• Spatial Information Research
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• v.17 no.4
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• pp.405-416
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• 2009

According to the changes of the city's paradigm, the demand on u-City increases rapidly. u-City has been built at 54 areas in Korea (as of May 2009). One of the important determinants of success or failure in the increasing of u-City is how to provide u-Services. Most current u-Services are Sensor Network-based monitoring services to manage urban infrastructure. u-GIS is one of fundamental requirements to implement 'any time and any where' u-Service which covers the essential meaningful term "ubiquitous". Hence, in this paper, we 1) describe the definition of the spatial awareness, 2) discuss how to converge (Spatial Embedding) among different spatial data : topographic spatial data, sensor spatial data, and more, 3) bring forth an advanced form of u-Service, 4) analyze the state-of-the-art u-GIS techniques.

• Journal of the Korean housing association
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• v.22 no.3
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• pp.73-82
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• 2011

The purpose of this study was to develop the curriculum of spatial convergence studies for the major field of housing and interior design. Documentary research and content analysis methods were used in this study. Data drawn from internet homepage of each universities which were 20 colleges the major field of housing and interior design and 160 colleges of architectural design, and public institutions the MLTM and the KRIHS. The major results of this study were as follows. 1) Government has been supported the specialized graduate school of spatial information and the university which they have spatial information curriculum during 5 years. 2) The department of spatial information has been educated the theory and practice about spatial issues in order to train the spatial specialist helpful to the new growing industry. 3) There were little changed curriculum which has related to the department of housing and interior design. The half of their curriculum were housing project and interior design. The spatial convergence studies educational program should be grow up step by step. The first is basic level to learn the basic theory of spatial studies for example the spatial introduction or the housing and the second is low level to learn the depth theory of spatial studies for example the design I or the housing development. The third is middle level to apply the depth theory of spatial studies for examples the design II, III or housing policy and institutions. And the last is high level to practice the depth theory of spatial studies for example housing construction or internship.

• Spatial Information Research
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• v.19 no.1
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• pp.117-126
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• 2011

Recently, AR applications provide location-based spatial information by GPS. Also, the spatial information is displayed by the angle of the camera. So far, traditional spatial indexes in spatial database field retrieve and filter spatial information by the minimum bounding rectangle (MBR) algorithm.(ex. R-tree) MBR strategy is a useful technique in the geographic information systems and location based services. But MBR technique doesn't reflect the characteristics of spatial queries in AR. Spatial queries of AR applications have high possibility of the dead space area between MBRs of non-leaf node and query area. We propose triangle node filtering algorithm that improved efficiency of spatial retrieval used the triangle node filtering techniques by exclusion the dead space. In this paper, the proposed algorithm has been implemented on PostgreSQL/PostGIS. Experimental results show the spatial retrieval that using the proposed algorithm better performance than the spatial retrieval that of the minimum bounding rectangle algorithm.

• Education of Primary School Mathematics
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• v.16 no.3
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• pp.303-313
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• 2013

Spatial ability has been valued as one component of intelligence and associated with the achievements in science, technology, engineering, and mathematics (STEM) disciplines and important in STEM education. The purpose of this study is to assess elementary school students' spatial ability and analyze the relationship with mathematical achievement, gender and grade level. This study explored the spatial visualization ability of 1288 elementary school students (grade 4-6) in Seoul and Gangwon province and investigated association between spatial ability and students' mathematics achievement, the students' spatial ability according to their gender and grade level. As a result, this study showed that there were significant correlations between spatial ability and mathematical achievement. And also, boy students were better than girl students in spatial ability and higher grader were better than lower graders in spatial ability. According to these results, spatial ability should be included as one of the important components in identifying students for gifted education programs. Furthermore, more research is needed on how to effectively structure educational opportunities to students both who have high spatial ability and have low spatial ability.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.452-457
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• 2015

In this paper, we propose a spatial query processor, SQUERY, which can derive rich query results through spatial reasoning on the initial knowledge base, as well as, process both qualitative and quantitative queries about the topological and directional relationships between spatial objects. In order to derive richer query results, the query processor expands the knowledge base by applying forward spatial reasoning into the initial knowledge base in a preprocessing step. The proposed query processor uses not only qualitative spatial knowledge describing topological/directional relationship between spatial objects, but also utilizes quantitative spatial knowledge including geometric data of individual spatial objects through geometric computation. The results of an experiment with the OSM(Open Street Map) spatial knowledge base demonstrates the high performance of our spatial query processing system.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.193-204
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• 2010

