Shohei Yokoyama, Ágnes Bogárdi-Mészöly, H. Ishikawa
The remarkable growth of social networking services on global positioning system (GPS)-enabled handheld devices has produced enormous amounts of georeferenced big data. Given a large spatial dataset, the challenge is to effectively discover dense regions from the dataset. Dense regions might be the most attractive area in a city or the most dangerous zone of a town. A solution to this problem can be useful in many applications, including marketing, tourism, and social research. Density-based clustering methods, such as DBSCAN, are often used for this purpose. Nevertheless, current spatial clustering methods emphasize density while neglecting human behavior derived from geographical features. In this paper, we propose EBSCAN, which is based on the novel idea of an entanglement-based approach. Our method considers not only spatial information but also human behavior derived from geographical features. Another problem is that competing methods such as DBSCAN have two input parameters. Thus, it is difficult to determine optimal values. EBSCAN requires only a single intuitive parameter, tooFar, to discover dense regions. Finally, we evaluate the effectiveness of the proposed method using both toy examples and real datasets. Our experimentally obtained results reveal the properties of EBSCAN and show that it is >10 times faster than the competitor.
{"title":"EBSCAN: An Entanglement-based Algorithm for Discovering Dense Regions in Large Geo-social Data Streams with Noise","authors":"Shohei Yokoyama, Ágnes Bogárdi-Mészöly, H. Ishikawa","doi":"10.1145/2830657.2830661","DOIUrl":"https://doi.org/10.1145/2830657.2830661","url":null,"abstract":"The remarkable growth of social networking services on global positioning system (GPS)-enabled handheld devices has produced enormous amounts of georeferenced big data. Given a large spatial dataset, the challenge is to effectively discover dense regions from the dataset. Dense regions might be the most attractive area in a city or the most dangerous zone of a town. A solution to this problem can be useful in many applications, including marketing, tourism, and social research. Density-based clustering methods, such as DBSCAN, are often used for this purpose. Nevertheless, current spatial clustering methods emphasize density while neglecting human behavior derived from geographical features. In this paper, we propose EBSCAN, which is based on the novel idea of an entanglement-based approach. Our method considers not only spatial information but also human behavior derived from geographical features. Another problem is that competing methods such as DBSCAN have two input parameters. Thus, it is difficult to determine optimal values. EBSCAN requires only a single intuitive parameter, tooFar, to discover dense regions. Finally, we evaluate the effectiveness of the proposed method using both toy examples and real datasets. Our experimentally obtained results reveal the properties of EBSCAN and show that it is >10 times faster than the competitor.","PeriodicalId":198109,"journal":{"name":"Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125871147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A growing body of literature has been devoted to harnessing the crowdsourcing power of social media by extracting knowledge from the huge amounts of information available online. This paper discusses how social media data can be used indirectly and with minimal cost to extract travel attributes such as trip purpose and activity location. As a result, the capacity of Twitter data in complementing other sources of transport related data such as household travel surveys or traffic count data is examined. Further, a detailed discussion is provided on how short term travellers, such as tourists, can be identified using Twitter data and how their travel pattern can be analysed. Having appropriate information about tourists/visitors -- such as the places they visit, their origin and the pattern of their movements at their destination -- is of great importance to urban planners. The available profile information of users and self-reported geo-location data on Twitter are used to identify tourists visiting Sydney as well as also those Sydney residents who made a trip outside Sydney. The presented data and analysis enable us to understand and track tourists' movements in cities for better urban planning. The results of this paper open up avenues for travel demand modellers to explore the possibility of using big data (in this case Twitter data) to model short distance (day-to-day or activity based) and long distance (vacation) trips.
{"title":"Utilising Location Based Social Media in Travel Survey Methods: bringing Twitter data into the play","authors":"A. Abbasi, T. Rashidi, M. Maghrebi, S. Waller","doi":"10.1145/2830657.2830660","DOIUrl":"https://doi.org/10.1145/2830657.2830660","url":null,"abstract":"A growing body of literature has been devoted to harnessing the crowdsourcing power of social media by extracting knowledge from the huge amounts of information available online. This paper discusses how social media data can be used indirectly and with minimal cost to extract travel attributes such as trip purpose and activity location. As a result, the capacity of Twitter data in complementing other sources of transport related data such as household travel surveys or traffic count data is examined. Further, a detailed discussion is provided on how short term travellers, such as tourists, can be identified using Twitter data and how their travel pattern can be analysed. Having appropriate information about tourists/visitors -- such as the places they visit, their origin and the pattern of their movements at their destination -- is of great importance to urban planners. The available profile information of users and self-reported geo-location data on Twitter are used to identify tourists visiting Sydney as well as also those Sydney residents who made a trip outside Sydney. The presented data and analysis enable us to understand and track tourists' movements in cities for better urban planning. The results of this paper open up avenues for travel demand modellers to explore the possibility of using big data (in this case Twitter data) to model short distance (day-to-day or activity based) and long distance (vacation) trips.","PeriodicalId":198109,"journal":{"name":"Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115428740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Users tend to check in and post their statuses in location-based social networks (LBSNs) to describe that their interests are related to a point-of-interest (POI). Since the relevance of the data to the POI varies according to the geographic 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 LBSNs, 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.
