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An empirical study on the intra-urban goods movement patterns using logistics big data / Pengxiang Zhao in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 6 (June 2020) . - pp 1089 - 1116 Titre : An empirical study on the intra-urban goods movement patterns using logistics big data Type de document : Article/Communication Auteurs : Pengxiang Zhao, Auteur ; Wenzhong Shi, Auteur ; Tao Jia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1089 - 1116 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] analyse systémique [Termes descripteurs IGN] circulation urbaine [Termes descripteurs IGN] fret [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] Hong-Kong [Termes descripteurs IGN] interaction spatiale [Termes descripteurs IGN] logistique [Termes descripteurs IGN] objet mobile [Termes descripteurs IGN] origine - destination [Termes descripteurs IGN] plan de déplacement urbain [Termes descripteurs IGN] réseau de transport [Termes descripteurs IGN] série temporelle Résumé : (auteur) Movement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning. Numéro de notice : A2020-268 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1520236 date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1080/13658816.2018.1520236 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95039 [article]

Mapping urban grey and green structures for liveable cities using a 3D enhanced OBIA approach and vital statistics / E. Banzhaf in Geocarto international, vol 35 n° 6 ([01/05/2020])  

Public [article]  inGeocarto international > vol 35 n° 6 [01/05/2020] . - pp 623 - 640 Titre : Mapping urban grey and green structures for liveable cities using a 3D enhanced OBIA approach and vital statistics Type de document : Article/Communication Auteurs : E. Banzhaf, Auteur ; H. Kollai, Auteur ; A. Kindler, Auteur Année de publication : 2020 Article en page(s) : pp 623 - 640 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] base de données orientée objet [Termes descripteurs IGN] base de données urbaines [Termes descripteurs IGN] bati [Termes descripteurs IGN] bien-être collectif [Termes descripteurs IGN] cartographie urbaine [Termes descripteurs IGN] développement durable [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] écosystème urbain [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] orthophotographie [Termes descripteurs IGN] population urbaine [Termes descripteurs IGN] santé [Termes descripteurs IGN] télédétection Résumé : (auteur) Mapping urban structures is a vital prerequisite for urban planners to enhance their database for a liveable city dedicated to sustainable development. Therefore, it is significant to measure urban grey and green structures at the scale of local districts to understand the urban structure and residential needs for urban ecosystem services. For a detailed analysis we exploit digital orthophotos (DOP), LiDAR data, and vital statistics. We use remote sensing techniques to create an Object-based Image Analysis (OBIA) that differentiates grey and green structures with high precision and at refined scale. This spatial information is linked with allocated population and health-related indicators to identify built-up types with highest population densities and local districts with deficits in the provision of different green structures. Our results show the share of built-up structures and the contribution of green structures to urban ecosystem services, human health and well-being at local district level. Numéro de notice : A2020-202 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1524514 date de publication en ligne : 23/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1524514 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94877 [article]

A deep learning architecture for semantic address matching / Yue Lin in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 3 (March 2020) . - pp 559 - 576 Titre : A deep learning architecture for semantic address matching Type de document : Article/Communication Auteurs : Yue Lin, Auteur ; Mengjun Kang, Auteur ; Yuyang Wu, Auteur Année de publication : 2020 Article en page(s) : pp 559 - 576 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] appariement d'adresses [Termes descripteurs IGN] appariement sémantique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] géocodage par adresse postale [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] inférence sémantique [Termes descripteurs IGN] repésentation vectorielle [Termes descripteurs IGN] réseau de neurones profond [Termes descripteurs IGN] Shenzhen [Termes descripteurs IGN] similitude sémantique [Termes descripteurs IGN] traitement du langage naturel Résumé : (auteur) Address matching is a crucial step in geocoding, which plays an important role in urban planning and management. To date, the unprecedented development of location-based services has generated a large amount of unstructured address data. Traditional address matching methods mainly focus on the literal similarity of address records and are therefore not applicable to the unstructured address data. In this study, we introduce an address matching method based on deep learning to identify the semantic similarity between address records. First, we train the word2vec model to transform the address records into their corresponding vector representations. Next, we apply the enhanced sequential inference model (ESIM), a deep text-matching model, to make local and global inferences to determine if two addresses match. To evaluate the accuracy of the proposed method, we fine-tune the model with real-world address data from the Shenzhen Address Database and compare the outputs with those of several popular address matching methods. The results indicate that the proposed method achieves a higher matching accuracy for unstructured address records, with its precision, recall, and F1 score (i.e., the harmonic mean of precision and recall) reaching 0.97 on the test set. Numéro de notice : A2020-106 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1681431 date de publication en ligne : 24/10/2019 En ligne : https://doi.org/10.1080/13658816.2019.1681431 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94702 [article]

A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data / Sheng Hu in Computers, Environment and Urban Systems, vol 80 (March 2020)  

