• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.1
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• pp.26-39
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• 2011

This paper reviews the strategy of activating spatial data community and its impacts, focusing on the Cooperative Agreements Program(CAP) of National Spatial Data Infrastructure(NSDI), established by Federal Geographic Data Committee(FGDC) in U.S. to facilitate cooperative data sharing. After thoroughly reviewing the 20 research categories followed by 350 cases, supported by CAP for the last decade, it turned out that since NSDI issued "Future Directions Initiative" in 2004, the CAP adopted all components of NSDI as CAP categories in attempting to reinforce the partnership rather than mainly deploying standardized meta-data. Also, CAP has been utilized as a means to enforce the role of NSDI as spatial data user and provider as well, recognizing that the spatial data community is essence for expandable and sustainable NSDI. Implementing CAP resulted in the sound structure of spatial data marketplaces increasing spatial data demand and promoting the relevant business due to diverse users.

• Proceeding of KASS Symposium
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• 2008.05a
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• pp.155-158
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• 2008

As the span of spatial structure is getting longer, the law frequency of the structure makes the wind-induced response much increased. However, there are lots of hardships to establish the economical structural systems due to the fact that an relative equation between the frequency and the span of the domestic spatial structures is not existed in the stage of the basic planning design. Therefore, among the large-span structures, this paper focused on the relationship between the frequency and the span of the world-cup stadium built in 2000s. The relative equation between the frequency and span is compared with the data measured in Japan. Moreover, we are willing to provide the basic study by suggesting the summary equation in this paper.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.851-858
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• 2009

In the last decades, there has been much interest in climate variability because its change has dramatic effects on humanity. Especially, the precipitation data are measured over space and their spatial association is so complicated. So we should take into account such a spatial dependency structure while analyzing the data. However, in linear models for analyzing the data, data sets show severely skewed distribution. In the paper, we consider the Box-Cox transformation to satisfy the normal distribution prior to the analysis, and employ a Bayesian hierarchical framework to investigate the spatial patterns. The data set we considered is monthly average precipitation of the third quarter of 2007 obtained from 347 automated monitoring stations in Contiguous South Korea.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1395-1409
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• 2006

A spatial data warehouse has spatial data cube composed of multi-dimensional data for efficient OLAP(On-Line Analytical Processing) operations. A spatial data cube supporting concept hierarchies holds huge amount of data so that many researches have studied a incremental update method for minimum modification of a spatial data cube. The Cube, however, compressed by eliminating prefix and suffix redundancy has coalescing paths that cause update inconsistencies for some updates can affect the aggregate value of coalesced cell that has no relationship with the update. In this paper, we propose incremental batch update method of a spatial data cube. The proposed method uses duplicated nodes and extended node structure to avoid update inconsistencies. If any collision is detected during update procedure, the shared node is duplicated and the duplicate is updated. As a result, compressed spatial data cube that includes concept hierarchies can be updated incrementally with no inconsistency. In performance evaluation, we show the proposed method is more efficient than other naive update methods.

• 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.

• Asia-Pacific Journal of Business
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• v.13 no.1
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• pp.277-288
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• 2022

Purpose - This study analyzed the determinants of local unemployment rate in Korea using panel data from 16 metropolitan cities and provinces from 2000 to 2018. Design/methodology/approach - We use a dynamic spatial panel model that considers characteristics of the regional unemployment rate such as the common factors effect, spatial dependence, and serial correlations. Findings - The local unemployment rate is affected by the past and present values of the national unemployment rate. And it is significantly affected by the past local unemployment rate and the past neighboring unemployment rate because spatial dependence and serial correlations are clearly present. In addition, when the industrial structure diversity and labor productivity were high, the regional unemployment rate decreased, and when the education level was high, the regional unemployment rate increased. Research implications or Originality - In order to reduce regional unemployment rate, it is necessary to plan and establish regional customized industrial structure policies under the stance of diversification rather than specializing the regional industrial structure and accompany improvement of the quality of education with the number of years of education. In addition, the redistribution of labor from low labor productivity sectors to high labor productivity sectors through technology development will help to reduce the local unemployment rate.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.1
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• pp.69-88
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• 2023

Efficient spatial planning is one of the necessary factors to successfully respond to climate change. And researchers often use LULC(Land-Use/Cover) data to conduct land use and spatial planning research. However, LULC data has a limited number of grades related to urban surface, so each different urban structure appearing in several cities is not easily analyzed with existing land cover products. This limitation of land cover data seems to be overcome through LCZ(Local Climate Zone) data used in the urban heat island field. Therefore, this study aims to first discuss whether LCZ data can be applied not only to urban heat island fields but also to other fields, and secondly, whether LCZ data still have problems with existing LULC data. Research methodology is largely divided into two categories. First, through literature review, studies in the fields of climate, land use, and urban spatial structure related to LCZ are synthesized to analyze what research LCZ data is currently being used, and how it can be applied and utilized in the fields of land use and urban spatial structure. Next, the GIS spatial analysis methodology is used to analyze whether LCZ still has several errors that are found in the LULC.

• Journal of Korean Society of Rural Planning
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• v.22 no.1
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• pp.49-56
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• 2016

Throughout the economic development era of Korea, migration occurred within a spatial hierarchy, with upward flows from rural areas to urban. The concept of step migration is a typical theory to explain these upward migration flows. Recent migration data and trends, however, indicate that migration-pattern regime shows strongly opposite-direction flows, with many of the major migration flowing downward on this national-spatial hierarchy, away from urban areas. In this study, we examine the most recent structure of migration flows up and down within the national-spatial hierarchy. We define seven tiers to tabulate origin-destination migration flows from population density of local administrative districts for the period 2001-2014, and then analyze the migration patterns between the tiers over time. The results show differentiated patterns of migration within the national-spatial hierarchy over time including specific states of migrants' life cycles.

• Spatial Information Research
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• v.20 no.2
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• pp.93-103
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• 2012

Recently metropolitan cities pursue multi centric urban spatial structure for sustainable development and efficient urban management. Thus, this study calculated population potential using data on population distributed among road nodes for the last 50 years, and based on the results. We measured the spatial variability of the multi centric structure of Busan Metropolitan City. According to the results, the multi centralization process has been continued up to recently in Busan Metropolitan City. As population potential is concentrated on sub centers, Hadan, Gupo and Haeundae areas were playing an increasingly powerful role as the center of the respective district, and Sasang and Dongrae had been losing their role as the center of their respective districts since 2000 and 1990, respectively. Additionally, in all the multi centric districts except Haeundae was observed the increase of oblongity, which is the change of spatial structure in an unbalanced way toward a specific area or direction.

• Spatial Information Research
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• v.8 no.2
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• pp.213-232
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• 2000

It is important to scrutinize spatial patterns in crime analysis since crime data has geographical attribute in itself. We focus on the development of ¨Crime Analysis GIS¨ prototype which can discover spatial patterns in crime data by integrating mapping functions of GIS and spatial analysis techniques. The structure of this system involves integration of DBMS and GIS, and the major functions of the system include (i) exploring spatial distribution of point data, (ii) mapping hot-spot, (iii) clustering analysis of crime occurrence, and (iv) analyzing aggregated areal data. The process of design and implementation of this system is based on object-oriented methodologies. A web-based extension of the prototype using 3-tier architecture is currently under development.