Category: xSpr14 Assignments

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.

Lying with Maps

Posted on Author Minh Anh Nguyen

Both of the following maps are based on the same data of employment rates across all census tracts in Connecticut. However, due to the number of intervals that the data is divided into in each map, the two maps look very different from each other. In the first map, the data is divided into only 2 buckets, showing a clear distinction between the census tracts with high employment rates (64.9% – 79.6%) and the ones with low employment rates (less than 64.9%). Highly contrasted colors are also used to emphasize the difference between the 2 baskets.

On the other hand, the data is divided into 8 buckets in the second map. Colors of the same hue and slightly different shades are used in symbolizing the baskets. It seems like the employment rates across the census tracts in Connecticut are quite even, with only a few census tracts having slightly higher rates of employment than the others.

The above maps have demonstrated how mapmakers can lie with maps and give totally opposite impressions to viewers, just by changing some of their decisions on map elements in making maps.

Assignment 8

Posted on Author Raymond Li

This sample map shows the percentage of minorities in the school districts. Using many gradients, I was able to create ranges that are very similar in color. Therefore, I was able to show that in the small area of CT, the percentage of minorities was similar throughout the towns/cities. If I wanted to show extreme differences, I would have used less gradients and ranges and had colors that contracted each other. In this map, There was a big range, but the colors representing each gradient was a similar color.
In the second map, I used data from IPC. I calculated the total number of injuries in CT and also added in population statistics. Using those stats, I was able to calculate the injuries per capita. Most cities/towns had a relatively low level of injuries. However, to make the map show an extreme difference, I used many gradients, but also chose different colors to represent each. For instance I used blue, orange, and red to show a greater contract between colors.
The third map shows the same data, but using the same color. Because the ratios are so low and similar, it looks like as if there is no difference in ratio of injuries in each town/city. However, this is not the case since the Hartford area as well as Cromwell, have the highest ratio, but it is not shown in the third map