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An investigation into heat storage by adopting local climate zones and nocturnal-diurnal urban heat island differences in the Tokyo Prefecture / Christopher O'Malley in Sustainable Cities and Society, vol 83 (August 2022)  

Public [article]  inSustainable Cities and Society > vol 83 (August 2022) . - n° 103959 Titre : An investigation into heat storage by adopting local climate zones and nocturnal-diurnal urban heat island differences in the Tokyo Prefecture Type de document : Article/Communication Auteurs : Christopher O'Malley, Auteur ; Hideki Kikumoto, Auteur Année de publication : 2022 Article en page(s) : n° 103959 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] cartographie thématique [Termes IGN] climat local [Termes IGN] distribution spatiale [Termes IGN] ilot thermique urbain [Termes IGN] image Landsat-8 [Termes IGN] image Terra-MODIS [Termes IGN] nuit [Termes IGN] pente [Termes IGN] stockage [Termes IGN] température au sol [Termes IGN] Tokyo (Japon) [Termes IGN] variation diurne Résumé : (auteur) This study aims to identify urban forms that are prone to heat storage in the Tokyo Prefecture in Japan. First, local climate zones (LCZ) were identified with 100 m pixel resolution using Landsat 8 data. LCZs include urban forms that are predominantly defined by building compactness and height. The spatial distribution of urban heat island intensity was obtained using LCZs and MODIS 100 m resolution land surface temperatures from 2013 to 2021. The difference between diurnal and nocturnal heat island intensity (∆UHI) was evaluated as an indicator of the relative heat storage effect between the LCZs. Lower ∆UHIs suggest increased relative heat-storage capacities. Seasonal average ∆UHIs for compact and super high-rise, high-rise, mid-rise, and low-rise LCZs were 3.1 °C, 4.1 °C, 5.8 °C, and 8.3 °C, respectively. Additionally, ∆UHIs for open and super high-rise, high-rise, and mid-rise LCZs were 5.8 °C, 6.4 °C, and 7.8 °C, respectively. Slope data also validated the LCZ height. LCZ and slope analyzes found lower ∆UHI magnitudes in all LCZs with high-rise buildings. Also, compact LCZs had lower ∆UHI magnitudes than open LCZs at corresponding heights. Therefore, higher-rise and compact LCZs are suggested to have larger relative heat storage effects than lower-rise and open LCZs. Numéro de notice : A2022-486 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.scs.2022.103959 Date de publication en ligne : 19/05/2022 En ligne : https://doi.org/10.1016/j.scs.2022.103959 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100951 [article]

Using attributes explicitly reflecting user preference in a self-attention network for next POI recommendation / Ruijing Li in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 8 (August 2022) . - n° 440 Titre : Using attributes explicitly reflecting user preference in a self-attention network for next POI recommendation Type de document : Article/Communication Auteurs : Ruijing Li, Auteur ; Jianzhong Guo, Auteur ; Chun Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 440 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] distance [Termes IGN] filtrage d'information [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] point d'intérêt [Termes IGN] réseau social géodépendant [Termes IGN] Tokyo (Japon) Résumé : (auteur) With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning models to capture user preferences by learning the nonlinear relations between POIs and user preference and pay little attention to mining or using the information that explicitly reflects user preference. In contrast, this paper proposes to utilize data that explicitly reflect user preference and include these data in a deep learning-based process to better capture user preference. Based on the self-attention network, this paper utilizes the attributes of the month of the check-ins and the categories of check-ins during this time, which indicate the periodicity of the user’s work and life and can reflect the habits of users. Moreover, considering that distance has a significant impact on a user’s decision of whether to visit a POI, we used a filter to remove candidate POIs that were more than a certain distance away when recommending the next POIs. We use check-in data from New York City (NYC) and Tokyo (TKY) as datasets, and experiments show that these improvements improve the recommended performance of the next POI. Compared with the state-of-the-art methods, the proposed method improved the recall rate by 7.32% on average. Numéro de notice : A2022-647 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080440 Date de publication en ligne : 04/08/2022 En ligne : https://doi.org/10.3390/ijgi11080440 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101463 [article]

