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Consolidation of crowd-sourced geo-ragged data for parameterized travel recommendations / Ago Luberg (2021)
Titre : Consolidation of crowd-sourced geo-ragged data for parameterized travel recommendations Type de document : Thèse/HDR Auteurs : Ago Luberg, Auteur ; Tanel Tammet, Directeur de thèse Editeur : Tallinn [Estonia] : Tallinn University of Technology Année de publication : 2021 Importance : 159 p. Format : 21 x 30 cm Note générale : bibliographie
Dissertation accepted for the defence of the degree of Doctor of Philosophy in Computer ScienceLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] apprentissage automatique
[Termes IGN] base de données
[Termes IGN] conception orientée utilisateur
[Termes IGN] données localisées des bénévoles
[Termes IGN] extraction de données
[Termes IGN] géoréférencement
[Termes IGN] point d'intérêt
[Termes IGN] Riga
[Termes IGN] site wiki
[Termes IGN] système de recommandation
[Termes IGN] Tallinn
[Termes IGN] taxinomie
[Termes IGN] tourismeRésumé : (auteur) The research covered in this thesis is focused on different aspects of the task of creating automated recommendations for tourism, focusing mostly on places of interest like beautiful views, architectural landmarks, charming areas etc. A significant amount of work has been spent on designing and developing actual recommender systems - Sightsplanner, Sightsmap and the automated recommender of Visit Estonia - and their data harvesting methods in order to create a platform for showing the feasibility of the new methods designed and experimented with. The main results of our research are split between three subfields:
• Knowledge engineering: we have shown how to formalize fuzzy and uncertain POI categories along with suitable ontologies and reasoner-based algorithms for object matching and score calculation in a real-life context of actual POI-s, available data and easily expressable user preferences.
• Machine learning: we have designed a learnable detection system for detecting duplicate POIs from different databases, usable for cross- category, cross-language and cross-city datasets.
• We show that learning on Tallinn eateries improved the algorithm parameters to such a degree that on Riga data containing also museums and galleries it gave us 98% accuracy versus 85% accuracy achieved by tuning the algorithm parameters manually.
• Knowledge extraction: we have designed an algorithm for high-quality keyword extraction from short crowd-sourced POI descriptions in different languages, able to find a suitable name and to add suitable types to the POI. Our clusterization algorithm is able to merge the POIs based on the extracted data: on the Panoramio and Wikipedia data about U.K. and French locations it was able to find 56% of Wikipedia objects from the textual titles/annotations of Panoramio pictures in the area.Note de contenu : 1- Introduction
2- Related work
3- Involvement in recommender projects
4- Data acquisition and information extraction
5- Data deduplication (using machine learning)
6- Location category and name detection
7- Data storage and object score calculation
8- Conclusions
9- Future workNuméro de notice : 28600 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse étrangère Note de thèse : PhD Thesis : Computer Science : Tallinn University of Technology : 2021 DOI : 10.23658/taltech.23/2021 En ligne : https://doi.org/10.23658/taltech.23/2021 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99407 Incorporating memory-based preferences and point-of-interest stickiness into recommendations in location-based social networks / Hang Zhang in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)
[article]
Titre : Incorporating memory-based preferences and point-of-interest stickiness into recommendations in location-based social networks Type de document : Article/Communication Auteurs : Hang Zhang, Auteur ; Mingxin Gan, Auteur ; Xi Sun, Auteur Année de publication : 2021 Article en page(s) : n° 10 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] approche participative
[Termes IGN] comportement
[Termes IGN] filtrage d'information
[Termes IGN] interprétation (psychologie)
[Termes IGN] mémoire
[Termes IGN] mobilité humaine
[Termes IGN] point d'intérêt
[Termes IGN] réseau social géodépendant
[Termes IGN] tourismeRésumé : (auteur) In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI recommendation methods. In psychological effect-based POI recommendations, the memory-based attenuation of people’s preferences with respect to POIs, e.g., the fact that more attention is paid to POIs that were checked in to recently than those visited earlier, is emphasized. However, the memory effect only reflects the changes in an individual’s check-in trajectory and cannot discover the important POIs that dominate their mobility patterns, which are related to the repeat-visit frequency of an individual at a POI. To solve this problem, in this paper, we developed a novel POI recommendation framework using people’s memory-based preferences and POI stickiness, named U-CF-Memory-Stickiness. First, we used the memory-based