• Spatial Information Research
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• v.19 no.1
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• pp.127-133
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• 2011

Recently, with the advances in sensor techniques and net work computing, Ubiquitous Sensor Network (USN) has been received a lot of attentions from various communities. The sensor nodes distributed in the sensor network tend to continuously generate a large amount of data, which is called stream data. Sensor stream data arrives in an online manner so that it is characterized as high-speed, real-time and unbounded and it requires fast data processing to get the up-to-date results. The data stream has many application domains such as traffic analysis, physical distribution, U-healthcare and so on. Therefore, there is an overwhelming need of a USN middleware for processing such online stream data to provide corresponding services to diverse applications. In this paper, we propose a novel USN middleware which can provide users both context-aware service and meaningful sequential patterns. Our proposed USN middleware is mainly focused on location based applications which use stream location data. We also show the implementation of our proposed USN middleware. By using the proposed USN middleware, we can save the developing cost of providing context aware services and stream sequential patterns mainly in location based applications.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.361-369
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• 1998

Recently, the improvement of on-board satellite sensors covering hyperspectral image sensors, high spatial resolution sensors provide data on earth in diverse aspect. The application field relating remotely sensed data also varies depending on what type of job one wants. The various resolution of sensors from low to extremely high is also available on the market with a user defined specific location. The expense to purchase remote sensed data is going down compare to the cost it need past few years ago in terms of research or private use. Now, the satellite remote sensed data is used on the field of forecasting, forestry, agriculture, urban reconstruction, geology, or other research field in order to extract meaningful information by applying special techniques of image processing. There are many image processing packages available worldwide and one common aspect is that they are expensive. There need to be a advanced satellite data processing package for people who can not afford commercial packages to apply special remote sensing techniques on their data and produce valued-added product. The study was carried out with the purpose of developing a special satellite data processing package which covers almost every satellite produced data with normal image processing functions and also special functions needed on specific research field with friendly graphical user interface (GUI). And for the people with any background of remote sensing with windows platform.

• Journal of the Korean housing association
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• v.25 no.4
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• pp.59-72
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• 2014

This study proposes a data-mining analysis method for examining variable multi-attribute big-data, which is considered to be more applicable in social science using a Correspondence Analysis of variables obtained by AIC model selection. The proposed method was applied on the Seoul Survey from 2005 to 2010 in order to extract interesting rules or patterns on characteristics of single household. The results found as follows. Firstly, this paper illustrated that the proposed method is efficiently able to apply on a big dataset of huge categorical multi attributes variables. Secondly, as a result of Seoul Survey analysis, it has been found that the more dissatisfied with residential environment the higher tendency of residential mobility in single household. Thirdly, it turned out that there are three types of single households based on the characteristics of their demographic characteristics, and it was different from recognition of home and partner of counselling by the three types of single households. Fourthly, this paper extracted eight significant variables with a spatial aggregated dataset which are highly correlated to the ratio of occupancy of single household in 25 Seoul Municipals, and to conclude, it investigated the relation between spatial distribution of single households and their demographic statistics based on the six divided groups obtained by Cluster Analysis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.507-516
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• 2019

The investigation of land characteristics is an important task for the calculation of officially land prices and standard comparison table of land price. Therefore, it should be done objectively and consistently. However, the current investigation system is mainly done by researcher's subjective judgment. Therefore, the objectivity and consistency of this investigation is not guaranteed and questionable. In this study, we first defined the problem by analyzing the current land topography investigation method. In addition, in order to investigate the land topography, the geometry of the parcel is quantified by spatial information and applied to the decision tree based method(C4.5) to produce the final result. This study intended to extract the parcel characteristics data of the topographic by the use of spatial information and to apply the information to the C4.5, there by suggesting a method for addressing the problems. The findings showed approximately 93.5% between the results of topography classification estimated with rules learned by C4.5.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.653-663
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• 2012

Using soft classification, it is possible to obtain the land cover proportions from the remotely sensed image. These land cover proportions are then used as input data for a procedure called "super-resolution mapping" to produce the predicted hard land cover layers at higher resolution than the original remotely sensed image. Superresolution mapping can be implemented using a number of algorithms in which the Hopfield Neural Network (HNN) has showed some advantages. The HNN has improved the land cover classification through superresolution mapping greatly with the high resolution data. However, the super-resolution mapping is based on the spatial dependence assumption, therefore it is predicted that the accuracy of resulted land cover classes depends on the relative size of spatial features and the spatial resolution of the remotely sensed image. This research is to evaluate the capability of HNN to implement the super-resolution mapping for SPOT image to create higher resolution land cover classes with different zoom factor.

