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. 2018 Jan 8;11:22. doi: 10.1186/s13071-017-2606-6

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© The Author(s). 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 8 — Phi coefficients indicating the correlation between the geographical distributions of Aedes aegypti and temperature assumptions based on a 348-township binary map for each season. a Spatial interpolation models calculated using inverse distance weighting. b Spatial interpolation models calculated using local polynomial interpolation. c Spatial interpolation models calculated using radial basis function. d Spatial interpolation models calculated using ordinary kriging