• IE interfaces
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• v.12 no.3
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• pp.401-405
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• 1999

We present a simulation model to generate the spatial distribution of mobiles in cellular communication network. Three types of spatial distributions are considered; biased, random, and ratio-based distributions. This study also points out and corrects the critical errors performed by Das and Morgera(1997) in getting random location of mobiles. By applying a simple path loss model, the effects of our correction on the signal-to-interference(SIR) ratio are discussed. The numerical results indicate that the variation of SIR in the Das's biased distribution is larger than that of other distributions. As compared with the random distribution, the average SIR error of the biased distribution is 91.1%.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.2
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• pp.112-121
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• 2001

In this study, a spatial location-allocation model of center villages (SLAMCV) was developed for selecting spatial center villages and allocating their rear villages. In SLAMCV, rural space was considered as a network with villages for nodes and roads for connecting lines. For quantification of residents’ accessibilities in the network, a disutility index (DUI) was developed and used, which is dependent upon travelling distance between the center and its hinter villages, design vehicle speed in connecting road and population of hinter villages. SLAMCV was verified to Christaller’s hexagonal hierachical settlement system to test the theoretical reasonability and computing algorithm of the model.

• Journal of the Economic Geographical Society of Korea
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• v.9 no.2
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• pp.149-166
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• 2006

The purpose of this study is to produce time-distance cartograms based on the various Korean transportation networks, and to analyze the spatial structure of time-distance accessibilities. For the purpose this study attempts to develop continuous area cartogram by utilizing Wolf's algorithm for creating contiguous cartogram in ArcGIS 9. The absolute physical space was transformed to the functional space indexed by time-distances along various transportation networks in Korea. We examined the differences in the time-distance accessibilities among transportation networks. This study makes a comparison between the spatial structure of time-distance accessibility of KTX network with them of existing railroad network, as well as with them of road network. In order to clarify the spatial structures of time-distance accessibilities, we restructured them on the time-distance circular diagrams and performing Multi-Dimensional Scaling analysis. Overall structure of time-distance accessibilities shows similar patterns: The time distances are shrinked a lot along the Seoul-Busan axis, while coastal parts, in particular the eastern part of the county, are shrinked relatively little via all transportation networks. The Road network affects significantly on the accessibilities of cities in Gangwon-do and Gyeonggi-do, while railway network affects on the accessibilities of cities in Jeolla-do and Gyeongsang-do in general. When we compared the time-distance accessibilities based on the 5 largest cities, Seoul appears as the most accessible city from/to all other cities in Korea. We can also find significant changes in the time-distance accessibility between KTX network and existing railroad network. Cities along the KTX network, in particular cities which has KTX stops, improved significantly in time-distance accessibilities.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.61-64
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• 2005

The purpose of this paper is to take a closer look at an area having shorted emergence facilities and to determine optional candidate sites instead of vulnerable area by using GIS spatial analysis. Newly determined new candidate is performed by concerning spatial efficiency and spatial equity for a public service. It was determined through using the analyzing of the physical accessibility measure, the Location-Allocation, sort of classic model in spatial statistics and general network analysis. The area of this research has been used in administrative boundary of Young-Dong in Gangneung including 13 emergency, medical hospitals, 46 fire-stations and sub-fire stations. In general terms, what all this show is that the way we are approached for geographical view from using GIS spatial analyzing technique of determined location and allocation problem by the social, economical, political factor and simple administrative discrimination at the meantime. At the same time, with problem occurred in the space it is possible to make an Effective proposal or means, policy, decision for new candidate location-allocation suggesting optimum model.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.433-436
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• 2008

A demand on the spatial data management has been rapidly increased with the introduction and diffusion process of ITS, Telematics, and Wireless Sensor Network, and many different people use the digital map that offers various thematic spatial data. Spatial data for digital map can manage to tile-based and feature-based data. The existing tile-based digital map management systems have difficult problems of data construction, history management, and updating based on a spatial object. In order to solve these problems, this paper proposed the data model for the feature-based digital map management system that is designed for feature-based seamless map, history management, real-time updating of spatial data, and analyzed the validity and utility of the proposed model.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.649-654
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• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1273-1283
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• 2019