preference-attenuation mechanism to emphasize personal psychological effects and memory-based preference evolution in human mobility patterns. Second, we took the visiting frequency of POIs into consideration and introduced the concept of POI stickiness to identify the important POIs that reflect the stable interests of an individual with respect to their mobility behavior decisions. Lastly, we incorporated the influence of both memory-based preferences and POI stickiness into a user-based collaborative filtering framework to improve the performance of POI recommendations. The results of the experiments we conducted on a real LBSN dataset demonstrated that our method outperformed other methods. Numéro de notice : A2021-148 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10010036 Date de publication en ligne : 15/01/2021 En ligne : https://doi.org/10.3390/ijgi10010036 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97056
in ISPRS International journal of geo-information > vol 10 n° 1 (January 2021) . - n° 10[article]Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London / Nilufer Sari Aslam in Annals of GIS, vol 27 n° 1 (January 2021)
[article]
Titre : Semantic enrichment of secondary activities using smart card data and point of interests: a case study in London Type de document : Article/Communication Auteurs : Nilufer Sari Aslam, Auteur ; Di Zhu, Auteur ; Tao Cheng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 29 - 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] carte à puce
[Termes IGN] collecte de données
[Termes IGN] données socio-économiques
[Termes IGN] données spatiotemporelles
[Termes IGN] enrichissement sémantique
[Termes IGN] loisir
[Termes IGN] Londres
[Termes IGN] méthode heuristique
[Termes IGN] planification urbaine
[Termes IGN] point d'intérêt
[Termes IGN] transport urbainRésumé : (auteur) The large volume of data automatically collected by smart card fare systems offers a rich source of information regarding daily human activities with a high resolution of spatial and temporal representation. This provides an opportunity for aiding transport planners and policy-makers to plan transport systems and cities more responsively. However, there are currently limitations when it comes to understanding the secondary activities of individual commuters. Accordingly, in this paper, we propose a framework to detect and infer secondary activities from individuals’ daily travel patterns from the smart card data and reduce the use of conventional surveys. First, we proposed a ‘heuristic secondary activity identification algorithm’, which uses commuters’ primary locations (home & work) and the direction (from & to) information to identify secondary activities for individuals. The algorithm provides a high-level classification of the activity types as before-work, midday and after-work activity patterns of individuals. Second, this classification is semantically enriched using Points of Interests to provide meaningful insights into individuals’ travel purposes and mobility in an urban environment. Lastly, using the transit data of London as a case study, the model is compared with a volunteer survey to demonstrate its effectiveness and offering a cost-effective method to travel demand research. Numéro de notice : A2021-319 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2020.1783359 Date de publication en ligne : 01/08/2020 En ligne : https://doi.org/10.1080/19475683.2020.1783359 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97550
in Annals of GIS > vol 27 n° 1 (January 2021) . - pp 29 - 41[article]Semantic trajectory segmentation based on change-point detection and ontology / Yuan Gao in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)
[article]
Titre : Semantic trajectory segmentation based on change-point detection and ontology Type de document : Article/Communication Auteurs : Yuan Gao, Auteur ; Longfei Huang, Auteur ; Jun Feng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2361 - 2394 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] base de données d'objets mobiles
[Termes IGN] base de données spatiotemporelles
[Termes IGN] détection de changement
[Termes IGN] enrichissement sémantique
[Termes IGN] modèle dynamique
[Termes IGN] objet mobile
[Termes IGN] ontologie
[Termes IGN] point d'intérêt
[Termes IGN] segmentation sémantique
[Termes IGN] trajectoire (véhicule non spatial)Résumé : (auteur) Trajectory segmentation is a fundamental issue in GPS trajectory analytics. The task of dividing a raw trajectory into reasonable sub-trajectories and annotating them based on moving subject’s intentions and application domains remains a challenge. This is due to the highly dynamic nature of individuals’ patterns of movement and the complex relationships between such patterns and surrounding points of interest. In this paper, we present a framework called SEMANTIC-SEG for automatic semantic segmentation of trajectories from GPS readings. For the decomposition component of SEMANTIC-SEG, a moving pattern change detection (MPCD) algorithm is proposed to divide the raw trajectory into segments that are homogeneous in their movement conditions. A generic ontology and a spatiotemporal probability model for segmentation are then introduced to implement a bottom-up ontology-based reasoning for semantic enrichment. The experimental results on three real-world datasets show that MPCD can more effectively identify the semantically significant change-points in a pattern of movement than four existing baseline methods. Moreover, experiments are conducted to demonstrate how the proposed SEMANTIC-SEG framework can be applied. Numéro de notice : A2020-689 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1798966 Date de publication en ligne : 04/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1798966 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96226
