• Journal of Korean Society of Forest Science
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• v.99 no.3
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• pp.259-266
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• 2010

Forest fire occurrence in Korea is highly related to human activities and its spatial distribution shows a strong spatial dependency with cluster pattern. In this study, we analyzed spatial distribution pattern of forest fire with point pattern analysis considering spatial dependency. Distributional pattern was derived from Ripley's K-function according to causes and distances. Spatially clustered intensity was found out using Kernel intensity estimation. As a result, forest fires in Korea show clustered pattern, although the degrees of clustering for each cause are different. Furthermore, spatial clustering pattern can be classified into two groups in terms of degrees of clustering and distance. The first group shows the national-wide cluster pattern related to the human activity near forests, such as human-induced accidental fire in mountain and field incineration. Another group shows localized cluster pattern which is clustered within a short distance. It is associated with the smoker fire, arson, accidental by children. The range of localized clustering was 30 km. Beyond of this range, the patterns of forest fire became random distribution gradually. Kernel intensity analysis showed that the latter group, which have localized cluster pattern, was occurred in near Seoul with high densed population.

• Journal of Ecology and Environment
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• v.41 no.8
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• pp.223-234
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• 2017

Background: Spatial structure of plants in a population reflects complex interactions of ecological and evolutionary processes. For dioecious plants, differences in reproduction cost between sexes and sizes might affect their spatial distribution. Abiotic heterogeneity may also affect adaptation activities, and result in a unique spatial structure of the population. Thus, we examined sex- and size-related spatial distributions of old-growth forest of dioecious tree Torreya nucifera in extremely heterogeneous Gotjawal terrain of Jeju Island, South Korea. Methods: We generated a database of location, sex, and size (DBH) of T. nucifera trees for each quadrat ($160{\times}300m$) in each of the three sites previously defined (quadrat A, B, C in Site I, II, and III, respectively). T. nucifera trees were categorized into eight groups based on sex (males vs. females), size (small vs. large trees), and sex by size (small vs. large males, and small vs. large females) for spatial point pattern analysis. Univariate and bivariate spatial analyses were conducted. Results: Univariate spatial analysis showed that spatial patterns of T. nucifera trees differed among the three quadrats. In quadrat A, individual trees showed random distribution at all scales regardless of sex and size groups. When assessing univariate patterns for sex by size groups in quadrat B, small males and small females were distributed randomly at all scales whereas large males and large females were clumped. All groups in quadrat C were clustered at short distances but the pattern changed as distance was increased. Bivariate spatial analyses testing the association between sex and size groups showed that spatial segregation occurred only in quadrat C. Males and females were spatially independent at all scales. However, after controlling for size, males and females were spatially separated. Conclusions: Diverse spatial patterns of T. nucifera trees across the three sites within the Torreya Forest imply that adaptive explanations are not sufficient for understanding spatial structure in this old-growth forest. If so, the role of Gotjawal terrain in terms of creating extremely diverse microhabitats and subsequently stochastic processes of survival and mortality of trees, both of which ultimately determine spatial patterns, needs to be further examined.

• Spatial Information Research
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• v.11 no.3
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• pp.251-260
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• 2003

Distribution pattern of seawater intrusion was analyzed from the spatial distribution map of chloride using the geostatistics and CIS analyses. The chloride distribution map made by kriging(ordinary kriging and co-kriging) after exploratory spatial data analysis. Kriging provides an advanced methodology which facilitates quantification of spatial features and enables spatial interpolation. TDS, Na$^{＋}$, Br$^{[-10]}$ were selected as second parameters of co-kriging which is higher value of correlation coefficients between chloride and others groundwater properties. Chloride concentration is highest in yeminchon and coastal area. And result in co-kriging was accurate than ordinary kriging.

• Ocean Science Journal
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• v.43 no.2
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• pp.67-79
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• 2008

We investigated the relationship between mesoscale spatial distribution of environmental parameters (temperature, salinity, and sigma-t), chlorophyll-a concentration and mesozooplankton in the Yellow Sea during May 1996, 1997, and 1998, with special reference to Yellow Sea Bottom Cold Water (YSBCW). Adult calanoid copepods, Calanus sinicus, Paracalanus parvus s.l., Acartia omorii, and Centropages abdominalis were isolated by BVSTEP analysis based on the consistent explainable percentage (-32.3%) of the total mesozooplankton distributional pattern. The copepods, which accounted for 60 to 87% of the total abundances, occupied 73-78% of the copepod community. The YSBCW consistently remained in the northern part of the study area and influenced the spatial distribution of the calanoid copepods during the study periods. Abundances of C. sinicus and P. parvus s.l., which were high outside the YSBCW, were positively correlated with the whole water average temperature (p<0.01). In contrast, the abundances of C. abdominalis and A. omorii, which were relatively high in the YSBCW, were associated with the integrated chl-a concentration based on factor analysis. These results indicate that the YSBCW influenced the mesoscale spatial heterogeneity of average temperature and integrated chl-a concentration through the water column. This consequently affected the spatial distribution pattern of the dominant copepods in association with their respective preferences for environmental and biological parameters in the Yellow Sea during spring.

