Descripteur



Etendre la recherche sur niveau(x) vers le bas
A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery / Bo Yang in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)
![]()
[article]
Titre : A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery Type de document : Article/Communication Auteurs : Bo Yang, Auteur ; Lin Liu, Auteur ; Minxuan Lan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1740 - 1764 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] coefficient de corrélation
[Termes descripteurs IGN] criminalité
[Termes descripteurs IGN] données spatiotemporelles
[Termes descripteurs IGN] géostatistique
[Termes descripteurs IGN] historique des données
[Termes descripteurs IGN] image VIIRS
[Termes descripteurs IGN] krigeage
[Termes descripteurs IGN] modèle dynamique
[Termes descripteurs IGN] nuit
[Termes descripteurs IGN] Ohio (Etats-Unis)
[Termes descripteurs IGN] prédiction
[Termes descripteurs IGN] prévention des risques
[Termes descripteurs IGN] prise de vue nocturne
[Termes descripteurs IGN] test statistique
[Termes descripteurs IGN] zone urbaineRésumé : (auteur) Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, and the other is based on environmental variables correlated with criminal patterns. Previous research on geo-statistical modeling mainly considered one type of data in space-time domain, and few sought to blend multi-source data. In this research, we proposed a spatio-temporal Cokriging algorithm to integrate historical crime data and urban transitional zones for more accurate crime prediction. Time-series historical crime data were used as the primary variable, while urban transitional zones identified from the VIIRS nightlight imagery were used as the secondary co-variable. The algorithm has been applied to predict weekly-based street crime and hotspots in Cincinnati, Ohio. Statistical tests and Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI) tests were used to validate predictions in comparison with those of the control group without using the co-variable. The validation results demonstrate that the proposed algorithm with historical crime data and urban transitional zones increased the correlation coefficient by 5.4% for weekdays and by 12.3% for weekends in statistical tests, and gained higher hit rates measured by PAI/PEI in the hotspots test. Numéro de notice : A2020-475 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1737701 date de publication en ligne : 13/03/2020 En ligne : https://doi.org/10.1080/13658816.2020.1737701 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95622
in International journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1740 - 1764[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 079-2020091 SL Revue Centre de documentation Revues en salle Disponible Real-time clock prediction of multi-GNSS satellites and its application in precise point positioning / Yaquan Peng in Advances in space research, vol 64 n°7 (1 October 2019)
![]()
[article]
Titre : Real-time clock prediction of multi-GNSS satellites and its application in precise point positioning Type de document : Article/Communication Auteurs : Yaquan Peng, Auteur ; Yidong Lou, Auteur ; Xiaopeng Gong, Auteur ; YinTong Wang, Auteur ; Xiaolei Dai, Auteur Année de publication : 2019 Article en page(s) : pp 1445 - 1454 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes descripteurs IGN] horloge atomique
[Termes descripteurs IGN] horloge du satellite
[Termes descripteurs IGN] positionnement par BeiDou
[Termes descripteurs IGN] positionnement par Galileo
[Termes descripteurs IGN] positionnement par GNSS
[Termes descripteurs IGN] positionnement ponctuel précis
[Termes descripteurs IGN] prédiction
[Termes descripteurs IGN] temps réelRésumé : (auteur) With the development of Global Navigation Satellite System (GNSS), multi-GNSS is expected to greatly benefit precise point positioning (PPP), especially during the outage of real time service (RTS). In this paper, we focus on the performance of multi-GNSS satellite clock prediction and its application in real-time PPP. Based on the statistical analysis of multi-system satellite clock products, a model consisting of polynomial and periodic terms is employed for multi-system satellite clock prediction. To evaluate the method proposed, both post-processed and real-time satellite clock products are employed in simulated real-time processing mode. The results show that the accuracy of satellite clock prediction is related to atomic clock type and satellite type. For GPS satellites, the average standard deviations (STDs) of Cs atomic clocks will reach as high as 0.65 ns while the STD of Rb atomic clocks is only about 0.15 ns. As for BDS and Galileo, the average STD of 2-hour satellite clock prediction are 0.30 ns and 0.06 ns, respectively. In addition, it is validated that real-time PPP can still achieve positioning accuracy of one to three decimeters by using products of 2-hour satellite clock prediction. Moreover, compared to the results of GPS-only PPP, multi-system can greatly enhance the accuracy of real-time PPP from 12.5% to 18.5% in different situations. Numéro de notice : A2019-410 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2019.06.040 date de publication en ligne : 08/07/2019 En ligne : https://doi.org/10.1016/j.asr.2019.06.040 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93525
