• The KIPS Transactions:PartA
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• v.9A no.1
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• pp.105-112
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• 2002

The purpose of this paper is to develop a courseware based on GIS (Geographic Information System) for improving visual and spatial learning efficiency of geography learning. The existing coursewares are not easy to encourage the learners in learning motivation, because these provide only the visual information using simple texts or imamges to the learners. To overcome these constraints, our courseware using GIS that can support spatial information can control the attribute information of map. In this paper, we define the courseware as the geography learning system. This courseware system enables the learners to take the perfect learning and the repetitive learning through the feedback after evaluating the learning degree. Also using geography learning application modules we implemented, the learners can participate directly in learning as well as search information in WWW.

• Hwankyungkyoyuk
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• v.9 no.1
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• pp.39-57
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• 1996

All kinds of environmental problems are related to each local and geographical environment. For this reason, it is necessary for schools in each region to provide environmental education which suits the geographical character of their particular region. In order to provide solutions to the environmental problems of each school's geographic region, the goal of this research is as follows: 1. We can make students realize the relationship between the human race and the environment by teaching according to the environmental conditions in each local area. 2. By teaching students about the problems in their own local environment, we can increase their concern about the state of their local surroundings. 3. When teaching about the environment, it is useful to use educational material which suits the character of each local region. 4. Students' interest in environmental preservation can be aroused through extracurricular environmental activities. The ares concerned are Chonnam and Kwangju City, which are divided into urban, industrial, rural, coastal, and mountainous areas. The conclusion about considering environmental education in environmental school social studies is as follows: 1. Kwangju and Chonnam should be divided into five sections, each with similar geographical environments. This will be an improvement over the old uniform approach to environmental studies in which all regions were treated as being the same each region will now receive special attention. 2. It is necessary to maximize the efficient use of the Environmental Education Building. When Media, environmental data and special materials for environmental education are used effectively, teachers can lead class effectively and students will be more interested in the class. 3. We can detect the cause of pollution, increase interest in the environment and easily solve environmental problems by collecting and displaying environmental educational materials. 4. An environmental education corner could boost students' interest in environmental problems and could act as a kind of bridge between theoretical and practical education. 5. Media and environmental data must be specialized according to the geographic character of each region. In this way, we can expect to improve the quality of environmental education over the simplistic environmental education of previous years. 6. Students will become interested in the problems of the region in which they live through social studies, and primarily through the environmental curriculum. 7. We can prevent learning deficiencies by making a consistent teaching plan. The teaching and learning methods will be improved and the teachers will be proud of what they teach. 8. The purpose of the Education Procedure Content Analysis is to make teaching and learning concise and easy by systematizing environmental and related subjects. This can be done by adding an environmental unit to the geographic section of social studies. 9. Citizens' interest in their own residential environment can be increased through action by sustaining environmental preservation movements to local region people.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4244-4274
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• 2021

The utilization of UAVs in various fields has led to the development of flying ad hoc network (FANET) technology. In a network environment with highly dynamic topology and frequent link changes, the traditional routing technology of FANET cannot satisfy the new communication demands. Traditional routing algorithm, based on geographic location, can "fall" into a routing hole. In view of this problem, we propose a geolocation routing protocol based on multi-agent reinforcement learning, which decreases the packet loss rate and routing cost of the routing protocol. The protocol views each node as an intelligent agent and evaluates the value of its neighbor nodes through the local information. In the value function, nodes consider information such as link quality, residual energy and queue length, which reduces the possibility of a routing hole. The protocol uses global rewards to enable individual nodes to collaborate in transmitting data. The performance of the protocol is experimentally analyzed for UAVs under extreme conditions such as topology changes and energy constraints. Simulation results show that our proposed QLGR-S protocol has advantages in performance parameters such as throughput, end-to-end delay, and energy consumption compared with the traditional GPSR protocol. QLGR-S provides more reliable connectivity for UAV networking technology, safeguards the communication requirements between UAVs, and further promotes the development of UAV technology.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• Journal of the Korean Geographical Society
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• v.46 no.4
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• pp.534-553
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• 2011

