Geo-tagged Twitter collection and visualization system

Fujita, Hideyuki

doi:10.1080/15230406.2013.800272

Cited by 15 publications

(12 citation statements)

References 6 publications

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“…Developing theory, methods, and tools to consider spatial and network factors simultaneously has the potential to achieve new insights about human processes. Similar are the research questions mentioned by (Fujita 2013), who proposes a function for visualising the collected data by focussing on the relationships between locations and communication among users.…”

Section: Social Relationships External Context and Information Diffumentioning

confidence: 99%

“…These approaches consider, e.g., the hourly number of tweets related to a given topic, the daily distribution of specific terms or topics, and number of tweets per user. Such an approach can be applied in several fields, for instance, to detect influenza epidemics (Culotta 2010), emergencies such as fires (Dobson and Fisher 2003), or other types of events spatially and temporally located (Fujita 2013).…”

Section: Methodology To Analyse Microblogging Content Based On the Exmentioning

confidence: 99%

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A Survey on Visual Analytics for the Spatio-Temporal Exploration of Microblogging Content

Bertone

Burghardt

2017

J geovis spat anal

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User-generated content is a valuable source of information whose production increases year after year. Twitter data is a form of user-generated content that is frequently adopted to comment on life activities in several contexts. Thus, scientific interest in that data has increased in recent years. This paper focusses on visual analytics approaches addressing the microblogging content exchanged through Twitter. In particular, we concentrate our interest on approaches that consider spatial and temporal aspects and provide visual support. Articles from the major conferences, journals, and digital libraries have been collected, organized and compared based on different criteria such as research questions, application focus, analytical and visual methods adopted, and interaction provided. In addition to these comparisons, opportunities and challenges are illustrated to inspire future research.

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Section: Social Relationships External Context and Information Diffumentioning

confidence: 99%

Section: Methodology To Analyse Microblogging Content Based On the Exmentioning

confidence: 99%

A Survey on Visual Analytics for the Spatio-Temporal Exploration of Microblogging Content

Bertone

Burghardt

2017

J geovis spat anal

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“…We can acquire when and where the check-in activities happened and extract the footprints of a large number of individuals [ 1 ]. Check-in data as geo-tagged information sources has been used for revealing some spatio-temporal regularity in urban areas such as identifying commercial centers [ 18 ], detecting local events [ 19 ] and determining population distribution [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Explorative Analysis of Wuhan Intra-Urban Human Mobility Using Social Media Check-In Data

Yang

Zhu

et al. 2015

PLoS ONE

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Social media check-in data as a geo-tagged information source have been used for revealing spatio-temporal patterns in the field of social and urban study, such as human behavior or public issues. This paper investigates a case study and presents a new method of representing the mobility of people within a city from check-in data. By dividing the data in a temporal sequence, this study examines the overall mobility in the case study city through the gradient/difference of population density with a series of time after computing the population density from the check-in data using an incorporated Thiessen polygon method. By classifying check-in data with their geo-tags into several groups according to travel purposes, and partitioning the data according to administrative district boundaries, various moving patterns for those travel purposes in those administrative districts are identified by scrutinizing a series of spatial geometries of a weighted standard deviational ellipse (WSDE). Through deep analyses of those data by the adopted approaches, the general pattern of mobility in the case city, such as people moving to the central urban area from the suburb from 4 am to 8 am, is ascertained, and different characteristics of movement in those districts are also depicted. Furthermore, it can tell that in which district less movement is likely for a certain purpose (e.g., for dinner or entertainment). This study has demonstrated the availability of the proposed methodology and check-in data for investigating intra-urban human mobility.

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“…Inicialmente, estos estudios se desarrollaron en el contexto de grandes metrópolis del tamaño de Nueva York, Londres o Tokio (Ratti et al 2006), a partir de redes sociales como Flickr (Dunkel, 2015), Twitter (Fujita, 2013), Facebook o Foursquare (Williams, 2012). Actualmente, el incremento en el uso de dispositivos móviles ha permitido obtener datos suficientes en otros ámbitos y extender este tipo de estudios también a ciudades de menor tamaño -entre 50 y 100 mil habitantes- (Morstatter et al, 2013;Leontidou et al, 2007;Lansley y Longley, 2016).…”

Section: Los Datos Generados En Redes Sociales Como Fuente Para El Esunclassified

“…Por tanto, estos datos permiten identificar, tanto los patrones espaciales de concentración de usuarios en una franja temporal -a lo largo del día o de la semana- (Fujita, 2013), como la afluencia de personas a eventos de todo tipo -culturales, festivos, de protesta, etc. - (Villatoro et al, 2013;Croitoru et al, 2015;Hua et al, 2016;Bolognesi y Galli, 2017;Panteras et al, 2015;Martín, Julián y Cos-Gayón, 2019) o cómo se distribuyen en la ciudad los usuarios que participan en un determinado tipo de evento.…”

Section: Patrones Espaciotemporales De Uso De Los Espacios Urbanosunclassified

Los datos geolocalizados de las redes sociales en el estudio de cuestiones urbanas complejas: cinco temas, cinco redes

Ciriquián¹,

Nolasco‐Cirugeda²,

Serrano-Estrada³

2019

ACE: Architecture, City and Environment

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Geolocated data generated by social networks allow various kinds of urban-related issues to be addressed, broadening knowledge on the different dynamics that occur in the city. This study has a twofold objective: (1) to identify which data variables from five well known social networks-Foursquare, Instagram, Twitter, Google Places and Airbnb-offer potential insights on the study of specific urban phenomena; and, (2) to delve into five exemplary urban topics that can be tackled by analysing the data from these social networks. Methodology Social media are analysed and specific urban issues that can be assessed through their data variables are identified. Specifically, five themes that encompass several research issues are identified through the combination of different data variables. Conclusions Data generated in social networks used for urban study purposes provide a valuable opportunity to address issues such as: [1] the use and preference for certain activities and places; [2] the image and perception of urban spaces; [3] spatiotemporal patterns of people presence and urban space-use; [4] supply and demand of economic activity; and, [5] identification of areas of concentration and specialization in the city. Originality This up to date and easily accessible information provides a thorough picture of complex issues in relation to urban dynamics that, until recently, were not easily measurable and quantifiable. Thus, the interpretation of these data broadens knowledge on city dynamics that can be useful for decision-making processes in the planning and design of the city.

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Geo-tagged Twitter collection and visualization system

Cited by 15 publications

References 6 publications

A Survey on Visual Analytics for the Spatio-Temporal Exploration of Microblogging Content

A Survey on Visual Analytics for the Spatio-Temporal Exploration of Microblogging Content

Explorative Analysis of Wuhan Intra-Urban Human Mobility Using Social Media Check-In Data

Los datos geolocalizados de las redes sociales en el estudio de cuestiones urbanas complejas: cinco temas, cinco redes

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