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Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling / Stefanos Georganos in Geocarto international, vol 36 n° 2 ([01/02/2021])  

Public [article]  inGeocarto international > vol 36 n° 2 [01/02/2021] . - pp 121 -1 36 Titre : Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling Type de document : Article/Communication Auteurs : Stefanos Georganos, Auteur ; Tais Grippa, Auteur ; Assane Niang Gadiaga, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 121 -1 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] autocorrélation spatiale [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] Dakar [Termes descripteurs IGN] densité de population [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] population [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) Machine learning algorithms such as Random Forest (RF) are being increasingly applied on traditionally geographical topics such as population estimation. Even though RF is a well performing and generalizable algorithm, the vast majority of its implementations is still ‘aspatial’ and may not address spatial heterogenous processes. At the same time, remote sensing (RS) data which are commonly used to model population can be highly spatially heterogeneous. From this scope, we present a novel geographical implementation of RF, named Geographical Random Forest (GRF) as both a predictive and exploratory tool to model population as a function of RS covariates. GRF is a disaggregation of RF into geographical space in the form of local sub-models. From the first empirical results, we conclude that GRF can be more predictive when an appropriate spatial scale is selected to model the data, with reduced residual autocorrelation and lower Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values. Finally, and of equal importance, GRF can be used as an effective exploratory tool to visualize the relationship between dependent and independent variables, highlighting interesting local variations and allowing for a better understanding of the processes that may be causing the observed spatial heterogeneity. Numéro de notice : A2021-080 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1595177 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1595177 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96822 [article]

Population dynamics and natural hazard risk management: conceptual and practical linkages for the case of Austrian policy making / Christoph Clar in Natural Hazards, Vol 105 n° 2 (January 2021)  

Public [article]  inNatural Hazards > Vol 105 n° 2 (January 2021) . - pp 765 - 1796 Titre : Population dynamics and natural hazard risk management: conceptual and practical linkages for the case of Austrian policy making Type de document : Article/Communication Auteurs : Christoph Clar, Auteur ; Lukas Löschner, Auteur ; Ralf Nordbeck, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 765 - 1796 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] aide à la décision [Termes descripteurs IGN] Autriche [Termes descripteurs IGN] décroissance urbaine [Termes descripteurs IGN] démographie [Termes descripteurs IGN] enquête [Termes descripteurs IGN] politique publique [Termes descripteurs IGN] population Résumé : (auteur) This contribution explores the conceptual and empirical linkages between population dynamics and natural hazard risk management (NHRM). Following a review of the international scholarly literature, we conduct a mixed-methods approach in Austria, combining an online survey among policy makers and other stakeholders with a thematic analysis of policy documents. The aim is to investigate the practical relevance of socio-demographic change in Austria’s NHRM. The study shows that many hazard-prone regions in Austria face population change, in particular demographic ageing and population decline. In addition, our findings from the online survey demonstrate the relevance of population dynamics in NHRM, especially with regard to hazard response and recovery. Nonetheless, policy formulation in NHRM overwhelmingly disregards demographic change as a relevant factor. Accordingly, the study underscores the importance of future-oriented risk management strategies to better account for ongoing and expected socio-demographic changes. Numéro de notice : A2021-202 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-020-04376-z date de publication en ligne : 24/10/2020 En ligne : https://doi.org/10.1007/s11069-020-04376-z Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97162 [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)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 12 (December 2020) . - pp 2475 -2 496 Titre : Exploring the heterogeneity of human urban movements using geo-tagged tweets Type de document : Article/Communication Auteurs : Ding Ma, Auteur ; Toshihiro Osaragi, Auteur ; Takuya Oki, Auteur ; Bin Jiang, Auteur Année de publication : 2020 Article en page(s) : pp 2475 -2 496 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] espace urbain [Termes descripteurs IGN] flux de données [Termes descripteurs IGN] géoétiquetage [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] hétérogénéité [Termes descripteurs IGN] Londres [Termes descripteurs IGN] migration humaine [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle orienté agent [Termes descripteurs IGN] population urbaine [Termes descripteurs IGN] Tokyo (Japon) [Termes descripteurs IGN] Twitter Résumé : (auteur) The availability of vast amounts of location-based data from social media platforms such as Twitter has enabled us to look deeply into the dynamics of human movement. The aim of this paper is to leverage a large collection of geo-tagged tweets and the street networks of two major metropolitan areas—London and Tokyo—to explore the underlying mechanism that determines the heterogeneity of human mobility patterns. For the two target cities, hundreds of thousands of tweet locations and road segments were processed to generate city hotspots and natural streets. User movement trajectories and city hotspots were then used to build a hotspot network capable of quantitatively characterizing the heterogeneous movement patterns of people within the cities. To emulate observed movement patterns, the study conducts a two-level agent-based simulation that includes random walks through the hotspot networks and movements in the street networks using each of three distance types—metric, angular and combined. Comparisons of the simulated and observed movement flows at the segment and street levels show that the heterogeneity of human urban movements at the collective level is mainly shaped by the scaling structure of the urban space. Numéro de notice : A2020-692 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1718153 date de publication en ligne : 24/01/2020 En ligne : https://doi.org/10.1080/13658816.2020.1718153 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96233 [article]

