Centre dedocumentation
scientifique

CIME: Context-aware geolocation of emergency-related posts / Gabriele Scalia in Geoinformatica [en ligne], vol 26 n° 1 (January 2022)  

Public [article]  inGeoinformatica [en ligne] > vol 26 n° 1 (January 2022) . - pp 125 - 157 Titre : CIME: Context-aware geolocation of emergency-related posts Type de document : Article/Communication Auteurs : Gabriele Scalia, Auteur ; Chiara Francalanci, Auteur ; Barbara Pernici, Auteur Année de publication : 2022 Article en page(s) : pp 125 - 157 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] cartographie d'urgence [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées des bénévoles [Termes IGN] exploration de données [Termes IGN] géolocalisation [Termes IGN] géoréférencement [Termes IGN] Grande-Bretagne [Termes IGN] implémentation (informatique) [Termes IGN] inondation [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] prise en compte du contexte [Termes IGN] tempête [Termes IGN] Twitter Résumé : (auteur) Information extracted from social media has proven to be very useful in the domain of emergency management. An important task in emergency management is rapid crisis mapping, which aims to produce timely and reliable maps of affected areas. During an emergency, the volume of emergency-related posts is typically large, but only a small fraction is relevant and help rapid mapping effectively. Furthermore, posts are not useful for mapping purposes unless they are correctly geolocated and, on average, less than 2% of posts are natively georeferenced. This paper presents an algorithm, called CIME, that aims to identify and geolocate emergency-related posts that are relevant for mapping purposes. While native geocoordinates are most often missing, many posts contain geographical references in their metadata, such as texts or links that can be used by CIME to filter and geolocate information. In addition, social media creates a social network and each post can be enhanced with indirect information from the post’s network of relationships with other posts (for example, a retweet can be associated with other geographical references which are useful to geolocate the original tweet). To exploit all this information, CIME uses the concept of context, defined as the information characterizing a post both directly (the post’s metadata) and indirectly (the post’s network of relationships). The algorithm was evaluated on a recent major emergency event demonstrating better performance with respect to the state of the art in terms of total number of geolocated posts, geolocation accuracy and relevance for rapid mapping. Numéro de notice : A2022-204 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-021-00446-x Date de publication en ligne : 28/07/2021 En ligne : https://doi.org/10.1007/s10707-021-00446-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100011 [article]

Geographical and temporal huff model calibration using taxi trajectory data / Shuhui Gong in Geoinformatica [en ligne], vol 25 n° 3 (July 2021)  

Public [article]  inGeoinformatica [en ligne] > vol 25 n° 3 (July 2021) . - pp 485 - 512 Titre : Geographical and temporal huff model calibration using taxi trajectory data Type de document : Article/Communication Auteurs : Shuhui Gong, Auteur ; John Cartlidge, Auteur ; Ruibin Bai, Auteur ; Yang Yue, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 485 - 512 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] attractivité (aménagement) [Termes IGN] étalonnage de modèle [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] régression des moindres carrés partiels [Termes IGN] régression géographiquement pondérée [Termes IGN] Shenzhen [Termes IGN] trajectoire (véhicule non spatial) Résumé : (auteur) The Huff model is designed to estimate the probability of shopping centre patronage based on a shopping centre’s attractiveness and the cost of a customer’s travel. In this paper, we attempt to discover some general shopping trends by calibrating the Huff model in Shenzhen, China, and New York, USA, using taxi trajectory GPS data and sharing bikes GPS data. Geographical and Temporal Weighted Regression (GTWR) is used to fit the model, and calibration results are compared with Ordinary Least Squares (OLS) regression, Geographical Weighted Regression (GWR), and Temporal Weighted Regression (TWR). Results show that GTWR gives the highest performance due to significant geographical and temporal variation in the Huff model parameters of attractiveness and travel cost. To explain the geographical variation, we use residential sales’ and rental prices in Shenzhen and New York as a proxy for customers’ wealth in each region. Pearson product-moment correlation results show a medium relationship between localised sales’ and rental prices and the Huff model parameter of attractiveness: that is, customer wealth explains geographic sensitivity to shopping area attractiveness. To explain temporal variation, we use census data in both Shenzhen and New York to provide job profile distributions for each region as a proxy to estimate customers’ spare leisure time. Regression results demonstrate that there is a significant linear relationship between the length of spare time and the parameter of shopping area attractiveness. In particular, we demonstrate that wealthy customers with less spare time are more sensitive to a shopping centre’s attractiveness. We also discover customers’ sensitivities to travel distance are related to their travel mode. In particular, people riding bikes to shopping areas care much more about trip distance compared with people who take taxi. Finally, results show a divergence in behaviours between customers in New York and Shenzhen at weekends. While customers in New York prefer to shop more locally at weekends, customers in Shenzhen care less about trip distance. We provide the GTWR calibration of the Huff model as our theoretical contribution. GTWR extends the Huff model to two dimensions (time and space), so as to analyse the differences of residents’ travel behaviours in different time and locations. We also provide the discoveries of factors affecting urban travel behaviours (wealth and employment) as practical contributions that may help optimise urban transportation design. In particular, the sensitivity of residents to the attraction of shopping areas has a significant positive linear relationship with the housing price and a significant negative linear relationship with the residents’ length of spare time. Numéro de notice : A2021-973 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1007/s10707-019-00390-x Date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1007/s10707-019-00390-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100392 [article]