in Advances in space research > vol 64 n°7 (1 October 2019) . - pp 1445 - 1454[article]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)
![]()
[article]
Titre : A methodology with a distributed algorithm for large-scale trajectory distribution prediction Type de document : Article/Communication Auteurs : QiuLei Guo, Auteur ; Hassan A. Karimi, Auteur Année de publication : 2019 Article en page(s) : pp 833 - 854 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes descripteurs IGN] circulation urbaine
[Termes descripteurs IGN] distribution spatiale
[Termes descripteurs IGN] données massives
[Termes descripteurs IGN] données spatiotemporelles
[Termes descripteurs IGN] gestion de trafic
[Termes descripteurs IGN] migration pendulaire
[Termes descripteurs IGN] mobilité urbaine
[Termes descripteurs IGN] New York (Etats-Unis ; ville)
[Termes descripteurs IGN] Pékin (Chine)
[Termes descripteurs IGN] population urbaine
[Termes descripteurs IGN] prédiction
[Termes descripteurs IGN] trajectoireRésumé : (Auteur) In this paper, we propose a method for predicting the distributions of people’s trajectories on the road network throughout a city. Specifically, we predict the number of people who will move from one area to another, their probable trajectories, and the corresponding likelihoods of those trajectories in the near future, such as within an hour. With this prediction, we will identify the hot road segments where potential traffic jams might occur and reveal the formation of those traffic jams. Accurate predictions of human trajectories at a city level in real time is challenging due to the uncertainty of people’s spatial and temporal mobility patterns, the complexity of a city level’s road network, and the scale of the data. To address these challenges, this paper proposes a method which includes several major components: (1) a model for predicting movements between neighboring areas, which combines both latent and explicit features that may influence the movements; (2) different methods to estimate corresponding flow trajectory distributions in the road network; (3) a MapReduce-based distributed algorithm to simulate large-scale trajectory distributions under real-time constraints. We conducted two case studies with taxi data collected from Beijing and New York City and systematically evaluated our method. Numéro de notice : A2019-218 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1536981 date de publication en ligne : 31/10/2018 En ligne : https://doi.org/10.1080/13658816.2018.1536981 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92690
in International journal of geographical information science IJGIS > Vol 33 n° 3-4 (March - April 2019) . - pp 833 - 854[article]Réservation
Réserver ce documentExemplaires (2)
Code-barres Cote Support Localisation Section Disponibilité 079-2019032 RAB Revue Centre de documentation En réserve 3L Disponible 079-2019031 SL Revue Centre de documentation Revues en salle Disponible Analysis of GPS satellite clock prediction performance with different update intervals and application to real-time PPP / H. Yang in Survey review, vol 51 n° 364 (January 2019)
![]()
[article]
Titre : Analysis of GPS satellite clock prediction performance with different update intervals and application to real-time PPP Type de document : Article/Communication Auteurs : H. Yang, Auteur ; C. Xu, Auteur ; Y. Gao, Auteur Année de publication : 2019 Article en page(s) : pp 43 - 52 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes descripteurs IGN] horloge du satellite
[Termes descripteurs IGN] positionnement cinématique en temps réel
[Termes descripteurs IGN] positionnement ponctuel précis
[Termes descripteurs IGN] prédiction
[Termes descripteurs IGN] temps réelRésumé : (Auteur) The GPS satellite clock offset prediction is investigated and applied to a real-time PPP system. First, the current situation of GPS satellite clock is introduced and analysed with respect to their stability. Then the satellite clock prediction with different update intervals is presented, in which the satellite clock day boundary jump is addressed. Afterwards, the investigation of the satellite clock prediction model for GPS satellite IIF clocks is carried out and the effects of periodic terms are discussed. After that, the verification of the satellite clock offset prediction will be carried out both in the time and positioning domain. Positioning accuracy at 0.021, 0.049, and 0.017 m in the east, north, and vertical directions can be obtained for 6-h static positioning using the predicted clock offset updating every hour, while the 3D RMS for kinematic real-time PPP is 0.360 m, with 28% improvement over that utilising the IGU predicted products. Numéro de notice : A2019-188 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2017.1359473 date de publication en ligne : 03/08/2017 En ligne : https://doi.org/10.1080/00396265.2017.1359473 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92620