• Journal of The Geomorphological Association of Korea
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• v.23 no.2
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• pp.61-70
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• 2016

This study intended to analyze the spatial distribution of two types of weeding song (Short Bang-a and Sangsa ryu) and how geomorphic elements influence the propagation of the songs in Chungcheongbuk-do area. The distribution of the two types of song was mapped as point data. According to the result, both types showed similar distribution pattern. In order to figure out the reason of this similarity, the distribution pattern of songs was analyzed at various scales based on geomorphic elements including river, mountain and lineament. The result showed that most of distribution pattern of songs followed the lineament direction. Also, the spatial continuity among mountain that was formed by large and small lineament in various directions could be the path of the cultural diffusion. If the lineament with same direction does not intersect other lineament that have different direction, spatial continuity would be blocked. Consequently it was confirmed that propagation of songs has not spread smoothly.

• Journal of Korean Society of Forest Science
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• v.95 no.3
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• pp.240-249
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• 2006

Recently, dispersion of damaged forest by pine wilt disease has been regarded as a serious social issue. Damages by pine wilt disease have been spreaded by natural area expansion of the vectors in the damaged area, while the national wide damage spread has induced by human-involved carrying infected trees out of damaged area. In this study, damaged trees were detected and located on the digital map by aerial photograph and terrestrial surveys. The spatial distribution pattern of damaged trees, and the relationship of spatial distribution of damaged trees and some geomorphological factors were geostatistically analysed. Finally, we maked natural spread pattern map of pine wilt disease using geostatistical CART(Classification and Regression Trees) model. This study verified that geostatistical analysis and CART model are useful tools for understanding spatial distribution and natural spread pattern of pine wilt diseases.

• Asian-Australasian Journal of Animal Sciences
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• v.10 no.5
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• pp.484-490
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• 1997

Spatial distribution of urination by Japanese Black heifers and steers was investigated, and compared with the distribution of defecation. The animals grazed a bahiagrass (Paspalum notatum $Fl\ddot{u}gge$) pasture in the daytime, and spent the rest of the day in a barn. The distribution of urination to the pasture was greater than that expected from the proportion of time that the animals spent in the pasture. Correspondingly, the distribution was smaller in the barn. Such a distribution pattern of urination to the pasture and barn was similar to that of defecation, and affected by the intake of supplement on the previous day. The distribution of urination within the pasture, i.e. the distribution to the paddock, alley and resting area, was often uneven on an area basis. The animals often urinated sparsely in the alley and resting area, while they urinated in the paddock almost proportionally to its area. This was a clear contrast to the distribution pattern of defecation, which was sparse in the paddock and dense in the resting area. The degree of aggregation of urination in the paddock, alley and resting area varied with the meteorological factors and the intake of supplement.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.269-281
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• 2016

The process of spatial big data, such as social media, is being paid more attention in the field of spatial information in recent years. This study, as an example of spatial big data analysis, analyzed the spatial and temporal distribution of Tweet data based on the location and time information. In addition, the characteristics of its spatial pattern by times were identified. Tweet data in Busan city are collected, processed, and analyzed to identify the characteristics of the temporal and spatial pattern. Then, the results of Tweet data analysis were compared with the characteristics of the land type. This study found that spatial pattern of tweeting in the city was associated with given time periods such as daytime and nighttime in both weekdays and weekends. The spatial distribution patterns of individual time periods were compared with the characteristics of the land for the spatially concentrated area. The results of this study showed that tweeted data would be related to different spatial distribution depending on the time, which potentially reflects the daily pattern and characteristics of the land type of urban area to some extent. This study presented the possible incorporation of social media data, e. g. Tweet data, into the field of spatial information. It is expected that there will be more advantage to use a variety of social media data in areas such as land planning and urban planning.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1260-1264
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• 2009

Precipitation time series is a mixture of complicate fluctuation and changes. The monthly precipitation data of 61 stations during 36 years (1973-2008) in Korea are comprehensively analyzed using the EOFs technique and CSEOFs technique respectively. The main motivation for employing this technique in the present study is to investigate the physical processes associated with the evolution of the precipitation from observation data. The twenty-five leading EOF modes account for 98.05% of the total monthly variance, and the first two modes account for 83.68% of total variation. The first mode exhibits traditional spatial pattern with annual cycle of corresponding PC time series and second mode shows strong North South gradient. In CSEOF analysis, the twenty-five leading CSEOF modes account for 98.58% of the total monthly variance, and the first two modes account for 78.69% of total variation, these first two patterns' spatial distribution show monthly spatial variation. The corresponding mode's PC time series reveals the annual cycle on a monthly time scale and long-term fluctuation and first mode's PC time series shows increasing linear trend which represents that spatial and temporal variability of first mode pattern has strengthened. Compared with the EOFs analysis, the CSEOFs analysis preferably exhibits the spatial distribution and temporal evolution characteristics and variability of Korean historical precipitation.

• 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.