Joint promotion partner recommendation systems using data from location-based social networks / Yi-Chung Chen in ISPRS International journal of geo-information, vol 10 n° 2 (February 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 2 (February 2021) . - n° 57 Titre : Joint promotion partner recommendation systems using data from location-based social networks Type de document : Article/Communication Auteurs : Yi-Chung Chen, Auteur ; Hsi-Ho Huang, Auteur ; Sheng-Min Chiu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 57 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] contenu généré par les utilisateurs [Termes IGN] Facebook [Termes IGN] Foursquare [Termes IGN] géomercatique [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] point d'intérêt [Termes IGN] politique commerciale [Termes IGN] réseau social géodépendant Résumé : (auteur) Joint promotion is a valuable business strategy that enables companies to attract more customers at lower operational cost. However, finding a suitable partner can be extremely difficult. Conventionally, one of the most common approaches is to conduct survey-based analysis; however, this method can be unreliable as well as time-consuming, considering that there are likely to be thousands of potential partners in a city. This article proposes a framework to recommend Joint Promotion Partners using location-based social networks (LBSN) data. We considered six factors in determining the suitability of a partner (customer base, association, rating and awareness, prices and star ratings, distance, and promotional strategy) and developed efficient algorithms to perform the required calculations. The effectiveness and efficiency of our algorithms were verified using the Foursquare dataset and real-life case studies. Numéro de notice : A2021-152 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10020057 Date de publication en ligne : 30/01/2021 En ligne : https://doi.org/10.3390/ijgi10020057 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97063 [article]

Local fuzzy geographically weighted clustering: a new method for geodemographic segmentation / George Grekousis in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 1 (January 2021) . - pp 152 - 174 Titre : Local fuzzy geographically weighted clustering: a new method for geodemographic segmentation Type de document : Article/Communication Auteurs : George Grekousis, Auteur Année de publication : 2021 Article en page(s) : pp 152 - 174 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] classification floue [Termes IGN] données démographiques [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] optimisation par essaim de particules [Termes IGN] pondération [Termes IGN] régression géographiquement pondérée [Termes IGN] santé [Termes IGN] segmentation [Termes IGN] voisinage (relation topologique) Résumé : (auteur) Fuzzy geographically weighted clustering has been proposed as an approach for improving fuzzy c-means algorithm when applied to geodemographic analysis. This clustering method allows a spatial entity to belong to more than one cluster with varying degrees, namely, membership values. Although fuzzy geographically weighted clustering attempts to create geographically aware clusters, it partially fails to trace spatial dependence and heterogeneity because, as a global metric, the membership values are calculated across the entire set of spatial entities. Here we introduce the first local version of fuzzy geographically weighted clustering, ‘local fuzzy geographically weighted clustering.’ In local fuzzy geographically weighted clustering, the membership values of a spatial entity are updated only according to the membership values of the spatial entities within its neighborhood and not across the entire set of entities, as originally proposed by the global metric. Additionally, we apply particle swarm optimization meta-heuristic to overcome the random initialization problem regarding the fuzzy c-means algorithm. To evaluate our method we compare local fuzzy geographically weighted clustering to global fuzzy geographically weighted clustering using a cancer incident benchmark dataset for Manhattan, New York. The results show that local fuzzy geographically weighted clustering outperforms the global version in all experimental settings. Numéro de notice : A2021-022 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808221 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808221 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96525 [article]