in Survey review > vol 51 n° 364 (January 2019) . - pp 43 - 52[article]Machine learning and geographic information systems for large-scale mapping of renewable energy potential / Dan Assouline (2019)
![]()
Titre : Machine learning and geographic information systems for large-scale mapping of renewable energy potential Type de document : Thèse/HDR Auteurs : Dan Assouline, Auteur ; Jean-Louis Scartezzini, Directeur de thèse ; Nahid Mohajeri Pour Rayeni, Directeur de thèse Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2019 Importance : 294 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée pour l'obtention du grade de Docteur ès Sciences à l'Ecole Polytechnique Fédérale de LausanneLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes descripteurs IGN] apprentissage automatique
[Termes descripteurs IGN] carte thématique
[Termes descripteurs IGN] classification dirigée
[Termes descripteurs IGN] classification par forêts aléatoires
[Termes descripteurs IGN] classification par séparateurs à vaste marge
[Termes descripteurs IGN] données météorologiques
[Termes descripteurs IGN] données topographiques
[Termes descripteurs IGN] énergie éolienne
[Termes descripteurs IGN] énergie géothermique
[Termes descripteurs IGN] énergie renouvelable
[Termes descripteurs IGN] énergie solaire
[Termes descripteurs IGN] méthode fondée sur le noyau
[Termes descripteurs IGN] modélisation spatio-temporelle
[Termes descripteurs IGN] occupation du sol
[Termes descripteurs IGN] prédiction
[Termes descripteurs IGN] SuisseRésumé : (auteur) A promising pathway to follow in order to reach sustainable development goals is an increased
reliance on renewable sources of energy. The optimized use of these energy sources, however, requires the assessment of their potential supply, along with the demand loads in locations of interest. In particular, large-scale supply estimation studies are needed in order to evaluate areas of high potential for each type of energy source for a particular region, and allow for the elaboration of efficient global energy strategies. In Switzerland, the “Energy Strategy 2050”, initiated in 2011 by the Swiss Federal Council, sets an example with the ambitious goal of reaching a 50-80% reduction of CO2 emissions by the year 2050, with a clear course of action: phasing-out nuclear power, improving energy efficiency, and greatly increasing the use of renewables. This thesis develops a general data-driven strategy combining Geographic Information Systems and Machine Learning methods to map the large-scale energy potential for three very popular sources of decentralized energy systems: wind energy (using horizontal axis wind turbines), geothermal energy (using very shallow ground source heat pumps) and solar energy (using photovoltaic solar panels over rooftops). For each of the three considered energy sources, an adapted methodology is suggested to assess its large-scale potential, by estimating multiple variables of interest (with a suitable time resolution, e.g. monthly or yearly), using widely available data, and combining these variables into potential values. These latter estimated variables, dictating the potential, include: (i) the monthly wind speed, and rural and urban topographic/obstacle configuration for wind energy, (ii) the ground thermal conductivity, volumetric heat capacity and monthly temperature gradient for geothermal energy, (iii) the monthly solar radiation, available area for PV panels over rooftops, geometrical characteristics of rooftops and monthly shading factors over rooftops for solar energy. The use of Machine Learning algorithms (notably Support Vector Machines and Random Forests) allows, given adequate features and training data (examples for some locations), for the prediction of the latter variables at unknown locations, along with the uncertainty attached to the predictions. In each case, the developed methodology is set-up with an aim to be applied for Switzerland, meaning that it relies on Swiss available energy-related data. Such data, however, including meteorological, topographic, ground/soil-related and building-related data, is becoming progressively available for most countries, making it possible to widely generalize the proposed methodologies.
