I chose to use the measurement as percentages of households that make less than $10,000 annually to demonstrate the amount of severe poverty in differing census tracts in central Connecticut. In the “lying” map, someone that takes a quick look would make the assumption that there isn’t much severe poverty or disparity in the region. Well that’s because one color represents zero to thirty percent of people in the tract that are below the $10,000 threshold. Zero to thirty is a very large range, and few tracts, even in the poorest cities, will have more than 30 percent. The second map is much less deceptive, and actually depicts the income inequality on the West Hartford-Hartford border with its better use of cutpoints of percentages.
Category: 6 Lying with Polygon Maps
How to Lie with Maps using Police Data
The Rate of Police Arrests in 2014 in Connecticut (Inequality)
The Rate of Police Arrests in 2014 in Connecticut (Equality)
Above are two shaded polygon maps that contain the same data about the rate of police arrests in 2014; yet, the information portrayed is different. From the shaded red polygon, the person reading that map would interpret disparity in the number of assault arrests in Connecticut. For instance, the Hartford area shows consistency in arresting 50-1,601 people for assault (light pink) while there are less than fifty arrests (dark red) that are spread across Connecticut. On the other hand, the blue shaded polygon contains the same data, but why can it be interpreted differently? Well, the person reading the blue polygon map would see a consistency of equal assault arrests in Connecticut. This is how you can lie with Maps. If you look closely at the two polygon maps, the red shaded polygon represents the TOTAL OFFENSE ARREST while the blue shaded polygon represents only ASSAULT OFFENSES. I have learned not to be fooled by maps easily by learning how to lie with maps. The reason why the person who created the map would show different interpretations with the same data is to emphasize their main point. For instance, if I wanted to target an audience about the importance of reducing the total amount of offense arrests, I would show the red polygon. However, if I want to emphasize the equality of arrests to show that the arrest rates are not increasing, I would show the blue polygon map. I have learned to carefully assess the map by using the legend as a guide as well as pay attention to details to better understand the purpose of the map.
(Note: The polygon is not filled because the police departments do not have the data for those towns in Connecticut)
Lying with Maps of Total Adult Served by Capital Workforce Partners
The two maps above show the total adults served by my community partner, the Capital Workforce Partner. For both maps I used gradient of purple to fill in the polygons, because the gradient can illustrate difference in quantity of the same data type, which in this case is the number of people served. While the first map emphasizes sameness, the second one highlights extreme inequality. For the first map, I narrowed down the ranges for the towns with lowest and highest number of people served. As the range of the medium number of people served are very wide, almost all towns fall into this category, which creates a sense of equality. On the other hand, for the second map, I expanded the range for the high and low number of people served. This creates more disparity among the towns. The towns in the middle of the state, such as Hartford have high number of people served, whereas many towns in the boundary still have not many people served. Hence, by changing the range of number of adult served, I can make two contrast maps of the same data, one emphasizes sameness, while the other highlights inequality.