Recently, researchers of the various fields where the spatial analysis is needed have more interested in spatial statistics. In case of data with spatial correlation, methodologies accounting for the correlation are required and there have been developments in methods for spatial data analysis. Lattice data among spatial data is analyzed with following three procedures: (1) definition of the spatial neighborhood, (2) definition of spatial weight, and (3) the analysis using spatial models. The present paper shows a spatial statistical analysis method superior to a general statistical method in aspect estimation by using the trimmed mean squared error statistic, when we analysis the spatial lattice data that outliers are included. To show validation and usefulness of contents in this paper, we perform a small simulation study and show an empirical example with a criminal data in BusanJin-Gu, Korea.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.119-132
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• 2009

Owing to the GIS technology, a vast volume of spatial data has been accumulated, thereby incurring the necessity of spatial data mining techniques. In this paper, we propose a new spatial data mining system named SD-Miner. SD-Miner consists of three parts: a graphical user interface for inputs and outputs, a data mining module that processes spatial mining functionalities, a data storage model that stores and manages spatial as well as non-spatial data by using a DBMS. In particular, the data mining module provides major data mining functionalities such as spatial clustering, spatial classification, spatial characterization, and spatio-temporal association rule mining. SD-Miner has own characteristics: (1) It supports users to perform non-spatial data mining functionalities as well as spatial data mining functionalities intuitively and effectively; (2) It provides users with spatial data mining functions as a form of libraries, thereby making applications conveniently use those functions. (3) It inputs parameters for mining as a form of database tables to increase flexibility. In order to verify the practicality of our SD-Miner developed, we present meaningful results obtained by performing spatial data mining with real-world spatial data.

• Journal of the Korean Geographical Society
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• v.42 no.4
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• pp.616-631
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• 2007

Residential differentiation is an academic theme which has been given enormous attention in urban studies. This is due to the fact that residential segregation can be seen as one of the best indicators for socio-spatial dialectics occurring on urban space. Measuring how one population group is differentiated from the other group in terms of residential space has been a focal point in the residential segregation studies. The index of dissimilarity has been the most extensively used one. Despite its popularity, however, it has been accused of inability to capture the degree of spatial clustering that unevenly distributed population groups usually display. Further, the spatial indices of segregation which have been introduced to edify the problems of the index of dissimilarity also have some drawbacks: significance testing methods have never been provided; recent advances in spatial statistics have not been extensively exploited. Thus, the main purpose of the research is to devise a spatial separation measure which is expected to gauge not only how unevenly two population groups are distributed over urban space, but also how much the uneven distributions are spatially clustered (spatial dependence). The main results are as follows. First, a new measure is developed by integrating spatial association measures and spatial chi-square statistics. A significance testing method based on the generalized randomization test is also provided. Second, a case study of residential differentiation among groups by educational attainment in major Korean metropolitan cities clearly shows the applicability of the analytical framework presented in the paper.

• Spatial Information Research
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• v.21 no.2
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• pp.23-33
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• 2013

W ith the recent advancement of IoT (Internet of Things) technology, there has been much interest in the spatial query processing which energy-efficiently acquires sensor readings from sensor nodes inside specified geographical area of interests. Therefore, various kinds of spatial query processing algorithms and distributed spatial indexing methods have been proposed. They can minimize energy consumption of sensor nodes by reducing wireless communication among them using in-network spatial filtering technology. However, they cannot optimize multiple spatial queries which w ill be w idely used in IoT, because most of them have focused on a single spatial query optimization. Therefore, we propose a new multiple spatial query optimization algorithm which can energy-efficiently process multiple spatial queries in a sensor network. The algorithm uses a concept of 'query merging' that performs the merged set after merging multiple spatial queries located at adjacent area. Here, our algorithm makes a decision on which is better between the merged and the separate execution of queries. For such the decision making, we additionally propose the cost estimation method on the spatial query execution. Finally, we analyze and clarify our algorithm's distinguished features using the spatial indexing methods of GR-tree, SPIX, CPS.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.29-36
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• 2017

In this paper, we implement a spatial big data analysis prototype based on Spark which is an in-memory system and compares the performance by the spatial split algorithm on this basis. In cluster computing environments, big data is divided into blocks of a certain size order to balance the computing load of big data. Existing research showed that in the case of the Hadoop based spatial big data system, the split method by spatial is more effective than the general sequential split method. Hadoop based spatial data system stores raw data as it is in spatial-divided blocks. However, in the proposed Spark-based spatial analysis system, there is a difference that spatial data is converted into a memory data structure and stored in a spatial block for search efficiency. Therefore, in this paper, we propose an in-memory spatial big data prototype and a spatial split block storage method. Also, we compare the performance of existing spatial split algorithms in the proposed prototype. We presented an appropriate spatial split strategy with the Spark based big data system. In the experiment, we compared the query execution time of the spatial split algorithm, and confirmed that the BSP algorithm shows the best performance.