{"title":"Low-Complexity Detection of POI Boundaries Using Geo-Tagged Tweets: A Geographic Proximity Based Approach","authors":"Dung D. Vu, Won-Yong Shin","doi":"10.1145/2830657.2830663","DOIUrl":"https://doi.org/10.1145/2830657.2830663","url":null,"abstract":"Users tend to check in and post their statuses in location-based social networks (LBSNs) to describe that their interests are related to a point-of-interest (POI). Since the relevance of the data to the POI varies according to the geographic 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 LBSNs, 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.","PeriodicalId":198109,"journal":{"name":"Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks","volume":"9 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133042865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
LBSN data are well-suited for research questions and perspectives on social or spatial phenomena. Researchers often subset large LBSN datasets into different social networks (using snowball sampling), temporal or spatial granularities, to test for statistical patterns. Yet, researchers lack a way to examine how human interpersonal behavior results in digital traces of geolocated social events, although macro global flows of movement and communication are built from micro individual human intentions. To help navigate between the individual mind and the resultant big LBSN data that researchers use to understand society and space, I list a 14-tier scale of connectivity typologies. Each step can provide different a perspective of a single LBSN dataset. This scale can illustrate how perturbations at one level affect another level. E.g. How will reported escalating rates of autism affect the future network of connectivity between global cities? Will a change in migration policy strain emotional ties between an international family? The scale allows us to track changes at different levels between micro-, meso- and macro-scale social-spatial phenomena in a computationally-friendly way.
{"title":"LBSN Data and the Social Butterfly Effect (Vision Paper)","authors":"Clio Andris","doi":"10.1145/2830657.2830658","DOIUrl":"https://doi.org/10.1145/2830657.2830658","url":null,"abstract":"LBSN data are well-suited for research questions and perspectives on social or spatial phenomena. Researchers often subset large LBSN datasets into different social networks (using snowball sampling), temporal or spatial granularities, to test for statistical patterns. Yet, researchers lack a way to examine how human interpersonal behavior results in digital traces of geolocated social events, although macro global flows of movement and communication are built from micro individual human intentions. To help navigate between the individual mind and the resultant big LBSN data that researchers use to understand society and space, I list a 14-tier scale of connectivity typologies. Each step can provide different a perspective of a single LBSN dataset. This scale can illustrate how perturbations at one level affect another level. E.g. How will reported escalating rates of autism affect the future network of connectivity between global cities? Will a change in migration policy strain emotional ties between an international family? The scale allows us to track changes at different levels between micro-, meso- and macro-scale social-spatial phenomena in a computationally-friendly way.","PeriodicalId":198109,"journal":{"name":"Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130780116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The landscape of social media applications is littered with novel approaches to using location information. The latest platform to emerge in this geosocial media realm is Yik Yak, an application that allows users to share geo-tagged, (currently) text-based, and most importantly, anonymous content. The fast adoption of this platform by college students as well as the recent availability of data offers a unique research opportunity. This work takes a first step in exploring this novel type of data through a range of textual, topical, and spatial data exploration methods. We are particularly interested in the question of whether Yik Yak differs from other geosocial data sources such as Twitter. Is it just another location-based social network or does it differ from existing social networks, establishing itself as a valuable resource for feature extraction?
{"title":"Of Oxen and Birds: Is Yik Yak a useful new data source in the geosocial zoo or just another Twitter?","authors":"Grant McKenzie, B. Adams, K. Janowicz","doi":"10.1145/2830657.2830659","DOIUrl":"https://doi.org/10.1145/2830657.2830659","url":null,"abstract":"The landscape of social media applications is littered with novel approaches to using location information. The latest platform to emerge in this geosocial media realm is Yik Yak, an application that allows users to share geo-tagged, (currently) text-based, and most importantly, anonymous content. The fast adoption of this platform by college students as well as the recent availability of data offers a unique research opportunity. This work takes a first step in exploring this novel type of data through a range of textual, topical, and spatial data exploration methods. We are particularly interested in the question of whether Yik Yak differs from other geosocial data sources such as Twitter. Is it just another location-based social network or does it differ from existing social networks, establishing itself as a valuable resource for feature extraction?","PeriodicalId":198109,"journal":{"name":"Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks","volume":"6 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116866187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Akhlaq Ahmad, Imad Afyouni, Abdullah Murad, Mohamed Abdur Rahman, F. Rehman, Bilal Sadiq, Saleh M. Basalamah, M. Wahiddin
The intensive use of social media through mobile devices has leveraged the development of digital diary applications that keep track of social events as well as geotagged multimedia content. In a large crowd where users with cultural diversity perform spatio-temporal activities, such geotagged multimedia content facilitates users' navigation through points of interest (POI) based on their preferences. This work presents a crowdsourced geo-spatial multimedia data aggregation tool that allows users to develop diary chapters relevant to forthcoming users' spatio-temporal activities. Our proposed solution provides users with the ability to add POIs through an authoring environment with multiple dimensions, such as spatio-temporal filters, multimedia categories, and event types. Specific application domains such as emergency situations, leisure trips, journalism, and tourism can take benefit of this technique. This authoring environment also visualizes geo-spatial multimedia content for collocated points of interest (CPOI) with moving users' timelines. We plan to integrate our proposed authoring environment as a proof of concept into our existing large-scale crowdsourcing environment that is envisioned to support millions of users during the Hajj 2015 event.
