• Journal of Wetlands Research
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• v.19 no.1
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• pp.103-111
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• 2017

In this study, we estimate the NNI(Nearest Neighbor Index) which is considered altitude of rain gauge network as a method for evaluating appropriateness of spatial distribution and the current rain gauge network is evaluated. The altitude is divided by equal-area-ratio and optimal NNI within given basin condition is estimated using harmony search method for considering geographical conditions that vary from altitude to altitude. After calculating current state and optimal NNI for each altitude, the distribution of the rain gauge network is evaluated based on the difference between the two NNIs. As a result, it founds that the density of rain gauge networks is relatively thin as the altitude increases. Furthermore, it will be possible to construct an efficient rain gauge network if the characteristics of different altitudes are considered when a new rain gauge network is newly constructed.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.352-352
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• 2023

본 연구에서는 제주도권역 강우관측소의 고도별 공간분포의 적정성을 평가하기 위한 방안으로 고도별 강우관측소의 최근린지수(Nearest Neighbor Index, NNI)를 산정하고 현재 강우관측소 공간분포의 적정성을 평가하였다. 또한, 제주도권역을 고도에 따라 등면적으로 구분하고, 고도마다 상이한 지형조건을 고려하기 위해 등면적으로 구분된 각 강우관측소의 최대 NNI를 최적화 기법의 하나인 화음탐색법(Harmony Search, HS)을 이용하여 산정하였다. 이와같이 현재 강우관측소설치위치를 기준으로 산정한 NNI와 HS를 이용하여 산정한 최대 NNI의 차이를 바탕으로 지형적인 특성을 고려한 제주도권역 강우관측소 분포를 비교·검토하였다. 그 결과 고도가 높아짐에 따라 강우관측소의 개수가 낮은 고도에 비해 상대적으로 적어 관측소 밀도가 작은 것으로 산정되었다. 향후 제주도권역 강우관측소의 지형적인 특성을 반영한다면 보다 효율적인 제주도권역 강우량관측이 가능할 것으로 판단된다.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.2
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• pp.164-171
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• 2007

This research used GIS spatial analysis model and Huff probability model and achieved trade area analysis of area center. we constructed basic maps that were surveyed according to types of business, number of households etc. using a land registration map of LMIS(Land Management Information System) in Bokdae-dong, Cheongju-si. Kernel density function and NNI(Nearest Neighbor Index) was used to estimate store distribution center area in neighborhood life zones. The center point of area and scale were estimated by means of the center area. Huff probability model was used in abstracting trade areas according to estimated center areas, those was drew map. Therefore, this study describes method that can apply in Huff probability model through kernel density function and NNI of GIS spatial analysis techniques. A trade area was abstracted more exactly by taking advantage of this method, which will can aid merchant for the foundation of small sized enterprises.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.1
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• pp.117-126
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• 2018

The purpose of this study is to analyze the spatial distribution characteristics and spatial changes of Automatic Weather Stations (AWS) in mountainous areas with altitude more than 200 meters in South Korea. In order to analyze the spatial distribution patterns, spatial analysis was performed on 203 Automatic Mountain Meteorology Observation Station (AMOS) points from 2012 to 2016 by Euclidean distance analysis, nearest neighbor index analysis, and Kernel density analysis methods. As a result, change of the average distance between 2012 and 2016 decreased up to 16.4km. The nearest neighbor index was 0.666632 to 0.811237, and the result of Z-score test was -4.372239 to -5.145115(P<0.01). The spatial distributions of AMOSs through Kernel density analysis were analyzed to cover 129,719ha/a station in 2012 and 50,914ha/a station in 2016. The result of a comparison between 2012 and 2016 on the spatial distribution has decreased about 169,399ha per a station for the past 5 years. Therefore it needs to be considered the mountainous regions with low density when selecting the site of AMOS.