This paper proposes an advanced crop classification model that combines a procedure for weighted combination of spatial features extracted from multi-scale input images with a conventional convolutional neural network (CNN) structure. The proposed model first extracts spatial features from patches with different sizes in convolution layers, and then assigns different weights to the extracted spatial features by considering feature-specific importance using squeeze-and-excitation block sets. The novelty of the model lies in its ability to extract spatial features useful for classification and account for their relative importance. A case study of crop classification with multi-temporal Landsat-8 OLI images in Illinois, USA was carried out to evaluate the classification performance of the proposed model. The impact of patch sizes on crop classification was first assessed in a single-patch model to find useful patch sizes. The classification performance of the proposed model was then compared with those of conventional two CNN models including the single-patch model and a multi-patch model without considering feature-specific weights. From the results of comparison experiments, the proposed model could alleviate misclassification patterns by considering the spatial characteristics of different crops in the study area, achieving the best classification accuracy compared to the other models. Based on the case study results, the proposed model, which can account for the relative importance of spatial features, would be effectively applied to classification of objects with different spatial characteristics, as well as crops.

• Journal of Korean Society for Atmospheric Environment
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• v.20 no.5
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• pp.583-591
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• 2004

In this study, methodologies for the rational organization of air pollution monitoring network were examined by understanding the characteristics of temporal and spatial distribution of secondary air pollution, whose significance would increase hereafter. The data on $O_3$ and $NO_2$ concentrations during high ozone period in 1998~1999 recorded at the nine air pollution monitoring station in Busan were analysed using principal component analysis (PCA) and cumulative semivariogram. It was found that the ozone concentration was deeply associated with the daily emission characteristics or the $O_3$ precusors, and nitrogen dioxide concentration largely depends on the emission strength of regional sources. According to the spatial distribution analysis of ozone and nitrogen dioxide in Busan using cumulative semivariograms, the number of monitoring stations for the secondary air pollution can be reduced in east-west direction, but reinforced in north-south direction to explain the spacial variability. More scientific and rational relocation of air pollution monitoring network in Busan would be needed to investigate pollution status accurately and to plan and implement the pollution reduction policies effectively.

• Journal of the Korean Geographical Society
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• v.51 no.3
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• pp.311-326
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• 2016

Assemblage has become a popular concept in modern socio-spatial theories with relational and material turns. This article investigates the concept of assemblage focusing on Deleuze and Guattari. By comparing similar concepts such as Foucault's apparatus and Actor-Network Theory, this article demonstrates that assemblage emphasizes not only deterritorialization but also (re)territorialization, and that the exteriality of relations is a critical aspect that differentiate assemblage from other relational spatial concepts. Assemblage can highlight the value of empiricism as an analytical tool, and be open to new spatial imaginations as well as multiple existences and possiblities of alternative political projects and practices.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.25-33
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• 2016

In order to visualize point based Location-Based Social Network Services(LBSNS) data on multi-scaled tile map effectively, it is necessary to apply tile-based clustering method. Then determinating reasonable numbers and size of tiles is required. However, there is no such criteria and the numbers and size of tiles are modified based on data type and the purpose of analysis. In other words, researchers' subjectivity is always involved in this type of study. This is when Modifiable Areal Unit Problem(MAUP) occurs, that affects the results of analysis. Among LBSNS, geotagged Twitter data were chosen to find the influence of MAUP in scale effects perspective. For this purpose, the degree of spatial autocorrelation using spatial error model was altered, and change of distributions was analyzed using Morna's I. As a result, positive spatial autocorrelation showed in the original data and the spatial autocorrelation was decreased as the value of spatial autoregressive coefficient was increasing. Therefore, the intensity of the spatial autocorrelation of Twitter data was adjusted to five levels, and for each level, nine different size of grid was created. For each level and different grid sizes, Moran's I was calculated. It was found that the spatial autocorrelation was increased when the aggregation level was being increased and decreased in a certainpoint. Another tendency was found that the scale effect of MAUP was decreased when the spatial autocorrelation was high.