• Economic and Environmental Geology
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• v.30 no.4
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• pp.371-377
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• 1997

This paper presents the geophysical applications to the environmenml problem in an abandoned mine area. We would like to focus our attention on the mapping of the soil contamination and the detection of the buried mine tailings. For mapping the soil contamination. measurements of both in-situ magnetic susceptibility (k) and terrain conductivity were carried out. In-situ magnetic susceptibilities of the contaminated soil due to the acid mine drainage show higher values than those of the uncontaminated area. However. those data do not show the correlation with the degree of the soil contamination observed on the surface. The least-squares fitted formula obtained with the measured insitu magnetic susceptibilities is $k=4.8207{\times}W^{0.6332}$, where W is the $Fe^{+2}$ weight percentage. This weight gives most effect to magnetic susceptibility of the soil. Lateral variations of the soil contamination in the shallow subsurface can be detected by the electrical conductivity distributions from EM induction survey. TDIP (Time Domain Induced Polarization) and EM induction surveys were conducted to detect the buried mine tailings. From the results of TDIP, the spatial zone, which shows high chargeability-low resistivity, is interpreted as the buried mine tailings. Therefore, it is concluded that it is possible to discriminate the spatial zone from the uncontaminated ground.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.85-95
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• 2021

Recently, as data mining and artificial neural network techniques are developed, analyzing large amounts of data is proposed to reduce the dimension of the data. In general, empirical orthogonal function (EOF) used to reduce the dimension in the ocean data and recently, Self-organizing maps (SOM) algorithm have been investigated to apply to the ocean field. In this study, both algorithms used the monthly Sea level anomaly (SLA) data from 1993 to 2018 around the East Asia Coasts. There was dominated by the influence of the Kuroshio Extension and eddy kinetic energy. It was able to find the maximum amount of variance of EOF modes. SOM algorithm summarized the characteristic of spatial distributions and periods in EOF mode 1 and 2. It was useful to find the change of SLA variable through the movement of nodes. Node 1 and 5 appeared in the early 2000s and the early 2010s when the sea level was high. On the other hand, node 2 and 6 appeared in the late 1990s and the late 2000s, when the sea level was relatively low. Therefore, it is considered that the application of the SOM algorithm around the East Asia Coasts is well distinguished. In addition, SOM results processed by SLA data, it is able to apply the other climate data to explain more clearly SLA variation mechanisms.

• Proceedings of the KGS Conference
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• 2004.05a
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• pp.102-102
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• 2004

많은 데이터들이 데이터베이스로 구축되면서, 데이터로부터 의미 있는 정보나 지식을 도출하기 위한 새로운 분석법이 제기 되었는데, 그 중 하나가 데이터 마이닝이다. 데이터 마이닝은 급격하게 증가하는 데이터들을 보다 효과적으로 분석하여 유용하고 의미 있는 정보나 지식을 찾기 위해 수행하는 데이터 분석 방법이다. 하지만 이러한 방법이 공간데이터에 적용될 때는 공간 데이터의 특수성으로 인해 그 효과를 기대하기가 어렵다. (중략)

• The KIPS Transactions:PartD
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• v.13D no.5 s.108
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• pp.633-640
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• 2006

Spatial clustering, which groups similar objects based on their distance, connectivity, or their relative density in space, is an important component of spatial data mining. Density-based and grid-based clustering are two main clustering approaches. The former is famous for its capability of discovering clusters of various shapes and eliminating noises, while the latter is well known for its high speed. Clustering large data sets has always been a serious challenge for clustering algorithms, because huge data set would make the clustering process extremely costly. In this paper, we propose an enhanced Density-Grid based Clustering algorithm for Large spatial database by setting a default number of intervals and removing the outliers effectively with the help of a proper measurement to identify areas of high density in the input data space. We use a density threshold DT to recognize dense cells before neighbor dense cells are combined to form clusters. When proposed algorithm is performed on large dataset, a proper granularity of each dimension in data space and a density threshold for recognizing dense areas can improve the performance of this algorithm. We combine grid-based and density-based methods together to not only increase the efficiency but also find clusters with arbitrary shape. Synthetic datasets are used for experimental evaluation which shows that proposed method has high performance and accuracy in the experiments.

• Spatial Information Research
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• v.13 no.3 s.34
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• pp.239-252
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• 2005

The major reason that spatial data warehousing has attracted a great deal of attention in business GIS in recent years is due to the wide availability of huge amount of spatial data and the imminent need for fuming such data into useful geographic information. Therefore, this research has been focused on designing and implementing the pilot tested system for spatial decision making. The purpose of the system is to predict targeted marketing area by discriminating the customers by using both transaction quantity and the number of customer using credit card in department store. Moreover, the pilot tested system of this research provides OLAP tools for interactive analysis of multidimensional data of geographically various granularities, which facilitate effective spatial data mining. focused on the analysis methodology, the case study is aiming to use GIS and clustering for knowledge discovery. Especially, the importance of this study is in the use of snowflake schema model capabilities for GIS framework.