An exact statistical method for analyzing co-location on a street network and its computational implementation / Wataru Morioka in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 4 (April 2022) . - pp 773 - 798 Titre : An exact statistical method for analyzing co-location on a street network and its computational implementation Type de document : Article/Communication Auteurs : Wataru Morioka, Auteur ; Mei-Po Kwan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 773 - 798 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] co-positionnement [Termes IGN] distance euclidienne [Termes IGN] fonction K de Ripley [Termes IGN] implémentation (informatique) [Termes IGN] méthode statistique [Termes IGN] réseau routier [Termes IGN] Tokyo (Japon) [Termes IGN] zone tampon Résumé : (auteur) In many central districts in cities across the world, different types of stores form clusters resulting from the benefits of spatial agglomeration. To precisely analyze co-location relationships in a micro-scale space, this study develops a new statistical method by addressing the limitations of the ordinary cross K function method. The objectives of this paper are, first, to formulate an exact statistical method for analyzing co-location along streets in a central district constrained by a street network; second, to implement this statistical method in computational procedures. Third, this method is extended to the analysis of repulsive-location, i.e. phenomena of stores locating repulsively among different types of stores. Fourth, the paper shows a graph-theoretic diagram illustrating the spatial structure of stores in a central district consisting of bilateral, unilateral co-location and repulsive-location. Last, the proposed method is applied to eight different types of stores in a trendy district in Tokyo. The results show that the method is useful for revealing the spatial structure consisting of co-location and repulsive-location in the central district. Numéro de notice : A2022-257 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1976409 Date de publication en ligne : 16/09/2021 En ligne : https://doi.org/10.1080/13658816.2021.1976409 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100230 [article]

Early warning of COVID-19 hotspots using human mobility and web search query data / Takahiro Yabe in Computers, Environment and Urban Systems, vol 92 (March 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 92 (March 2022) . - n° 101747 Titre : Early warning of COVID-19 hotspots using human mobility and web search query data Type de document : Article/Communication Auteurs : Takahiro Yabe, Auteur ; Kota Tsubouchi, Auteur ; Yoshihide Sekimoto, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101747 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aide à la localisation [Termes IGN] analyse de données [Termes IGN] analyse de groupement [Termes IGN] épidémie [Termes IGN] exploration de données [Termes IGN] maladie virale [Termes IGN] mobilité urbaine [Termes IGN] modèle de simulation [Termes IGN] prévention des risques [Termes IGN] requête spatiale [Termes IGN] ressources web [Termes IGN] surveillance sanitaire [Termes IGN] Tokyo (Japon) Résumé : (auteur) COVID-19 has disrupted the global economy and well-being of people at an unprecedented scale and magnitude. To contain the disease, an effective early warning system that predicts the locations of outbreaks is of crucial importance. Studies have shown the effectiveness of using large-scale mobility data to monitor the impacts of non-pharmaceutical interventions (e.g., lockdowns) through population density analysis. However, predicting the locations of potential outbreak occurrence is difficult using mobility data alone. Meanwhile, web search queries have been shown to be good predictors of the disease spread. In this study, we utilize a unique dataset of human mobility trajectories (GPS traces) and web search queries with common user identifiers (> 450 K users), to predict COVID-19 hotspot locations beforehand. More specifically, web search query analysis is conducted to identify users with high risk of COVID-19 contraction, and social contact analysis was further performed on the mobility patterns of these users to quantify the risk of an outbreak. Our approach is empirically tested using data collected from users in Tokyo, Japan. We show that by integrating COVID-19 related web search query analytics with social contact networks, we are able to predict COVID-19 hotspot locations 1–2 weeks beforehand, compared to just using social contact indexes or web search data analysis. This study proposes a novel method that can be used in early warning systems for disease outbreak hotspots, which can assist government agencies to prepare effective strategies to prevent further disease spread. Human mobility data and web search query data linked with common IDs are used to predict COVID-19 outbreaks. High risk social contact index captures both the contact density and COVID-19 contraction risks of individuals. Real world data was collected from 200 K individual users in Tokyo during the COVID-19 pandemic. Experiments showed that the index can be used for microscopic outbreak early warning. Numéro de notice : A2022-114 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101747 Date de publication en ligne : 17/12/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101747 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99637 [article]

