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Semantic‐based urban growth prediction / Marvin Mc Cutchan in Transactions in GIS, Vol 24 n° 6 (December 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 6 (December 2020) . - 1482 - 1503 Titre : Semantic‐based urban growth prediction Type de document : Article/Communication Auteurs : Marvin Mc Cutchan, Auteur ; Simge Özdal‐Oktay, Auteur ; Ioannis Giannopoulos, Auteur Année de publication : 2020 Article en page(s) : 1482 - 1503 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] croissance urbaine [Termes IGN] dynamique spatiale [Termes IGN] Europe (géographie politique) [Termes IGN] information sémantique [Termes IGN] modèle de simulation [Termes IGN] modélisation spatiale [Termes IGN] occupation du sol [Termes IGN] OpenStreetMap [Termes IGN] organisation spatiale [Termes IGN] OWL [Termes IGN] prévision [Termes IGN] réseau neuronal artificiel [Termes IGN] urbanisation [Termes IGN] ville durable Résumé : (Auteur) Urban growth is a spatial process which has a significant impact on the earth’s environment. Research on predicting this complex process makes it therefore especially fruitful for decision‐making on a global scale, as it enables the introduction of more sustainable urban development. This article presents a novel method of urban growth prediction. The method utilizes geospatial semantics in order to predict urban growth for a set of random areas in Europe. For this purpose, a feature space representing geospatial configurations was introduced which embeds semantic information. Data in this feature space was then used to perform deep learning, which ultimately enables the prediction of urban growth with high accuracy. The final results reveal that geospatial semantics hold great potential for spatial prediction tasks. Numéro de notice : A2020-766 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12655 Date de publication en ligne : 14/07/2020 En ligne : https://doi.org/10.1111/tgis.12655 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96657 [article]

How urban places are visited by social groups? Evidence from matrix factorization on mobile phone data / Chaogui Kang in Transactions in GIS, Vol 24 n° 6 (December 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 6 (December 2020) . - pp 1504 - 1525 Titre : How urban places are visited by social groups? Evidence from matrix factorization on mobile phone data Type de document : Article/Communication Auteurs : Chaogui Kang, Auteur ; Li Shi, Auteur ; Fahui Wang, Auteur ; Yu Liu, Auteur Année de publication : 2020 Article en page(s) : pp 1504 - 1525 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Chine [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données spatiotemporelles [Termes IGN] ethnographie [Termes IGN] factorisation de matrice non-négative [Termes IGN] matrice de co-occurrence [Termes IGN] production participative [Termes IGN] réseau social [Termes IGN] site urbain [Termes IGN] téléphonie mobile [Termes IGN] urbanisme Résumé : (Auteur) This research attempts to build a unified framework for distinguishing the spatiotemporal visit patterns of urban places by different social groups using mobile phone data in Harbin, China. Social groups are detected by their social ties in the ego‐to‐ego mobile phone call network and are embedded in physical space according to their home locations. Popular urban places are detected from user‐generated content as the basic spatial analysis unit. Coupling subscribers’ footprints and urban places in physical space, the spatiotemporal visit patterns of urban places by distinct social groups are uncovered and interpreted by non‐negative matrix factorization. The proposed framework enables us to answer several critical questions from three perspectives: (1) How to model popular urban places in terms of vague boundary, land use, and semantic features based on crowdsourcing data?; (2) How to evaluate interaction between individuals for inspecting the relationship between spatial proximity and social ties based on spatiotemporal co‐occurrence?; and (3) How to distinguish urban place visit preferences for social groups associated with different socio‐demographic characteristics? Our research could assist urban planners and municipal managers to identify critical urban places frequented by different population groups according to their roles and social/cultural characteristics for improvement of urban facility allocation. Numéro de notice : A2020-767 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12654 Date de publication en ligne : 30/06/2020 En ligne : https://doi.org/10.1111/tgis.12654 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96658 [article]

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]

Automated labeling of schematic maps by optimization with knowledge acquired from existing maps / Tian Lan in Transactions in GIS, Vol 24 n° 6 (December 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 6 (December 2020) . - pp 1722 - 1739 Titre : Automated labeling of schematic maps by optimization with knowledge acquired from existing maps Type de document : Article/Communication Auteurs : Tian Lan, Auteur ; Zhilin Li, Auteur ; Qian Peng, Auteur ; Xinyu Gong, Auteur Année de publication : 2020 Article en page(s) : pp 1722 - 1739 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] automatisation [Termes IGN] calcul d'itinéraire [Termes IGN] Chine [Termes IGN] données cartographiques [Termes IGN] Hong-Kong [Termes IGN] optimisation (mathématiques) [Termes IGN] réseau métropolitain [Termes IGN] segmentation sémantique Résumé : (Auteur) Schematic maps are simplified representations of line networks, aiming to help people quickly and accurately perform route planning and orientation tasks. The automated generation of such maps is generally treated as an optimization problem. Most researchers prefer to optimize network layouts and name labels separately, because optimizing them simultaneously is still intractable. It is found that optimizing network layouts is extensively studied, while optimizing name labels is rarely considered. In the optimization of name labels, constraints can be established with rules from cartographic experts, literature (e.g., specification and technical documents), and/or existing maps. However, some rules from experts and literature cannot be explicitly and mathematically expressed. This study aims to develop an automated labeling method with rules from existing maps. We first acquire the rules (i.e., the potential positions and the preferences of these positions) from some existing schematic maps and then integrate them into an optimization algorithm. Experimental evaluation is conducted by a questionnaire in terms of “ease level of finding name labels,” “congestion level,” and “satisfaction level” using Tianjin and Hong Kong metro schematic maps and the labels of our method. The results show that the proposed method can automatically generate effective labels. Numéro de notice : A2020-769 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12671 Date de publication en ligne : 06/08/2020 En ligne : https://doi.org/10.1111/tgis.12671 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96661 [article]