• Journal of Korea Spatial Information System Society
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• v.8 no.1 s.16
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• pp.1-16
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• 2006

Recently, human based computing environment for supporting users to concentrate only user task without sensing other changes from users is being progressively researched and developed. But middleware deletes steream data processed for reducing process load of massive information from RFID sensor in this computing. So, this kind of middleware have problems when user demands probability or statistics needed for data warehousing or data mining and when user demands very important stream data repeatedly but already discarded in the middleware every former time. In this paper, we designs Sensor Middleware Architecture on Multi Level Spatial DBMS with Snapshot and manage repeatedly required stream datas to solve reusing problems of historical stream data in current middleware. This system uses disk databse that manages historical stream datas filtered in middleware for requiring services using historical stream information as data mining or data warehousing from user, and uses memory database that mamages highly reuseable data as a snapshot when stream data storaged in disk database has high reuse frequency from user. For the more, this system processes memory database management policy in a cycle to maintain high reusement and rapid service for users. Our paper system solves problems of repeated requirement of stream datas, or a policy decision service using historical stream data of current middleware. Also offers variant and rapid data services maintaining high data reusement of main memory snapshot datas.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.127-136
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• 2009

This paper proposes an on-line mining algorithm of moving trajectory patterns in RFID data streams considering changing characteristics over time and constraints of single-pass data scan. Since RFID, sensor, and mobile network technology have been rapidly developed, many researchers have been recently focused on the study of real-time data gathering from real-world and mining the useful patterns from them. Previous researches for sequential patterns or moving trajectory patterns based on stream data have an extremely time-consum ing problem because of multi-pass database scan and tree traversal, and they also did not consider the time-changing characteristics of stream data. The proposed method preserves the sequential strength of 2-lengths frequent patterns in binary relationship table using the time-evolving graph to exactly reflect changes of RFID data stream from time to time. In addition, in order to solve the problem of the repetitive data scans, the proposed algorithm infers candidate k-lengths moving trajectory patterns beforehand at a time point t, and then extracts the patterns after screening the candidate patterns by only one-pass at a time point t+1. Through the experiment, the proposed method shows the superior performance in respect of time and space complexity than the Apriori-like method according as the reduction ratio of candidate sets is about 7 percent.

• Journal of Korea Spatial Information System Society
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• v.8 no.1 s.16
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• pp.27-35
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• 2006

As the increasing interest about geographical information, such researches and applied fields become wide. OGC(Open GIS Consortium) developed GML(Geography Markup Language) which is adopted XML(extensible Markup Language) in GIS field. In various applied field, GML is used and studied continuously. This paper try to find out the meaningful rules using Apriori algorithm from GML documents, one of the data mining techniques which is studied based on existing XML documents There are two ways to find out the rules. One is the way that find out the related rules as extracting the content in GML documents, the other find out the related rules based on used tags and attributes. This paper describes searching the rules through two ways and shows the system adopted two ways.

• Korean Journal of Remote Sensing
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• v.33 no.5_3
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• pp.773-786
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• 2017

This study was to analyze data of climate change impacts on social infrastructure using text-mining methodology, and to visualize the spatial information by integrating those with regional data layers. First of all, the study identified that the following social infrastructure; power, oil and resource management, transport and urban, environment, and water supply infrastructures, were affected by five kinds of climate factors (heat wave, cold wave, heavy rain, heavy snow, strong wind). Climate change impacts on social infrastructure were then analyzed and visualized by regions. The analysis resulted that transport and urban infrastructures among all kinds of infrastructure were highly impacted by climate change, and the most severe factors of the climate impacts on social infrastructure were heavy rain and heavy snow. In addition, it found out that social infrastructure located in Seoul and Gangwon-do region were relatively largely affected by climate change. This study has significance that atypical data in media was used to analyze climate change impacts on social infrastructure and the results were translated into spatial information data to analyze and visualize the climate change impacts by regions.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.245-253
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• 2016

Background in a novel is most important elements with characters and events, and means time, place and situation that characters appeared. Among the background, spatial background can help conveys topic of a novel. So, it may be helpful for choosing a novel that readers want to read. In this paper, we are targeting Korean historical novels. In case of English text, It can be recognize spatial background easily because it use upper and lower case and words used with the spatial information such as Bank, University and City. But, in case Korean text, it is difficult to recognize that spatial background because there is few information about usage of letter. In the previous studies, they use machine learning or dictionaries and rules to recognize about spatial information in text such as news and text messages. In this paper, we build a nation dictionaries that refer to information such as 'Korean history' and 'Google maps.' We Also propose a method for recognizing spatial background based on patterns of postposition in Korean sentences comparing to previous works. We are grasp using of postposition with spatial background because Korean characteristics. And we propose a method based on result of morpheme analyze and frequency in a novel text for raising accuracy about recognizing spatial background. The recognized spatial background can help readers to grasp the atmosphere of a novel and to understand the events and atmosphere through recognition of the spatial background of the scene that characters appeared.