• Journal of the Korean Geographical Society
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• v.48 no.6
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• pp.897-913
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• 2013

The main purpose of the paper is to examine the spatial characteristics of school choice through residential mobility by conducting a correlation analysis on the relationships between the middle schools' entrance rates to special high schools and the elementary schools' net transfer rates. Analyses are done at both the individual school level and the school catchment area level. Prior to the calculation, the two variables involved in the correlation analysis are transformed via a standardization equation, and the standardized scores are mapped and explored. Both the global and local correlation analyses are done for the standardized variables. Main findings are twofold. First, the global correlation analysis reports that there exists a statistically significant correlation between the two variables at both the analytical levels. Second, albeit the prominent positive correlation at the global level, the local analysis reveals the existence of a considerable level of spatial heterogeneity in terms of bivariate association. While several school catchment areas with very high local correlation coefficients (the HH association type) are popped up, other areas with various types of bivariate association including ones even opposite to the global trend are also observed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.129-141
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• 2019

In recent years, fast growing cities in Korea are showing signs of being vulnerable to more disasters as their population and facilities increase and intensify. In particular, fire is one of the most common disasters in Korea's cities, along with traffic accidents. Therefore, in this study, we analyze what type of factors affect the fire that threatens urban people. Fire data were acquired for 10 years, from 2007 to 2017, in Jinju, Korea. Spatial distribution pattern of fire occurrence in Jinju was assessed through the spatial autocorrelation analysis. First, spatial autocorrelation analysis was carried out to grasp the spatial distribution pattern of fire occurrence in Jinju city. In addition, correlation and multiple regression analysis were used to confirm spatial dependency and abnormality among factors. Based on this, OLS (Ordinary Least Square) regression analysis was performed using space weighting considering fire location and spatial location of each facility. As a result, First, LISA (Local Indicator of Spatial Association) analysis of the occurrence of fire in Jinju shows that the most central commercial area are fire department, industrial area, and residential area. Second, the OLS regression model was analyzed by applying spatial weighting, focusing on the most derived factors of multiple regression analysis, by integrating population and social variables and physical variables. As a result, the second kind of neighborhood living facility showed the highest correlation with the fire occurrence, followed by the following in the order of single house, sales facility, first type of neighborhood living facility, and number of households. The results of this study are expected to be useful for analyzing the fire occurrence factors of each facility in urban areas and establishing fire safety measures.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.11
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• pp.111-118
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• 1990

In this paper, pSDF-based referance image is realized. Using BJTC (binary joint transform correlator) as the spatial plane correlator, optical pattern recognition for interclass identification and interclass discrimination is performed. Computer simulation shows that the correlation performance of BJTC is superior to that of JTC. Experimental results using BJTC reveal that correlation peak intensity is constant within the error rang from $4.1{\%}\to\9.6{\%}$ in interclass identification and correlation peak intensity of one class is two times higher than that of the other class in interclass discrimination, which indicates its superiority in discrimination sensitivity.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.581-590
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• 2017

This study analyzes the effect of floating population, locational characteristics and spatial autocorrelation on foodservice sales using big data provided by the Seoul Institute. Although big data provided by public sector is growing recently, research difficulties are occurred due to the difference of aggregation units of data. In this study, the aggregation unit of a dependent variable, sales of foodservice is SKT unit but those of independent variables are various, which are provided as the aggregation unit of Korea National Statistical Office, administration dong unit and point. To overcome this problem, we convert all data to the SKT aggregation unit. The spatial error model, SEM is used for analysing spatial autocorrelation. Floating population, the number of nearby workers, and the area of aggregation unit effect positively on foodservice sales. In addition, the sales of Jung-gu, Yeongdeungpo-gu and Songpa-gu are less than that of Gangnam-gu. This study provides implications for further study by showing the usefulness and limitations of converting aggregation units of heterogeneous spatial data.

• Proceedings of the KGS Conference
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• 2003.11a
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• pp.247-252
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• 2003

최근 원격탐사기법과 지리정보시스템들이 급격하게 발달하면서, 공간상의 각종 지리사상들의 시계열적인 자료들이 증가하고 있다. 일반적으로 지리학계의 관심은 특정 지리사상의 시간적인 변화(Time series analyses)와 공간상의 분포 (Spatial statistics)로 크게 대별되어 왔으며, 이 두 가지의 요인들을 종합적으로 고려하는 경우는 극히 드물다고 하겠다. (중략)

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.09a
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• pp.99-101
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• 2010

• The Journal of the Acoustical Society of Korea
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• v.31 no.6
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• pp.410-418
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• 2012

Parametric stereo(PS) and MPEG surround(MPS) are major spatial audio coding(SAC) tools. In this paper, the problem of the inter-channel correlation(ICC) synthesis in the conventional SAC is analyzed. Conventional methods assume that ambient components mixed to two output channels are anti-phased, while the primary components are assumed to be in-phased. This assumption can cause excessive ambient mixing for a negative-valued ICC. As a remedy to this problem, we propose a new ICC synthesis method based on an assumption that the primary components are anti-phased each other for a negative ICC. The proposed method is also applied to the approximation which works in practice. The performance of the proposed method was evaluated by computer simulations and the subjective listening tests verified that the proposed method is effective in not only headphones but also loudspeakers playback.

• 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 Association of Geographic Information Studies
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• v.22 no.1
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• pp.1-18
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• 2019

This research measured and visualized the spatial dependency and the spatial heterogeneity of the small business in Cheonan-si, Asan-si with $100m{\times}100m$ grids based on global and local spatial autocorrelation. First, we confirmed positive spatial autocorrelation of small business in the research area using Moran's I Index, which is ESDA(Exploratory Spatial Data Analysis). And then, through Getis-Ord $GI{\ast}$, one kind of LISA(Local Indicators of Spatial Association), local patterns of spatial autocorrelation were visualized. These verified that Spatial Regression Model is valid for the location factor analysis on small business commercial buildings. Next, GWR(Geographically Weighted Regression) was used to analyze the spatial relations between the distribution of small business, hourly mobile traffic-based floating population, land use attributes index, residence, commercial building, road networks, and the node of traffic networks. Final six variables were applied and the accessibility to bus stops, afternoon time floating population, and evening time floating population were excluded due to multicollinearity. By this, we demonstrated that GWR is statistically improved compared to OLS. We visualized the spatial influence of the individual variables using the regression coefficients and local coefficients of determinant of the six variables. This research applied the measured population information in a practical way. Reflecting the dynamic information of the urban people using the commercial area. It is different from other studies that performed commercial analysis. Finally, this research has a differentiated advantage over the existing commercial area analysis in that it employed hourly changing commercial service population data and it applied spatial statistical models to micro spatial units. This research proposed new framework for the commercial analysis area analysis.

• 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.