# How to Lie With Maps

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When analyzing maps, it is important to look at the scale in which the author used to make the map.  In addition to this, the reader should also investigate what the scale does in comparison to what point the author is trying to prove.  In the maps I made below, I used the same statistics of percent minority composition in the different towns in Hartford’s metropolitan area.  Although I used the same data, and the same color scheme, the first map shows a large contrast between Hartford, Bloomfield, East Hartford and Windsor with the rest of the metropolitan area, while the second map portrays a more even minority composition throughout the metropolitan area of Hartford.

To show stark contrast, I made the gradient much darker for those areas whose minority was above a certain percent, and those below much lighter for the above map.  By creating only three gradients for towns to reside in, the contrast between towns became very apparent. This is only because the settings made it so the towns surrounding Hartford, Bloomfield, Windsor and East Hartford all resided under a certain percentage mark, so they all became very light green, appearing that they all have the same minority percentage.  This is untrue of course, but since the towns are all grouped in the same category in this map, it is easy for the reader to make this conclusion.

In the map above, the contrast between the different areas is much less identifiable.  In this map, I adjusted the gradients so there were more categories of around the same tint of green.  Since there are more categories, the difference in percentages of the towns are easier to see, showing an area that looks much more homogeneous than the previous map.

This stark contrast between two maps with the same data exemplifies that the reader must be careful when coming to conclusions about maps.  They should ask themselves questions such as, “Does the scale use rational increments?” and “What is the range of this color–is it small or large?”.  After the reader asks questions such as these, they can start to make conclusions, but it can be dangerous to assume that one color means all the regions with that color have the same minority composition (or whatever is being measured), instead it is wise to look at how large the range is; then the reader can determine how alike the regions actually are.