Roadside tree extraction and diameter estimation with MMS lidar by using point-cloud image / Genki Takahashi in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2021 (July 2021) . - pp 67 - 74 Titre : Roadside tree extraction and diameter estimation with MMS lidar by using point-cloud image Type de document : Article/Communication Auteurs : Genki Takahashi, Auteur ; H. Masuda, Auteur Année de publication : 2021 Article en page(s) : pp 67 - 74 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] alignement d'arbres [Termes IGN] apprentissage automatique [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction d'arbres [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] route [Termes IGN] semis de points [Termes IGN] Tokyo (Japon) [Termes IGN] zone urbaine Résumé : (auteur) Efficient management of roadside trees for local governments is important. Mobile Mapping System (MMS) equipped with a high-density LiDAR scanner has the possibility to be applied to estimate DBH of roadside trees using point clouds. In this study, we propose a method for detecting roadside trees and estimating their DBHs automatically from MMS point clouds. In our method, point clouds captured using the MMS are mapped on a 2D image plane, and they are converted into a wireframe model by connecting adjacent points. Then, geometric features are calculated for each point in the wireframe model. Tree points are detected using a machine learning technique. The DBH of each tree is calculated using vertically aligned circles extracted from the wireframe model. Our method allows robustly calculating the DBH even if there is a hump at breast height. We evaluated our method using actual MMS data measured in an urban area in Tokyo. Our method achieved a high extraction performance of 100 percent of precision and 95.1 percent of recall for 102 roadside trees. The average accuracy of the DBH was 2.0 cm. These results indicate that our method is useful for the efficient management of roadside trees. Numéro de notice : A2021-491 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2021-67-2021 Date de publication en ligne : 17/06/2021 En ligne : http://dx.doi.org/10.5194/isprs-annals-V-2-2021-67-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97956 [article]

Exploring the heterogeneity of human urban movements using geo-tagged tweets / Ding Ma in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

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A comparison of neighbourhood relations based on ordinary Delaunay diagrams and area Delaunay diagrams: an application to define the neighbourhood relations of buildings / Hiroyuki Usui in International journal of geographical information science IJGIS, vol 34 n° 11 (November 2020)  

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Prediction of RTK positioning integrity for journey planning / Ahmed El-Mowafy in Journal of applied geodesy, vol 14 n° 4 (October 2020)  

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A two-stage estimation method with bootstrap inference for semi-parametric geographically weighted generalized linear models / Dengkui Li in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

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A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area / Guodong Du in International journal of geographical information science IJGIS, vol 32 n° 3-4 (March - April 2018)  

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Assessment of urban energy performance through integration of BIM and GIS for smart city planning / Shinji Yamamura in Procedia Engineering, vol 180 ([01/06/2017])  

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Measuring deformations using SAR interferometry and GPS observables with geodetic accuracy: Application to Tokyo, Japan / Tamer Elgarbawi in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)  

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Usage of large scale mobile GPS data : detection of weather effect to after work habits of office workers in Tokyo / Karlvin David C. Cuaresma (2014) 

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Comparison between several feature extraction/classification methods for mapping complicated agricultural land use patches using airborne hyperspectral data / S. Lu in International Journal of Remote Sensing IJRS, vol 28 n°5-6 (March 2007)  

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Étude de la gouvernance des métropoles mondiales / C. Lefevre (2006)

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