation. In this paper, we apply several nonresponse imputation methods including spatial imputation for the analysis of farm household economy data of the Gangwon-Do in 2002 as an example. We show that spatial imputation is more efficient than other methods through the numerical simulations.

• Journal of Korean Society of Transportation
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• v.29 no.2
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• pp.101-109
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• 2011

In most of the urbanized cities, socio-economic attributes tend to cluster as patterns of similarity in space, namely spatial autocorrelation, by agglomeration forces. The classical linear regression model, the most frequently adopted in the trip generation step, cannot sufficiently represent this effect. In order to take into account the effect properly, we need a model which adequately deals with the spatial dependence patterns. In this study, the Geographically Weighted Regression (GWR) model is adopted as an alternative method for the local analysis of relationships in multivariate data sets; that is GWR extends this traditional regression framework by estimating local rather than global parameters. This study shows the existence of spatial effects in the production and attraction of home base/non-home based trips through the GWR model using travel data collected in Daegu metropolitan area. Furthermore, LISA is employed to verify the fact that the local spatial autocorrelation exists.

• Journal of Fisheries and Marine Sciences Education
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• v.23 no.3
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• pp.477-485
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• 2011

For the identifying of spatial distribution pattern, Moran's Index(I) which has the range of values from -1 to +1 is common method for the spatial autocorrelation measurement. When I is close to 1, all neighboring features have close to the same value, indicating clustered pattern. Conversely, if the spatial pattern is dispersed, I is close to -1. And I closing to 0 means spatially random pattern. However, this index equation is influenced by how defining the neighboring features for target feature. To compare and understand the difference of neighborhood definition methods, fixed distance neighboring method and Gabriel Network method were used for I. In this study, these two methods were applied to two marine environments with water quality data. One is Gwangyang Bay which has complex geometric coastal structure located in South Sea of Korea. Another is Uljin area adjacent to open sea located in east coast of Korea. The distances between water quality observed locations were relatively regular in Gwangyang Bay, however, irregular in Uljin area. And for the fixed distance method popular Arc GIS tool was used, but, for the Gabriel Network, Visual Basic program was developed to produce Gabriel Network and calculate Moran's I and its Z-score automatically. According to this experimental results, different spatial pattern was showed differently for some data with using of neighboring definition methods. Therefore there is need to choose neighboring definition method carefully for spatial pattern analysis.

• Journal of Fisheries and Marine Sciences Education
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• v.26 no.5
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• pp.979-990
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• 2014

Recently the frequency of coastal disasters caused by global warming is increasing and the damage is becoming greater. Therefore, the Korean government is establishing various policies and measures to minimize damage. For disaster prevention, this study will evaluate the disaster response capacity of each local resident(Eup/Myeun/Dong) in coastal areas through the survey. The purpose of this study is to quantitatively understand the disaster response capacity and analyze spatial autocorrelation between hot spots(vulnerable area) and cold spots. Thus this study was conducted a survey of 311 towns(Eup/Myeun/Dong) about the disaster response capacity of coastal residents. As a result, Namhae has the highest average score(4.9). On the contrary, Hampyeong has the lowest(1.6). Coastal residents in Namhae seem to have better understanding of first aid and preventive maintenance. But coastal residents in Hampyeong seem to not have these characteristics. Afterwards, this study builds a database of disaster response capacity, and analyzes it using the spatial autocorrelation method. Finally, the area of hot spots and cold spots for disaster response capacity was quantitatively detected.

• Journal of Climate Change Research
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• v.7 no.1
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• pp.69-75
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• 2016

Recently, many countries all over the world have been suffered from disaster caused by climate change. Especially in case of developed countries, the disaster is concentrated in the industry sector. In this research, we analyzed industrial vulnerable homogeneous hotspot for the climate change using spatial autocorrelation analysis on the south Korea. Homogeneous hot spot areas through autocorrelation analysis indicate the spatial pattern of areas interacted each other. Industry sector have responsibility of green house gas emissions, and should adapt to the climate change caused by greenhouse gas already released. So, we integrated the areas sensitive to mitigation option with the areas hardly adapt to climate change because of vulnerable infrastructure. We expected that the result of this research could contribute to the decision-making system of climate change polices.

• Ecology and Resilient Infrastructure
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• v.4 no.2
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• pp.93-96
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• 2017

Spatial variations of physicochemical and microbiological variables were examined to understand spatial heterogeneity of those variables in intertidal flat. Variograms were constructed for understanding spatial autocorrelations of variables by a geostatistical analysis and spatial correlations between two variables were evaluated by applications of a Cross-Mantel test with a Monte Carlo procedure (with 999 permutations). Water content, organic matter content, pH, nitrate, sulfate, chloride, dissolved organic carbon (DOC), four extracellular enzyme activities (${\beta}-glucosidase$, N-acetyl-glucosaminidase, phosphatase, arylsulfatase), and bacterial diversity in soil were measured along a transect perpendicular to shore line. Most variables showed strong spatial autocorrelation or no spatial structure except for DOC. It was suggested that complex interactions between physicochemical and microbiological properties in sediment might controls DOC. Intertidal flat sediment appeared to be spatially heterogeneous. Bacterial diversity was found to be spatially correlated with enzyme activities. Chloride and sulfate were spatially correlated with microbial properties indicating that salinity in coastal environment would influence spatial distributions of decomposition capacities mediated by microorganisms. Overall, it was suggested that considerations on the spatial distributions of physicochemical and microbiological properties in intertidal flat sediment should be included when sampling scheme is designed for decomposition processes in intertidal flat sediment.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.3
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• pp.55-63
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• 2012

Soil properties are not random values which is represented by mean and standard deviation but show spatial correlation. Especially, soils are highly variable in their properties and rarely homogeneous. Thus, the accuracy and reliability of probabilistic analysis results is decreased when using only one random variable as design parameter. In this paper, to consider spatial variability of soil property, one-dimensional random fields of coefficient of consolidation ($C_v$) were generated based on a Karhunen-Loeve expansion. A Latin hypercube Monte Calro simulation coupled with finite difference method for Terzaghi's one dimensional consolidation theory was then used to probabilistic analysis. The results show that the failure probability is smaller when consider spatial variability of $C_v$ than not considered and the failure probability increased when the autocorrelation distance increased. Thus, the uncertainty of soil can be overestimated when spatial variability of soil property is not considered, and therefore, to perform a more accurate probabilistic analysis, spatial variability of soil property needed to be considered.