Impact of extreme weather events on urban human flow: A perspective from location-based service data / Zhenhua Chen in Computers, Environment and Urban Systems, vol 83 (September 2020)  

Public [article]  inComputers, Environment and Urban Systems > vol 83 (September 2020) . - n° 101520 Titre : Impact of extreme weather events on urban human flow: A perspective from location-based service data Type de document : Article/Communication Auteurs : Zhenhua Chen, Auteur ; Zhaoya Gong, Auteur ; Yang Shan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 101520 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] cyclone [Termes descripteurs IGN] données de flux [Termes descripteurs IGN] phénomène climatique extrême [Termes descripteurs IGN] plan de déplacement urbain [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] population urbaine [Termes descripteurs IGN] Shenzhen [Termes descripteurs IGN] système d'information géographique Résumé : (auteur) This study investigates the impact of extreme weather events on urban human flow disruptions using location-based service data obtained from Baidu Map. Utilizing the 2018 Typhoon Mangkhut as an example, the spatial and temporal variations of urban human flow patterns in Shenzhen are examined using GIS and spatial flow analysis. In addition, the variation of human flow by different urban functions (e.g. transport, recreational, institutional, commercial and residential related facilities) is also examined through an integration of flow data and point-of-interest (POI) data. The study reveals that urban flow patterns varied substantially before, during, and after the typhoon. Specifically, urban flows were found to have reduced by 39% during the disruption. Conversely, 56% of flows increased immediately after the disruption. In terms of functional variation, the assessment reveals that fundamental urban functions, such as industrial (work) and institutional - (education) related trips experienced less disruption, whereas the typhoon event appears to have a relatively larger negative influence on recreational related trips. Overall, the study provides implications for planners and policy makers to enhance urban resilience to disasters through a better understanding of the urban vulnerability to disruptive events. Numéro de notice : A2020-699 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2020.101520 date de publication en ligne : 07/07/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101520 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96253 [article]

Estimating and interpreting fine-scale gridded population using random forest regression and multisource data / Yun Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 18 p. Titre : Estimating and interpreting fine-scale gridded population using random forest regression and multisource data Type de document : Article/Communication Auteurs : Yun Zhou, Auteur ; Mingguo Ma, Auteur ; Kaifang Shi, Auteur ; Zhenyu Peng, Auteur Année de publication : 2020 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] cartographie urbaine [Termes descripteurs IGN] catastrophe naturelle [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] densité de population [Termes descripteurs IGN] données maillées [Termes descripteurs IGN] données multisources [Termes descripteurs IGN] migration humaine [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] population urbaine [Termes descripteurs IGN] risque sanitaire [Termes descripteurs IGN] secours d'urgence [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Gridded population results at a fine resolution are important for optimizing the allocation of resources and researching population migration. For example, the data are crucial for epidemic control and natural disaster relief. In this study, the random forest model was applied to multisource data to estimate the population distribution in impervious areas at a 30 m spatial resolution in Chongqing, Southwest China. The community population data from the Chinese government were used to validate the estimation accuracy. Compared with the other regression techniques, the random forest regression method produced more accurate results (R2 = 0.7469, RMSE = 2785.04 and p Numéro de notice : A2020-308 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060369 date de publication en ligne : 03/06/2020 En ligne : https://doi.org/10.3390/ijgi9060369 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95155 [article]

Extracting activity patterns from taxi trajectory data: a two-layer framework using spatio-temporal clustering, Bayesian probability and Monte Carlo simulation / Shuhui Gong in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

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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])  

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A review of assessment methods for cellular automata models of land-use change and urban growth / Xiaohua Tong in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

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Measuring differential access to facilities between population groups using spatial Lorenz curves and related indices / Gordon A. Cromley in Transactions in GIS, Vol 23 n° 6 (November 2019)  

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High‐resolution national land use scenarios under a shrinking population in Japan / Haruka Ohashi in Transactions in GIS, vol 23 n° 4 (August 2019)  

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A methodology with a distributed algorithm for large-scale trajectory distribution prediction / QiuLei Guo in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)  

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Contemporary co-housing in Europe / Pernilla Hagbert (2019)  

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Fine-grained prediction of urban population using mobile phone location data / Jie Chen in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

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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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Gen*: a generic toolkit to generate spatially explicit synthetic populations / Kevin Chapuis in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)  

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