• 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.

• Atmosphere
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• v.20 no.4
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• pp.495-503
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• 2010

Satellite data have an intrinsic problem due to a number of various physical parameters, which can have a similar effect on measured radiance. Most evaluations of satellite performance have relied on comparisons with limited spatial and temporal resolution of ground-based measurements such as soundings and in-situ measurements. In order to overcome this problem, a new way of satellite data evaluation is suggested with statistical tools such as empirical orthogonal function(EOF), and singular value decomposition(SVD). The EOF analyses with OMI and OMI HCHO over northeast Asia show that the spatial pattern show high correlation with population density. This suggests that human activity is a major source of as well as HCHO over this region. However, this analysis is contradictory to the previous finding with GOME HCHO that biogenic activity is the main driving mechanism(Fu et al., 2007). To verify the source of HCHO over this region, we performed the EOF analyses with vegetation and HCHO distribution. The results showed no coherence in the spatial and temporal pattern between two factors. Rather, the additional SVD analysis between $NO_2$ and HCHO shows consistency in spatial and temporal coherence. This outcome suggests that the anthropogenic emission is the main source of HCHO over the region. We speculate that the previous study appears to be due to low temporal and spatial resolution of GOME measurements or uncertainty in model input data.

• Journal of The Geomorphological Association of Korea
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• v.23 no.3
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• pp.67-78
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• 2016

Soil carbon(C) is an essential property for characterizing soil quality. Understanding spatial patterns of soil C is particularly limited for mountain areas. This study aims to predict the spatial pattern of soil C using terrain analysis in a steep mountainous area. Specifically, model performances and prediction uncertainties were investigated based on the number of resampling repetitions. Further, important predictors for soil C were also identified. Finally, the spatial distribution of uncertainty was analyzed. A total of 91 soil samples were collected via conditioned latin hypercube sampling and a digital soil C map was developed using support vector regression which is one of the powerful machine learning methods. Results showed that there were no distinct differences of model performances depending on the number of repetitions except for 10-fold cross validation. For soil C, elevation and surface curvature were selected as important predictors by recursive feature elimination. Soil C showed higher values in higher elevation and concave slopes. The spatial pattern of soil C might possibly reflect lateral movement of water and materials along the surface configuration of the study area. The higher values of uncertainty in higher elevation and concave slopes might be related to geomorphological characteristics of the research area and the sampling design. This study is believed to provide a better understanding of the relationship between geomorphology and soil C in the mountainous ecosystem.

• 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 Forest and Environmental Science
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• v.34 no.6
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• pp.419-428
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• 2018

Spatial point-patterns analyses were conducted to provide insight into the ecological process behind competition and mortality in two lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) stands, one in the Lower Foothills, and the other in the Upper Foothills natural subregions in the boreal forest of Alberta, Canada. Spatial statistical tests were applied to live and dead trees and included Clark-Evans nearest neighbor statistic (R), nearest neighbor distribution function (G(r)), and a variant of Ripley's K function (L(r)). In both lodgepole pine plots, the results indicated that there was significant regularity in the spatial point-pattern of the surviving trees which indicates that competition has been a key driver of mortality and forest dynamics in these plots. Dead trees generally showed a clumping pattern in higher density patches. There were also significant bivariate relationships between live and dead trees, but the relationships differed by natural subregion. In the Lower Foothills plot there was significant attraction between live and dead tees which suggests mainly one-sided competition for light. In contrast, in the Upper Foothills plot, there was significant repulsion between live and dead trees which suggests two-sided competition for soil nutrients and soil moisture.

• 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 Economic Geographical Society of Korea
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• v.9 no.2
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• pp.225-242
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• 2006

This study aims to analyze the spatial distribution pattern of international trade. The method is to analyze the principal components by changing interaction attribute matrix of four dimensions (hinterland, gateway, foreland and commodities) into two dimension matrix. The study area is the territory region of Cheongju clearance depot in inland. The result are as follows : Major spatial patterns of regional connections by hinterland, gateway and foreland are, in the case of exports, ten patterns and in the case of imports come to nine. Composition of major export and import commodities in Cheongju clearance depot are similar, but precision instrument manufactured commodity and nonmetal mineral are remarkable in export and mineral manufactured commodity machinery and electronic manufactured commodity are remarkable in import. Gateway are similar to export and import, but Incheon international airport is used more in the case of import. And Cheongiu international airport is used for some commodities and is remarkable as a foreland of import for the areas outside of Chungcheongbuk-do.

• International Journal of High-Rise Buildings
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• v.7 no.2
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• pp.171-183
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• 2018

Turner and Penn (1) from UCL have proved that Visibility Graph Analysis (VGA) can be used as a more accurate method to predict the pedestrian distribution in building spaces. However, this methodology neglects certain elements that are of special influence on pedestrian distribution in buildings, especially the entrances and exits. Based on Space Syntax, this dissertation improves on the traditional method of Visibility Graph Analysis, using three shopping centers in Shenzhen as examples, attempts to explore a new parameter - "attenuation index of pedestrians at the entrances and exits" - using relevant data of the entrances and exits of the three cases, and combines it with traditional VGA analysis through weighted calculation, in order to provide more accurate predictions of pedestrian patterns in shopping centers.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.75-84
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• 2008

In 2002, the 5-day workweek policy was effective in Korea. As we have expected, the 5-day workweek policy has changed people's travel behavior during weekdays and weekends. Several studies have been done to understand these changes and impacts on transportation systems. However, these studies have only focused on travel pattern changes without considering spatial factors. Said in another way, although individual travel pattern changes are usually investigated, indices adopted cannot describe travel pattern changes in a proper way due to lack of the spatial distribution measure. This study aims to analyze travel change since the 5-day work week policy in effect using a new index (i.e. Travel Vector Index) developed in this study, which can explain travel pattern changes in terms of magnitude and spatial point of views. The new index uses a GIS technology and TCS (Toll Collection System) databases in Korea. The results in this study show that the index is very useful and reliable to measure the travel patterns changes. They are applied to TCS data set and the results show that the 5-day workweek policy significantly affects on travel behaviors.

• Journal of the Economic Geographical Society of Korea
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• v.19 no.3
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• pp.437-456
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• 2016

The purpose of this study is to understand the locational characteristics of performing art industries and to investigate the linkages with local economy. For the purpose, we examine the spatial concentration of cultural and artistic resources in Korea first, and than focus on Seoul where the resources of performing art industries are concentrated to the utmost. To distinguish the distribution aspect and locational characteristics of performing art industries, we apply the Kernel density analysis and LISA (Local Indicator of Spatial Association) on the address data of performing art theater, gallery, and movie theater. In contrast to galleries and movie theaters. the spatial distribution pattern of performing art theaters reveals a unique local cluster centered on the Daehakro area. We confirm that the Daehakro area constitutes a performing art industry cluster in their dense distribution of various related activities making up the value chain of the performing art industry. Multiple regression analysis probes the related economic activities to explain the distribution of performing art theaters as well as the linkages with the local economic landscape.