Centre dedocumentation
scientifique

Delineating and modeling activity space using geotagged social media data / Lingqian Hu in Cartography and Geographic Information Science, vol 47 n° 3 (March 2020)  

Public [article]  inCartography and Geographic Information Science > vol 47 n° 3 (March 2020) . - pp 277 - 288 Titre : Delineating and modeling activity space using geotagged social media data Type de document : Article/Communication Auteurs : Lingqian Hu, Auteur ; Zhenhong Li, Auteur ; Xinyue Ye, Auteur Année de publication : 2020 Article en page(s) : pp 277 - 288 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] distance [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données massives [Termes descripteurs IGN] données socio-économiques [Termes descripteurs IGN] logement [Termes descripteurs IGN] loisir [Termes descripteurs IGN] Los Angeles [Termes descripteurs IGN] quartier [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] sport [Termes descripteurs IGN] Twitter [Termes descripteurs IGN] voisinage (topologie) [Termes descripteurs IGN] zone urbaine Résumé : (auteur) It has become increasingly important in spatial equity studies to understand activity spaces – where people conduct regular out-of-home activities. Big data can advance the identification of activity spaces and the understanding of spatial equity. Using the Los Angeles metropolitan area for the case study, this paper employs geotagged Twitter data to delineate activity spaces with two spatial measures: first, the average distance between users’ home location and activity locations; and second, the area covered between home and activity locations. The paper also finds significant relationship between the spatial measures of activity spaces and neighborhood spatial and socioeconomic characteristics. This research enriches the literature that aims to address spatial equity in activity spaces and demonstrates the applicability of big data in urban socio-spatial research. Numéro de notice : A2020-135 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1705187 date de publication en ligne : 10/02/2020 En ligne : https://doi.org/10.1080/15230406.2019.1705187 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94843 [article]

Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN / Xinyi Liu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 5-6 (May - June 2019) . - pp 1196 - 1223 Titre : Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN Type de document : Article/Communication Auteurs : Xinyi Liu, Auteur ; Qunying Huang, Auteur ; Song Gao, Auteur Année de publication : 2019 Article en page(s) : pp 1196 - 1223 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] mobilité urbaine [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] Twitter Mots-clés libres : density-based spatial clustering of applications with noise (DBSCAN) Résumé : (Auteur) The density-based spatial clustering of applications with noise (DBSCAN) method is often used to identify individual activity clusters (i.e., zones) using digital footprints captured from social networks. However, DBSCAN is sensitive to the two parameters, eps and minpts. This paper introduces an improved density-based clustering algorithm, Multi-Scaled DBSCAN (M-DBSCAN), to mitigate the detection uncertainty of clusters produced by DBSCAN at different scales of density and cluster size. M-DBSCAN iteratively calibrates suitable local eps and minpts values instead of using one global parameter setting as DBSCAN for detecting clusters of varying densities, and proves to be effective for detecting potential activity zones. Besides, M-DBSCAN can significantly reduce the noise ratio by identifying all points capturing the activities performed in each zone. Using the historic geo-tagged tweets of users in Washington, D.C. and in Madison, Wisconsin, the results reveal that: 1) M-DBSCAN can capture dispersed clusters with low density of points, and therefore detecting more activity zones for each user; 2) A value of 40 m or higher should be used for eps to reduce the possibility of collapsing distinctive activity zones; and 3) A value between 200 and 300 m is recommended for eps while using DBSCAN for detecting activity zones. Numéro de notice : A2019-445 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1563301 date de publication en ligne : 09/01/2019 En ligne : https://doi.org/10.1080/13658816.2018.1563301 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92781 [article]

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2019052	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
079-2019051	SL	Revue	Centre de documentation	Revues en salle	Disponible

Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level / Jiang Juqin in Cartography and Geographic Information Science, vol 46 n° 3 (May 2019)  