• Proceedings of the KAIS Fall Conference
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• 2010.05b
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• pp.630-633
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• 2010

입지분석은 공간 및 비공간적 특성이 중요하게 다루어져야 함에도 불구하고 공간데이터 타입(spatial data type), 공간관계(spatial relationship), 그리고 공간 자기상관성(spatial autocorrelation)의 복잡성에 기인한 처리의 어려움으로 인해 기하학적거리나 공간적 위치와 같은 단순 공간적 특성만 이용되었다. 본 연구에서는 서울시 대형할인점을 사례로하여로 GIS에 의한 공간데이터와 비공간데이터(인구통계 등)를 통합 구축한 후, 공간데이터마이닝 기법을 이용하여 입지패턴(location pattern)을 분석 추출하여 보고자 한다.

• Journal of Korea Spatial Information System Society
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• v.11 no.4
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• pp.74-84
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• 2009

GIS based new postal marketing method is presented in this paper with spatiotemporal mining to cope with domestic mail volume decline and to strengthening competitiveness of postal business. Market segmentation technique for socialogy of population and spatiotemporal prediction of consumer propensity to purchase through spatiotemporal multi-dimensional analysis are suggested to provide meaningful and accurate marketing information with customers. Internal postal acceptance & external statistical data of local districts in the Seoul Metropolis are used for the evaluation of geo-lifestyle clustering and spatiotemporal cube mining. Successfully optimal 14 maketing clusters and spatiotemporal patterns are extracted for the prediction of consumer propensity to purchase.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5782-5799
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• 2018

With the development of GPS and the popularity of mobile devices with positioning capability, collecting massive amounts of trajectory data is feasible and easy. The daily trajectories of moving objects convey a concise overview of their behaviors. Different social roles have different trajectory patterns. Therefore, we can identify users or groups based on similar trajectory patterns by mining implicit life patterns. However, most existing daily trajectories mining studies mainly focus on the spatial and temporal analysis of raw trajectory data but missing the essential semantic information or behaviors. In this paper, we propose a novel trajectory semantics calculation method to identify groups that have similar behaviors. In our model, we first propose a fast and efficient approach for stay regions extraction from daily trajectories, then generate semantic trajectories by enriching the stay regions with semantic labels. To measure the similarity between semantic trajectories, we design a semantic similarity measure model based on spatial and temporal similarity factor. Furthermore, a pruning strategy is proposed to lighten tedious calculations and comparisons. We have conducted extensive experiments on real trajectory dataset of Geolife project, and the experimental results show our proposed method is both effective and efficient.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.485-489
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• 2003

Several clustering methods for spatial data mining have been devised in the literature, but have the following drawback: increase cost due to calculating distance among objects. To solve this problem, we propose a spatial clustering method using regular cells. In this paper, we design and implement file structures, data structures and algorithms to realize the proposed method, also, show experimental results after applying test data to the implemented method.

• Safety and Health at Work
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• v.11 no.1
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• pp.1-9
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• 2020

Background: As the impact of climate change intensifies, exposure to heat stress will grow, leading to a loss of work capacity for vulnerable occupations and affecting individual labor decisions. This study estimates the future work capacity under the Representative Concentration Pathways 8.5 scenario and discusses its regional impacts on the occupational structure in the Republic of Korea. Methods: The data utilized for this study constitute the local wet bulb globe temperature from the Korea Meteorological Administration and information from the Korean Working Condition Survey from the Occupational Safety and Health Research Institute of Korea. Using these data, we classify the occupations vulnerable to heat stress and estimate future changes in work capacity at the local scale, considering the occupational structure. We then identify the spatial cluster of diminishing work capacity using exploratory spatial data analysis. Results: Our findings indicate that 52 occupations are at risk of heat stress, including machine operators and elementary laborers working in the construction, welding, metal, and mining industries. Moreover, spatial clusters with diminished work capacity appear in southwest Korea. Conclusion: Although previous studies investigated the work capacity associated with heat stress in terms of climatic impact, this study quantifies the local impacts due to the global risk of climate change. The results suggest the need for mainstreaming an adaptation policy related to work capacity in regional development strategies.