• Spatial Information Research
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• v.8 no.2
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• pp.243-255
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• 2000

The spatial distribution characteristics and patterns of geographic features in space can be understood through a variety of analysis techniques. The scale is one of most important factors in spatial analysis techniques. This study is aimed at identifying the characteristics of spatial data with a coarser spatial resolution and finding procedures for spatial resolution in operational scale. To achieve these objectives, this study selected LANSAT TM imagery for Sunchon Bay, a coastal wetland for a study site, applied the indices for representing scale characteristics with resolution, and compared those indices. Local variance and fractal dimension developed by previous studies were applied to measure the textual characteristics. In this study, Moran s I was applied to measure spatial pattern change of variance data which were generated from the process of coarser resolution. Drawing upon the Moran s I of variancedata was optimum technique for analysing spatial structure than those of previous studies (local variance and fractal dimension). When the variance data represents maximum Moran´s I at certainly resolution, spatial data reveals maximum change at that resolution. The optimum resolution for spatial data can be explored by applying these results.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.25-45
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• 2020

The primary purpose of this study is to compare and analyze the performance of local spatial autocorrelation techniques in identifying spatial distribution patterns of green spaces. To achieve the objective, this researcher uses satellite image analysis and spatial autocorrelation techniques. The result of the study shows that the LISA cluster map with the spatial outlier cluster is superior to other analytical methods in identifying the spatial distribution pattern of urban green space. This study can contribute to the related fields in that it uses several different research methods than the existing ones. Despite this differentiation and usefulness, this study has limitations in using low-resolution satellite imagery and NDVI among vegetation indices in identifying spatial distribution patterns of green areas. These limitations may be overcome in future studies by using UAV images or by simultaneously using several vegetation indices.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.3
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• pp.492-507
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• 2008

Many classical network analysis methods approach networks in aspatial perspectives. Measuring network reliability and finding critical nodes in particular, the analyses consider only network connection topology ignoring spatial components in the network such as node attributes and edge distances. Using local network autocorrelation measure, this study handles the problem. By quantifying similarity or clustering of individual objects' attributes in space, local autocorrelation measures can indicate significance of individual nodes in a network. As an application, this study analyzed internet backbone networks in the United States using both classical disjoint product method and Getis-Ord local G statistics. In the process, two variables (population size and reliability) were applied as node attributes. The results showed that local network autocorrelation measures could provide local clusters of critical nodes enabling more empirical and realistic analysis particularly when research interests were local network ranges or impacts.

• Journal of the Korean Geographical Society
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• v.39 no.2
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• pp.195-211
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• 2004

The innovation of information and communication technology has brought the emergence of the digital economy in which the growing importance of the Internet for the production and consumption of information has caused a rapid increase of commercial domains. Domains are basic form of Internet address for the delivery of information, but the location of registered commercial domains is not free from a spatial context. Utilizing a database of commercial domain registrations, spatial statistical methods and GIS, the spatial dimensions of the commercial domains are explored for the city of Seoul. Through this research, it was found that the commercial domains were unevenly distributed, namely 44% of commercial domains are located at 3 Gus in Seoul. The locations of commercial domains by themselves represented a strong spatial autocorrelation among adjacent places. In order to identify factors affecting spatial variation in the development of the commercial domains among Dongs, a conditional spatial autoregressive model which effectively eliminates a spatial autocorrelation was used. As a result of this research, it is clearly shown that the selective location of firms having commercial domains and their role in economic activities are influencing the spatial structure of urban with dynamic mix of spatial characteristic.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.3
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• pp.64-73
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• 2010

This study aims to analyse the spatial characteristics and to draw the declining areas from the whole area of Chung-Cheong Province. For this purpose, the temporal and spatial conditions by the urban decline diagnosis indexes are utilized. Additionally, the spatial autocorrelation method was applied for extraction of those areas. The spatial autocorrelation method is one of the methods on exploring spatial characteristics and considering the spatial factors. We also adopted the concepts of economics and then discovered the characteristics of deprivation areas. In applying this method, the positively valued areas were classified as the complementary areas, and the negatively valued areas as the substitutional areas. The findings show the declining areas and the growing areas caused by the growth of periphery. This study supports the regeneration plan of Chung-Cheong Province in extracting depressed or activated areas and explaining the characteristics of those areas.

