On the effects of spatial relationships in spatial compositional multivariate models
On the effects of spatial relationships in spatial compositional multivariate models
复制标题
DOI：
10.1007/s12076-017-0199-5
复制
发表时间：
2018-01
影响因子：
1.8
通讯作者：
Takahiro Yoshida;M. Tsutsumi
中科院分区：
文献类型：
--
作者：
Takahiro Yoshida;M. Tsutsumi
空间成分多变量模型是近年来环境生态研究中发展起来的一个新课题。然而，很少有人注意到这些模型的结果是如何受到不同的空间关系设置的影响的，这些空间关系通常是用所谓的“空间权重矩阵”来表述的。许多研究将他们的模型设置为一阶连续性，这类似于国际象棋中车的移动，而没有检查这种空间关系类型选择的影响。在本研究中，我们研究了空间组成多元模型中空间关系的表述。本文利用土地利用成分数据和具有不同空间关系公式的成分多元条件自回归模型对预测精度问题进行了实证研究。结果表明，在我们的经验案例中，空间关系的宽范围和平滑设置更有利于预测精度。每个变量的结果表明，优选关系各不相同：对总数的优选设置并不总是对每个变量都优选。提高预测精度要求我们在多变量情况下为每个变量考虑不同的空间权重。
Spatial compositional multivariate models have recently been developed in environmental and ecological research. However, little attention has been paid to how the results of these models are affected by different settings of spatial relationships, which are generally formulated using the so-called “spatial weight matrix.” Many studies set their models to first order contiguity, which is analogous to the moves of a rook in chess, without examining the effects of this choice of spatial relationship type. In this study, we examine the formulation of spatial relationship in spatial compositional multivariate models. We investigate the question of prediction accuracy through an empirical illustration that uses land use compositional data and compositional multivariate conditionally autoregressive models with different spatial relationship formulations. The results indicate that the wide range and smoothed setting of spatial relationship is preferable for prediction accuracy in our empirical case. The results for each variate suggest that the preferable relationships vary: the preferable setting on the total is not always preferable for each variate. Improving prediction accuracy requires that we consider a different spatial weight for each variate in multivariate cases.