Display options
Cite
Chin WC, Wen TH, Sabel CE, et al. A geo-computational algorithm for exploring the structure of diffusion progression in time and space. Sci Rep. 2017;7(1):12565doi: 10.1038/s41598-017-12852-z.
Chin, W. C., Wen, T. H., Sabel, C. E., & Wang, I. H. (2017). A geo-computational algorithm for exploring the structure of diffusion progression in time and space. Scientific reports, 7(1), 12565. https://doi.org/10.1038/s41598-017-12852-z
Chin, Wei-Chien-Benny, et al. "A geo-computational algorithm for exploring the structure of diffusion progression in time and space." Scientific reports vol. 7,1 (2017): 12565. doi: https://doi.org/10.1038/s41598-017-12852-z
Chin WC, Wen TH, Sabel CE, Wang IH. A geo-computational algorithm for exploring the structure of diffusion progression in time and space. Sci Rep. 2017 Oct 03;7(1):12565. doi: 10.1038/s41598-017-12852-z. PMID: 28974752; PMCID: PMC5626785.
Copy Download .nbib Format: NLM
Share it on

Sci Rep. 2017 Oct 03;7(1):12565. doi: 10.1038/s41598-017-12852-z.

A geo-computational algorithm for exploring the structure of diffusion progression in time and space.

Scientific reports

Wei-Chien-Benny Chin, Tzai-Hung Wen, Clive E Sabel, I-Hsiang Wang

Affiliations

  1. Department of Geography, National Taiwan University, Taipei City, 10617, Taiwan.
  2. Department of Geography, National Taiwan University, Taipei City, 10617, Taiwan. [email protected].
  3. Department of Environmental Science, Aarhus University, 4000, Roskilde, Denmark.

PMID: 28974752 PMCID: PMC5626785 DOI: 10.1038/s41598-017-12852-z

Abstract

A diffusion process can be considered as the movement of linked events through space and time. Therefore, space-time locations of events are key to identify any diffusion process. However, previous clustering analysis methods have focused only on space-time proximity characteristics, neglecting the temporal lag of the movement of events. We argue that the temporal lag between events is a key to understand the process of diffusion movement. Using the temporal lag could help to clarify the types of close relationships. This study aims to develop a data exploration algorithm, namely the TrAcking Progression In Time And Space (TaPiTaS) algorithm, for understanding diffusion processes. Based on the spatial distance and temporal interval between cases, TaPiTaS detects sub-clusters, a group of events that have high probability of having common sources, identifies progression links, the relationships between sub-clusters, and tracks progression chains, the connected components of sub-clusters. Dengue Fever cases data was used as an illustrative case study. The location and temporal range of sub-clusters are presented, along with the progression links. TaPiTaS algorithm contributes a more detailed and in-depth understanding of the development of progression chains, namely the geographic diffusion process.

References

  1. Int J Environ Res Public Health. 2014 Jan 09;11(1):934-51 - PubMed
  2. Int J Infect Dis. 2010 Apr;14(4):e334-43 - PubMed
  3. Phys Life Rev. 2016 Dec;19:43-73 - PubMed
  4. Cancer Res. 1967 Feb;27(2):209-20 - PubMed
  5. PLoS Negl Trop Dis. 2009 Jul 21;3(7):e481 - PubMed
  6. Sci Rep. 2016 Jun 06;6:27381 - PubMed
  7. Am J Trop Med Hyg. 2012 Aug;87(2):349-58 - PubMed
  8. Proc Natl Acad Sci U S A. 2009 Dec 22;106(51):21484-9 - PubMed
  9. Am J Public Health. 1998 Sep;88(9):1377-80 - PubMed
  10. Acta Trop. 2014 Feb;130:17-23 - PubMed
  11. Nature. 2004 May 13;429(6988):180-4 - PubMed
  12. Soc Sci Med. 2000 Apr;50(7-8):1121-37 - PubMed
  13. PLoS Negl Trop Dis. 2010 Aug 03;4(8):e775 - PubMed
  14. PLoS Med. 2011 Aug;8(8):e1001082 - PubMed
  15. Am J Trop Med Hyg. 2007 Jan;76(1):42-7 - PubMed
  16. J Hist Geogr. 1983;9(1):29-46 - PubMed
  17. J Formos Med Assoc. 2012 Jun;111(6):297-9 - PubMed
  18. PLoS One. 2011;6(12):e29640 - PubMed
  19. PLoS Comput Biol. 2010 Nov 18;6(11):e1001005 - PubMed
  20. Sci Rep. 2017 Feb 22;7:42992 - PubMed
  21. Int J Environ Res Public Health. 2015 Apr 14;12(4):4170-84 - PubMed
  22. Sci Rep. 2015 Jun 18;5:11246 - PubMed
  23. Am J Trop Med Hyg. 2009 Jun;80(6):1039-46 - PubMed
  24. PLoS One. 2008 Mar 12;3(3):e1790 - PubMed
  25. Stat Med. 1990 Jun;9(6):691-701 - PubMed
  26. Sci Rep. 2016 Apr 15;6:24593 - PubMed
  27. J Hyg (Lond). 1980 Dec;85(3):451-7 - PubMed
  28. Biometrics. 1999 Jun;55(2):544-52 - PubMed
  29. Sci Rep. 2017 Mar 06;7:43467 - PubMed
  30. J R Soc Interface. 2005 Sep 22;2(4):295-307 - PubMed
  31. Sci Rep. 2011;1:62 - PubMed
  32. Int J Biometeorol. 2015 Mar;59(3):267-83 - PubMed
  33. Am J Trop Med Hyg. 2008 Sep;79(3):344-52 - PubMed
  34. Sci Rep. 2016 Dec 14;6:38865 - PubMed
  35. PLoS One. 2013 Oct 18;8(10):e76863 - PubMed
  36. Ecol Dis. 1983;2(4):377-96 - PubMed

Publication Types