Results show that Machine Learning is adequate for energy potential estimation, as the multiple required predictions and spatial extrapolations are achieved with reasonable accuracy. In addition, final values are validated with other existing data or studies when possible, and show general agreement. The application of the suggested potential methodologies in Switzerland outline the very significant potential for the considered renewables. In particular, there is a relatively high potential for RooftopMounted solar PV panels, as it is estimated that they could generate a total electricity production of 16.3 TWh per year, which corresponds to 25.3% of the annual electricity demand in 2017.In each case, the developed methodology is set-up with an aim to be applied for Switzerland, meaning that it relies on Swiss available energy-related data. Such data, however, including meteorological, topographic, ground/soil-related and building-related data, is becoming progressively available for most countries, making it possible to widely generalize the proposed methodologies. Results show that Machine Learning is adequate for energy potential estimation, as the multiple required predictions and spatial extrapolations are achieved with reasonable accuracy. In addition, final values are validated with other existing data or studies when possible, and show general agreement. The application of the suggested potential methodologies in Switzerland outline the very significant potential for the considered renewables. In particular, there is a relatively high potential for RooftopMounted solar PV panels, as it is estimated that they could generate a total electricity production of 16.3 TWh per year, which corresponds to 25.3% of the annual electricity demand in 2017.Note de contenu : 1- Introduction
2- Machine Learning
3- Theory and modeling of renewable energy systems
4- Wind energy: a theoretical potential estimation
5- Very shallow geothermal energy: a theoretical potential estimation
6- Solar energy: a technical potential estimation at commune scale
7- Solar energy: an improved potential estimation at pixel scale
8- ConclusionNuméro de notice : 25797 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences : EPFLausanne : 2019 En ligne : https://infoscience.epfl.ch/record/264971?ln=fr Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95038 Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)
PermalinkPermalinkImproving the prediction of African savanna vegetation variables using time series of MODIS products / Miriam Tsalyuk in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)
PermalinkQuantifying the sources of epistemic uncertainty in model predictions of insect disturbances in an uncertain climate / David R. Gray in Annals of Forest Science [en ligne], vol 74 n° 3 (September 2017)
PermalinkTM-Based SOC models augmented by auxiliary data for carbon crediting programs in semi-arid environments / Salahuddin M. Jaber in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 6 (June 2017)
PermalinkModeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules / Yongjiu Feng in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)
PermalinkPanda∗: A generic and scalable framework for predictive spatio-temporal queries / Abdeltawab M. Hendawi in Geoinformatica [en ligne], vol 21 n° 2 (April - June 2017)
PermalinkQuantifying early-seral forest composition with remote sensing / Rayma A Cooley in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 11 (November 2016)
PermalinkPropagating uncertainty through individual tree volume model predictions to large-area volume estimates / Ronald E. McRoberts in Annals of Forest Science [en ligne], vol 73 n° 3 (September 2016)
PermalinkPredicting palustrine wetland probability using random forest machine learning and digital elevation data-derived terrain variables / Aaron E. Maxwell in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)
Permalink