• Journal of the Korean Society of Clothing and Textiles
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• v.39 no.4
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• pp.613-624
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• 2015

This study examined: 1) the effect of store density on perceived crowding 2) the difference of perceived price benefit of sale by store density 3) the effect of perceived price benefit and store density on perceived crowding and 4) the effect of perceived crowding and price benefit on shopping behaviors. Store density and perceived crowding were categorized into social and spatial dimensions. Data were collected with 6 (high, medium, and low social and spatial densities) * 2 (sale and no-sale) between-subjects experimental designs. A total of 395 responses were analyzed. The results revealed that social density affected social crowding, but spatial density had no effect on perceived crowding. Price benefit of sale was not different by store density. The sale itself did not affect perceived crowding. Under the social density situation, perceived price benefit reduced spatial crowding and social crowding showed a positive effect on purchase behavior while spatial crowding had a negative effect. However, the most important effect on purchase behavior was price benefit. The study implies that social density (not spatial density) is important for consumer behavior and retail strategies.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.1090-1101
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• 2017

This study analyzes spatial density and integration of Space Syntax and Discrete Event Simulation (DEVS) of complex system theory and analyzes spatial structure by property, type and depth. The aim of this study is to secure the validity of the theoretical application. The study evaluated the correlation between spatial density and integration by setting up eight types of analysis models. In addition, analyzed the correlation of structural characteristics and approached the application of discrete event simulation of spatial syntax theory. It is confirmed that the concept of integration of spatial syntax theory and analysis using discrete event simulation are valid as new spatial analysis methodology. Also expect that realistic and concrete predictions will be possible if discrete event simulation evolves into research for space allocation and space efficiency optimization.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.3
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• pp.225-231
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• 2014

When a spatial regression model that uses kernel density values as a dependent variable is applied to retail business data, a unique model cannot be selected because kernel density values change following kernel bandwidths. To overcome this problem, this paper suggests how to use the point pattern analysis, especially the L-index to select a unique spatial regression model. In this study, kernel density values of retail business are computed by the bandwidth, the distance of the maximum L-index and used as the dependent variable of spatial regression model. To test this procedure, we apply it to meeting room business data in Seoul, Korea. As a result, a spatial error model (SEM) is selected between two popular spatial regression models, a spatial lag model and a spatial error model. Also, a unique SEM based on the real distribution of retail business is selected. We confirm that there is a trade-off between the goodness of fit of the SEM and the real distribution of meeting room business over the bandwidth of maximum L-index.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.1
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• pp.3-9
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• 2010

Drainage density, defined as the degree to which a landscape is dissected by streams, is a fundamental property of natural terrain that reflect the comprehensive morphologic response of watershed. In this study the spatial variability of drainage density is analyzed by statistical approach to it and its plotting method is proposed. Overland flow length is confirmed to be a highly variable spatial factor from the result of statistical analysis. Distribution map of drainage density based on spatial autocorrelation length in this study would be a superior tool to the classical definition of drainage density.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.739-745
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• 2011

This paper proposes a method for reducing the negative spatial harmonics of the radial flux density of an interior-type permanent magnet (IPM) motor. The reliability of the motor is increased by minimizing its vibrations under dynamic eccentricity (DE) state and normal state due to reduction of a negative spatial harmonics component through the influx of a zero spatial harmonics component in the radial flux density. To minimize the vibrations, optimal notches corresponding to the distribution shape of the magnetic field are designed on the rotor pole face. The variations of vibration computation by finite element method (FEM) and the validity of the analysis and rotor shape design are confirmed by vibration and performance experiments.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.32-38
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• 2021

The purpose of this study is to examine the spatial characteristics of violent and burglary crimes in South Korea. Violent crimes and burglary crimes depend on a spatial setting with good conditions for their criminal purposes. This study defines population density, racial heterogeneity, types of houses, and density of commercial facilities as variables of place affecting crime in cities and counties. The study collects data from 229 cities in Korea to analyze the effect of spatial characteristics on crime. We conduct additional analyses to meet the statistical requisites of the spatial econometrics model using the open-source software R and GeoDa 1.12.1.129. From the analytical result, population density, racial heterogeneity, apartments, and commercial areas relate to crime occurrence. We suggest the implication of the theoretical and practical contributions to the relationship between place and crime.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.11a
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• pp.257-270
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• 2005

