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Map of Corruption Perception Index

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#1
Gorky

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Hi,

I am making a world-map of Corruption Perception Index (http://en.wikipedia....rceptions_Index), which ranges from 10 (very clean) to 0 (highly corrupt).
I used a sequential color-scheme (see screenshot) . Is this ok or should I use a diverging color scheme?

thanks for any advice

Gorky

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#2
Strebe

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I used a sequential color-scheme (see screenshot) . Is this ok or should I use a diverging color scheme?
Gorky


Sequential seems best to me, but the progression seems to be the reverse of what feels natural to me. I would expect lighter colors to reflect a lower perception of corruption. Perhaps different cultures view this differently.

Best,
— daan

#3
Gorky

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Sequential seems best to me, but the progression seems to be the reverse of what feels natural to me. I would expect lighter colors to reflect a lower perception of corruption. Perhaps different cultures view this differently. Best, — daan


daan- thank you for your kind response

Gorky

#4
I make legends.

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Hi,

This corruption perception concept is something I've never heard of, so I checked out the wiki link. I'm curious why you're making this map? Is it a school assignment, something for practice, something for work?

Perception is a kind of opinion. It's not really quantifiable, so I question the use of numbers in the legend. I also don't think they can really claim to have good data because the polled samples aren't random samples of a population. TI uses data from 13 sources that have their own collection methods (some poll only business people) and doesn't have control over how their data is collected, or how those methods change year to year. Just move ahead knowing that the data is pretty soft.


Anyway, conveying exactly what the colors mean will be the big challenge of the map and should get the most careful treatment. I also agree that the progression of colors should be reversed.

Food for thought... good luck with it!

#5
Gorky

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Hi, This corruption perception concept is something I've never heard of, so I checked out the wiki link. I'm curious why you're making this map? Is it a school assignment, something for practice, something for work? Perception is a kind of opinion. It's not really quantifiable, so I question the use of numbers in the legend. I also don't think they can really claim to have good data because the polled samples aren't random samples of a population. TI uses data from 13 sources that have their own collection methods (some poll only business people) and doesn't have control over how their data is collected, or how those methods change year to year. Just move ahead knowing that the data is pretty soft. Anyway, conveying exactly what the colors mean will be the big challenge of the map and should get the most careful treatment. I also agree that the progression of colors should be reversed. Food for thought... good luck with it!


Hi I make legends,

thanks for your response. I know that that the data are "pretty soft", but better than nothing. ;) Its a work for a small project. I have already reversed the progresson of colors and replaced the green-color progression by a red-color-progression. thanks for the hint to replace the numbers by textual labels. of course there will be some addiitional textual info for the map.

#6
DaveB

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Part of the flaw, in my mind, is they call it a "Corruption Index", but a low value means a high level of perceived corruption. Seems a bit counter-intuitive, no? :\
Dave Barnes
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#7
Gorky

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Part of the flaw, in my mind, is they call it a "Corruption Index", but a low value means a high level of perceived corruption. Seems a bit counter-intuitive, no? :\


yeah, live is strange ...

#8
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I agree with Dave and Daan also... The low numbers / light colors should represent less perception of corruption. The higher numbers / darker colors should represent higher perception of corruption.

#9
Gorky

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I agree with Dave and Daan also... The low numbers / light colors should represent less perception of corruption. The higher numbers / darker colors should represent higher perception of corruption.


finally I made textual labels (for example very high corruption, low corruption....) and used darker colors for higher corruption (= lower values) and added explanatory text-infos
thanks
Gorky




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