• Journal of the HCI Society of Korea
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• v.7 no.1
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• pp.19-28
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• 2012

Why people share location context information? The purpose of our study is that why users share location context information and which factors is related to co-experiences in LBSNS. Through an exploratory approach, first, we conducted a qualitative study in order to understand experiences among people in LBSNS and analyzed according to Grounded Theory. As a result, we found concepts regarding co-experiences cooperatively and competitively in LBSNS. Second, the theoretical model of co-experience was constructed by mediating perceived empathy based on theoretical foundation. In this study, theoretically, we suggested exploratory research model of co-experiences in LBSNS. Practically, designers could adopt concepts in terms of competition and collaboration among users to build co-experience of LBSNS services as its guidelines.

• The Journal of the Korea Contents Association
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• v.13 no.8
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• pp.171-179
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• 2013

During 19th general election the customized LBSNS(Location Based SNS) application for some candidates of the National Assembly planned and developed based on the object identification, ePosition, comprising the candidate's name have been applied for an election campaign. The approval rating change before and after 19th election campaign period for each candidate was quantitatively studied how it would be affected by the candidate custom LBSNS application. Only 3 out of 24 candidates in 6 local electorates in the Daejon Metropolitan City have adopted the customized LBSNS application and the rest 21 candidates have not, whose approval rating change before and after an election campaign has been analyzed comparatively candidate by candidate. The approval rating for 3 candidates adopting LBSNS application went up by 12.6%, 11.4%, 11.2% respectively, but those for the rest 21 candidates all changed within 3%.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.5
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• pp.55-62
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• 2011

Recently, LBS(Location Based Service) is expanding its service areas with the spread of smart phones and the contents of the service are more individualized according to the customer's needs. Specially, LBSNS(Location Based Social Network Service) is emerging as the most promising service of LBS. This paper introduces a LBSNS model to form a community to share common contents dynamically centering around the place of user's smart phone. The methodology suitable for implementing the effective management and the automatic update of the community of social network is presented in this paper. In addition, we describe the significant future researches of LBSNS under the upcoming mobile environment.

• Proceedings of the Korea Contents Association Conference
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• 2013.05a
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• pp.111-112
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• 2013

19대 총선에서 특정 후보를 홍보하기 위한 목적으로 개인 맞춤형 위치기반소셜네트워크서비스(LBSNS, Location Based Social Network Service) 앱(App)을 기획하여 개발하고 선거기간 동안 활용함으로써 선거 전후 해당 후보의 지지율 변화에 어느 정도 기여했는지 그 영향을 분석하였다. 대전광역시 6개 지역구 24명의 후보를 대상으로 개인 맞춤형 LBSNS 앱을 활용한 후보와 그렇지 않은 후보를 구분하여 선거운동 기간 동안 언론에 공개적으로 발표된 지지율과 개표 결과를 바탕으로 개인별 지지율 변화를 비교하였다. 해당 앱을 활용한 3명의 후보는 각각 12.6%, 11.4% 및 11.2%씩 두 자리 수의 지지율 상승이 있었지만 나머지 21명의 후보들은 지지율 변화는 모두 3% 이내로 머물러 개인맞춤형 스마트폰 앱을 활용함으로써 후보 지지율 상승에 상당한 효과가 있었음이 밝혀졌다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.752-758
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• 2012

SNS(Social Networking Services) is oneline service that enable users to construct human network through their relation on web, such as following relation, friend relation, and etc. Recently, owing to the advent of digital devices (smart phone, tablet PC) which embedded GPS some applications which provide services with spatial relevance and social relevance have been released. Such an online service is called LBSNS. It is required to use spatial filtering so as to build the LBSNS system that enable users to subscribe information of interesting area. For spatial filtering, user and tweet attaches location information which divide into static property presenting fixed area and dynamic property presenting user's area changed along the moving user. In the case of using a location information including dynamic property, Continuous query occurred from the moving user causes the problem in server. In this paper, we propose spatial filtering algorithm using Virtual Grid for reducing frequency of query, and conclude that frequency of query on using Virtual Grid is 93% decreased than frequency of query on not using Virtual Grid.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.109-119
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• 2018

