• Journal of the Korean Geographical Society
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• v.47 no.4
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• pp.625-640
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• 2012

The purpose of this study is to introduce the current status of geography education research in South Korea. This article consists of mainly two parts: 1) source of knowledge and 2) content of knowledge in geography education research. The main academic journals are introduced as the sources of knowledge. The research trends in these journals are discussed as the content of knowledge. We classified geography education research into three types based on the framework suggested by Bednarz (2000): 1) nature of geographic knowledge, learning, and curriculum, 2) teacher education in geography, and 3) strategies in the geography classroom. Relevant research regarding each category is introduced. We hope this study serves as an access point where geography educators in South Korea and other countries facilitate interactions with each other.

• Journal of the Korean Society for information Management
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• v.28 no.2
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• pp.159-175
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• 2011

The purposes of this study are to evaluate the navigability of three web-based mapping applications and to suggest how to improve the navigability of all web-based mapping applications. With these purposes, this study conducted a web-based survey congruent with a think aloud and systematic observation for individual participants, followed up by a focus group with all participants. Based on the findings, recommendations are proposed for web-based mapping applications that include a standard click and drag panning function in mapping applications, a scaled zooming option, increased text for icons and buttons, and other potential changes to computer hardware for increased navigability in these applications. By improving the navigability of web-based mapping applications, the learning time may be reduced for each application and the speed at which users' geographic information needs are met will be quicker.

• Journal of Korea Game Society
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• v.8 no.2
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• pp.25-35
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• 2008

Seunglamdonoli was a traditional board game played during the Chosun Dynasty. In this article, I interpret its features as a board game and discuss the possibility of turning it into a mass market game like Milton Bradley's 'The Game of Life' in the U.S. Seunglamdonoli uses the dice to determine movements around a board shaped like Korea, teaching players about important geographic locations, and traditional characters in a game that relies on strategy as well as luck. With appropriate changes such as introducing a standardized game board or digitalization for use on computers, Seunglamdonoli has potential to again become a popular pastime as well as become tool for learning about Korean history, geography, and society. Furthermore it can be adapted as an educational game for any location with minimal changes.

• Korean journal of applied entomology
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• v.48 no.4
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• pp.453-457
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• 2009

This study was conducted to investigate insect fauna in the four insect garden sites of Suwon, Yeongyang, Buyeo and Yecheon from 2005 to 2007. Seasonal population size of insects was largest from June to August in all the four sites. In the four sites, Coleopteran insects were dominant followed by Hemiptera and Orthoptera. Unique education/learning programs are successfully run at the insect gardens based on the three geographic types (i.e. urban, mountainous and rural). These activities will help preserve insect biodiversity in the area and visitors better understand life forms such as insects found in the areas.

• Journal of the Korean Geographical Society
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• v.47 no.6
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• pp.899-920
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• 2012

The purpose of this study is to examine and better understand student's perception of territory in the deterritorialized world. Students' sketch maps were analyzed to investigate the way of which they recognized the form of national territory, border, geographic location, and neighboring countries. In addition, students' values, attitudes, and affection for their homeland and other countries were observed through the awareness of North Korea, DMZ, and Dok-do island. Scrutinizing students' sketch maps and follow-up interviews provided much more in-depth context for understanding students' narratives about territorial identity than did the analysis of structured surveys since they can freely draw and sketch their cognition. A qualitative data analysis builded a code list with 67 codes generated from 626 quotations. These data were exported to CSV format to elicit and evaluate differential variances of territorial identity along with student's Social Studies score and Grade Point Average for inferential statistics and quantitative data analysis using SPSS. Based on the results of data analysis and discussion, some suggestions to build a model of territorial education and to develop teaching and learning materials in the domain of geography education were provided.

• Journal of the Korean Geographical Society
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• v.52 no.1
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• pp.55-71
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• 2017

This paper points out the semantic unclearness of the jargon "cluster" and suggests the substitution of "industrial cluster" for "cluster". Industrial cluster is the intersection of industrial agglomeration and cluster phenomenon while the actual concept of cluster includes not only industry cluster but also political administration cluster, science research cluster, art cluster, religion cluster, education cluster, etc. Partially reconstructing the concept and significance of industry cluster, industrial cluster is a geographic agglomeration of interconnected productional businesses in a particular industry, forming close industrial networks. The advantage of the agglomeration includes reducing the transaction cost between the businesses, promoting technological innovation and dispersion, facilitating the utilization of the professional workforce, sharing and connecting the external customer. Moreover, this paper discusses the range of the industrial cluster and its distinctness from the other similar concepts. There is a need to discriminate it from the other related jargons and to clarify their relationship. In particular, there is a task to eradicate the mixed usage of industrial cluster with the jargons related to space for learning and innovation.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.81-99
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• 2015

