Centre de documentation
scientifique

A spatial distribution: Principal component analysis (SD-PCA) model to assess pollution of heavy metals in soil / Jiawei Liu in Science of the total environment, vol 859 n° 1 (February 2023)  

Public [article]  inScience of the total environment > vol 859 n° 1 (February 2023) . - n° 160112 Titre : A spatial distribution: Principal component analysis (SD-PCA) model to assess pollution of heavy metals in soil Type de document : Article/Communication Auteurs : Jiawei Liu, Auteur ; Hou Kang, Auteur ; Wendong Tao, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 160112 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse en composantes principales [Termes IGN] autocorrélation spatiale [Termes IGN] cartographie des risques [Termes IGN] Chine [Termes IGN] distribution spatiale [Termes IGN] métal lourd [Termes IGN] pollution des sols [Termes IGN] risque de pollution [Termes IGN] traçabilité Résumé : (auteur) With the rapid development of urbanization, heavy metal pollution of soil has received great attention. Over-enrichment of heavy metals in soil may endanger human health. Assessing soil pollution and identifying potential sources of heavy metals are crucial for prevention and control of soil heavy metal pollution. This study introduced a spatial distribution - principal component analysis (SD-PCA) model that couples the spatial attributes of soil pollution with linear data transformation by the eigenvector-based principal component analysis. By evaluating soil pollution in the spatial dimension it identifies the potential sources of heavy metals more easily. In this study, soil contamination by eight heavy metals was investigated in the Lintong District, a typical multi-source urban area in Northwest China. In general, the soils in the study area were lightly contaminated by Cr and Pb. Pearson correlation analysis showed that Cr was negatively correlated with other heavy metals, whereas the spatial autocorrelation analysis revealed that there was strong association in the spatial distribution of eight heavy metals. The aggregation forms were more varied and the correlation between Cr contamination and other heavy metals was lower. The aggregation forms of Mn and Cu, Zn and Pb, on the other hand, were remarkably comparable. Agriculture was the largest pollution source, contributing 65.5 % to soil pollution, which was caused by the superposition of multiple heavy metals. Additionally, traffic and natural pollution sources contributed 17.9 % and 11.1 %, respectively. The ability of this model to track pollution of heavy metals has important practical significance for the assessment and control of multi-source soil pollution. Numéro de notice : A2023-009 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scitotenv.2022.160112 Date de publication en ligne : 11/11/2022 En ligne : https://doi.org/10.1016/j.scitotenv.2022.160112 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102115 [article]

Spatial distribution analysis of seismic activity based on GMI, LMI, and LISA in China / Ziyi Cao in Open geosciences, vol 14 n° 1 (January 2023)  

Public [article]  inOpen geosciences > vol 14 n° 1 (January 2023) . - pp 89 - 97 Titre : Spatial distribution analysis of seismic activity based on GMI, LMI, and LISA in China Type de document : Article/Communication Auteurs : Ziyi Cao, Auteur ; Heng Zhang, Auteur ; Yan Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 89 - 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse diachronique [Termes IGN] autocorrélation spatiale [Termes IGN] Chine [Termes IGN] distribution spatiale [Termes IGN] séisme [Termes IGN] sismicité Résumé : (auteur) Recently, all kinds of geological disasters happen frequently on the earth. In China, there are countless earthquakes every year, which greatly affect the country’s economic level and development as well as the people’s life and health. The analysis of seismic activity is becoming more and more significant. In this article, the spatial distribution of China’s seismic activities was analyzed by using the provincial seismic data from 1970 to 2013. On the basis of spatial autocorrelation analysis theory, Global Moran’s I, Local Moran’s I, and the Local Indicators of Spatial Association are used to measure the geospatial distribution characteristics of China’s seismic activities. The research results show that earthquakes in mainland China have significant global autocorrelation characteristics as a whole, and the global autocorrelation coefficients are all positive. And the Z-value test (P Numéro de notice : A2023-052 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article En ligne : https://doi.org/10.1515/geo-2020-0332 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102383 [article]

