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Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation / Guiming Zhang in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 1 (January 2022) . - n° 55 Titre : Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation Type de document : Article/Communication Auteurs : Guiming Zhang, Auteur Année de publication : 2022 Article en page(s) : n° 55 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] données localisées des bénévoles [Termes IGN] données massives [Termes IGN] estimation par noyau [Termes IGN] exploration de données géographiques [Termes IGN] géovisualisation [Termes IGN] processeur graphique [Termes IGN] qualité des données [Termes IGN] réseau social [Termes IGN] tâche claire Résumé : (auteur) Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the bias. Enabled by the parallel kernel density estimation (KDE) computational tool that can run on multiple GPUs (graphics processing units), this study conducted point pattern analyses on tens of millions of iNaturalist observations to detect and visualize volunteers’ observation hot-spots across spatial scales. It was achieved by setting varying KDE bandwidths in accordance with the spatial scales at which hot-spots are to be detected. The succession of estimated density surfaces were then rendered at a sequence of map scales for visual detection of hot-spots. This study offers an effective geovisualization scheme for hierarchically detecting hot-spots in massive VGI datasets, which is useful for understanding the pattern-shaping drivers that operate at multiple spatial scales. This research exemplifies a computational tool that is supported by high-performance computing and capable of efficiently detecting and visualizing multi-scale hot-spots in geospatial big data and contributes to expanding the toolbox for geospatial big data analytics. Numéro de notice : A2022-091 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11010055 Date de publication en ligne : 12/01/2022 En ligne : https://doi.org/10.3390/ijgi11010055 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99507 [article]

A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena / Guiming Zhang in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n° 9 (September 2019) . - pp 1873 - 1893 Titre : A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena Type de document : Article/Communication Auteurs : Guiming Zhang, Auteur ; A - Xing Zhu, Auteur Année de publication : 2019 Article en page(s) : pp 1873 - 1893 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Aves [Termes IGN] carte thématique [Termes IGN] distribution spatiale [Termes IGN] données localisées des bénévoles [Termes IGN] échantillon [Termes IGN] erreur d'échantillon [Termes IGN] erreur de positionnement [Termes IGN] erreur systématique [Termes IGN] habitat (nature) [Termes IGN] modèle de simulation [Termes IGN] phénomène géographique [Termes IGN] pondération [Termes IGN] précision de localisation [Termes IGN] régression logistique [Termes IGN] representativité [Termes IGN] science citoyenne [Termes IGN] Wisconsin (Etats-Unis) Résumé : (auteur) Volunteered geographic information (VGI) contains valuable field observations that represent the spatial distribution of geographic phenomena. As such, it has the potential to provide regularly updated low-cost field samples for predictively mapping the spatial variations of geographic phenomena. The predictive mapping of geographic phenomena often requires representative samples for high mapping accuracy, but samples consisting of VGI observations are often not representative as they concentrate on specific geographic areas (i.e. spatial bias) due to the opportunistic nature of voluntary observation efforts. In this article, we propose a representativeness-directed approach to mitigate spatial bias in VGI for predictive mapping. The proposed approach defines and quantifies sample representativeness by comparing the probability distributions of sample locations and the mapping area in the environmental covariate space. Spatial bias is mitigated by weighting the sample locations to maximize their representativeness. The approach is evaluated using species habit suitability mapping as a case study. The results show that the accuracy of predictive mapping using weighted sample locations is higher than using unweighted sample locations. A positive relationship between sample representativeness and mapping accuracy is also observed, suggesting that sample representativeness is a valid indicator of predictive mapping accuracy. This approach mitigates spatial bias in VGI to improve predictive mapping accuracy. Numéro de notice : A2019-392 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1615071 Date de publication en ligne : 10/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1615071 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93490 [article]

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Validity of historical volunteered geographic information: Evaluating citizen data for mapping historical geographic phenomena / Guiming Zhang in Transactions in GIS, vol 22 n° 1 (February 2018)  

Public [article]  inTransactions in GIS > vol 22 n° 1 (February 2018) . - pp 149 - 164 Titre : Validity of historical volunteered geographic information: Evaluating citizen data for mapping historical geographic phenomena Type de document : Article/Communication Auteurs : Guiming Zhang, Auteur ; A - Xing Zhu, Auteur ; Zhi‐Pang Huang, Auteur ; Guopeng Ren, Auteur ; Cheng‐Zhi Qin, Auteur ; Wen Xiao, Auteur Année de publication : 2018 Article en page(s) : pp 149 - 164 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] cartographie historique [Termes IGN] distribution spatiale [Termes IGN] données localisées des bénévoles [Termes IGN] habitat animal [Termes IGN] phénomène géographique [Termes IGN] Simiiformes [Termes IGN] validité des données [Termes IGN] Yunnan (Chine) Résumé : (auteur) Studies on volunteered geographic information (VGI) have focused on examining its validity to reveal geographic phenomena in relatively recent periods. Empirical evaluation of the validity of VGI to reveal geographic phenomena in historical periods (e.g., decades ago) is lacking, although such evaluation is desirable for assessing the possibility of broadening the temporal scope of VGI applications. This article presents an evaluation of the validity of VGI to reveal historical geographic phenomena through a citizen data‐based habitat suitability mapping case study. Citizen data (i.e., sightings) of the black‐and‐white snub‐nosed monkey (Rhinopithecus bieti) were elicited from local residents through three‐dimensional (3D) geovisualization interviews in Yunnan, China. The validity of the elicited sightings to reveal the historical R. bieti distribution was evaluated through habitat suitability mapping using the citizen data in historical periods. The results of controlled experiments demonstrated that suitability maps predicted using the historical citizen data had a consistent spatial pattern (correlation above 0.60) that reflects the R. bieti distribution (Boyce index around 0.90) in areas free of significant environmental change across historical periods. This in turn suggests that citizen data have validity for mapping historical geographic phenomena. It provides supporting empirical evidence for potentially broadening the temporal scope of VGI applications. Numéro de notice : A2018-066 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12300 En ligne : https://doi.org/10.1111/tgis.12300 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89422 [article]

