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jiulong river basin exhibit spatial autocorrelation  (Zhangzhou Pientzehuang Pharmaceutical Co Ltd)

 
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    Zhangzhou Pientzehuang Pharmaceutical Co Ltd jiulong river basin exhibit spatial autocorrelation
    Jiulong River Basin Exhibit Spatial Autocorrelation, supplied by Zhangzhou Pientzehuang Pharmaceutical Co Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/kriging/basin+jiulong+river/pm41027084-162-4-3
    Average 86 stars, based on 1 article reviews
    jiulong river basin exhibit spatial autocorrelation - by Bioz Stars, 2026-11
    86/100 stars

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    Article Title: Exploring urban expansion pattern associations with flood Vulnerability: Spatial heterogeneity in multidimensional linkages across China's Min Delta urban agglomeration.
    Article Snippet: By 2020, substantial expansion of very high vulnerability occurred with pronounced changes in Zhangzhou’s Jiulong River basin driven by prolonged development pressures; concurrently, very low and low vulnerability zones contracted toward northeastern Quanzhou, where development remained persistently limited.

    Article Title: Exploring urban expansion pattern associations with flood Vulnerability: Spatial heterogeneity in multidimensional linkages across China's Min Delta urban agglomeration.
    Article Snippet: Concentrated zones like Zhangzhou’s Jiulong River basin exhibit spatial autocorrelation.

    Article Title: Exploring urban expansion pattern associations with flood Vulnerability: Spatial heterogeneity in multidimensional linkages across China's Min Delta urban agglomeration.
    Article Snippet: Spatially, EE exhibits the strongest association with FV, characterized by persistent expansion of correlated areas concentrated in Zhangzhou’s coastal zones and Jiulong River Basin during its peak phase (2000–2010), mirroring urban expansion trends where EE’s relative share declined while absolute area grew before stabilizing post-2010.



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    The workflow of the improved tensor completion algorithm under the C2F framework. The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary <t>Kriging</t> <t>interpolation</t> method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).
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    The workflow of the improved tensor completion algorithm under the C2F framework. The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Journal: Scientific Reports

    Article Title: A prediction model for soil heavy metal content based on improved tensor completion

    doi: 10.1038/s41598-025-07565-7

    Figure Lengend Snippet: The workflow of the improved tensor completion algorithm under the C2F framework. The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Article Snippet: The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Techniques: Generated, Software

    Kriging interpolation results for different metals. The interpolation results in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Journal: Scientific Reports

    Article Title: A prediction model for soil heavy metal content based on improved tensor completion

    doi: 10.1038/s41598-025-07565-7

    Figure Lengend Snippet: Kriging interpolation results for different metals. The interpolation results in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Article Snippet: The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Techniques: Generated, Software

    As completion process. The original heatmap in Fig. 3 are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Journal: Scientific Reports

    Article Title: A prediction model for soil heavy metal content based on improved tensor completion

    doi: 10.1038/s41598-025-07565-7

    Figure Lengend Snippet: As completion process. The original heatmap in Fig. 3 are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Article Snippet: The interpolated data in this figure are generated from the soil heavy metal dataset, which was created using the ordinary Kriging interpolation method with ESRI ArcGIS 10.8 software ( https://www.esri.com ).

    Techniques: Generated, Software