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An agent-based modeling approach for public charging demand estimation and charging station location optimization at urban scale / Zhiyan Yi in Computers, Environment and Urban Systems, vol 101 (April 2023)  

Public [article]  inComputers, Environment and Urban Systems > vol 101 (April 2023) . - n° 101949 Titre : An agent-based modeling approach for public charging demand estimation and charging station location optimization at urban scale Type de document : Article/Communication Auteurs : Zhiyan Yi, Auteur ; Bingkun Chen, Auteur ; Xiaoyue Cathy Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101949 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] chaîne de Markov [Termes IGN] distribution spatiale [Termes IGN] équipement collectif [Termes IGN] modèle orienté agent [Termes IGN] optimisation spatiale [Termes IGN] planification urbaine [Termes IGN] véhicule électrique [Termes IGN] zone urbaine Résumé : (auteur) As the market penetration of electric vehicles (EVs) increases, the surge of charging demand could potentially overload the power grid and disrupt infrastructure planning. Hence, an efficient deployment strategy of electrical vehicle supply equipment (EVSE) is much needed. This study attempts to address the EVSE problem from a microscopic perspective by formulating the problem in two steps: public charging demand simulation and charging station location optimization. Specifically, we apply agent-based modeling approach to produce high-resolution daily driving profiles within an urban-scale context using MATSim. Subsequently, we perform EV assignment based on socioeconomic attributes to determine EV adopters. Energy consumption model and public charging rule are specified for generating synthetic public charging demand and such demand is validated against real-world public charging records to guarantee the robustness of simulation results. In the second step, we apply a location approach – capacitated maximal coverage location problem (CMCLP) model – to reallocate existing charging stations with the objective of maximizing the coverage of total charging demands generated from the previous step under the budget and load capacity constraints. The entire framework is capable of modeling the spatiotemporal distribution of public charging demand in a bottom-up fashion, and provide practical support for future public EVSE installation. Numéro de notice : A2023-186 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2023.101949 Date de publication en ligne : 15/02/2023 En ligne : https://doi.org/10.1016/j.compenvurbsys.2023.101949 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102960 [article]

Mapping population distribution from open address data: application to mainland Portugal / Nelson Mileu in Journal of maps, vol 18 n° 3 (March 2023)  

Public [article]  inJournal of maps > vol 18 n° 3 (March 2023) . - pp 585 - 593 Titre : Mapping population distribution from open address data: application to mainland Portugal Type de document : Article/Communication Auteurs : Nelson Mileu, Auteur ; Margarida Queirós, Auteur ; Paolo Morgado, Auteur Année de publication : 2023 Article en page(s) : pp 585 - 593 Note générale : bilbliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] base de données d'adresses [Termes IGN] carte thématique [Termes IGN] distribution spatiale [Termes IGN] grille [Termes IGN] planification urbaine [Termes IGN] population [Termes IGN] Portugal [Termes IGN] QGIS Résumé : (auteur) Mapping population distribution remains a common need in various fields of studies. Several approaches and methodologies have been adopted to obtain high-resolution population distribution grids. The use of addresses data to obtain gridded population distribution maps emerges as one of the more recent and accurate approaches. The increasing dissemination and availability of geo-data and more specifically address data allow us to obtain updated, granular and high spatial resolution population distribution maps. This paper describes a bottom-up open addresses data mapping-based approach of gridded population distribution with a fine spatial resolution. Through a QGIS plugin, an adaptation of the housing unit methodology was implemented to obtain 500 m × 500 and 250 m × 250 m population grids for mainland Portugal. The results showed that the use of reliable addresses databases can generate gridded population distribution maps with a high degree of adjustment to reality. Numéro de notice : A2023-154 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17445647.2022.2114862 Date de publication en ligne : 07/09/2022 En ligne : https://doi.org/10.1080/17445647.2022.2114862 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102839 [article]

Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal / Cristina Alegria in Forests, vol 14 n° 3 (March 2023)  

