Category: 8 How to Lie with Maps

A8: Lying with Maps

Posted on Author Shaina Lo

The first Google Fusion table here shows three different SmartChoices types of schools in Connecticut. To learn more about the description of SmartChoices schools, please click here. SmartChoices includes public schools in the city of Hartford as well as 19 other suburban towns. It is important to note that it does not include other types of schools, such as private.

Below I have divided my schools into three different SmartChoices: 1=interdistrict, 2=district, and 3=more PreK centers. By combining the data with the city KML layer, the polygon map shows the most prevalent types of schools in each city area with a shade of blue.

The second map I formed with the same data and boundary lines. However, the map shows points of the three types of schools present, instead of polygon layers. It is interesting to view these maps together because by looking at the one below, it is clear that especially within the Hartford city area, one may get easily confused by the amounts of dots present. They can overwhelm an audience unfamiliar with this type of data. The map above only shows one color per city and identifies that city with a type color. One may think that that particular city only contains mainly those types of schools. However, the other types are available as well.

How to Lie with Maps

Posted on Author Marissa Block

The first map below shows the four school zones of the Hartford school district, which are shaded based on the average percentage of CMT proficiency of the schools in the that zone in 2013. The averages range from 50.4 to 74.09, so in the first map the red shaded areas are 50.4-62.23 and the green shaded areas are 62.24-74.09. This splits the zones into either good or bad, which is obvious in the map. Additionally, the use of red also further highlights the negative performance of the school zones. In shading an entire school zone in red, such as Zone 2 (top right), viewers might react negatively towards all the schools in that zone, even though one point (Capital Prep) is green, meaning the percentage at proficient is 70% and above.

The second map shows the exact same data, but rather than dividing school zones into good versus bad, zones are shaded as a gradient, so identifying the worse performing school zones does not quite jump out at the viewer as much as the last map. Again, the range for the gradient is 50.4-74.09, and as the shading gets darker the percentage at proficient increases. In this map, the viewer can determine that Zone 1 (top left) seems to be the darkest shaded, and this is confirmed by the yellow and green points, which are schools with percentage at proficient of 51% and above. In terms of the other zones, it is not quite clear which are better performing. For example, it does not seem that Zone 2 (top right) and Zone 3 (bottom left) are any worse than Zone 4 (bottom right), whereas in the first map, Zone 2 and 3 were shaded red, while Zone 4 was green.

Assignment 8: How to Lie With Maps

Posted on Author Minh Nguyen

The two maps below both display the percentage of college graduation in Connecticut, in which data is provided by Social Explorer. By using different colours and break intervals, these two maps provide two completely different portrayals of a same dataset.

In the first map, I use different shades of green to show the college graduation rate across Connecticut. Since there is no dramatic change in variation,  the similarity among various census tract polygons are highlighted. Moreover, by breaking the data into 9 buckets, I intentionally vary the length of each interval to manipulate the number of census tracts in each group. The top 2 and bottom 2 groups have the less data sample, while 5 middle buckets have the most data sample. Even though the legend shows wide range of rates, my dataset is mostly concentrated in the middle group. Therefore, it seems like the percentages of people who graduated from college are quite similar among all the census tracts in Connecticut.

Using the same data but with different choice of colours and intervals, I display my second map as a highly unequal distribution of college graduation rate. The data is divided into only two buckets, which are assigned with two highly contrast colours: extremely dark and light green. As illustrated on the map, it seems like more people graduated from college in Northern and South West of Connecticut.

MoveUP! Lying with Maps

Posted on Author Sarah Keane

This first visualization is a gradient polygon map of the poverty status for individuals 18-64 years of age within the various census tracts of Hartford County. The goal here was to make each tract look very similar data-wise, which is why each tract is a very similar shade green. The map is showing that throughout the county, and within each tract, there aren’t many individuals living in poverty. It does show, though, where the higher concentration of poverty is, so it doesn’t hide it.

 

The second visualization is a two-bucket polygon map of the poverty status for individuals 18-64 years of age within the various census tracts of Hartford County. My goal was to show an extreme difference in data in this particular visualization, which is why I used the two-bucket technique. I found a number boundary that accentuated how many individuals were in poverty and where they were primarily located.