Public [article]  inCartography and Geographic Information Science > vol 46 n° 3 (May 2019) . - pp 228 - 242 Titre : Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level Type de document : Article/Communication Auteurs : Jiang Juqin, Auteur ; Li Zhenlong, Auteur ; Xinyue Ye, Auteur Année de publication : 2019 Article en page(s) : pp 228 - 242 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] agrégation spatiale [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données démographiques [Termes descripteurs IGN] données massives [Termes descripteurs IGN] données socio-économiques [Termes descripteurs IGN] erreur systématique [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] géoétiquetage [Termes descripteurs IGN] régression géographiquement pondérée [Termes descripteurs IGN] Twitter Résumé : (Auteur) Massive social media data produced from microblog platforms provide a new data source for studying human dynamics at an unprecedented scale. Meanwhile, population bias in geotagged Twitter users is widely recognized. Understanding the demographic and socioeconomic biases of Twitter users is critical for making reliable inferences on the attitudes and behaviors of the population. However, the existing global models cannot capture the regional variations of the demographic and socioeconomic biases. To bridge the gap, we modeled the relationships between different demographic/socioeconomic factors and geotagged Twitter users for the whole contiguous United States, aiming to understand how the demographic and socioeconomic factors relate to the number of Twitter users at county level. To effectively identify the local Twitter users for each county of the United States, we integrate three commonly used methods and develop a query approach in a high-performance computing environment. The results demonstrate that we can not only identify how the demographic and socioeconomic factors relate to the number of Twitter users, but can also measure and map how the influence of these factors vary across counties. Numéro de notice : A2019-093 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2018.1434834 date de publication en ligne : 09/02/2018 En ligne : https://doi.org/10.1080/15230406.2018.1434834 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92338 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2019031	SL	Revue	Centre de documentation	Revues en salle	Disponible

Modeling and visualizing semantic and spatio-temporal evolution of topics in interpersonal communication on Twitter / Caglar Koylu in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 3-4 (March - April 2019) . - pp 805 - 832 Titre : Modeling and visualizing semantic and spatio-temporal evolution of topics in interpersonal communication on Twitter Type de document : Article/Communication Auteurs : Caglar Koylu, Auteur Année de publication : 2019 Article en page(s) : pp 805 - 832 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] analyse géovisuelle [Termes descripteurs IGN] analyse sémantique [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] comportement [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] traitement interactif [Termes descripteurs IGN] Twitter Résumé : (Auteur) Interpersonal communication on online social networks has a significant impact on the society by not only diffusing information, but also forming social ties, norms, and behaviors. Knowing how the conversational discourse semantically and geographically vary over time can help uncover the changing dynamics of interpersonal ties and the digital traces of social events. This article introduces a framework for modeling and visualizing the semantic and spatio-temporal evolution of topics in a spatially embedded and time-stamped interpersonal communication network. The framework consists of (1) a topic modeling workflow for modeling topics and extracting the evolution of conversational discourse; (2) a geo-social network modeling and smoothing approach to projecting connection characteristics and semantics of communication onto geographic space and time; (3) a web-based geovisual analytics environment for exploring semantic and spatio-temporal evolution of topics in a spatially embedded and time-stamped interpersonal communication network. To demonstrate, geo-located and reciprocal user mention and reply tweets over the course of the 2016 primary and presidential elections in the United States from 1 August 2015 to 15 November 2016 were analyzed. The large portion of the topics extracted from mention tweets were related to daily life routines, human activities, and interests such as school, work, sports, dating, wearing, birthday celebration, music, food, and live-tweeting. Specific focus on the analysis of political conversations revealed that the content of conversational discourse was split between civil rights and election-related discussions of the political campaigns and candidates. These political topics exhibited major shifts in terms of content and the popularity in reaction to primaries, debates, and events throughout the study period. While civil rights discussions were more dominant and in higher intensity across the nation and throughout the whole time period, election-specific conversations resulted in temporally varying local hotspots that correlated with locations of primaries and events. Numéro de notice : A2019-217 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1458987 date de publication en ligne : 25/04/2018 En ligne : https://doi.org/10.1080/13658816.2018.1458987 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92689 [article]

