Selected article for: "discrete wavelet transform and wavelet transform"

Author: Wang, Jin-Feng; Christakos, George; Han, Wei-Guo; Meng, Bin
Title: Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China
  • Document date: 2008_4_26
  • ID: 2nko37oo_16
    Snippet: We measured the degree of global spatial data clustering using Moran's I M coefficient (Appendix 4). A positive value of the coefficient meant that adjacent districts had similar values, whereas a negative value indicated that adjacent districts were dissimilar. A temporal measure of spatial clustering was obtained by calculating this coefficient on a daily basis. This time series was then filtered using a discrete wavelet transform implemented b.....
    Document: We measured the degree of global spatial data clustering using Moran's I M coefficient (Appendix 4). A positive value of the coefficient meant that adjacent districts had similar values, whereas a negative value indicated that adjacent districts were dissimilar. A temporal measure of spatial clustering was obtained by calculating this coefficient on a daily basis. This time series was then filtered using a discrete wavelet transform implemented by the MatLab computer library (http://www.mathworks.com/). In this way, the time series was decomposed into low-frequency components (reflecting the fundamental trend of a time series) and highfrequency components (reflecting noise caused by random factors).

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