Category: maps

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.

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.

Like lying with statistics and charts, one can also lie with maps. While we can manipulate charts to tell a different story, maps can do the same thing, depending on the way one manipulates it.

These maps below show the percent minority in the Hartford region. However, they show it much differently and one can assume different things by looking at them both.

 

 

 

 

 

 

 

 

 

 

 

 

These maps above, using the gradient feature on google fusion, show where minority rates are concentrated more in the Hartford region. It shows the contrast and one is able to understand that looking at this, where more minority rates live.

These maps below, on the other hand, show a different story and are much harder to understand. A gradient feature shows the differences within the areas through different shades of one color, while using the bucket feature, you manipulate the graph with completely different colors, and makes it harder to understand which area has more amounts of minority. One can see that different areas have different concentrations, but people could think that oh, a color is darker, which means there’s more; however, this is not true necessarily. Also, one can also change the percentage scale, as I did.

 

 

 

 

 

 

 

 

 

 

The way I scaled the percentages is not even in any sense, so it is harder to understand and grasp which areas really have more minority. In addition, the colors I used also make it more confusing, because 90%-100% minority is a lighter shade of color. This exercise shows how important it is for one to really look at the legend and keys of maps to make sure they really are understanding what the map is supposed to show.

Similar to last week, this post is about manipulations of statistics in terms of how they are presented. However, instead of charts, this post focuses on cartography: MAPS. In this case, data was drawn from

Using the same data, these maps were made to show the same information in different ways. Unlike the charts, both of these maps illustrate completely different situations.

Using “Buckets” on Google Fusion tables, shows a stark contrast of mostly white suburbs in a ring around higher percent minority areas in the center.

Using “Gradient” on Google Fusion tables, the map created is able to illustrate a larger amount of racial diversity and integration in the area.

This post seconds the prior post to warn consumers of knowledge to be skeptical when being presented with maps in the media and in research. Look at legends and keys being presented and the colors being utilized.

go to the most up-to-date version of this tutorial