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I'm working with a database about internally displaced persons in Colombia. All data are absolutes values, so I calculate the rate per 1000 people.

I started to visualize all data using QGis. I choosed std dev, quantiles and jenks methods to determine the arrangement of values into different classes.

Here are the examples:

Clusters

However, I got a question in this proccess: what is the best arrangement of values when we have big gaps between them?

This is a list of values I am working with.

Values = [290 161 154 133 126 126 118 112 112 103 102 102 101 100 96 96 92  87  87  86  85  84  84  80  79  79  76  73  71  70  70  69  65  60  59  58  57  57  56  55  54  53  53  53  53  52  51  51  50  50  50  50  49  49  49  49  49  48  47  47  47  46  45  44  44  44  44  43  42  42  41  41  40  40  40  40  40  39  39  39  38  38  38  38  37  37  37  37  37  37  37  36  36  35  35  35  35  35  34  34  34  32  32  32  32  32  31  31  31  31  31  31  31  31  31  31  30  30  30  30  30  30  30  30  29  29  29  29  29  29  29  29  28  28  28  28  28  27  27  27  27  27  26  26  26  26  26  26  26  26  26  26  25  25  25  25  25  24  24  24  24  24  23  23  23  23  23  23  23  23  23  23  22  22  22  22  22  22  21  21  21  21  21  21  21  21  21  21  21  20  20  20  20  20  20  20  19  19  19  19  19  19  19  19  19  19  19  19  19  18  18  18  18  18  18  18  18  18  18  17  17  17  17  17  17  17  17  17  17  17  17  17  16  16  16  16  16  16  16  16  16  16  16  16  16  16  16  15  15  15  15  15  15  15  15  15  15  15  15  15  15  14  14  14  14  14  14  14  14  14  14  14  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  13  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  12  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  11  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  10  9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   8   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   7   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   6   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   5   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   4   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   3   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   2   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0]

Any suggestions to understand better data clustering are appreciate!

Thanks!

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  • 4
    $\begingroup$ All colour schemes are a compromise. We can't tell you what the "best" is without you telling us your criteria for "bestness". What questions are being asked of the map? Do you want a value of 10 to be perceived as half as bad as a value of 20? Then linear scale. Do you want to stretch the contrast as much as possible to emphasise the spatial pattern? Then use quantiles. Every graphic is an answer - make sure the question is well posed. $\endgroup$ – Spacedman Jun 30 '16 at 16:28
  • $\begingroup$ +1 . And maybe you can start by searching other desplazados maps on the web, see what scales were created and how (well) it renders. $\endgroup$ – tagoma Sep 23 '17 at 20:44
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One solution is binning the data into groups. For example, create three groups - "High", "Medium", "Low". The numerical splits groups can be handpicked to emphasize the signal in the data.

Another solution is to threshold the data and not show data above or below the threshold.

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I believe that you should use Logarithmic Scale for better clustering results.

Take a look at the photo on the following link. wikimedia/Logarithmic_scale

Let me know if that helped.

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