CAJ | 학술논문

属性数据空间化是利用矢量数据生成栅格数据产品的有效方法，它有助于不同来源、不同格式之间的数据的综合分析。空间化是一种必然有误差伴随的过程，为探讨空间化误差与数据源密度、空间化模型方法，以及空间化分辨率之间的关系，本文利用7种水平的气象站点密度、5种空间化模型方法和19种栅格分辨率分析多年平均气温数据空间化误差与这3类影响因子之间的关系。分析发现：（1）气象站点密度的降低导致多年平均气温数据的空间化误差增加；（2）在IDW、Kriging、Adjusted IDW、Regression和Anusplin 5种空间化模型方法中，Adjusted IDW、Regression、Anusplin比IDW、Kriging的精度高；（3）随着栅格分辨率的变粗，多年平均气温数据空间化误差增大；（4）在影响空间化精度的3类因子中，空间化模型方法对空间化精度的影响最大，栅格分辨率次之，气象站点密度的影响最小。通过多元回归分析，建立了多年平均气温数据空间化误差与这3类影响因子之间的定量模型，可为空间化技术方案的制定提供参考和依据。
속성수거공간화시이용시량수거생성책격수거산품적유효방법，타유조우불동래원、불동격식지간적수거적종합분석。공간화시일충필연유오차반수적과정，위탐토공간화오차여수거원밀도、공간화모형방법，이급공간화분변솔지간적관계，본문이용7충수평적기상참점밀도、5충공간화모형방법화19충책격분변솔분석다년평균기온수거공간화오차여저3류영향인자지간적관계。분석발현：（1）기상참점밀도적강저도치다년평균기온수거적공간화오차증가；（2）재IDW、Kriging、Adjusted IDW、Regression화Anusplin 5충공간화모형방법중，Adjusted IDW、Regression、Anusplin비IDW、Kriging적정도고；（3）수착책격분변솔적변조，다년평균기온수거공간화오차증대；（4）재영향공간화정도적3류인자중，공간화모형방법대공간화정도적영향최대，책격분변솔차지，기상참점밀도적영향최소。통과다원회귀분석，건립료다년평균기온수거공간화오차여저3류영향인자지간적정량모형，가위공간화기술방안적제정제공삼고화의거。
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 pro-cess 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, Adjust-ed 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. There-fore 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 im-pact 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 er-rors and the three kinds of affecting factors was established. The model can be used to predict spatialization er-rors when some of the affecting factors change, so it can provide the basis for drawing up a plan for spatializa-tion of air temperature data.