• Journal of Korea Multimedia Society
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• v.15 no.7
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• pp.910-919
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• 2012

We have observed huge success in social network services like Facebook and Twitter, and many researchers have done their analysis on these services. As massive data observed by users is produced on Twitter, many researchers have been conducting research to detect an event on Twitter. Some of them developed a system to detect the earthquakes or to find the local festivals. However, they did not consider the credibility of location information on Twitter although their systems were using the location information. In this paper, we analyze the credibility of the profile location and the correlation between the spatial attributes on Twitter as the preliminary research of the event detection system on Twitter. We analyzed 0.5 million Twitter users in Korea and 2.8 million users around the world. 49.73% of the users in Korea and 90.64% of the users in the world posted tweets in their profile locations. This paper will be helpful to understand the credibility of the spatial attributes on Twitter when the researchers develop an application using them.

• Spatial Information Research
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• v.23 no.2
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• pp.69-81
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• 2015

If one can infer the residential area of SNS users by analyzing the SNS big data, it can be an alternative by replacing the spatial big data researches which result from the location sparsity and ecological error. In this study, we developed the way of utilizing the daily life activity pattern, which can be found from timeline data of tweet users, to infer the residential areas of tweet users. We recognized the daily life activity pattern of tweet users from user's movement pattern and the regional cognition words that users text in tweet. The models based on user's movement and text are named as the daily movement pattern model and the daily activity field model, respectively. And then we selected the variables which are going to be utilized in each model. We defined the dependent variables as 0, if the residential areas that users tweet mainly are their home location(HL) and as 1, vice versa. According to our results, performed by the discriminant analysis, the hit ratio of the two models was 67.5%, 57.5% respectively. We tested both models by using the timeline data of the stress-related tweets. As a result, we inferred the residential areas of 5,301 users out of 48,235 users and could obtain 9,606 stress-related tweets with residential area. The results shows about 44 times increase by comparing to the geo-tagged tweets counts. We think that the methodology we have used in this study can be used not only to secure more location data in the study of SNS big data, but also to link the SNS big data with regional statistics in order to analyze the regional phenomenon.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.151-161
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• 2009

In Location-Based Services (LBSs), users requesting a location-based query send their exact location to a database server and thus the location information of the users can be misused by adversaries. Therefore, a privacy protection method is required for using LBS in a safe way. In this paper, we propose a new cloaking region generation algorithm using grids for privacy protection in LBSs. The proposed algorithm creates a m inimum cloaking region by finding L buildings and then performs K-anonymity to search K users. For this, we make use of not only a grid-based index structure, but also an efficient pruning techniques. Finally, we show from a performance analysis that our cloaking region generation algorithm outperforms the existing algorithm in term of the size of cloaking region.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.6
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• pp.650-662
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• 2011

The statistical tools such as empirical orthogonal function (EOF), and singular value decomposition (SVD) have been applied to analyze the characteristic of air pollutant over southeast Asia as well as to evaluate Zimeke's tropospheric column ozone (ZTO) determined by tropospheric residual method. In this study, we found that the EOF and SVD analyses are useful methods to extract the most significant temporal and spatial pattern from enormous amounts of satellite data. The EOF analyses with OMI $NO_2$ and OMI HCHO over southeast Asia revealed that the spatial pattern showed high correlation with fire count (r=0.8) and the EOF analysis of CO (r=0.7). This suggests that biomass burning influences a major seasonal variability on $NO_2$ and HCHO over this region. The EOF analysis of ZTO has indicated that the location of maximum ZTO was considerably shifted westward from the location of maximum of fire count and maximum month of ZTO occurred a month later than maximum month (March) of $NO_2$, HCHO and CO. For further analyses, we have performed the SVD analyses between ZTO and ozone precursor to examine their correlation and to check temporal and spatial consistency between two variables. The spatial pattern of ZTO showed latitudinal gradient that could result from latitudinal gradient of stratospheric ozone and temporal maximum of ZTO in March appears to be associated with stratospheric ozone variability that shows maximum in March. These results suggest that there are some sources of error in the tropospheric residual method associated with cloud height error, low efficiency of tropospheric ozone, and low accuracy in lower stratospheric ozone.

