Abstract
Users tend to check in and post their statuses in location- based social networks (LBSNs) to describe that their in- Terests are related to a point-of-interest (POI). Since the relevance of the data to the POI varies according to the ge- ographic distance between the POI and the locations where the data are generated, it is important to characterize an area-of-interest (AOI) that enables to utilize the location information in a variety of businesses, services, and place advertisements. While previous studies on discovering AOIs were conducted based mostly on density-based clustering methods with the collection of geo-tagged photos from LB- SNs, we focus on detecting a POI boundary, which corresponds to only one cluster containing its POI center. Using geo-tagged tweets recorded from Twitter users, this paper introduces a low-complexity two-phase strategy to detect a POI boundary by finding a suitable radius reachable from the POI center. We detect a polygon-type boundary of the POI as the convex hull (i.e., the outermost region) of selected geo-tags through our two-phase approach, where each phase proceeds on with different sizes of radius increment, thus yielding a more precise boundary. It is shown that our approach outperforms the conventional density-based clustering method in terms of runtime complexity.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2015 - Held in Conjunction with ACM SIGSPATIAL 2015 |
| Editors | Sen Xu, Alexei Pozdnoukhov, Dimitris Sacharidis |
| Publisher | Association for Computing Machinery, Inc |
| ISBN (Electronic) | 9781450339759 |
| DOIs | |
| Publication status | Published - 2015 Nov 3 |
| Event | 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2015 - Seattle, United States Duration: 2015 Nov 3 → 2015 Nov 6 |
Publication series
| Name | Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2015 - Held in Conjunction with ACM SIGSPATIAL 2015 |
|---|
Conference
| Conference | 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2015 |
|---|---|
| Country | United States |
| City | Seattle |
| Period | 15/11/3 → 15/11/6 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
Cite this
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Low-complexity detection of POI boundaries using geo-tagged Tweets : A geographic proximity based approach. / Vu, Dung D.; Shin, Won-Yong.
Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2015 - Held in Conjunction with ACM SIGSPATIAL 2015. ed. / Sen Xu; Alexei Pozdnoukhov; Dimitris Sacharidis. Association for Computing Machinery, Inc, 2015. a5 (Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2015 - Held in Conjunction with ACM SIGSPATIAL 2015).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Low-complexity detection of POI boundaries using geo-tagged Tweets
T2 - A geographic proximity based approach
AU - Vu, Dung D.
AU - Shin, Won-Yong
PY - 2015/11/3
Y1 - 2015/11/3
N2 - Users tend to check in and post their statuses in location- based social networks (LBSNs) to describe that their in- Terests are related to a point-of-interest (POI). Since the relevance of the data to the POI varies according to the ge- ographic distance between the POI and the locations where the data are generated, it is important to characterize an area-of-interest (AOI) that enables to utilize the location information in a variety of businesses, services, and place advertisements. While previous studies on discovering AOIs were conducted based mostly on density-based clustering methods with the collection of geo-tagged photos from LB- SNs, we focus on detecting a POI boundary, which corresponds to only one cluster containing its POI center. Using geo-tagged tweets recorded from Twitter users, this paper introduces a low-complexity two-phase strategy to detect a POI boundary by finding a suitable radius reachable from the POI center. We detect a polygon-type boundary of the POI as the convex hull (i.e., the outermost region) of selected geo-tags through our two-phase approach, where each phase proceeds on with different sizes of radius increment, thus yielding a more precise boundary. It is shown that our approach outperforms the conventional density-based clustering method in terms of runtime complexity.
AB - Users tend to check in and post their statuses in location- based social networks (LBSNs) to describe that their in- Terests are related to a point-of-interest (POI). Since the relevance of the data to the POI varies according to the ge- ographic distance between the POI and the locations where the data are generated, it is important to characterize an area-of-interest (AOI) that enables to utilize the location information in a variety of businesses, services, and place advertisements. While previous studies on discovering AOIs were conducted based mostly on density-based clustering methods with the collection of geo-tagged photos from LB- SNs, we focus on detecting a POI boundary, which corresponds to only one cluster containing its POI center. Using geo-tagged tweets recorded from Twitter users, this paper introduces a low-complexity two-phase strategy to detect a POI boundary by finding a suitable radius reachable from the POI center. We detect a polygon-type boundary of the POI as the convex hull (i.e., the outermost region) of selected geo-tags through our two-phase approach, where each phase proceeds on with different sizes of radius increment, thus yielding a more precise boundary. It is shown that our approach outperforms the conventional density-based clustering method in terms of runtime complexity.
UR - http://www.scopus.com/inward/record.url?scp=84956643126&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84956643126&partnerID=8YFLogxK
U2 - 10.1145/2830657.2830663
DO - 10.1145/2830657.2830663
M3 - Conference contribution
T3 - Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2015 - Held in Conjunction with ACM SIGSPATIAL 2015
BT - Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, LBSN 2015 - Held in Conjunction with ACM SIGSPATIAL 2015
A2 - Xu, Sen
A2 - Pozdnoukhov, Alexei
A2 - Sacharidis, Dimitris
PB - Association for Computing Machinery, Inc
ER -