<p>Usually, when people use non-equidistant axis, it is because they want to emphasize some variation more than others. For example, if most of your data is between 0 and 1, but you have one outlier on 100, then an equidistant axis would make it hard to analyze most the variation, because it emphasizes the wrong parts.</p>

<p>In this case you have a log-scale, which is a simple nonlinear axis that (in general) emphasizes small values over larger ones. Whether that makes sense with oil prices, is - to me - unclear.</p>

CC BY-SA 3.0
2015-05-06T20:44:13.657Z
It might be worth pointing out that specifically, a log scale emphasizes volatility (b/c equal % changes show up as equal movements, rather than equal absolute changes having equal movements), which can definitely be of interest with oil prices. Also, a log scale can be very appropriate for series (like GDP level) that are characterized by compound growth. The use of both a log scale and an inflation adjustment (inflation-adjusting oil prices complicates many analyses, due to the powerful endogeneity) is pretty hard to justify, though.
dismalscience
CC BY-SA 3.0
2015-05-07T11:49:58.887Z
@dismalscience, you should write this up as an alternative answer. It is often good to graph transformed data to present a stationary time series. This is especially important when time series are long. While it is true that as foobar says, this isn't very important for oil prices because the technology of extraction and refining have improved almost as fast at nominal prices have increased, it is true for most long run price series. Another alternative is to graph the levels but to deflate the series to show real (rather than nominal) prices.
BKay
CC BY-SA 3.0
2015-05-06T16:09:20.827Z
2015-05-06T16:09:20.827Z
FooBar
<p>As it has already been explained, it's a logarithmic scale (also called log scale for short). The distance between values doesn't depend on their linear difference, but on their relative difference. The distance between 20 and 40 is the same as the distance between 40 and 80 or the distance between 60 and 120, because 40/20 = 80/40 = 120/60 = 2 or a 100% increase, a fixed rate.</p>

<p>An additional caveat about log scales is that they never go down to 0. After all, if you increase 0 by any percentage, you still get 0. The distance between 0 and any positive value is infinite.</p>

CC BY-SA 3.0
2015-05-06T17:54:44.413Z
Your caveat is negligible when plotting prices, though.
FooBar
CC BY-SA 3.0
2015-05-06T18:27:55.043Z
Yeah. I was thinking about log scales for any kind of data, not necessarily prices. My comment also referred to the André Peseur's last paragraph saying that the Y axis was heavily truncated. If "truncated" means that it doesn't go down to 0, well, log scales just need to be truncated at some point - maybe the lowest value in the data set or some value not much lower than that.
dgstranz
CC BY-SA 3.0
2015-05-06T17:13:31.957Z
2015-05-06T17:13:31.957Z
dgstranz
<p>I found the axis on the following <a href="http://www.macrotrends.net/1369/crude-oil-price-history-chart" rel="nofollow noreferrer">graph of crude oil prices over 1950-2015 from MacroTrends</a> surprising. 
<img src="https://i.sstatic.net/EqDLy.png" alt="enter image description here">
The y-axis labels are 20, 40, 60, 80, 100, ...but they are not equidistant. Why? </p>

<price-level><commodities>
Oil Price Chart Scale
user2321323
# Oil Price Chart Scale

(Asked by: username_0 on 2015-05-06)

I found the axis on the following [graph of crude oil prices over 1950-2015 from MacroTrends](http://www.macrotrends.net/1369/crude-oil-price-history-chart) surprising.
![enter image description here](https://i.sstatic.net/EqDLy.png)
The y-axis labels are 20, 40, 60, 80, 100, ...but they are not equidistant. Why?

**Tags:** price-level, commodities

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## 2 answers

### Answer by username_1 on 2015-05-06. Score: 6

Usually, when people use non-equidistant axis, it is because they want to emphasize some variation more than others. For example, if most of your data is between 0 and 1, but you have one outlier on 100, then an equidistant axis would make it hard to analyze most the variation, because it emphasizes the wrong parts.

In this case you have a log-scale, which is a simple nonlinear axis that (in general) emphasizes small values over larger ones. Whether that makes sense with oil prices, is - to me - unclear.

> **Comments:**
> **username_2**: It might be worth pointing out that specifically, a log scale emphasizes volatility (b/c equal % changes show up as equal movements, rather than equal absolute changes having equal movements), which can definitely be of interest with oil prices. Also, a log scale can be very appropriate for series (like GDP level) that are characterized by compound growth. The use of both a log scale and an inflation adjustment (inflation-adjusting oil prices complicates many analyses, due to the powerful endogeneity) is pretty hard to justify, though.  
> **username_3**: @dismalscience, you should write this up as an alternative answer. It is often good to graph transformed data to present a stationary time series. This is especially important when time series are long. While it is true that as foobar says, this isn't very important for oil prices because the technology of extraction and refining have improved almost as fast at nominal prices have increased, it is true for most long run price series. Another alternative is to graph the levels but to deflate the series to show real (rather than nominal) prices.  

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### Answer by username_4 on 2015-05-06. Score: 3

As it has already been explained, it's a logarithmic scale (also called log scale for short). The distance between values doesn't depend on their linear difference, but on their relative difference. The distance between 20 and 40 is the same as the distance between 40 and 80 or the distance between 60 and 120, because 40/20 = 80/40 = 120/60 = 2 or a 100% increase, a fixed rate.

An additional caveat about log scales is that they never go down to 0. After all, if you increase 0 by any percentage, you still get 0. The distance between 0 and any positive value is infinite.

> **Comments:**
> **username_1**: Your caveat is negligible when plotting prices, though.  
> **username_4**: Yeah. I was thinking about log scales for any kind of data, not necessarily prices. My comment also referred to the André Peseur's last paragraph saying that the Y axis was heavily truncated. If "truncated" means that it doesn't go down to 0, well, log scales just need to be truncated at some point - maybe the lowest value in the data set or some value not much lower than that.  

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