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2021011	SL	Revue	Centre de documentation	Revues en salle	Disponible
079-2021012	SL	Revue	Centre de documentation	Revues en salle	Disponible

STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities / Chao Wang in Transactions in GIS, Vol 24 n° 6 (December 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 6 (December 2020) . - pp 1559 - 1577 Titre : STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities Type de document : Article/Communication Auteurs : Chao Wang, Auteur ; Zhenhong Du, Auteur ; Yuhua Gu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1559 - 1577 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] classification barycentrique [Termes IGN] données spatiotemporelles [Termes IGN] exploration de données [Termes IGN] exploration de données géographiques [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] origine - destination [Termes IGN] Pékin (Chine) [Termes IGN] taxi Résumé : (Auteur) Clustering on spatiotemporal point events with multiple types is an important step for exploratory data mining and can help us reveal the correlation of event types. In this article, we present an effective method for discovering spatiotemporal multi‐type clusters containing events with different densities and event types (STME). Particularly, the type of events in a cluster can be different, and clusters with similar densities but different internal compositions should be distinguished. We use the distance to the kth nearest neighbour to define the size of the searched neighbourhood, and expand clusters by the concept of cluster reachable, ensuring that the proportion of various types of events in the cluster remains stable. The concept of clustering priority is also proposed to make the cluster always expand from the region with the highest density, which improves the robustness of clustering. Moreover, the density of multiple types of events in clusters is estimated to discover the internal structure of clusters and further explore the correlation between events. The effectiveness of the STME algorithm is demonstrated in several simulated and real data sets, including points of interest data in Beijing and the origins and destinations of taxi trips in New York. Numéro de notice : A2020-768 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12662 Date de publication en ligne : 19/07/2020 En ligne : https://doi.org/10.1111/tgis.12662 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96660 [article]

A preliminary exploration of the cooling effect of tree shade in urban landscapes / Qiuyan Yu in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)  

Permalink

Extracting commuter-specific destination hotspots from trip destination data – comparing the boro taxi service with Citi Bike in NYC / Andreas Keler in Geo-spatial Information Science, vol 23 n° 2 (June 2020)  

Permalink

A reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy / Yan Zhou in Transactions in GIS, Vol 23 n° 5 (October 2019)  

Permalink

Space, time, and situational awareness in natural hazards: a case study of Hurricane Sandy with social media data / Zheye Wang in Cartography and Geographic Information Science, Vol 46 n° 4 (July 2019)  

Permalink

A methodology with a distributed algorithm for large-scale trajectory distribution prediction / QiuLei Guo in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)  

Permalink

Modeling use of space from social media data using a biased random walker / Steven D. Prager in Transactions in GIS, vol 18 n° 6 (December 2014)  

Permalink

The signature of self-organisation in cities: Temporal patterns of clustering and growth in street networks / Kinda Al-Sayed in Revue internationale de géomatique, vol 23 n° 3 - 4 (septembre 2013 - février 2014)  

Permalink

Probationers Monitored and Public Safety Improved with GIS in ArcNews [en ligne], Vol 30 n° 2 (Summer 2008)  

Permalink

Étude de la gouvernance des métropoles mondiales / C. Lefevre (2006)

Permalink

True orthoimage generation in urban areas with very tall buildings / G. Zhou in International Journal of Remote Sensing IJRS, vol 25 n° 22 (November 2004)  

Permalink