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Tourism land use simulation for regional tourism planning using POIs and cellular automata / Hong Shi in Transactions in GIS, Vol 24 n° 4 (August 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 4 (August 2020) . - 20 p. Titre : Tourism land use simulation for regional tourism planning using POIs and cellular automata Type de document : Article/Communication Auteurs : Hong Shi, Auteur ; Xia Li, Auteur ; Zhenzhi Yang, Auteur Année de publication : 2020 Article en page(s) : 20 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] chaîne de Markov [Termes IGN] Chine [Termes IGN] distribution spatiale [Termes IGN] modèle de simulation [Termes IGN] montagne [Termes IGN] planification [Termes IGN] point d'intérêt [Termes IGN] tourisme Résumé : (auteur) Previous studies on tourism land use primarily focus on the spatial distribution, and its related impacts on the environment. Here, we propose a future tourism land use simulation model for mountain vacations based on the cellular automata and Markov chain methods, having verified and simulated tourism land use in Emeishan city at a spatial resolution of 30 × 30 m using remote sensing and GIS. In addition, we introduced a tourism land use intensity index to study the spatial expansion mode of tourism land use. The results have confirmed the validity of the model and demonstrated its ability to simulate future tourism land use. The average growth rate of tourism land use from 2010 to 2015 is 33.36%, and tourism land use will rise from 1.26% of Emeishan city’s land area in 2015 to 2.95% in 2030. Tourism land use shows a spatial expansion pattern along channels from scenic spots to the urban area. The growth of tourism land use in the protected area has an increasing trend when there is no restriction on development, especially in the Eshan region. The simulation results can provide useful implications and guides for regional tourism planning and management. Numéro de notice : A2020-673 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12626 Date de publication en ligne : 23/05/2020 En ligne : https://doi.org/10.1111/tgis.12626 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96158 [article]

Integration of spatialization and individualization: the future of epidemic modelling for communicable diseases / Meifang Li in Annals of GIS, vol 26 n° 3 (July 2020)  

Public [article]  inAnnals of GIS > vol 26 n° 3 (July 2020) . - pp 219 - 226 Titre : Integration of spatialization and individualization: the future of epidemic modelling for communicable diseases Type de document : Article/Communication Auteurs : Meifang Li, Auteur ; Xun Shi, Auteur ; Xia Li, Auteur Année de publication : 2020 Article en page(s) : pp 219 - 226 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] épidémie [Termes IGN] historique des données [Termes IGN] modèle orienté objet [Termes IGN] modélisation spatio-temporelle [Termes IGN] risque sanitaire [Termes IGN] système d'information géographique [Termes IGN] transmissibilité Résumé : (auteur) In the past several decades, epidemic modelling for communicable diseases has experienced transitions from treating the entire study area as a whole to addressing spatial variation within the area, and from targeting the entire population to incorporating characteristics of categorized subpopulations and finally going down to the individual level. These transitions have been first driven by the recognition that generalizations of space and population in conventional epidemic modelling may have hampered the effectiveness of the modelling; they then have been supported by increasingly available data that allow depiction of detailed spatiotemporal characteristics of an epidemic, as well as those characteristics of the environment in both human and natural aspects; and finally they have been facilitated by developments in geographic information science, data science, computer science, and computing technologies. Based on a review of a variety of recently developed communicable disease models, we explicitly put forward the notions of spatialization and individualization in this area, and point out that the integration of the two is the future of communicable disease modelling. We also point out that in this area models based on the object conceptualization are good at modelling spatiotemporal process, whereas models based on the field conceptualization are good at representing spatialized information. We propose a procedural framework of epidemic modelling that implements the integration of individualization and spatialization, integration of object-based process and field-based representation, and integration of modelling that retrospectively traces infection relationships based on historical patient data and modelling that prospectively predicts such relationships of future epidemics. Numéro de notice : A2020-581 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2020.1768438 Date de publication en ligne : 25/05/2020 En ligne : https://doi.org/10.1080/19475683.2020.1768438 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95903 [article]