A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data / Qunying Huang in Computers, Environment and Urban Systems, vol 66 (November 2017)  

Public [article]  inComputers, Environment and Urban Systems > vol 66 (November 2017) . - pp 23 - 37 Titre : A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data Type de document : Article/Communication Auteurs : Qunying Huang, Auteur ; Guido Cervone, Auteur ; Guiming Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 23 - 37 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] caractérisation [Termes IGN] catastrophe naturelle [Termes IGN] données issues des réseaux sociaux [Termes IGN] exploration de données géographiques [Termes IGN] exploration de texte [Termes IGN] image numérique [Termes IGN] informatique en nuage [Termes IGN] inondation [Termes IGN] intégration de données [Termes IGN] interface web [Termes IGN] prototype [Termes IGN] tempête [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Social media streams and remote sensing data have emerged as new sources for tracking disaster events, and assessing their damages. Previous studies focus on a case-by-case approach, where a specific event was first chosen and filtering criteria (e.g., keywords, spatiotemporal information) are manually designed and used to retrieve relevant data for disaster analysis. This paper presents a framework that synthesizes multi-sourced data (e.g., social media, remote sensing, Wikipedia, and Web), spatial data mining and text mining technologies to build an architecturally resilient and elastic solution to support disaster analysis of historical and future events. Within the proposed framework, Wikipedia is used as a primary source of different historical disaster events, which are extracted to build an event database. Such a database characterizes the salient spatiotemporal patterns and characteristics of each type of disaster. Additionally, it can provide basic semantics, such as event name (e.g., Hurricane Sandy) and type (e.g., flooding) and spatiotemporal scopes, which are then tuned by the proposed procedures to extract additional information (e.g., hashtags for searching tweets), to query and retrieve relevant social media and remote sensing data for a specific disaster. Besides historical event analysis and pattern mining, the cloud-based framework can also support real-time event tracking and monitoring by providing on-demand and elastic computing power and storage capabilities. A prototype is implemented and tested with data relative to the 2011 Hurricane Sandy and the 2013 Colorado flooding. Numéro de notice : a2017-430 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2017.06.004 En ligne : https://doi.org/10.1016/j.compenvurbsys.2017.06.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86330 [article]

A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data / Guiming Zhang in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 9-10 (September - October 2017) . - pp 2068 - 2097 Titre : A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data Type de document : Article/Communication Auteurs : Guiming Zhang, Auteur ; A - Xing Zhu, Auteur ; Qunying Huang, Auteur Année de publication : 2017 Article en page(s) : pp 2068 - 2097 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] données massives [Termes IGN] estimation par noyau [Termes IGN] jeu de données localisées [Termes IGN] optimisation (mathématiques) [Termes IGN] processeur graphique Résumé : (Auteur) Kernel density estimation (KDE) is a classic approach for spatial point pattern analysis. In many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over KDE with an invariant bandwidth (fixed KDE). However, bandwidths determination for adaptive KDE is extremely computationally intensive, particularly for point pattern analysis tasks of large problem sizes. This computational challenge impedes the application of adaptive KDE to analyze large point data sets, which are common in this big data era. This article presents a graphics processing units (GPUs)-accelerated adaptive KDE algorithm for efficient spatial point pattern analysis on spatial big data. First, optimizations were designed to reduce the algorithmic complexity of the bandwidth determination algorithm for adaptive KDE. The massively parallel computing resources on GPU were then exploited to further speed up the optimized algorithm. Experimental results demonstrated that the proposed optimizations effectively improved the performance by a factor of tens. Compared to the sequential algorithm and an Open Multiprocessing (OpenMP)-based algorithm leveraging multiple central processing unit cores for adaptive KDE, the GPU-enabled algorithm accelerated point pattern analysis tasks by a factor of hundreds and tens, respectively. Additionally, the GPU-accelerated adaptive KDE algorithm scales reasonably well while increasing the size of data sets. Given the significant acceleration brought by the GPU-enabled adaptive KDE algorithm, point pattern analysis with the adaptive KDE approach on large point data sets can be performed efficiently. Point pattern analysis on spatial big data, computationally prohibitive with the sequential algorithm, can be conducted routinely with the GPU-accelerated algorithm. The GPU-accelerated adaptive KDE approach contributes to the geospatial computational toolbox that facilitates geographic knowledge discovery from spatial big data. Numéro de notice : A2017-509 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1324975 En ligne : http://dx.doi.org/10.1080/13658816.2017.1324975 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86455 [article]

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Enabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley’s K function / Guiming Zhang in International journal of geographical information science IJGIS, vol 30 n° 11-12 (November - December 2016)  

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