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hey there welcome back hey today we're
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diving to unpack different interpolation
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methods ever wondered how we predict
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values for places where we don't
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actually have any data that's where
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interpolation comes in and I'll break
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down each method so you'll know exactly
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when to use which one if you're new to
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interpolation I would suggest checking
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out our Basics video on what and why
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interpolation you'll find the link in
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the description and the I button above
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let's look into the main event of this
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video types of interpolation methods in
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GIS their advantages disadvantages and
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uses each method has unique strengths
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depending on your data type and what
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kind of results you need first inverse
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IDW let's look at inverse distance
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weighted or IDW with IDW we assume that
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points closer to each other are more
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similar than those farther away think of
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it as a weighted average where nearby
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points carry more influence than distant
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ones advantage of IDW IDW is excellent
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for capturing details in areas with
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Cliffs plus you can adjust the number of
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points used which lets you control the
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results based on your data's
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density disadvantages IDW struggles with
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very steep or mountainous areas where
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values might be extreme also it can't
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estimate values beyond the highest or
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lowest points in your data second
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natural neighbor inverse distance
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interpolation it's similar to IDW but
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takes a more geometric approach by
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finding the closest neighboring points
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around the unknown location and waiting
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them based on the area they cover
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advantages of natural neighbor
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interpolation natural neighbor is
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efficient for large data sets and
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handles clustered data points well
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unlike IDW it doesn't need you to set
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parameters like radius or Point
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count disadvantages natural neighbor
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interpolation it might might not capture
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sharp changes in your data as well so if
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your data has large jumps this method
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May smooth them out too much third
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spline the spline method is all about
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surfaces think of it like bending a
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flexible sheet to pass through your
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known data points producing a continuous
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and smooth surface a surface created
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with spline interpolation passes through
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each sample point and may exceed the
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value range of the sample Point set
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spline can estimate above or below your
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sample data range which is great for
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creating a flexible surface
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disadvantages of spline sharp features
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like Cliffs may not come through well
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because spline tends to smooth over
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extreme changes Close sample points with
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big differences can also be a problem as
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spline relies on smooth curves fourth
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cing cing is a more advanced method that
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considers not only the distance between
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points but also how values vary it's
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more complex but very useful when you
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need highly accurate predictions
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advantages of craing craing accounts for
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directional influences in your data like
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wind or water flow it's also highly
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customizable allowing you to model
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spatial relationships based on your
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traits disadvantages of cing on the
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downside cing is computationally intense
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and needs more setup than simpler
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methods like IDW or spline fifth lastly
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we have Trend interpolation which uses
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least Square regression to fit a smooth
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surface over your data it's great for
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spotting large scale patterns though it
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doesn't pass through the actual data
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points advantages of trend interpolation
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Trend interpolation is perfect for
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identifying broad trends like
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temperature gradients or general terrain
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disadvantages Trend interpolation if you
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need precise values Trend interpolation
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is not ideal especially for detailed
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variations since it doesn't match the
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sample points exactly use of
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interpolation in GIS so how do we use
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these methods in GIS interpolation is
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widely used for tasks like building
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elevation models creating weather maps
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or estimating pollution levels which
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method you pick depends on your data for
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smooth General patterns spline or Trend
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may work best but for highly variable
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data IDW or cing might be a better
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choice for example spline is perfect for
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data with with gentle changes like
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pollution or water tables these are five
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most used interpolation methods we
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discussed here there are more methods
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such as Point inter Topo terter Etc and
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that wraps up our introduction to
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interpolation methods in Gs I hope this
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gave you a clear overview of each method
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if you found it helpful hit that like
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button we have more in-depth tutorials
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on coming soon so be sure to subscribe
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leave them in the comments and feel free
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to share this with anyone learning GIS
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thanks for watching and see you next