• Journal of The Korea Institute of Healthcare Architecture
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• v.23 no.2
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• pp.47-61
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• 2017

Purpose: This study aims to suggest the spatial composition and area of the intensive care units through analysis of current situation for the standards establishment of the intensive care units in public district hospitals in the future. Methods: Three Methods have been used in this paper. 1) Literature reviews about composition, location, foreign standards on intensive care units. 2) Comparative analysis on the architectural drawing for the space program and the area. 3) Field survey in order to confirm differences between architectural drawing and current situation. Results: 1) The intensive care units is required to be linked by the operating department, the emergency department and the sterile supply department upon considerations of accessibility of emergency patients and the infection control. 2) The intensive care unit consists of five areas such as the patient area, the medical staff area, the material and equipment area, the patient family area, and the public area. 3) The location of spaces in the intensive care unit is classified by three methods such as connection by view, circulation and connection to corridor. The [table 20] summarizes the type of space and the average size of the space in intensive care unit. Implications: This study is a basic research for guideline of intensive care units, and need to be followed by further study using various perspectives and methods in the future.

• Journal of Cadastre & Land InformatiX
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• v.51 no.1
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• pp.125-136
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• 2021

Recently, Unmanned Aerial Vehicle utilization and image processing technology for remote sensing have diversified remarkably with Orthophoto and Digital Surface Model. In particular, It uses more application fields such as spatial information analysis and hazardous areas as well as land surveying. This study analyses the accuracy of the coordinate on Orthophoto and DSM height on slope area with high and low differences by using UAV images. As the result of this study, in the case of GCP on 2D orthophoto, the location error was not produced significantly. The vertical position of the DSM showed the highest accuracy when the height difference between GCPs is under 30m(RMSEZ=0.07m). The location of the GCPs was divided into approximately 10m, 20m, 30m, and 40m with analysis for each of the eight points of GCP and inspection points in general. This study expects that producing both horizontal accuracy of Orthophoto and vertical accuracy of DSM using UAV on the sloped area which similar to this research area will help in spatial information fields.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.2
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• pp.89-96
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• 2015

Location-based social media data have high potential to be used in various area such as big data, location based services and so on. In this study, we applied a series of analysis methodology to figure out how the important keywords in location-based social media are spatially distributed by analyzing text information. For this purpose, we collected tweet data with geo-tag in Gangnam district and its environs in Seoul for a month of August 2013. From this tweet data, principle keywords are extracted. Among these, keywords of three categories such as food, entertainment and work and study are selected and classified by category. The spatial clustering is conducted to the tweet data which contains keywords in each category. Clusters of each category are compared with buildings and benchmark POIs in the same position. As a result of comparison, clusters of food category showed high consistency with commercial areas of large scale. Clusters of entertainment category corresponded with theaters and sports complex. Clusters of work and study showed high consistency with areas where private institutes and office buildings are concentrated.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2006.05a
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• pp.1-6
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• 2006

위치기반서비스(Location-based Services: LBS)는 현재 이동통신 기술의 발달과 더불어 전 세계 이동통신시장에서 가장 활발하게 연구되고 있는 차세대 기술 분야이다. 국내에서는 최근 이동통신 회사에서 위치기반서비스에 기반을 둔 다양한 부가서비스를 제공하면서 위치기반서비스에 대한 높은 관심을 받고 있다. 위치기반서비스는 기술별로 위치측위기술, LBS플랫폼 기술, LBS 응용서비스 기술로 분류될 수 있다. 본 논문에서는 1991년부터 현재까지 위치기반서비스의 기술 분야별로 출원 및 등록된 특허를 분석함으로서 위치기반서비스의 기술동향에 대해 살펴본다.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.23-33
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• 2021

"China's Western Development Policy" has brought multiple opportunities to the development of tourism in Southwest China including Sichuan, Guizhou, Yunnan, Chongqing and Tibet. The 4 provinces and 1 municipality overall show a certain degree of accumulation effect and coordinated development in tourism due to their location, traffic and traditional economic cooperation. This study takes the Southwest China as the research object and utilized the spatial dislocation model and the tourism spatial misalignment index to estimate the mismatch degree between tourism resources and tourism income among provinces and try to find out the internal reason background. The results show that each of the five provinces has its own advantages in index of economy, tourism resources, human resource, and transportation, leading to differences in the center of gravity of the entire region in all aspects. In view of the results of spatial dislocation analysis, suggestions for improvement and optimization are put forward to promote the high-quality development of tourism in Southwest region. development.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.445-456
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• 2003

Spatial data are usually obtained at selected locations even though they are potentially available at all locations in a continuous region. Moreover the monitoring locations are clustered in some regions, sparse in other regions. One important goal of spatial data analysis is to predict unknown response values at any location throughout a region of interest. Thus, an appropriate space model should be set up and their estimates and predictions must be accompanied by measures of uncertainty. In this study we see that a space model proposed allows a best interpolation to annual rainfall data in South Korea.