Scale Effect of Errors on Spatialization of Annual Mean Air Temperature Data

  • 1. College of Environment and Planning, Henan University, Kaifeng 475004, China;
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received date: 2013-04-07

  Revised date: 2013-05-28

  Online published: 2014-01-05


Spatialization of attribute data is a way to output grid data products from vector data. It is beneficial to integrated analysis of geosciences data from various sources and in different formats. However, it is also a process companied with errors, and the errors are closely related to density of data sources, spatializing models and resolution of grid cells. In this paper, 7 levels of density of meteorological stations, 5 spatializing models and 19 levels of resolutions of grid cells were used to analyze the relationships between the errors of annual mean air temperature data spatialization and these affecting factors. The following conclusions were drawn: (a) Reduction of density of meteorological stations led to increasing of the spatialization errors. (b) Of the five models, Adjusted IDW, Regression and Anusplin had higher accuracy than IDW and Kriging. The reason is that both IDW and Kriging are spatial autocorrelation based interpolation methods. They neglect influence of underlying surface on air temperature. But, elevation factor is taken into account for Adjusted IDW, Regression and Anusplin. Therefore higher accuracy can be gained with the three interpolation methods. (c) The accuracy generally decreased with increasing of size of grid cells. The trend was significant especially for Adjusted IDW, Regression and Anusplin. (d) Of the three kinds of factors affecting accuracy of spatialization, the models had the greatest impact on the accuracy, the resolution of grid cells second and the density of meteorological stations the lowest. (e) For spatialization products of annual mean air temperature data at national scale, some spatial hetero-correlation interpolation methods, such as Adjusted IDW, Regress and Anusplin should be applied, and the size of grid cells should be smaller than ten kilometers by ten kilometers. In such a case, the mean absolute error for spatialization can be less than one degree centigrade. At last, a quantitative multiple regression model between spatialization errors and the three kinds of affecting factors was established. The model can be used to predict spatialization errors when some of the affecting factors change, so it can provide the basis for drawing up a plan for spatialization of air temperature data.

Cite this article

LIAO Shunbao, ZHANG Sai . Scale Effect of Errors on Spatialization of Annual Mean Air Temperature Data[J]. Journal of Geo-information Science, 2014 , 16(1) : 8 -14 . DOI: 10.3724/SP.J.1047.2014.00008


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