Many studies states that improperly uprising of infrastructure may cause leading the forest degradation and canopy reduction in many tropical forest of Asian countries. Other studies revealed that habitat destruction and fragmentation, edge effects, exotic species invasions, pollution are provoked by roads. Similarly, environmental effects of road construction in forests are problematic. Similarly, many researches have been indicated that roads have a far greater impact on forests than simply allowing greater access for human use. Moreover, people using river as means of transportation hence illegal logging and felling cause canopy depletion in many countries. Therefore, it is important to comprehend the study about spatial relation of road, river and suburb followed by temporal change of forest canopy phenomena. This study also tried to examine the effect of road, river and suburb in forest canopy density change of Terai forest of Nepal from you 1988 to 2001. So, Landsat TM88, 92 and 001 and FCD (Forest Canopy Density) mapper were used to perform the spatial .elation of canopy density change. ILWIS (Integrated Land and Water Information System) which is GIS software and compatible with remote sensing data was used to execute analysis and visualize the results. Study found that influence of distance to suburb and river had statistically significance influenced in canopy change. Though road also influenced canopy density much but didn't show a statistical relation. It can be concluded from this research that understanding of spatial relation of factors respect with canopy change is quite complex phenomena unless detail analysis of surrounding environment. Hence, it is better to carry out comprehensive analysis with other additional factors such as biophysical, anthropogenic, social, and institutional factors for proper approach of their effect on canopy change.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.397-404
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• 1999

For two-dimensional causal spatial autoregressive processes, we propose and illustrate a method for determining asymptotic simultaneous confidence regions using Yule-Walker, unbiased Yule-Walker and least squres estimators. The spectral density for first-order spatial autoregressive model are looked at in more detail. Finite sample properties based on simulation study we also presented.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.60.2-60.2
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• 2018

We present the near infrared JHK photometric properties and the spatial distribution of red supergiants(RSGs) of NGC 3623, NGC 3627 and NGC 3628 in the Leo Triplet system using the data obtained with 3.8m UKIRT(United Kingdom Infra-Red Telescope) at Hawaii. We checked interaction between the three galaxies by making a spatial density map of RSGs. From (J-K,K)0 Color-Magnitude Diagram which include resolved stars in three galaxy and control field with PARSEC isochrone, we figured out the RSG candidates of the Leo triplet are at 0.9<(J-K)0<1.2, mK<17.5 and separated them from background and foreground sources. Using gaussian kernel density estimation, we drew spatial density map of RSGs in the Leo triplet with an assumption that all RSGs are an identical population. The density map shows extended features of NGC 3628 to NGC 3627 along the declination direction. The asymmetries between NGC 3627 and NGC 3628 might be evidence for that the distribution of actual star components(RSGs) follows the neutral hydrogen distribution and also for interaction between two galaxies. And the extended features along the right ascension direction might be a supporting evidence for the existence of a TDG(Tidal Dwarf Galaxy). In case of NGC 3623, we could not see any sign of interaction in density map.

• The KIPS Transactions:PartD
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• v.13D no.5 s.108
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• pp.633-640
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• 2006

Spatial clustering, which groups similar objects based on their distance, connectivity, or their relative density in space, is an important component of spatial data mining. Density-based and grid-based clustering are two main clustering approaches. The former is famous for its capability of discovering clusters of various shapes and eliminating noises, while the latter is well known for its high speed. Clustering large data sets has always been a serious challenge for clustering algorithms, because huge data set would make the clustering process extremely costly. In this paper, we propose an enhanced Density-Grid based Clustering algorithm for Large spatial database by setting a default number of intervals and removing the outliers effectively with the help of a proper measurement to identify areas of high density in the input data space. We use a density threshold DT to recognize dense cells before neighbor dense cells are combined to form clusters. When proposed algorithm is performed on large dataset, a proper granularity of each dimension in data space and a density threshold for recognizing dense areas can improve the performance of this algorithm. We combine grid-based and density-based methods together to not only increase the efficiency but also find clusters with arbitrary shape. Synthetic datasets are used for experimental evaluation which shows that proposed method has high performance and accuracy in the experiments.