Public [article]  inComputers, Environment and Urban Systems > vol 80 (March 2020) Titre : A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data Type de document : Article/Communication Auteurs : Sheng Hu, Auteur ; Zhanjun He, Auteur ; Liang Wu, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] données massives [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] extraction de données [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] regroupement de données [Termes descripteurs IGN] télédétection spatiale [Termes descripteurs IGN] traitement du langage naturel [Termes descripteurs IGN] Wuhan (Chine) [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Many studies are in an effort to explore urban spatial structure, and urban functional regions have become the subject of increasing attention among planners, engineers and public officials. Attempts have been made to identify urban functional regions using high spatial resolution (HSR) remote sensing images and extensive geo-data. However, the research scale and throughput have also been limited by the accessibility of HSR remote sensing data. Recently, big geo-data are becoming increasingly popular for urban studies since research is still accessible and objective with regard to the use of these data. This study aims to build a novel framework to provide an alternative solution for sensing urban spatial structure and discovering urban functional regions based on emerging geo-data – points of interest (POIs) data and an embedding learning method in the natural language processing (NLP) field. We started by constructing the intraurban functional corpus using a center-context pairs-based approach. A word embeddings representation model for training that corpus was used to extract multiprototype vectors in the second step, and the last step aggregated the functional parcels based on an introduced spatial clustering method, hierarchical density-based spatial clustering of applications with noise (HDBSCAN). The clustering results suggested that our proposed framework used in this study is capable of discovering the utilization of urban space with a reasonable level of accuracy. The limitation and potential improvement of the proposed framework are also discussed. Numéro de notice : A2020-191 Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2019.101442 date de publication en ligne : 15/11/2019 En ligne : https://doi.org/10.1016/j.compenvurbsys.2019.101442 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94853 [article]

A method for urban population density prediction at 30m resolution / Krishnachandran Balakrishnan in Cartography and Geographic Information Science, vol 47 n° 3 (March 2020)  

Public [article]  inCartography and Geographic Information Science > vol 47 n° 3 (March 2020) . - pp 193 - 213 Titre : A method for urban population density prediction at 30m resolution Type de document : Article/Communication Auteurs : Krishnachandran Balakrishnan, Auteur Année de publication : 2020 Article en page(s) : pp 193 - 213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] densité de population [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] hauteur du bâti [Termes descripteurs IGN] image Cartosat-1 [Termes descripteurs IGN] Inde [Termes descripteurs IGN] logiciel de traitement d'image [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modélisation du bâti [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] véhicule automobile Résumé : (auteur) This paper proposes a new method for urban population density prediction at 30 m resolution. Using data for Bangalore, the paper demonstrates that population within each 30 m residential built-up cell can be modeled as a function of cell-level data on street density and building heights and ward-level data on car ownership. Building-height data were generated from Cartosat-1 stereo imagery using an open-source satellite stereo image processing software. Using this building-height data in conjunction with the other datasets, the paper demonstrates that a 30 m resolution population density surface can be generated such that, when summed to the ward level, the median absolute percentage error between predicted population and known census population at the ward level is 8.29%. The paper also shows that the relationship between population density, street density, building height, and ward level car ownership is spatially non-stationary. A fine-grained understanding of urban population densities, as enabled by the proposed method, can be beneficial to research, policy, and practice related to cities. Numéro de notice : A2020-168 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1687014 date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1080/15230406.2019.1687014 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94839 [article]

A novel method of spatiotemporal dynamic geo-visualization of criminal data, applied to command and control centers for public safety / Mayra Salcedo-Gonzalez in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)  

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Road network structure and ride-sharing accessibility: A network science perspective / Mingshu Wang in Computers, Environment and Urban Systems, vol 80 (March 2020)  

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3D immersive underground navigation [short paper] / Quoc Dinh Nguyen (2018)    

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Development of a Protocol to Convert and Manage Underground Infrastructure Maps into Geographic Information Systems (GIS) Format / Guillemette Fonteix (2018) 

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La géomatique comme aide à la décision dans le domaine de la santé / Dorothée Dumay (2018) 

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Mise en place d’un outil de classification et d’utilisation des données LiDAR pour l’étude du couvert arboré à Florence / Florian Thill (2018) 

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L'île de Ré, un SIG très aérien / Anonyme in Géomatique expert, n° 115 (mars - avril 2017)

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Crossing boundaries: mapping spatial dynamics of urban phenomena at micro scale to support urban management in the Amsterdam urban region / Els Veldhuizen in Belgeo, vol 2016 n° 2 (2016-2)  

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La ville intelligente / Hubert d' Erceville in SIGmag, n° 9 (juin 2016)

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Classified and clustered data constellation: An efficient approach of 3D urban data management / Suhaibah Azri in ISPRS Journal of photogrammetry and remote sensing, vol 113 (March 2016)  

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