Calibrating a Land Parcel Cellular Automaton (LP-CA) for urban growth simulation based on ensemble learning / Yimin Chen in International journal of geographical information science IJGIS, vol 31 n° 11-12 (November - December 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 11-12 (November - December 2017) . - pp 2480 - 2504 Titre : Calibrating a Land Parcel Cellular Automaton (LP-CA) for urban growth simulation based on ensemble learning Type de document : Article/Communication Auteurs : Yimin Chen, Auteur ; Xiaoping Liu, Auteur ; Xia Li, Auteur Année de publication : 2017 Article en page(s) : pp 2480 - 2504 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] automate cellulaire [Termes IGN] changement d'occupation du sol [Termes IGN] croissance urbaine [Termes IGN] données vectorielles [Termes IGN] étalonnage de modèle [Termes IGN] Kouangtoung (Chine) [Termes IGN] maillage [Termes IGN] parcelle cadastrale [Termes IGN] simulation Résumé : (Auteur) The reliability of raster cellular automaton (CA) models for fine-scale land change simulations has been increasingly questioned, because regular pixels/grids cannot precisely represent irregular geographical entities and their interactions. Vector CA models can address these deficiencies due to the ability of the vector data structure to represent realistic urban entities. This study presents a new land parcel cellular automaton (LP-CA) model for simulating urban land changes. The innovation of this model is the use of ensemble learning method for automatic calibration. The proposed model is applied in Shenzhen, China. The experimental results indicate that bagging-Naïve Bayes yields the highest calibration accuracy among a set of selected classifiers. The assessment of neighborhood sensitivity suggests that the LP-CA model achieves the highest simulation accuracy with neighbor radius r = 2. The calibrated LP-CA is used to project future urban land use changes in Shenzhen, and the results are found to be consistent with those specified in the official city plan. Numéro de notice : A2017-702 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1367004 En ligne : https://doi.org/10.1080/13658816.2017.1367004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88083 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017061	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2017062	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data / Yao Yao in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 1220 - 1244 Titre : Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data Type de document : Article/Communication Auteurs : Yao Yao, Auteur ; Xiaoping Liu, Auteur ; Xia Li, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1220 - 1244 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] bâtiment [Termes IGN] Canton (Kouangtoung) [Termes IGN] cartographie statistique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] densité de population [Termes IGN] données localisées des bénévoles [Termes IGN] données massives [Termes IGN] données statistiques [Termes IGN] habitat collectif [Termes IGN] habitat urbain [Termes IGN] intégration de données [Termes IGN] point d'intérêt [Termes IGN] population urbaine [Termes IGN] répartition géographique Résumé : (auteur) Fine-scale population distribution data at the building level play an essential role in numerous fields, for example urban planning and disaster prevention. The rapid technological development of remote sensing (RS) and geographical information system (GIS) in recent decades has benefited numerous population distribution mapping studies. However, most of these studies focused on global population and environmental changes; few considered fine-scale population mapping at the local scale, largely because of a lack of reliable data and models. As geospatial big data booms, Internet-collected volunteered geographic information (VGI) can now be used to solve this problem. This article establishes a novel framework to map urban population distributions at the building scale by integrating multisource geospatial big data, which is essential for the fine-scale mapping of population distributions. First, Baidu points-of-interest (POIs) and real-time Tencent user densities (RTUD) are analyzed by using a random forest algorithm to down-scale the street-level population distribution to the grid level. Then, we design an effective iterative building-population gravity model to map population distributions at the building level. Meanwhile, we introduce a densely inhabited index (DII), generated by the proposed gravity model, which can be used to estimate the degree of residential crowding. According to a comparison with official community-level census data and the results of previous population mapping methods, our method exhibits the best accuracy (Pearson R = .8615, RMSE = 663.3250, p Numéro de notice : A2017-245 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1290252 En ligne : http://dx.doi.org/10.1080/13658816.2017.1290252 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85188 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Knowledge transfer for large-scale urban growth modeling based on formal concept analysis / Jinyao Lin in Transactions in GIS, vol 20 n° 5 (October 2016)  

Public [article]  inTransactions in GIS > vol 20 n° 5 (October 2016) . - pp 684 – 700 Titre : Knowledge transfer for large-scale urban growth modeling based on formal concept analysis Type de document : Article/Communication Auteurs : Jinyao Lin, Auteur ; Xia Li, Auteur Année de publication : 2016 Article en page(s) : pp 684 – 700 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] croissance urbaine [Termes IGN] découverte de connaissances [Termes IGN] étalonnage des données [Termes IGN] Kouangtoung (Chine) [Termes IGN] modèle conceptuel de données localisées [Termes IGN] modélisation spatiale Résumé : (auteur) Cellular automata (CA) are useful for studies on urban growth and land-use changes. Although various methods have been developed to define transition rules, modeling urban growth of large areas remains a tough challenge owing to heterogeneous geographical features. To address the problem, we present a novel method based on the combination of Formal Concept Analysis (FCA) and knowledge transfer techniques. FCA is used to solicit association rules among cities within a large area. This method can provide a theoretical basis for the knowledge transfer process. A cutting-edge algorithm called TrAdaBoost is then integrated with the commonly-used Logistic-CA as the modeling framework. The proposed method is applied to the urban growth modeling of Guangdong Province, a large region with 21 cities in China, from 2005 to 2008. Compared with traditional methods, this method can achieve better results at the provincial and local levels, according to the experiments. The combination of FCA and knowledge transfer is expected to provide a useful tool for calibrating large-scale urban CA models. Numéro de notice : A2016-997 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12172 En ligne : http://dx.doi.org/10.1111/tgis.12172 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83777 [article]

Domain adaptation for land use classification: A spatio-temporal knowledge reusing method / Yilun Liu in ISPRS Journal of photogrammetry and remote sensing, vol 98 (December 2014)  

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