Inverse Distance Weighted Interpolation Worked Example

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  • เผยแพร่เมื่อ 4 ต.ค. 2024
  • Shows an explanation and worked example of IDW interpolation.

ความคิดเห็น • 9

  • @tylerdurden5924
    @tylerdurden5924 4 ปีที่แล้ว +7

    Interesting. I had wonder if that could be correct. My nose told me it isn't. 107.95 don't fit very well. And indeed the correct value is 103.7
    But thank you very much. Good explanation.👌

    • @TheGeomatician
      @TheGeomatician  4 ปีที่แล้ว

      Of course, it cannot be larger than the surrounding values. I will fix the video. Thanks for noticing. Cheers.

    • @anthonator1033
      @anthonator1033 ปีที่แล้ว

      @@TheGeomatician cap

  • @jackcampbell4165
    @jackcampbell4165 ปีที่แล้ว +1

    I love your videos and will continue to watch them, they have been an enormous help. However, you may consider getting a new microphone or doing some audio preprocessing, the volume fluctuations almost blow out my eardrums every time

  • @SuilujChannel
    @SuilujChannel ปีที่แล้ว

    really great video!!! thanks a lot! :)

  • @neekhilsingh2114
    @neekhilsingh2114 3 ปีที่แล้ว

    Explain the weight value how u caculated weight

    • @abdullamasud4278
      @abdullamasud4278 2 ปีที่แล้ว

      As mentioned in the video, w = 1/d^p
      take p as whatever you want (0.25 is most used as mentioned in the video)

  • @ushakachari4473
    @ushakachari4473 3 ปีที่แล้ว +1

    How the weight is plotted against the distance directly. Please elaborate

    • @abdullamasud4278
      @abdullamasud4278 2 ปีที่แล้ว

      I don't really know if this is what you are asking for but,
      To get that graph you need to plot distance with weight.
      For Distance:
      You take a series of values for distance, say 0 to 100.
      For Weight:
      Weight is just a function of distance,
      w = 1/d^p
      where p = power (determines how strongly distance affect weight)
      take p=1 for now and substitute it in the formula.
      Now you simply plot distance as x and weight as y. You get that graph.