• Journal of the Korean association of regional geographers
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• v.23 no.1
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• pp.118-135
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• 2017

The decline in urban center, changes in the population structure, economic slump and etc. have caused empty or deserted houses in the city. The government recognizes the houses as the reason for the accelerated formation of local slum, and as the negative element threatening the residential environment, urban landscape, social stability and others. This research aims at investigating the spatial distribution of empty or deserted houses in Gwangju metro city, identifying hotspots and classifying those hotspot according to the socioeconomic indicators as well as physical ones, and examining their characteristics and problems in the urban space. The results of this study are as follows. First of all, there is a positive spatial autocorrelation in the spatial distribution of empty and deserted houses in Gwangju metro city. Second, several hotspots are identified mainly around the old CBD area showing a sign of urban decline. Third, the indicators of urban decline were visualized using triangulation charts, and hotspots of empty(deserted) houses are classified so that the classification could serve for effective urban regeneration policy making tailored for each region.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.29-40
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• 2021

Risk factors that threaten public safety such as crime, fire, and traffic accidents have spatial characteristics. Since each region has different dangerous environments, it is necessary to analyze the spatial pattern of risk factors for each sector such as traffic accident, fire, crime, and living safety. The purpose of this study is to analyze the spatial distribution pattern of local safety level index, which act as an index that rates the safety level of each sector (traffic accident, fire, crime, living safety, suicide, and infectious disease) for basic local governments across the nation. The following analysis tools were used to analyze the spatial autocorrelation of local safety level index : Global Moran's I, Local Moran's I, and Getis-Ord's G⁎i. The result of the analysis shows that the distribution of safety level on traffic accidents, fire, and suicide tends to be more clustered spatially compared to the safety level on crime, living safety, and infectious disease. As a result of analyzing significant spatial correlations between different regions, it was found that the Seoul metropolitan areas are relatively safe compared to other cities based on the integrated index of local safety. In addition, hot spot analysis using statistical values from Getis-Ord's G⁎i derived three hot spots(Samchuck, Cheongsong-gun, and Gimje) in which safety-vulnerable areas are clustered and 15 cold spots which are clusters of areas with high safety levels. These research findings can be used as basic data when the government is making policies to improve the safety level by identifying the spatial distribution and the spatial pattern in areas with vulnerable safety levels.

• 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 the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1133-1146
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• 2016

Counts or averages over arbitrary regions are often analyzed using conditionally autoregressive (CAR) models. The spatial neighborhoods within CAR model are generally formed using only the inter-distance or boundaries between the sub-regions. Kyung and Ghosh (2009) proposed a new class of models to accommodate spatial variations that may depend on directions, using different weights given to neighbors in different directions. The proposed model, directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Bayesian inference method is discussed based on efficient Markov chain Monte Carlo (MCMC) sampling of the posterior distributions of the parameters. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.150-159
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• 2015

This study attempted to test the scale effect of MAUP, particularly focusing on the spatial autocorrelation of sex crime, correlations among neighborhood structural variables, and causal mechanism leading to sex crime. Analysis results of nation-wide Eup-Myon-Dong and Si-Gun-Gu data discovered that the spatial autocorrelation, correlations among independent variables, and determinant coefficient of multiple regression of Si-Gun-Gu level were generally bigger and stronger than those of Eup-Myon-Dong, which appeared to be due to the averaging effect. Regarding the causal effect to sex crime, two interesting results were found: First, the ratio of non-apartment residency lowered sex crime at both levels contrary to the hypothesis. Second, the ratio of food and lodging increased sex crime only at Eup-Myon-Dong level. These suggested that future research need to perform more detailed analyses dividing data into subsets such as urban vs. rural and/or economically advantaged vs. disadvantaged areas.