scholarly journals Covariance Tapering for Interpolation of Large Spatial Datasets

2006 ◽  
Vol 15 (3) ◽  
pp. 502-523 ◽  
Author(s):  
Reinhard Furrer ◽  
Marc G Genton ◽  
Douglas Nychka
Keyword(s):  
Covariance Tapering ◽  
Spatial Datasets

scholarly journals Discussion on Competition for Spatial Statistics for Large Datasets

2021 ◽  
Author(s):  
Roman Flury ◽  
Reinhard Furrer
Keyword(s):  
Spatial Statistics ◽  
Covariance Function ◽  
Likelihood Function ◽  
Large Datasets ◽  
Missing Observations ◽  
Covariance Model ◽  
Compactly Supported ◽  
Covariance Tapering ◽  
Simple Kriging ◽  
Estimated Parameters

AbstractWe discuss the experiences and results of the AppStatUZH team’s participation in the comprehensive and unbiased comparison of different spatial approximations conducted in the Competition for Spatial Statistics for Large Datasets. In each of the different sub-competitions, we estimated parameters of the covariance model based on a likelihood function and predicted missing observations with simple kriging. We approximated the covariance model either with covariance tapering or a compactly supported Wendland covariance function.

scholarly journals How much cattle can brazil pastures support? An analysis based on “YIELD GAP”

2017 ◽  
Vol 8 (4) ◽  
pp. 162-169
Author(s):  
Christiane Cavalcante Leite ◽  
Britaldo Silveira Soares Filho ◽  
Marcos Heil Costa ◽  
Ranieri Carlos Ferreira de Amorim
Keyword(s):  
Spatial Variation ◽  
Agricultural Intensification ◽  
Animal Production ◽  
Yield Gap ◽  
Potential Yield ◽  
Biophysical Factors ◽  
Spatial Datasets ◽  
Degraded Pasture ◽  
Different Climates

The degradation of pastures is one of Brazil's biggest problems today and directly affects the sustainability of livestock. The animal production in a degraded pasture can be six times smaller than a grazing or recovered in good maintenance state. So we can consider that productivity could be increased in pasture areas, and analyze how productivity is limited by biophysical factors (climate, for example) versus management. Using spatial datasets, we compare yield patterns for the pasturelands within regions of similar climate. We use this comparison to evaluate the potential yield obtainable for pasturelands in different climates around the Brazil using the limits of Brazilian biomes. We then compare the actual yields currently being achieved with their ‘potential yield’ to estimate the ‘yield gap’, present spatial datasets of both the potential yields and yield gap patterns for pasturelands around the year 1995 and 2006. This study is intended to be an important new resource for scientists and policymakers alike, helping to more accurately understand spatial variation of yield and agricultural intensification potential, as well as employing these data to better utilize existing infrastructure and optimize the distribution of development and aid capital.

Mixture of Regression Models for Large Spatial Datasets

Technometrics ◽  
2019 ◽  
Vol 61 (4) ◽  
pp. 507-523 ◽  
Author(s):  
Karen Kazor ◽  
Amanda S. Hering
Keyword(s):  
Regression Models ◽  
Spatial Datasets

scholarly journals Global restoration opportunities in tropical rainforest landscapes

2019 ◽  
Vol 5 (7) ◽  
pp. eaav3223 ◽  
Author(s):  
Pedro H. S. Brancalion ◽  
Aidin Niamir ◽  
Eben Broadbent ◽  
Renato Crouzeilles ◽  
Felipe S. M. Barros ◽  
...  
Keyword(s):  
Tropical Rainforest ◽  
Anthropogenic Impacts ◽  
Cost Effective ◽  
Well Being ◽  
Area Network ◽  
Spatial Datasets ◽  
Potential Benefits ◽  
The Tropics

Over 140 Mha of restoration commitments have been pledged across the global tropics, yet guidance is needed to identify those landscapes where implementation is likely to provide the greatest potential benefits and cost-effective outcomes. By overlaying seven recent, peer-reviewed spatial datasets as proxies for socioenvironmental benefits and feasibility of restoration, we identified restoration opportunities (areas with higher potential return of benefits and feasibility) in lowland tropical rainforest landscapes. We found restoration opportunities throughout the tropics. Areas scoring in the top 10% (i.e., restoration hotspots) are located largely within conservation hotspots (88%) and in countries committed to the Bonn Challenge (73%), a global effort to restore 350 Mha by 2030. However, restoration hotspots represented only a small portion (19.1%) of the Key Biodiversity Area network. Concentrating restoration investments in landscapes with high benefits and feasibility would maximize the potential to mitigate anthropogenic impacts and improve human well-being.

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