Public [article]  inForests > vol 14 n° 3 (March 2023) . - n° 591 Titre : Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal Type de document : Article/Communication Auteurs : Cristina Alegria, Auteur ; Alice M. Almeida, Auteur ; Natalia Roque, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 591 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] changement climatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] distribution spatiale [Termes IGN] entropie maximale [Termes IGN] gestion forestière [Termes IGN] modèle de simulation [Termes IGN] modélisation de la forêt [Termes IGN] Pinus pinaster [Termes IGN] Portugal [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) To date, a variety of species potential distribution mapping approaches have been used, and the agreement in maps produced with different methodological approaches should be assessed. The aims of this study were: (1) to model Maritime pine potential distributions for the present and for the future under two climate change scenarios using the machine learning Maximum Entropy algorithm (MaxEnt); (2) to update the species ecological envelope maps using the same environmental data set and climate change scenarios; and (3) to perform an agreement analysis for the species distribution maps produced with both methodological approaches. The species distribution maps produced by each of the methodological approaches under study were reclassified into presence–absence binary maps of species to perform the agreement analysis. The results showed that the MaxEnt-predicted map for the present matched well the species’ current distribution, but the species ecological envelope map, also for the present, was closer to the species’ empiric potential distribution. Climate change impacts on the species’ future distributions maps using the MaxEnt were moderate, but areas were relocated. The 47.3% suitability area (regular-medium-high), in the present, increased in future climate change scenarios to 48.7%–48.3%. Conversely, the impacts in species ecological envelopes maps were higher and with greater future losses than the latter. The 76.5% suitability area (regular-favourable-optimum), in the present, decreased in future climate change scenarios to 58.2%–51.6%. The two approaches combination resulted in a 44% concordance for the species occupancy in the present, decreasing around 30%–35% in the future under the climate change scenarios. Both methodologies proved to be complementary to set species’ best suitability areas, which are key as support decision tools for planning afforestation and forest management to attain fire-resilient landscapes, enhanced forest ecosystems biodiversity, functionality and productivity. Numéro de notice : A2023-167 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f14030591 Date de publication en ligne : 16/03/2023 En ligne : https://doi.org/10.3390/f14030591 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102904 [article]

A spatial distribution: Principal component analysis (SD-PCA) model to assess pollution of heavy metals in soil / Jiawei Liu in Science of the total environment, vol 859 n° 1 (February 2023)  

Public [article]  inScience of the total environment > vol 859 n° 1 (February 2023) . - n° 160112 Titre : A spatial distribution: Principal component analysis (SD-PCA) model to assess pollution of heavy metals in soil Type de document : Article/Communication Auteurs : Jiawei Liu, Auteur ; Hou Kang, Auteur ; Wendong Tao, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 160112 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse en composantes principales [Termes IGN] autocorrélation spatiale [Termes IGN] cartographie des risques [Termes IGN] Chine [Termes IGN] distribution spatiale [Termes IGN] métal lourd [Termes IGN] pollution des sols [Termes IGN] risque de pollution [Termes IGN] traçabilité Résumé : (auteur) With the rapid development of urbanization, heavy metal pollution of soil has received great attention. Over-enrichment of heavy metals in soil may endanger human health. Assessing soil pollution and identifying potential sources of heavy metals are crucial for prevention and control of soil heavy metal pollution. This study introduced a spatial distribution - principal component analysis (SD-PCA) model that couples the spatial attributes of soil pollution with linear data transformation by the eigenvector-based principal component analysis. By evaluating soil pollution in the spatial dimension it identifies the potential sources of heavy metals more easily. In this study, soil contamination by eight heavy metals was investigated in the Lintong District, a typical multi-source urban area in Northwest China. In general, the soils in the study area were lightly contaminated by Cr and Pb. Pearson correlation analysis showed that Cr was negatively correlated with other heavy metals, whereas the spatial autocorrelation analysis revealed that there was strong association in the spatial distribution of eight heavy metals. The aggregation forms were more varied and the correlation between Cr contamination and other heavy metals was lower. The aggregation forms of Mn and Cu, Zn and Pb, on the other hand, were remarkably comparable. Agriculture was the largest pollution source, contributing 65.5 % to soil pollution, which was caused by the superposition of multiple heavy metals. Additionally, traffic and natural pollution sources contributed 17.9 % and 11.1 %, respectively. The ability of this model to track pollution of heavy metals has important practical significance for the assessment and control of multi-source soil pollution. Numéro de notice : A2023-009 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scitotenv.2022.160112 Date de publication en ligne : 11/11/2022 En ligne : https://doi.org/10.1016/j.scitotenv.2022.160112 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102115 [article]

