• 한국태양에너지학회:학술대회논문집
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• 2011.04a
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• pp.17-22
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• 2011

There are some degradation factors for amorphous silicon solar cells. Light inducing is one of the factor that explained by Staebler-Wronski effect. Also, hotspot heating could be the reason that makes amorphous silicon solar cell degrade. Hotspot heating is occurred when a solar cell is shaded so this work is investigated into two types of shading condition and how these affect to solar cell differently. Reduced irradiance for whole cell and partially shaded as 0($W/m^2$) while the other part of cell is soaking as 1000($W/m^2$) of irradiance are two conditions that are experimented. The two types of shading show different characteristics of degradations. The result shows that partially shaded cell dropped maximum powerless and slower. Also sudden drop points have shown that should be concerned to decide the number of cells for a string. Otherwise, the current through a shaded cell might flow more than cell's capability. It makes cell and module damaged. This work would help to manufacture modules.

• ETRI Journal
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• v.43 no.2
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• pp.272-287
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• 2021

This article presents a multi-dimensional spatial pattern analysis of crime events in San Francisco. Our analysis includes the impact of spatial resolution on hotspot identification, temporal effects in crime spatial patterns, and relationships between various crime categories. In this work, crime prediction is viewed as a classification problem. When predictions for a particular category are made, a binary classification-based model is framed, and when all categories are considered for analysis, a multiclass model is formulated. The proposed crime-prediction model (HotBlock) utilizes spatiotemporal analysis for predicting crime in a fixed spatial region over a period of time. It is robust under variation of model parameters. HotBlock's results are compared with baseline real-world crime datasets. It is found that the proposed model outperforms the standard DeepCrime model in most cases.

• Journal of Korean Society of Forest Science
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• v.106 no.3
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• pp.310-319
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• 2017

This study categorized the damaged trees by Supervised Classification using time-series-aerial photographs of Bukhan, Cheonggae and Suri mountains because oak wilt disease seemed to be concentrated in the metropolitan regions. In order to analyze the spatial characteristics of the damaged areas, the geographical characteristics such as elevation and slope were statistically analyzed to confirm their strong correlation. Based on the results from the statistical analysis of Moran's I, we have retrieved the following: (i) the value of Moran's I in Bukhan mountain is estimated to be 0.25, 0.32, and 0.24 in 2009, 2010 and 2012, respectively. (ii) the value of Moran's I in Cheonggye mountain estimated to be 0.26, 0.32 and 0.22 in 2010, 2012 and 2014, respectively and (iii) the value of Moran's I in Suri mountain estimated to be 0.42 and 0.42 in 2012 and 2014. respectively. These numbers suggest that the damaged trees are distributed in clusters. In addition, we conducted hotspot analysis to identify how the damaged tree clusters shift over time and we were able to verify that hotspots move in time series. According to our research outcome from the analysis of the entire hotspot areas (z-score>1.65), there were 80 percent probability of oak wilt disease occurring in the broadleaf or mixed-stand forests with elevation of 200~400 m and slope of 20~40 degrees. This result indicates that oak wilt disease hotspots can occur or shift into areas with the above geographical features or forest conditions. Therefore, this research outcome can be used as a basic resource when predicting the oak wilt disease spread-patterns, and it can also prevent disease and insect pest related harms to assist the policy makers to better implement the necessary solutions.

• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.5-18
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• 2017

The AMOEBA approach in this study supplements the Hotspot method that had not been fully capable of dealing with the ecotone issues in designating the Development Impact Fee (DIF) zones as had been seen in the preceding study by Kim and Choei (2017). The AMOEBA procedure shares the common Getis-Ord statistic with the Hotspot technique but is more adequate to figure out the ecotones. For the comparative purpose, simulations are run by both methods for a series of different scenarios in terms of analytic spatial units (here, the square grids) from 100m up to 400m; and the zonal outcomes by both methods are compared using a set of evaluative indicators. In terms of the numerical scores, the performances by the two methods are much comparable except that the former is slightly superior with respect to the avoidance of the oversized spread of the selected zones whereas so is the latter with respect to the ease of infrastructure installation. It remains yet to be investigated by the extended studies that include in-depth field surveys to figure out the causes as well as the meanings of such differences in zonal determinations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3529-3550
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• 2016

In heterogeneous networks (HetNets), energy saving is vital for a sustainable network development. Many techniques, such as spectrum allocation, network planning, etc., are used to improve the network energy efficiency (EE). In this paper, micro BSs utilizing cell range expansion (CRE) and spectrum allocation are considered in multi-channel heterogeneous networks to improve EE. Hotspot region is assumed to be covered by micro BSs which can ensure that the hotspot capacity is greater than the average demand of hotspot users. The expressions of network energy efficiency are derived under shared, orthogonal and hybrid subchannel allocation schemes, respectively. Particle swarm optimization (PSO) algorithm is used to solve the optimal ratio of subchannel allocation in orthogonal and hybrid schemes. Based on the results of the optimal analysis, we propose three service control strategies on the basis of large-scale user behaviors, i.e., adjust micro cell rang expansion (AmCRE), adjust micro BSs density (AmBD) and adjust micro BSs transmit power (AmBTP). Both theoretical and simulation results show that using shared subchannel allocation scheme in AmBD strategies can obtain maximal EE with a very small area ratio. Using orthogonal subchannel allocation scheme in AmCRE strategies can obtain maximal EE when area ratio is larger. Using hybrid subchannel allocation scheme in AmCRE strategies can obtain maximal EE when area ratio is large enough. No matter which service control strategy is used, orthogonal spectrum scheme can obtain the maximal hotspot user rates.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.459-471
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• 2023

