基于不确定数据理论的道路相关度度量方法

Measurement method of road correlation based on uncertain data theory

现实世界中各数据元组的概率维构成不确定数据集合,在不确定数据集中的各数据元组间存在大量相关性问题。将不确定性理论应用于真实旅游数据中,并引出旅游收入统计中的重复计算问题。为了解决这一问题,需要解决道路相关度的度量问题。借助传统的Moran’s Index,提出了通用空间相关性度量方法及受限空间的自相关指标,对MapInfo格式的贵州省城市主干道数据进行了实验,实验结果表明:uncertain-srp算法的时间复杂性满足线性规律,其内存空间几乎不受影响,进一步表明了算法的有效性。

In real world situations,the uncertain data set is formed by the probability dimension of each tuple.There exists several problems related to probability dimension of each tuple in uncertain data sets.The theory of uncertain in database research areas is applied to real tourism data in order to solve the common problem of duplication calculation in terms of tourism revenues in statistics.To handle this issue,the measure of path correlation in generic and constrained space is first defined.By the traditional Moran＇s Index,a new approach called CSA is proposed.Experiments that conducted on city trunk road data in MapInfo format from Guizhou Province show that the time performance of CSA agrees with the linear complexity as the memory cost is nearly unchanged,which further shows the effectiveness and efficiency.