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2019032	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
079-2019031	SL	Revue	Centre de documentation	Revues en salle	Disponible

CarSenToGram: geovisual text analytics for exploring spatiotemporal variation in public discourse on Twitter / Caglar Koylu in Cartography and Geographic Information Science, Vol 46 n° 1 (January 2019)  

Public [article]  inCartography and Geographic Information Science > Vol 46 n° 1 (January 2019) . - pp 57 - 71 Titre : CarSenToGram: geovisual text analytics for exploring spatiotemporal variation in public discourse on Twitter Type de document : Article/Communication Auteurs : Caglar Koylu, Auteur ; Ryan Larson, Auteur ; Bryce J. Dietrich, Auteur ; Kang-Pyo Lee, Auteur Année de publication : 2019 Article en page(s) : pp 57 - 71 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse du discours [Termes descripteurs IGN] analyse géovisuelle [Termes descripteurs IGN] cartogramme [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] corpus [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] sentiment [Termes descripteurs IGN] Twitter [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Assessing the impact of events on the evolution of online public discourse is challenging due to the lack of data prior to the event and appropriate methodologies for capturing the progression of tenor of public discourse, both in terms of their tone and topic. In this article, we introduce a geovisual analytics framework, CarSenToGram, which integrates topic modeling and sentiment analysis with cartograms to identify the changing dynamics of public discourse on a particular topic across space and time. The main novelty of CarSenToGram is coupling comprehensible spatiotemporal overviews of the overall distribution, topical and sentiment patterns with increasing levels of information supported by zoom and filter, and details-on-demand interactions. To demonstrate the utility of CarSenToGram, in this article, we analyze tweets related to immigration the month before and after the 27 January 2017 travel ban in order to reveal insights into one of the defining moments of President Trump’s first year in office. Not only do we find that the travel ban influenced online public discourse and sentiment on immigration, but it also highlighted important partisan divisions within the US. Numéro de notice : A2019-012 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2018.1510343 date de publication en ligne : 18/09/2018 En ligne : https://doi.org/10.1080/15230406.2018.1510343 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91661 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2019011	SL	Revue	Centre de documentation	Revues en salle	Disponible

Spatialities, social Media and sentiment analysis: Exploring the potential of the detection tool SentiStrength / Christina Reithmeier in GI Forum, vol 2018 n° 2 ([01/09/2018])  

Permalink

SensePlace3: a geovisual framework to analyze place–time–attribute information in social media / Scott Pezanowski in Cartography and Geographic Information Science, Vol 45 n° 5 (August 2018)  

Permalink

A spatial analysis of non‐English Twitter activity in Houston, TX / Matthew Haffner in Transactions in GIS, vol 22 n° 4 (August 2018)  

Permalink

A framework for annotating OpenStreetMap objects using geo-tagged tweets / Xin Chen in Geoinformatica [en ligne], vol 22 n° 3 (July 2018)  

Permalink

TAGGS : grouping tweets to improve global geoparsing for disaster response / Jens A. de Bruijn in Journal of Geovisualization and Spatial Analysis, vol 2 n° 1 (June 2018)  

Permalink

Information extraction and visualization from twitter considering spatial structure / Hideyuki Fujita in Cartographica, vol 52 n° 2 (Summer 2017)  

Permalink

Improving large area population mapping using geotweet densities / Nirav N. Patel in Transactions in GIS, vol 21 n° 2 (April 2017)  

Permalink

Automatic targeted-domain spatiotemporal event detection in twitter / Ting Hua in Geoinformatica [en ligne], vol 20 n° 4 (October - December 2016)  

Permalink

Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us? / Qunying Huang in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)  

Permalink

Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks / Enrico Steiger in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)  

Permalink