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]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
139-2022031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Measuring COVID-19 vulnerability for Northeast Brazilian municipalities: Social, economic, and demographic factors based on multiple criteria and spatial analysis / Ciro José Jardim De Figueiredo 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° 449 Titre : Measuring COVID-19 vulnerability for Northeast Brazilian municipalities: Social, economic, and demographic factors based on multiple criteria and spatial analysis Type de document : Article/Communication Auteurs : Ciro José Jardim De Figueiredo, Auteur ; Caroline Maria de Miranda Mota, Auteur ; Kaliane Gabriele Dias de Araújo, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 449 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse multicritère [Termes IGN] autocorrélation spatiale [Termes IGN] Brésil [Termes IGN] densité de population [Termes IGN] données socio-économiques [Termes IGN] épidémie [Termes IGN] maladie virale [Termes IGN] vulnérabilité Résumé : (auteur) COVID-19 has brought several harmful consequences to the world from many perspectives, including social, economic, and well-being in addition to health issues. However, these harmful consequences vary in intensity in different regions. Identifying which cities are most vulnerable to COVID-19 and understanding which variables could be associated with the advance of registered cases is a challenge. Therefore, this study explores and builds a spatial decision model to identify the characteristics of the cities that are most vulnerable to COVID-19, taking into account social, economic, demographic, and territorial aspects. Hence, 18 features were separated into the four groups mentioned. We employed a model joining the dominance-based rough set approach to aggregate the features (multiple criteria) and spatial analysis (Moran index, and Getis and Ord) to obtain final results. The results show that the most vulnerable places have characteristics with high population density and poor economic conditions. In addition, we conducted subsequent analysis to validate the results. The case was developed in the northeast region of Brazil. Numéro de notice : A2022-646 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11080449 Date de publication en ligne : 16/08/2022 En ligne : https://doi.org/10.3390/ijgi11080449 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101462 [article]

Mapping monthly population distribution and variation at 1-km resolution across China / Zhifeng Cheng in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 6 (June 2022) . - pp 1166 - 1184 Titre : Mapping monthly population distribution and variation at 1-km resolution across China Type de document : Article/Communication Auteurs : Zhifeng Cheng, Auteur ; Jianghao Wang, Auteur ; Yong Ge, Auteur Année de publication : 2022 Article en page(s) : pp 1166 - 1184 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] analyse spatiale [Termes IGN] autocorrélation spatiale [Termes IGN] Chine [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] densité de population [Termes IGN] distribution spatiale [Termes IGN] figuration de la densité [Termes IGN] krigeage [Termes IGN] population [Termes IGN] série temporelle [Termes IGN] téléphonie mobile Résumé : (auteur) Fine-grained inner-annual population data are instrumental in climate change response, resource allocation, and epidemic control. However, such data are currently scarce due to the lack of human-related indicators with both high temporal resolution and long-term coverage that can be used in the process of population spatialization. Here, we estimate monthly 1-km gridded population distribution across China in 2015 using time-series mobile phone positioning data. We construct a hybrid downscaling model to map the gridded population by incorporating random forest and area-to-point kriging. The estimated monthly population products appear to capture inner-annual population variations, especially during special periods, such as the festival, holiday, and short-term labor flow period, which are characterized by large-scale population movements. Additionally, compared with census data, the hybrid model-based results obtained exhibit higher consistency than popular global population products across all spatial extents. Our monthly 1-km data products for the population distribution across China in 2015 provide a credible dataset that can be employed in studies aimed at accurate population-dependent decisions. Numéro de notice : A2022-407 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1854767 Date de publication en ligne : 07/12/2020 En ligne : https://doi.org/10.1080/13658816.2020.1854767 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100724 [article]

Discovering co-location patterns in multivariate spatial flow data / Jiannan Cai in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)  

Permalink

Mapping forest site quality at national level / Ana Aguirre in Forest ecology and management, vol 508 (March-15 2022)  

Permalink

Assessing COVID-induced changes in spatiotemporal structure of mobility in the United States in 2020: a multi-source analytical framework / Evgeny Noi in International journal of geographical information science IJGIS, vol 36 n° 3 (March 2022)  

Permalink

Influence of determinant factors towards soil erosion using ordinary least squared regression in GIS domain / Imran Ahmad in Applied geomatics, vol 14 n° 1 (March 2022)  

Permalink

Replication and the search for the laws in the geographic sciences / Peter Kedron in Annals of GIS, vol 28 n° 1 (January 2022)  

Permalink

Spatial distribution of lead (Pb) in soil: a case study in a contaminated area of the Czech Republic / Nicolas Francos in Geomatics, Natural Hazards and Risk, vol 13 (2022)  

Permalink

Spatial biodiversity modeling using high-performance computing cluster: A case study to access biological richness in Indian landscape / Hariom Singh in Geocarto international, vol 36 n° 18 ([01/10/2021])  

Permalink

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])  

Permalink

FOSTER - An R package for forest structure extrapolation / Martin Queinnec in Plos one, vol 16 n° 1 (January 2021)  

Permalink

Geospatial analysis of the spreading of COVID-19 In the United States / Otto Heimonen (2021)  

Permalink