{"title":"ST-Diary: A Multimedia Authoring Environment for Crowdsourced Spatio-Temporal Events","authors":"Akhlaq Ahmad, Imad Afyouni, Abdullah Murad, Mohamed Abdur Rahman, F. Rehman, Bilal Sadiq, Saleh M. Basalamah, M. Wahiddin","doi":"10.1145/2830657.2830664","DOIUrl":"https://doi.org/10.1145/2830657.2830664","url":null,"abstract":"The intensive use of social media through mobile devices has leveraged the development of digital diary applications that keep track of social events as well as geotagged multimedia content. In a large crowd where users with cultural diversity perform spatio-temporal activities, such geotagged multimedia content facilitates users' navigation through points of interest (POI) based on their preferences. This work presents a crowdsourced geo-spatial multimedia data aggregation tool that allows users to develop diary chapters relevant to forthcoming users' spatio-temporal activities. Our proposed solution provides users with the ability to add POIs through an authoring environment with multiple dimensions, such as spatio-temporal filters, multimedia categories, and event types. Specific application domains such as emergency situations, leisure trips, journalism, and tourism can take benefit of this technique. This authoring environment also visualizes geo-spatial multimedia content for collocated points of interest (CPOI) with moving users' timelines. We plan to integrate our proposed authoring environment as a proof of concept into our existing large-scale crowdsourcing environment that is envisioned to support millions of users during the Hajj 2015 event.","PeriodicalId":198109,"journal":{"name":"Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133763288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michael Weiler, Andreas Züfle, Felix Borutta, Tobias Emrich
Location-based social networks are a source of geo-spatial data enriched by textual information, such as news, travel blogs, tweets and user recommendations. Such data may describe an event, an experience or a point of interest that is relevant to a user. In this vision paper we propose to describe a spatial region by the thoughts, ideas and emotions frequently and recently expressed by people in that region. For this purpose, we envision to extract features from geo-textual data, which capture not only the vocabulary, but also current topics and current general interests. We formally define the problem of drawing a socio textual map using geo-textual data and identify the necessary steps towards this vision: We represent each region as a stream of text messages such as tweets. In each region, we maintain a feature representation of text messages. We define a dissimilarity measure between such collections to assess the similarity between two regions. Using this measure, we utilize a metric clustering approach to obtain a social map of similar regions. We present a proof of concept by implementing the aforementioned steps with initial solutions. This proof of concept shows that an initial solution, which clusters the feature representations of regions, also yields clusters having regions that are spatially close. We theoretically explain this proof of concept by Tobler's first law of geography.
{"title":"Socio Textual Mapping","authors":"Michael Weiler, Andreas Züfle, Felix Borutta, Tobias Emrich","doi":"10.1145/2830657.2830662","DOIUrl":"https://doi.org/10.1145/2830657.2830662","url":null,"abstract":"Location-based social networks are a source of geo-spatial data enriched by textual information, such as news, travel blogs, tweets and user recommendations. Such data may describe an event, an experience or a point of interest that is relevant to a user. In this vision paper we propose to describe a spatial region by the thoughts, ideas and emotions frequently and recently expressed by people in that region. For this purpose, we envision to extract features from geo-textual data, which capture not only the vocabulary, but also current topics and current general interests. We formally define the problem of drawing a socio textual map using geo-textual data and identify the necessary steps towards this vision: We represent each region as a stream of text messages such as tweets. In each region, we maintain a feature representation of text messages. We define a dissimilarity measure between such collections to assess the similarity between two regions. Using this measure, we utilize a metric clustering approach to obtain a social map of similar regions. We present a proof of concept by implementing the aforementioned steps with initial solutions. This proof of concept shows that an initial solution, which clusters the feature representations of regions, also yields clusters having regions that are spatially close. We theoretically explain this proof of concept by Tobler's first law of geography.","PeriodicalId":198109,"journal":{"name":"Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130745195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks","authors":"","doi":"10.1145/2830657","DOIUrl":"https://doi.org/10.1145/2830657","url":null,"abstract":"","PeriodicalId":198109,"journal":{"name":"Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116284207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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