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Assessing road accidents in spatial context via statistical and non-statistical approaches to detect road accident hotspot using GIS / Yegane Khosravi in Geodetski vestnik, vol 66 n° 3 (September - November 2022)  

Public [article]  inGeodetski vestnik > vol 66 n° 3 (September - November 2022) . - pp 412 - 431 Titre : Assessing road accidents in spatial context via statistical and non-statistical approaches to detect road accident hotspot using GIS Type de document : Article/Communication Auteurs : Yegane Khosravi, Auteur ; Farhad Hosseinali, Auteur ; Mostafa Adresi, Auteur Année de publication : 2022 Article en page(s) : pp 412 - 431 Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] accident de la route [Termes IGN] analyse de groupement [Termes IGN] autocorrélation spatiale [Termes IGN] classification par nuées dynamiques [Termes IGN] corrélation automatique de points homologues [Termes IGN] distance de Manhattan [Termes IGN] estimation par noyau [Termes IGN] Iran [Termes IGN] méthode statistique [Termes IGN] pente [Termes IGN] processus de hiérarchisation analytique [Termes IGN] regroupement de données [Termes IGN] système d'information géographique Résumé : (auteur) Road accidents are among the most critical causes of fatality, personal injuries, and financial damage worldwide. Identifying accident hotspots and the causes of accidents and improving the condition of these hotspots is an economical way to improve road traffic safety. In this study, to identify the accident hotspots of “Dehbala” road located in Yazd province-Iran, statistical and non-statistical clustering methods were used. First, the weighting of the criteria was performed by an expert using the AHP method. Hence, the spatial correlation of slope and curvature was calculated by Global Moran’I. Anselin Local Moran index and Getis-Ord Gi* and Kernel Density Estimation were used to identify accident hotspots based on accident location due to the density of points. As a result, four accident hotspots were obtained by the Anselin Local Moran index, three accident hotspots by Getis-Ord Gi*and one accident-prone area were obtained by Kernel Density Estimation method. Three algorithms, k-means, k-medoids, and DBSCAN, were used to identify accident-prone areas or points using non-statistical methods. The dense cluster of each method was considered as an accident-prone cluster. Then the results of statistical and non- statistical methods were intersected with each other and the final accident-prone area was obtained. This study revealed the effect of geometric charcateristics of the road (slope and curvature) on the occurance of accidents. Numéro de notice : A2022-781 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.15292/geodetski-vestnik.2022.03.412-431 Date de publication en ligne : 04/08/2022 En ligne : https://doi.org/10.15292/geodetski-vestnik.2022.03.412-431 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101864 [article]

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Crowdsource mapping of target buildings in hazard: the utilization of smartphone technologies and geographic services / Mohammad H. Vahidnia in Applied geomatics, vol 12 n° 1 (April 2020)  

Public [article]  inApplied geomatics > vol 12 n° 1 (April 2020) . - pp 3 - 14 Titre : Crowdsource mapping of target buildings in hazard: the utilization of smartphone technologies and geographic services Type de document : Article/Communication Auteurs : Mohammad H. Vahidnia, Auteur ; Farhad Hosseinali, Auteur ; Maryam Shafiei, Auteur Année de publication : 2020 Article en page(s) : pp 3 - 14 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] bâtiment [Termes IGN] cartographie collaborative [Termes IGN] données GPS [Termes IGN] données localisées des bénévoles [Termes IGN] géocodage [Termes IGN] gestion de crise [Termes IGN] instrument embarqué [Termes IGN] OpenStreetMap [Termes IGN] secours d'urgence [Termes IGN] système d'information géographique [Termes IGN] Téhéran [Termes IGN] téléphone intelligent [Termes IGN] web 2.0 Résumé : (auteur) Volunteered geographical information (VGI) refers to geographical information that the general public voluntarily collects and shares in the environment instead of for-profit businesses or government entities. Crowdsourcing such information on urgent needs in a disaster can improve the quick emergency responses. This study incorporates the capability of smartphone sensors, GPS, Web 2.0, VGI, and server-based technologies to design and develop a system for collecting target hazard information from volunteers. One of the most important contributions in designing this system is considering the improvement of the positional accuracy of the target buildings based on the position of the mobile device. Several approaches have been recommended for this purpose. The solutions include the use of online map services, geocoding services, and trigonometric methods based on the measurements of sensors such as camera, accelerometer, and magnetic field embedded in a smart mobile phone. The accuracy assessment showed that the trigonometric method by the means of embedded sensors would yield the best result. However, geocoding is more economical in terms of time than other methods. Potentially, the evaluation of the mobile application provided by a group of volunteers showed the overwhelming preference of crowdsource mapping over current telephone communication systems in disaster management. Numéro de notice : A2020-556 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00280-9 Date de publication en ligne : 16/07/2019 En ligne : https://doi.org/10.1007/s12518-019-00280-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95861 [article]