• Journal of Korea Water Resources Association
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• v.46 no.3
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• pp.277-285
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• 2013

The purpose of this research is to evaluate the scenic value of the Gyungin Ara waterway in real estate prices. Apart from the multi-functionality such as transportation of passengers and freight, prevention of floods, and provision of leisure areas, the Ara waterway possesses a scenic function which offers people esthetic value through unique and beautiful scenery. This scenic function is an externality for apartment residents living nearby. The applied methodology for this research is the Hedonic Price Model (HPM) which creates a cause and effect model between real estate prices and attributes. Variables such as apartment sale prices, complex characteristics, location characteristics, timely characteristics have been deduced through data collected from a total of 4,207 households that have experienced actual transactions during the same period, all located within the scenic benefit boundaries of the waterway. Landscape variable has been derived from algorithm designed by a combination of digital map and Google Mapview. The scenic value of the waterway estimated through the application of HPM on these variables is 165,000 Won per area (pyeong). The regional asset enhancing effect caused by the landscape view of the waterway is estimated to be 89.1 billion won.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.4
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• pp.469-476
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• 2009

The new laser scanning technology allows to attain 3D information faster with higher accuracy on surface ground, vegetation and buildings of the earth surface. This acquired information can be used in many areas after modifying them appropriately by users. The contour production for accurate landform is an advanced technology that can reveal the mountain area landscapes hidden by the trees in detail. However, if extremely precise LiDAR data is used in constructing the contour, massive-sized data intricates the contour diagram and could amplify the data size inefficiently. This study illustrates the algorithm producing contour that is filtered in stages for more efficient utilization using the LiDAR contour produced by the detailed landscape data. This filtering stages allow to preserve the original landscape shape and to keep the data size small. Point Filtering determines the produced contour diagram shape and could minimize data size. Thus, in this study we compared experimentally filtered contour with the current digital map(1:5,000).

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.