This study identified the characteristics of changes in forest areas of North Korea's major regions (Gaesong, Goseong, Pyongyang, and Hyesan·Samsu) using data on degraded lands collected via monitoring by the National Institute of Forest Science. The data, spanning 1999 to 2018, were cross-analyzed to determine trends in land cover change, and hotspot analysis was conducted to confirm evident changes in the forest areas. The results showed that the areas of interest substantially transitioned to other land use types from 1999 to 2008. Contrastingly, the range of changes decreased from 2008 to 2018, with some areas regenerating into forests. Nevertheless, the hotspot analysis indicated that hotspots occurred more intensively in the outskirts of cities and forest edges from 2008 to 2018 than from 1999 to 2008. The analysis also showed that the aforementioned changes were caused by various aspects, depending on regional characteristics and social factors. This study can be used as a basic reference for decision-making on the selection of basic forest restoration targets and restoration methods in inter-Korean forest cooperation initiatives.

• Journal of the Korean Regional Science Association
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• v.35 no.4
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• pp.61-73
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• 2019

Urban development and densification have led to the Urban Heat Island Effect, in which the temperature of urban space is higher than the surrounding areas, and the intensity is increasing with climate change. In addition, when the city's air temperature rises in summer, low-income, elderly population, and socially vulnerable people who have health problems lack the ability to cope with the elevated heat environment. Therefore, this study aimed to identify the urban heat island area of Seoul through Hotspot analysis, which is a spatial statistics technique, and explored physical environments, demographic and socioeconomic characteristics of urban heat island effect areas using logistic regression models. This study performed urban heat island hotspot analysis using the average air temperatures of the 423 administrative dongs in Seoul. Analysis results identified that the urban heat islands were concentrated in Jung-gu, Jongno-gu, Yongsan-gu, and Yeongdeungpo-gu. Logistic regression analysis results indicated that urban heat island areas of Seoul were affected by residential floor area ratio, commercial facility floor area ratio, overall floor area ratio, impervious surface ratio, and normalized difference vegetation index(NDVI). In addition, as a result of analyzing the vulnerable area of thermal environment considering the demographic and socioeconomic characteristics of the heat island area, urban heat island areas of Seoul were significantly associated with the proportion of low-income elderly living alone. The result of this study provided useful insights for urban thermal environmental design and policy development that could improve the thermal environment for the socially disadvantaged urban population.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.201-213
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• 2018

In recent years, the number of traffic accidents caused by car accidents has been decreasing steadily due to traffic accident prevention activities in Korea. However, the number of accidents in Seoul is higher than that of other regions. Various studies have been conducted to prevent traffic accidents, which are human disasters. In particular, previous studies have performed the spatial analysis of traffic accidents by counting the number of traffic accidents by administrative districts or by estimating the density through kernel density method in order to identify the traffic accident cluster areas. However, since traffic accidents take place along the road, it would be more meaningful to investigate them concentrated on the road network. In this study, traffic accidents were assigned to the nearest road network in two ways and analyzed by hotspot analysis using Getis-Ord Gi* statistics. One of them was investigated with a fixed road link of 10m unit, and the other by computing the average traffic accidents per unit length per road section. As a result by the first method, it was possible to identify the specific road sections where traffic accidents are concentrated. On the other hand, the results by the second method showed that the traffic accident concentrated areas are extensible depending on the characteristic of the road links. The methods proposed here provide different approaches for visualizing the traffic accidents and thus, make it possible to identify those sections clearly that need improvement as for the traffic environment.

• Journal of Climate Change Research
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• v.7 no.1
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• pp.69-75
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• 2016

Recently, many countries all over the world have been suffered from disaster caused by climate change. Especially in case of developed countries, the disaster is concentrated in the industry sector. In this research, we analyzed industrial vulnerable homogeneous hotspot for the climate change using spatial autocorrelation analysis on the south Korea. Homogeneous hot spot areas through autocorrelation analysis indicate the spatial pattern of areas interacted each other. Industry sector have responsibility of green house gas emissions, and should adapt to the climate change caused by greenhouse gas already released. So, we integrated the areas sensitive to mitigation option with the areas hardly adapt to climate change because of vulnerable infrastructure. We expected that the result of this research could contribute to the decision-making system of climate change polices.

• Journal of the Korean Geographical Society
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• v.43 no.3
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• pp.392-411
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• 2008

The purpose of this paper is to develop a method for exploring hotspot patterns of house price volatility where there is a high fluctuation in price and homogeneity of direction of price volatility. These patterns are formed when the majority of householders in an area show an adaptive tendency in their decision making. This paper suggests a method that consists of two analytical parts. The first part uses spatial scan statistics to detect spatial clusters of houses with a positive range of price volatility. The second part utilizes local Moran's I to evaluate the homogeneity of direction of price volatility within each cluster. The method is applied to the areas of Gangnam-Gu, Seocho-Gu, and Songpa-Gu in Seoul from August to November of 2003; the Participatory Government of Korea designated these areas and this period as the most speculative. The results of the analysis show that the area around Gaepo-Dong was as a hotspot before the Government's anti-speculative 10.29 policy in 2003; the house prices in the same area stabilized in October, 2003 and the area was identified as a coldspot in December, 2003. This case study shows that the suggested method enables exploration of hotspot of house price volatility at micro spatial scales which had not been detected by visual analysis.