<blockquote>
<p>Is it a solution to multiply the B Average Value with 0.7, so the weighted average value will be 3.5?</p>
</blockquote>
<p>No, this is not a solution. Doing this effectively assigns a value of zero to the stations that do not have data, which is the wrong thing to do.</p>
<p>If you want to fill in the missing data you have to apply data interpolation techniques. Such techniques are only valid if there is correlation between the sampling points.</p>
<p>To fill in those missing gaps you will have to do additional calculations. Which technique you use depends on the result you want or what is <strong>now</strong> considered state of the art for the field the data is applicable to.</p>
<p>Until the mid 1990s to early 2000s, when estimating values of grade for ore reserves, using block modelling methods, <a href="https://en.wikipedia.org/wiki/Inverse_distance_weighting" rel="noreferrer">inverse distance weighting</a> methods were used. Initially, <span class="math-container">$\frac{1}{d}$</span> was used as this was a relatively quick calculation to perform. It was later discovered that <a href="https://en.wikipedia.org/wiki/Inverse_distance_weighting" rel="noreferrer">inverse distance squared</a> <span class="math-container">$(\frac{1}{d^2})$</span> gave better results. In some instances, inverse distance to the power <span class="math-container">$n$</span>, <span class="math-container">$(\frac{1}{d^n})$</span>, where <span class="math-container">$n$</span> generally ranged between 2 and 2.5 was also used. Generally, however, inverse distance squared became industry &quot;standard&quot; for a period of time.</p>
<p>The benefit of inverse distance squared weighting compared to simple inverse distance weighting was it gave less weighting to more distance samples. This is based on the logic that samples that are close to one another are more likely to have been deposited under more similar conditions and thus would more likely be related to one another.</p>
<p>Since the early 2000s inverse distance weighting methods have been replaced by <a href="https://en.wikipedia.org/wiki/Kriging" rel="noreferrer">kriging</a>. One of the main differences between kriging and all forms of inverse distance weighting is with kriging, the sum of the weights equals 1. With the other methods, there is no limit, or specification to what the sum of the weight should be.</p>

CC BY-SA 4.0
2022-06-03T13:56:53.850Z
Thanks a lot :)
Weiss
CC BY-SA 4.0
2022-06-03T11:29:12.710Z
2022-06-03T11:29:12.710Z
Fred
<p>With year long data-gaps, any type of interpolation which does not account for environmental forcing (i.e. models) would be an inadequate comparison.
For example, extreme years that are included in A but not in B would affect your comparison greatly. I would rather mask the A dataset to match the years available in B or fill the missing years using a process based model with environmental forcing data outputting the isotope data you need.</p>

CC BY-SA 4.0
2022-06-06T15:42:49.903Z
2022-06-06T15:42:49.903Z
Töröö
<p>Hi I want to create two isotope data (1970-1980) and (2010-2020). As you can mention, the interpolation for the two maps is based on the average value measured at different stations in each time period.</p>
<p>However, I now have the problem that I do not have the data from the particular years for all stations.</p>
<p><a href="https://i.sstatic.net/LisXS.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/LisXS.png" alt="![enter image description here" /></a></p>
<p>For example, station B has data from 1970-1974 and 1979-1980.
So the data from 1975-1978 are missing!</p>
<p>But if I now calculate the average at this station and interpolate it with the data that includes a data set (A) for all 10 years, I will distort my model!
Is there a simple mathematical solution for weighting the data?</p>
<p>Is it a solution to multiply the B Average Value with 0.7, so the weighted average value will be 3.5?</p>

CC BY-SA 4.0
2022-06-02T15:06:51.127Z
You state "As you can see" but there is no table, image, or link included for us to look at.
GrapefruitIsAwesome
CC BY-SA 4.0
2022-06-03T07:53:50.743Z
@GrapefruitIsAwesome so I fixed it :)
Weiss
CC BY-SA 4.0
2022-06-03T10:11:08.413Z
Thank you, the question is clearer now.
GrapefruitIsAwesome
<climate-models><python><time><database>
Problems when interpolating time series data (average problem)
Weiss
# Problems when interpolating time series data (average problem)

(Asked by: username_0 on 2022-06-02)

Hi I want to create two isotope data (1970-1980) and (2010-2020). As you can mention, the interpolation for the two maps is based on the average value measured at different stations in each time period.

However, I now have the problem that I do not have the data from the particular years for all stations.

[![![enter image description here](https://i.sstatic.net/LisXS.png)](https://i.sstatic.net/LisXS.png)

For example, station B has data from 1970-1974 and 1979-1980.
So the data from 1975-1978 are missing!

But if I now calculate the average at this station and interpolate it with the data that includes a data set (A) for all 10 years, I will distort my model!
Is there a simple mathematical solution for weighting the data?

Is it a solution to multiply the B Average Value with 0.7, so the weighted average value will be 3.5?

**Tags:** climate-models, python, time, database

> **Comments:**
> **username_1**: You state "As you can see" but there is no table, image, or link included for us to look at.  
> **username_0**: @username_1 so I fixed it :)  
> **username_1**: Thank you, the question is clearer now.  

---

## 2 answers

### Answer by username_2 on 2022-06-03. Score: 5 | Accepted answer

> Is it a solution to multiply the B Average Value with 0.7, so the weighted average value will be 3.5?

No, this is not a solution. Doing this effectively assigns a value of zero to the stations that do not have data, which is the wrong thing to do.

If you want to fill in the missing data you have to apply data interpolation techniques. Such techniques are only valid if there is correlation between the sampling points.

To fill in those missing gaps you will have to do additional calculations. Which technique you use depends on the result you want or what is **now** considered state of the art for the field the data is applicable to.

Until the mid 1990s to early 2000s, when estimating values of grade for ore reserves, using block modelling methods, [inverse distance weighting](https://en.wikipedia.org/wiki/Inverse_distance_weighting) methods were used. Initially, $\frac{1}{d}$ was used as this was a relatively quick calculation to perform. It was later discovered that [inverse distance squared](https://en.wikipedia.org/wiki/Inverse_distance_weighting) $(\frac{1}{d^2})$ gave better results. In some instances, inverse distance to the power $n$, $(\frac{1}{d^n})$, where $n$ generally ranged between 2 and 2.5 was also used. Generally, however, inverse distance squared became industry "standard" for a period of time.

The benefit of inverse distance squared weighting compared to simple inverse distance weighting was it gave less weighting to more distance samples. This is based on the logic that samples that are close to one another are more likely to have been deposited under more similar conditions and thus would more likely be related to one another.

Since the early 2000s inverse distance weighting methods have been replaced by [kriging](https://en.wikipedia.org/wiki/Kriging). One of the main differences between kriging and all forms of inverse distance weighting is with kriging, the sum of the weights equals 1. With the other methods, there is no limit, or specification to what the sum of the weight should be.

> **Comments:**
> **username_0**: Thanks a lot :)  

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### Answer by username_3 on 2022-06-06. Score: 0

With year long data-gaps, any type of interpolation which does not account for environmental forcing (i.e. models) would be an inadequate comparison.
For example, extreme years that are included in A but not in B would affect your comparison greatly. I would rather mask the A dataset to match the years available in B or fill the missing years using a process based model with environmental forcing data outputting the isotope data you need.


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