The acceleration of global warming has required better understanding of carbon cycles over local and regional areas such as the Korean peninsula. Since forests serve as a carbon sink, which stores a large amount of terrestrial carbon, there has been a demand to accurately estimate such forest carbon sequestration. In Korea, the National Forest Inventory(NFI) has been used to estimate the forest carbon stocks based on the amount of growing stocks per hectare measured at sampled location. However, as such data are based on point(i.e., plot) measurements, it is difficult to identify spatial distribution of forest carbon stocks. This study focuses on urban areas, which have limited number of NFI samples and have shown rapid land cover change, to estimate grid-based forest carbon stocks based on UNFCCC Approach 3 and Tier 3. Land cover change and forest carbon stocks were estimated using Landsat 5 TM data acquired in 1991, 1992, 2010, and 2011, high resolution airborne images, and the 3rd, 5th~6th NFI data. Machine learning techniques(i.e., random forest and support vector machines/regression) were used for land cover change classification and forest carbon stock estimation. Forest carbon stocks were estimated using reflectance, band ratios, vegetation indices, and topographical indices. Results showed that 33.23tonC/ha of carbon was sequestrated on the unchanged forest areas between 1991 and 2010, while 36.83 tonC/ha of carbon was sequestrated on the areas changed from other land-use types to forests. A total of 7.35 tonC/ha of carbon was released on the areas changed from forests to other land-use types. This study was a good chance to understand the quantitative forest carbon stock change according to the land cover change. Moreover the result of this study can contribute to the effective forest management.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.83-98
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• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.73-82
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• 2021

Rural roads are the significant infrastructure for developing and managing the rural areas, hence the utilization of the remote sensing datasets for managing the rural roads is necessary for expanding the rural transportation infrastructure and improving the life quality of the rural residents. In this research, the two different methods such as image classification and image segmentation were compared for mapping the rural road based on the given high-resolution satellite image acquired in the rural areas. In the image classification method, the deep learning with the multiple neural networks was employed to the given high-resolution satellite image for generating the object classification map, then the rural roads were mapped by extracting the road objects from the generated object classification map. In the image segmentation method, the multiresolution segmentation was employed to the same satellite image for generating the segment image, then the rural roads were mapped by merging the road objects located on the rural roads on the satellite image. We used the 100 checkpoints for assessing the accuracy of the two rural roads mapped by the different methods and drew the following conclusions. The image segmentation method had the better performance than the image classification method for mapping the rural roads using the give satellite image, because some of the rural roads mapped by the image classification method were not identified due to the miclassification errors occurred in the object classification map, while all of the rural roads mapped by the image segmentation method were identified. However some of the rural roads mapped by the image segmentation method also had the miclassfication errors due to some rural road segments including the non-rural road objects. In future research the object-oriented classification or the convolutional neural networks widely used for detecting the precise objects from the image sources would be used for improving the accuracy of the rural roads using the high-resolution satellite image.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.169-186
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• 2018

The social influence of the elderly population will accelerate in a rapidly aging society. The purpose of this study is to establish a methodology for extracting vulnerable districts of the welfare of the aged through machine learning(ML), artificial neural network(ANN) and geospatial analysis. In order to establish the direction of analysis, this progressed after an interview with volunteers who over 65-year old people, public officer and the manager of the aged welfare facility. The indicators are the geographic distance capacity, elderly welfare enjoyment, officially assessed land price and mobile communication based on old people activities where 500 m vector areal unit within 15 minutes in Yongin-city, Gyeonggi-do. As a result, the prediction accuracy of 83.2% in the support vector machine(SVM) of ML using the RBF kernel algorithm was obtained in simulation. Furthermore, the correlation result(0.63) was derived from ANN using backpropagation algorithm. A geographically weighted regression(GWR) was also performed to analyze spatial autocorrelation within variables. As a result of this analysis, the coefficient of determination was 70.1%, which showed good explanatory power. Moran's I and Getis-Ord Gi coefficients are analyzed to investigate spatially outlier as well as distribution patterns. This study can be used to solve the welfare imbalance of the aged considering the local conditions of the government recently.