in International journal of geographical information science IJGIS > vol 34 n° 12 (December 2020) . - pp 2361 - 2394[article]Follow the road: historical GIS for evaluating the development of routes in the Negev region during the twentieth century / Motti Zohar in Cartography and Geographic Information Science, vol 47 n° 6 (October 2020)
[article]
Titre : Follow the road: historical GIS for evaluating the development of routes in the Negev region during the twentieth century Type de document : Article/Communication Auteurs : Motti Zohar, Auteur Année de publication : 2020 Article en page(s) : pp 532 - 546 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] carte militaire
[Termes IGN] cartographie historique
[Termes IGN] Palestine
[Termes IGN] point d'appui
[Termes IGN] point d'intérêt
[Termes IGN] réseau routier
[Termes IGN] Sinai
[Termes IGN] transformation polynomiale
[Termes IGN] vingtième siècleRésumé : (auteur) At the beginning of the twentieth century, a British mapping team led by Captain S. F. Newcombe surveyed and mapped the Negev region, Sinai, and western Jordan. The map was mainly produced for military use. Consequently, it included a network of branched routes, water supplies and facilities, and topographic contours. This study used this map to examine the development of routes in the Negev region between the beginning of and until the end of the twentieth century. First, the individual sheets comprising the study area were pieced together and the accuracy of the map was evaluated. The accuracy found on the Newcombe map was 0.76 mm on the map scale, equivalent to 100.3 m. Route development during the twentieth century was then evaluated by comparing the routes digitized from the Newcombe map to digitized routes on a late twentieth-century map. The results do not reveal tremendous changes in path, shape, or number of routes. Instead, they merely indicate the natural development in their quality. This Historical GIS-based approach provided a useful technique for analyzing and comparing the line segments extracted from historical and modern maps. The implemented approach may also serve other geographical or historical studies aiming to examine the development of branched networks throughout history. Numéro de notice : A2020-605 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1577176 Date de publication en ligne : 26/02/2019 En ligne : https://doi.org/10.1080/15230406.2019.1577176 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95965
in Cartography and Geographic Information Science > vol 47 n° 6 (October 2020) . - pp 532 - 546[article]A graph convolutional network model for evaluating potential congestion spots based on local urban built environments / Kun Qin in Transactions in GIS, Vol 24 n° 5 (October 2020)PermalinkMachine‐learning prediction models for pedestrian traffic flow levels: Towards optimizing walking routes for blind pedestrians / Achituv Cohen in Transactions in GIS, Vol 24 n° 5 (October 2020)PermalinkImpact of extreme weather events on urban human flow: A perspective from location-based service data / Zhenhua Chen in Computers, Environment and Urban Systems, vol 83 (September 2020)PermalinkMeasuring accessibility of bus system based on multi-source traffic data / Yufan Zuo in Geo-spatial Information Science, vol 23 n° 3 (September 2020)PermalinkA name‐led approach to profile urban places based on geotagged Twitter data / Juntao Lai in Transactions in GIS, Vol 24 n° 4 (August 2020)PermalinkTourism land use simulation for regional tourism planning using POIs and cellular automata / Hong Shi in Transactions in GIS, Vol 24 n° 4 (August 2020)PermalinkObjets connectés et mobilité urbaine : visualiser les déplacements des usagers de Twitter avec des graphes dynamiques / Françoise Lucchini in Mappemonde, n° 128 (juillet 2020)PermalinkDeveloping shopping and dining walking indices using POIs and remote sensing data / Yingbin Deng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkEstimating and interpreting fine-scale gridded population using random forest regression and multisource data / Yun Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkExtracting activity patterns from taxi trajectory data: a two-layer framework using spatio-temporal clustering, Bayesian probability and Monte Carlo simulation / Shuhui Gong in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)Permalink