Analogical thinking is a problem-solving strategy to use a familiar problem (or base analog) to solve a novel problem of the same type (the target problem). The purpose of this study is to provide new insight into geography teaching and learning by connecting cognitive science research on analogical thinking with issues of geography education and suggest that teaching with analogies can be a productive instructional strategy for geography. In this study, using the various examples of analogical thinking used in geography we defined analogical thinking, addressed the theoretical models on analogical transfer, and discussed conditions that make an effective analogical transfer. The major research findings include the following: a) the spatial analogy, indicating skills to find places that may be far apart but have similar locations, and therefore have other similar conditions and/or connections, can provide a useful way to design contents for place learning; b) representational transfer, specifying a common representation for two problems, can play a key role in solving geographic problems requiring data visualization and spatialization processes; and c) either asking learners to compare/analyze similar examples sharing common structure or providing them examples bridging the gap between concrete, real-life phenomena and the ideas and models can contribute to learning in geographic concepts and skills. The spatial analogy requiring both geographic content knowledge and visual/spatial thinking has the potential to become a content-specific problem-solving strategy. We ended with recommendations for future research on analogy that is important in geography education.

• Journal of Distribution Science
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• v.22 no.8
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• pp.37-53
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• 2024

Purpose: Global warming is increasingly aggravated by environmental degradation, a challenge that can be mitigated through strategic logistic policies. This study introduces the dynamics of green trade in environmental goods for the Organisation of Islamic Cooperation (OIC) nations. It is a region known for its high environmental degradation, political risk and instability. This study examines how technological innovation and political factors influence the geographic distribution of green trade among OIC nations from 1994 to 2021 using the structural gravity model. The COVID-19 pandemic further emphasised the need for resilient and eco-friendly approaches. Research design, data and methodology: The main objective of the study is to analyse the impact of technological innovation along with scrutinising political determinants of green trade in the OIC region from 1994 to 2021 using the structural gravity model. Results: The results reveal geographic proximity, RTA, and innovation significantly boost green trade. Similarly, OIC's green trade performance has been impeded by high political risk and instability. Conclusions: The research recommends fostering political stability, and conducting further research using longitudinal studies and machine learning to strengthen the understanding of innovation and green trade in the OIC. This will inform policies for sustainable economic growth through green trade.

• Journal of the Korean association of regional geographers
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• v.23 no.1
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• pp.213-226
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• 2017

Geography textbooks and atlases are typical printed materials broadly used in the classroom and the public education as certified materials. The students' atlas, as supplementary materials, is useful in teaching and learning geography in the classroom. This study examines and evaluates the supplementary role of the students' atlas in the institutional and pedagogical perspectives. The textbook and the atlas were written by different writers and publishers based on the interpretation of the geography curriculum, the pedagogical beliefs and interests. The textbook is submitted to be certificated by Ministry of Education. Then the textbook and the atlas which is passed in the certification process can be distributed to schools to use in the classroom. Most maps and materials are supplied in the textbook and the atlas variously depended on the authors' interpretation of geography curriculum, which are designed complementally. But, that maps and materials of respective book of even different author are similar and duplicated. Sometimes maps and materials of textbook and atlas are independent, not mutual complement. It needed that geography textbook and student's atlas take a efficient role supplementarily in these certification system of textbook.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.97-115
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• 2023

In recent years, the structure of the population has been changing rapidly, with a declining birthrate and aging population, and the inequality of population distribution is expanding. At this point, changes in population estimation methods are required, and more accurate estimates are needed at the subregional level. This study aims to estimate the population in 2040 at the 500m grid level by applying an explainable machine learning to Busan in order to respond to this need for a change in population estimation method. Comparing the results of population estimation by applying the explainable machine learning and the cohort component method, we found that the machine learning produces lower errors and is more applicable to estimating areas with large population changes. This is because machine learning can account for a combination of variables that are likely to affect demographic change. Overestimated population values in a declining population period are likely to cause problems in urban planning, such as inefficiency of investment and overinvestment in certain sectors, resulting in a decrease in quality in other sectors. Underestimated population values can also accelerate the shrinkage of cities and reduce the quality of life, so there is a need to develop appropriate population estimation methods and alternatives.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.3
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• pp.43-60
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• 2016

Coastal cities need disaster planning that accounts for the complex causes of environmental disasters such as high tides or tsunamis generated by typhoons, and of river or lowland flooding caused by heavy rains, etc. The elements of the disaster map were initially defined using a Geographic Information System (GIS) to allow for efficient information management. Complex disaster information elements were thus established in this study to create a disaster map of coastal cities. The range of information required for coastal cities includes the type of disaster, evacuation methods, available sheltering facilities, and learning content. These informational elements are intended to build on spatial information based on data available from the Ministry of Public Safety and Security as well as local governments.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.