Thematic Patterns in Georeferenced Tweets through Space-Time Visual Analytics

Andrienko, Gennady; Bosch, Harald; Ertl, Thomas; Fuchs, Georg; Jankowski, Piotr; Thom, Dennis

doi:10.1109/mcse.2013.70

Cited by 96 publications

(80 citation statements)

References 7 publications

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“…Twitter for example is used as an information foraging source (MacEachren et al 2011), in journalism to disseminate data to the public in near real-time basis (O'Connor 2009, Castillo et al 2011, detect disease spreading (Chunara et al 2012), event detection (Bosch et al 2013), and for gaining insights on social interaction behavior (Huberman et al 2008) or trajectories of people (Andrienko et al 2013, Senaratne et al 2014.…”

Section: Text-based Vgimentioning

confidence: 99%

A review of volunteered geographic information quality assessment methods

Senaratne

Mobasheri

Ali

et al. 2016

International Journal of Geographical Information Science

398

315

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With the ubiquity of advanced web technologies and location-sensing hand held devices, citizens regardless of their knowledge or expertise, are able to produce spatial information. The phenomena is known as Volunteered Geographic Information (VGI). During the last decade VGI has been used as a data source supporting a wide range of services such as environmental monitoring, events reporting, human movement analysis, disaster management etc. However, these volunteer contributed data also come with varying quality. Reasons for this are: data is produced by heterogeneous contributors, using various technologies and tools, having different level of details and precision, serving heterogeneous purposes, and a lack of gatekeepers. Crowd-sourcing, social, and geographic approaches have been proposed and later followed to develop appropriate methods to assess the quality measures and indicators of VGI. In this paper, we review various quality measures and indicators for selected types of VGI, and existing quality assessment methods. As an outcome, the paper presents a classification of VGI with current methods utilized to assess the quality of selected types of VGI. Through these findings we introduce data mining as an additional approach for quality handling in VGI.

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Section: Text-based Vgimentioning

confidence: 99%

A review of volunteered geographic information quality assessment methods

Senaratne

Mobasheri

Ali

et al. 2016

International Journal of Geographical Information Science

398

315

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“…These data have the potential to enrich existing data collection methods for mapping spatio-temporal activity patterns and location-based experiences of people. The potential of geotagged social media for mapping people's activities and movements has been mainly illustrated in urban environments [2][3][4][5][6][7]. There are also promising results for utilizing spatio-temporal information of social media posts for visitor monitoring in recreational revenues [10], landscape values [11], and travel patterns [34].…”

Section: Introductionmentioning

confidence: 99%

User-Generated Geographic Information for Visitor Monitoring in a National Park: A Comparison of Social Media Data and Visitor Survey

Heikinheimo

Minin

Tenkanen

et al. 2017

IJGI

226

101

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Abstract:Protected area management and marketing require real-time information on visitors' behavior and preferences. Thus far, visitor information has been collected mostly with repeated visitor surveys. A wealth of content-rich geographic data is produced by users of different social media platforms. These data could potentially provide continuous information about people's activities and interactions with the environment at different spatial and temporal scales. In this paper, we compare social media data with traditional survey data in order to map people's activities and preferences using the most popular national park in Finland, Pallas-Yllästunturi National Park, as a case study. We compare systematically collected survey data and the content of geotagged social media data and analyze: (i) where do people go within the park; (ii) what are their activities; (iii) when do people visit the park and if there are temporal patterns in their activities; (iv) who the visitors are; (v) why people visit the national park; and (vi) what complementary information from social media can provide in addition to the results from traditional surveys. The comparison of survey and social media data demonstrated that geotagged social media content provides relevant information about visitors' use of the national park. As social media platforms are a dynamic source of data, they could complement and enrich traditional forms of visitor monitoring by providing more insight on emerging activities, temporal patterns of shared content, and mobility patterns of visitors. Potentially, geotagged social media data could also provide an overview of the spatio-temporal activity patterns in other areas where systematic visitor monitoring is not taking place.

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“…Some of the photo posts have descriptive titles or tags indicating what attracted the photographers' attention. Other social media sites, such as Twitter, may have geo-coordinates embedded in the data, and recent works [4] [37] demonstrate the possibility of using these data for extracting information about people's interests in terms of places and events they like to visit.…”

Section: People's Activities and Interestsmentioning

confidence: 99%

“…Specifically, they refer the trip origins and destinations to the categories of the places of interest located nearby, which may be indicative of the trip purposes. Andrienko et al [4] explored the potential of georeferenced Tweets as a source of semantic information concerning people's activities and movements. They classified tweets according to the topics of the messages, such as 'food', 'coffee', 'education', 'sports', 'transportation', etc., and found that the topics corresponding to some activities tend to occur at the typical times of these activities.…”

Section: People's Activities and Interestsmentioning

confidence: 99%

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

Andrienko

Chen

Maciejewski

et al. 2017

IEEE Trans. Intell. Transport. Syst.

Self Cite

123

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This is the accepted version of the paper.This version of the publication may differ from the final published version. City Research Online  Abstract-Many cities and countries are now striving to create intelligent transportation systems that utilize the current abundance of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility, interests, and lifestyles. Such intelligent transportation systems aim to provide novel services that can enable transportation consumers and managers to be better informed and make safer and more efficient use of the infrastructure. However, the transportation domain is characterized by both complex data and complex problems, which calls for visual analytics approaches. The science of visual analytics is continuing to develop principles, methods, and tools to enable synergistic work between humans and computers through interactive visual interfaces. Such interfaces support the unique capabilities of humans (such as the flexible application of prior knowledge and experiences, creative thinking, and insight) and couple these abilities with machines' computational strengths, enabling the generation of new knowledge from large and complex data. In this paper, we describe recent developments in visual analytics that are related to the study of movement and transportation systems and discuss how visual analytics can enable and improve the intelligent transportation systems of the future. We provide a survey of literature from the visual analytics domain and organize the survey with respect to different types of transportation data, movement and its relationship to infrastructure and behavior, and modeling and planning. We conclude with lessons learned and future directions including social transportation, recommender systems, and policy implications. Permanent repository link

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Thematic Patterns in Georeferenced Tweets through Space-Time Visual Analytics

Cited by 96 publications

References 7 publications

A review of volunteered geographic information quality assessment methods

A review of volunteered geographic information quality assessment methods

User-Generated Geographic Information for Visitor Monitoring in a National Park: A Comparison of Social Media Data and Visitor Survey

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

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