Multi-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services / Mingyue Xu in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 2 (February 2023) . - pp 380 - 402 Titre : Multi-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services Type de document : Article/Communication Auteurs : Mingyue Xu, Auteur ; Peng Yue, Auteur ; Fan Yu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 380 - 402 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] appariement de données localisées [Termes IGN] apprentissage profond [Termes IGN] autopartage [Termes IGN] Chine [Termes IGN] distribution spatiale [Termes IGN] interaction humain-espace [Termes IGN] modèle de Markov [Termes IGN] système d'information urbain [Termes IGN] système multi-agents [Termes IGN] taxi [Termes IGN] transmission de données [Termes IGN] zone d'activité économique Résumé : (auteur) The popularity of ride-hailing platforms has significantly improved travel efficiency by providing convenient and personalized transportation services. Designing an effective ride-hailing service generally needs to address two tasks: order matching that assigns orders to available vehicles and proactive vehicle repositioning that deploys idle vehicles to potentially high-demand regions. Recent studies have intensively utilized deep reinforcement learning to solve the two tasks by learning an optimal dispatching strategy. However, most of them generate actions for the two tasks independently, neglecting the interactions between the two tasks and the communications among multiple drivers. To this end, this paper provides an approach based on multi-agent deep reinforcement learning where the two tasks are modeled as a unified Markov decision process, and the colossal state space and competition among drivers are addressed. Additionally, a modifiable agent-specific state representation is proposed to facilitate knowledge transferring and improve computing efficiency. We evaluate our approach on a public taxi order dataset collected in Chengdu, China, where a variable number of simulated vehicles are tested. Experimental results show that our approach outperforms seven existing baselines, reducing passenger rejection rate, driver idle time and improving total driver income. Numéro de notice : A2023-058 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2119477 Date de publication en ligne : 07/09/2022 En ligne : https://doi.org/10.1080/13658816.2022.2119477 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102396 [article]

Evaluation of GNSS-based volunteered geographic information for assessing visitor spatial distribution within protected areas: A case study of the Bavarian Forest National Park, Germany / Laura Horst in Applied Geography, vol 150 (January 2023)  

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A GIS-based study on the layout of the ecological monitoring system of the Grain for Green project in China / Ke Guo in Forests, vol 14 n° 1 (January 2023)  

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Landscape metrics regularly outperform other traditionally-used ancillary datasets in dasymetric mapping of population / Heng Wan in Computers, Environment and Urban Systems, vol 99 (January 2023)  

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Rapid mapping of seismic intensity assessment using ground motion data calculated from early aftershocks selected by GIS spatial analysis / Huaiqun Zhao in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)  

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Sensing urban soundscapes from street view imagery / Tianhong Zhao in Computers, Environment and Urban Systems, vol 99 (January 2023)  

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Spatial distribution analysis of seismic activity based on GMI, LMI, and LISA in China / Ziyi Cao in Open geosciences, vol 14 n° 1 (January 2023)  

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Eco-environment and coupling coordination and quantification of urbanization in Yangtze River delta considering spatial non-stationarity / Yaqiu Zhang in Geocarto international, vol 37 n° 27 ([20/12/2022])  

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GIS-based land-use suitability analysis for urban agriculture development based on pollution distributions / Fatemeh Kazemi in Land use policy, vol 123 (December 2022)  

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Identification and spatial extent of understory plant species requiring vegetation control to ensure tree regeneration in French forests / Noé Dumas in Annals of Forest Science, vol 79 n° 1 (2022)  

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Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution / Zhenfeng Shao in Geo-spatial Information Science, vol 25 n° 4 (December 2022)  

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