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Streets of London: Using Flickr and OpenStreetMap to build an interactive image of the city / Azam Raha Bahrehdar in Computers, Environment and Urban Systems, vol 84 (November 2020)  

Public [article]  inComputers, Environment and Urban Systems > vol 84 (November 2020) . - n° 101524 Titre : Streets of London: Using Flickr and OpenStreetMap to build an interactive image of the city Type de document : Article/Communication Auteurs : Azam Raha Bahrehdar, Auteur ; Benjamin Adams, Auteur ; Ross S. Purves, Auteur Année de publication : 2020 Article en page(s) : n° 101524 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] autocorrélation spatiale [Termes IGN] collecte de données [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données localisées des bénévoles [Termes IGN] exploration de données [Termes IGN] image Flickr [Termes IGN] Londres [Termes IGN] mesure de similitude [Termes IGN] métadonnées [Termes IGN] OpenStreetMap [Termes IGN] orthoimage géoréférencée [Termes IGN] perception [Termes IGN] segmentation sémantique Résumé : (auteur) In his classic book “The Image of the City” Kevin Lynch used empirical work to show how different elements of the city were perceived: such as paths, landmarks, districts, edges, and nodes. Streets, by providing paths from which cities can be experienced, were argued to be one of the key elements of cities. Despite this long standing empirical basis, and the importance of Lynch's model in policy associated areas such as planning, work with user generated content has largely ignored these ideas. In this paper, we address this gap, using streets to aggregate filtered user generated content related to more than 1 million images and 60,000 individuals and explore similarity between more than 3000 streets in London across three dimensions: user behaviour, time and semantics. To perform our study we used two different sources of user generated content: (1) a collection of metadata attached to Flickr images and (2) street network of London from OpenStreetMap. We first explore global patterns in the distinctiveness and spatial autocorrelation of similarity using our three dimensions, establishing that the semantic and user dimensions in particular allow us to explore the city in different ways. We then used a Processing tool to interactively explore individual patterns of similarity across these four dimensions simultaneously, presenting results here for four selected and contrasting locations in London. Before drilling into the data to interpret in more detail, the identified patterns demonstrate that streets are natural units capturing perception of cities not only as paths but also through the emergence of other elements of the city proposed by Lynch including districts, landmarks and edges. Our approach also demonstrates how user generated content can be captured, allowing bottom-up perception from citizens to flow into a representation. Numéro de notice : A2020-710 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2020.101524 Date de publication en ligne : 05/08/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101524 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96255 [article]

Crowdsourcing the character of a place : Character‐level convolutional networks for multilingual geographic text classification / Benjamin Adams in Transactions in GIS, vol 22 n° 2 (April 2018)  

Public [article]  inTransactions in GIS > vol 22 n° 2 (April 2018) . - pp 394 - 408 Titre : Crowdsourcing the character of a place : Character‐level convolutional networks for multilingual geographic text classification Type de document : Article/Communication Auteurs : Benjamin Adams, Auteur ; Grant McKenzie, Auteur Année de publication : 2018 Article en page(s) : pp 394 - 408 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie [Termes IGN] classification [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données localisées des bénévoles [Termes IGN] exploration de texte [Termes IGN] géocodage [Termes IGN] méthode robuste [Termes IGN] réseau neuronal convolutif [Termes IGN] toponyme [Termes IGN] traitement du langage naturel Résumé : (Auteur) This article presents a new character‐level convolutional neural network model that can classify multilingual text written using any character set that can be encoded with UTF‐8, a standard and widely used 8‐bit character encoding. For geographic classification of text, we demonstrate that this approach is competitive with state‐of‐the‐art word‐based text classification methods. The model was tested on four crowdsourced data sets made up of Wikipedia articles, online travel blogs, Geonames toponyms, and Twitter posts. Unlike word‐based methods, which require data cleaning and pre‐processing, the proposed model works for any language without modification and with classification accuracy comparable to existing methods. Using a synthetic data set with introduced character‐level errors, we show it is more robust to noise than word‐level classification algorithms. The results indicate that UTF‐8 character‐level convolutional neural networks are a promising technique for georeferencing noisy text, such as found in colloquial social media posts and texts scanned with optical character recognition. However, word‐based methods currently require less computation time to train, so currently are preferable for classifying well‐formatted and cleaned texts in single languages. Numéro de notice : A2018-214 Affiliation des auteurs : non IGN Thématique : TOPONYMIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12317 Date de publication en ligne : 29/01/2018 En ligne : https://doi.org/10.1111/tgis.12317 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90004 [article]

Thematic signatures for cleansing and enriching place-related linked data / Benjamin Adams in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 4 (April 2015) . - pp 556 - 579 Titre : Thematic signatures for cleansing and enriching place-related linked data Type de document : Article/Communication Auteurs : Benjamin Adams, Auteur ; Krzysztof Janowicz, Auteur Année de publication : 2015 Article en page(s) : pp 556 - 579 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] base de connaissances [Termes IGN] données massives [Termes IGN] graphe [Termes IGN] ontologie [Termes IGN] répertoire toponymique [Termes IGN] réseau sémantique [Termes IGN] web des données [Termes IGN] web sémantique Résumé : (Auteur) There has been significant progress transforming semi-structured data about places into knowledge graphs that can be used in a wide variety of geographic information systems such as digital gazetteers or geographic information retrieval systems. For instance, in addition to information about events, actors, and objects, DBpedia contains data about hundreds of thousands of places from Wikipedia and publishes it as Linked Data. Repositories that store data about places are among the most interlinked hubs on the Linked Data cloud. However, most content about places resides in unstructured natural language text, and therefore it is not captured in these knowledge graphs. Instead, place representations are limited to facts such as their population counts, geographic locations, and relations to other entities, for example, headquarters of companies or historical figures. In this paper, we present a novel method to enrich the information stored about places in knowledge graphs using thematic signatures that are derived from unstructured text through the process of topic modeling. As proof of concept, we demonstrate that this enables the automatic categorization of articles into place types defined in the DBpedia ontology (e.g., mountain) and also provides a mechanism to infer relationships between place types that are not captured in existing ontologies. This method can also be used to uncover miscategorized places, which is a common problem arising from the automatic lifting of unstructured and semi-structured data. Numéro de notice : A2015-588 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.989855 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.989855 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77873 [article]