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来源类型Book Section
Conditionally autoregressive model for spatial disaggregation of activity data in GHG inventory: Application for agriculture sector in Poland.
Horabik-Pyzel J; Charkovska N; Danylo O; Nahorski Z; Bun R
发表日期2015
出处Proceedings, 4th International Workshop on Uncertainty in Atmospheric Emissions, 7-9 October 2015, Krakow, Poland. pp. 25-31 Warsaw, Poland: Systems Research Institute, Polish Academy of Sciences. ISBN 83-894-7557-X
出版年2015
语种英语
摘要This report presents a novel approach for allocation of spatially correlated data, such as emission inventories, into finer spatial scales conditional on covariate information observable in a fine grid. Spatial dependence is modelled with the conditional autoregressive structure introduced into a linear model as a random effect. The maximum likelihood approach to inference is employed, and the optimal predictors are developed to assess missing values in a fine grid. The usefulness of the proposed technique is shown for agricultural sector of GHG inventory in Poland. An example of allocation of livestock data (a number of horses) from district to municipality level is analysed. The results indicate that the proposed method outperforms a naive and commonly used approach of proportional distribution.
主题Ecosystems Services and Management (ESM)
关键词4th International Workshop on Uncertainty in Atmospheric Emissions
URLhttp://pure.iiasa.ac.at/id/eprint/11885/
来源智库International Institute for Applied Systems Analysis (Austria)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/134438
推荐引用方式
GB/T 7714
Horabik-Pyzel J,Charkovska N,Danylo O,et al. Conditionally autoregressive model for spatial disaggregation of activity data in GHG inventory: Application for agriculture sector in Poland.. 2015.
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