Clustering Geo-tagged Tweets for Advanced Big Data Analytics

Bordogna, Gloria; Frigerio, Luca; Cuzzocrea, Alfredo; Psaila, Giuseppe

doi:10.1109/bigdatacongress.2016.78

Cited by 25 publications

(19 citation statements)

References 22 publications

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“…In option E about "movement of tourist based in geolocalized tweets" is mainly developed in References [134,230,244]. It is based on taking information from a collection of geolocated tweets from a specific area and reconstructing the movement of users.…”

Section: Discussionmentioning

confidence: 99%

Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability

et al. 2019

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The tracking of tourist movements is an essential aspect in the management of sustainable tourist destinations. The current information and communication technologies provide innovative ways of collecting data on tourist movements, but it is still necessary to evaluate tools and methods of study for this challenge. At this point, mobile technologies are the best candidate for this task. Given the relevance of the topic, this paper proposes a mapping science analysis of publications on "movement of tourists" and "traceability." It has been carried out in the two main sources WOS and SCOPUS. The term "traceability" is brought from industry and technology areas to be applied to the tourist movement/mobility tracking and management. The methodological scheme is based on a selection of search criteria with combinations of terms. The sources of specialized information in applied social sciences and technology were then selected. From there, the searches have been executed for their subsequent analysis in three stages-(I) relevance analysis filtering the results to obtain the most pertinent; (II) analysis of articles with similarity thematic, authors, journals or citations; (III) analysis of selected papers as input for the mapping analysis using Citespace. The automatic naming of clusters under the selected processing confirms that the analysis of movements is a valid scientific trend but research-oriented from the perspective of traceability is non-existent, so this approach is novel and complementary to existing ones and a potential contribution to knowledge about tourist movements. Finally, a set of methodological considerations and a classification of information capture tools are proposed. In this classification, mobile technology is the best option to enable tourist movement analysis.

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Section: Discussionmentioning

confidence: 99%

Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability

et al. 2019

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“…This study showed how demographic, geographical, technological and contextual properties of social media (and their users) can provide very different reflections and interpretations of the reality of an urban environment. The work of Bordogana [32] exploited the timestamped geo-labelled messages posted by Twitter users from their smartphones when they travel to track their journeys. To learn more about how social media data can be used to infer knowledge about urban dynamics and mobility patterns in an urban area, the reader is invited to read [33].…”

Section: Related Workmentioning

confidence: 99%

Urban Crowd Detection Using SOM, DBSCAN and LBSN Data Entropy: A Twitter Experiment in New York and Madrid

2019

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The surfer and the physical location are two important concepts associated with each other in the social network-based localization service. This work consists of studying urban behavior based on location-based social networks (LBSN) data; we focus especially on the detection of abnormal events. The proposed crowd detection system uses the geolocated social network provided by the Twitterapplication programming interface (API) to automatically detect the abnormal events. The methodology we propose consists of using an unsupervised competitive learning algorithm (self-organizing map (SOM)) and a density-based clustering method (density-based spatial clustering of applications with noise (DBCSAN)) to identify and detect crowds. The second stage is to build the entropy model to determine whether the detected crowds fit into the daily pattern with reference to a spatio-temporal entropy model, or whether they should be considered as evidence that something unusual occurs in the city because of their number, size, location and time of day. To detect an abnormal event in the city, it is sufficient to determine the real entropy model and to compare it with the reference model. For the normal day, the reference model is constructed offline for each time interval. The obtained results confirm the effectiveness of our method used in the first stage (SOM and DBSCAN stage) to detect and identify clusters dynamically, and imitating human activity. These findings also clearly confirm the detection of special days in New York City (NYC), which proves the performance of our proposed model.

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“…We named this project FollowMe, because we traced (and we are still tracing) travellers that post geo-located messages on Twitter, detecting them in a pool of 30 airports potentially connected with the airport of Bergamo (northern Italy). Next [7], we integrated the FollowMe project within a framework for analysing trips of Twitter users, furthermore [8], we experimented a clustering technique for identifying common paths followed by users during their trips. That was a preliminary work of the Urban Nexus project, in which huge numbers of trips of Twitter users represented as JSON objects have to be analysed on the basis of multi-paradigmatic approach (see [12]).…”

Section: Motivation Of the Proposalmentioning

confidence: 99%

Why we need a Novel Framework to Integrate and Transform Heterogeneous Multi-Source Geo referenced Information : The J-Co Proposal

Bordogna¹,

Psaila²

2018

Computer Science &Amp; Information Technology (CS &Amp; IT)

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The large number of geo referenced data sets provided by Open Data portals, social media networks and created by volunteers within citizen science projects (Volunteered Geographical Information) is pushing analysts to define and develop novel frameworks for analysing these multisource heterogeneous data sets in order to derive new data sets that generate social value. For analysts, such an activity is becoming a common practice for studying, predicting and planning social dynamics. The convergence of various technologies related with data representation formats, database management and GIS (Geographical Information Systems) can enable analysts to perform such complex integration and transformation processes. JSON has become the de-facto standard for representing (possibly geo-referenced) data sets to share; NoSQL databases (and MongoDB in particular) are able to natively deal with collections of JSON objects; the GIS community has defined the GeoJSON standard, a JSON format for representing georeferenced information layers, and has extended GIS software to support it. However, all these technologies have been separately developed, consequently, there is actually a gap that shall be filled to easily manipulate GeoJSON objects by performing spatial operations. In this paper, we pursue the objective of defining both a unifying view of several NoSQL databases and a query language that is independent of specific database platforms to easily integrate and transform collections of GeoJSON objects. In the paper, we motivate the need for such a framework, named J-CO, able to execute novel high-level queries, written in the J-CO-QL language, for JSON objects and will show its possible use for generating open data sets by integrating various collections of geo-referenced JSON objects stored in different databases.

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Clustering Geo-tagged Tweets for Advanced Big Data Analytics

Cited by 25 publications

References 22 publications

Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability

Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability

Urban Crowd Detection Using SOM, DBSCAN and LBSN Data Entropy: A Twitter Experiment in New York and Madrid

Why we need a Novel Framework to Integrate and Transform Heterogeneous Multi-Source Geo referenced Information : The J-Co Proposal

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