Point of interest (POI) of the city is a special place that has what importance to the user. For example, it is such landmark, restaurants, museums, hotels, and theaters. Because of its role in the social and economic life of us, these have attracted a lot of interest in location-based applications such as social networks and online map. However, while it can easily be obtained through the Web, the basic information of POI such as geographic location, another effort is required to obtain detailed information such as Wi-Fi, accepting credit cards, opening hours, romper room and the assessment and evaluation of other users. To solve these problems, a new method for correcting position error is required to link location-based social network service (LBSNS) data and POIs. This paper attempts to propose a position error correction method of POI and LBSNS data to enrich POI information from the vast information that is accumulated in LBSNS. Through this study, we can overcome the limitation of individual POI information via the information fusion method of LBSNS and POI, and we have discovered the possibility to be able to provide additional information which users need. As a result, we expect to be able to collect a variety of POI information quickly.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.277-287
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• 2014

In LBSNS(Location-based Social Network Service), users can share locations and communicate with others by using check-in data. The check-in data consists of POI name, category, coordinate and address of locations, nickname of users, evaluating grade of locations, related article/photo/video, and etc. If you analyze the check-in data from the location-based social network service in accordance with your situation, you can provide various customized services. Therefore, In this paper, we develop a location recommendation system based on LBSNS that can utilize the check-in data efficiently. This system analyzes the location category of the check-in data, determines the weighted value of it, and finds out the similarity between users by using the Pearson correlation coefficient. Also, it obtains the preference score of recommended locations by using the collaborated filtering algorithm and then, finds out the distance score by applying the Euclidean's algorithm to the recommended locations and the current users' locations. Finally, it recommends appropriate locations by applying the weighted value to the preference score and the distance score. In addition, this paper approved excellence of the proposed system throughout the experiment using real data.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.25-33
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• 2016

In order to visualize point based Location-Based Social Network Services(LBSNS) data on multi-scaled tile map effectively, it is necessary to apply tile-based clustering method. Then determinating reasonable numbers and size of tiles is required. However, there is no such criteria and the numbers and size of tiles are modified based on data type and the purpose of analysis. In other words, researchers' subjectivity is always involved in this type of study. This is when Modifiable Areal Unit Problem(MAUP) occurs, that affects the results of analysis. Among LBSNS, geotagged Twitter data were chosen to find the influence of MAUP in scale effects perspective. For this purpose, the degree of spatial autocorrelation using spatial error model was altered, and change of distributions was analyzed using Morna's I. As a result, positive spatial autocorrelation showed in the original data and the spatial autocorrelation was decreased as the value of spatial autoregressive coefficient was increasing. Therefore, the intensity of the spatial autocorrelation of Twitter data was adjusted to five levels, and for each level, nine different size of grid was created. For each level and different grid sizes, Moran's I was calculated. It was found that the spatial autocorrelation was increased when the aggregation level was being increased and decreased in a certainpoint. Another tendency was found that the scale effect of MAUP was decreased when the spatial autocorrelation was high.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.230-232
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• 2011

Owing to the advent of digital devices which equipped with GPS, such as smartphone and tablet pc, a number of LBSNS applications have been released and even SNS applications serve various Location-Based Services. In twitter's case, the news of interesting area is provided to user not by being subscribed them automatically, but by being searched on web-site. This paper describes the system designed for users want to subscribe the local news without procedure like searching using operators. This system uses PBSM(Partition Based Spatial-Merge Join) which has no index for batch processing and against a massive query. The results from Spatial Join are stored in Materialized View then provided to user.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.91-94
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• 2011

The LBSNS(Location-Based Social Networking Service) service has been well-received by researchers and end-users, such as Twitter. Location-Based service of Twitter is now structured that users could not subscribe the information of their interesting local area. Those who being following from someone tweet message included information of local area to them just for their own interesting. However, follower may receive that kind of tweet. In order to handle the problem, we propose filtering technique using spatial join. The first work for filtering technique is to add a location information to tweets and users. In this paper, location information is represented by MBR(Minimum Bounding Rectangle). Location information is divided into dynamic property and static property. Suppose that users are continuously moving, that means one of the dynamic property's example. At this time, a massive continous query could cause the problem in server. In this paper, we create Virtual Grid on Google Map for reducing frequency of query, and conclude that it is useful for server.