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.
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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.
Lunch Locations using different mapping tools
When using Mapme I found it very easy to edit my data and change around the look of the map but it became very tedious to impute each data separately. With the large data sets I will be using this mapping tool will not be the most usefully. I did find this tool to be very user friendly and helpful with the message board on the side to help you trouble shoot any problems you had. I would use this mapping tool for smaller data sets but overall I do not think this will be my first choice for mapping my girl scouts data.
BatchGeo was very useful for imputing large amount of data quickly. I also found this the easiest to embed on to this web site. I did find it hard to add other information to the data points. I did like that the data points would display all data presented but I wanted a way to filter out the information so it would only show what I wanted. This tool is a very user friendly tool and easy to learn which was helpful for this assignment.
Google fusion tables was the most advanced of all the tools I tried. It was not hard to figure out because of the example in class but I think I would have found it harder if we had not had that knowledge. I liked this tool a lot because it had so many options in changing how the data looked on the map. Also the fact that you can import your data straight form google docs was very easy and useful. I did have a lot of issues with embedding the map onto my post. I kept getting a video instead of my map and I am still trying to edit the embed link so it will show my map. I think that out of all the tools I would most likely use Google fusion tables because of the may options.
Comparisons of 3 Mapping Tools on the Number of Arrests in Different Police Departments
BatchGeo
MapMe
Google Fusion
Pros and Cons of BatchGeo, MapMe, and Google Fusion:
After creating the three maps based on the number of arrests in different police departments, I have found that MapMe was the easiest and most efficient mapping tool for police data. The hardest mapping tool to work with was Google Fusion Tables because I was not able to map large amounts of data, had to separate addresses by street, city, and zip code, and the mapping process did not pose many obstacles when I manually inputted data into Google Fusion. When I uploaded the Google Sheets/Excel file, it was difficult to map out the points because in police data, there are many categories and subcategories of arrest. For BatchGeo, the mapping was simple and efficient, but I was not allowed to input any additional information. For example, within each police department, there were a number of offenses such as assaults, kidnapping, homicides, etc and in BatchGeo, I would have to click on the police department which would then lead me to different offenses. This visual is not as effective because it is easier to hover over a point and have the information pop out than have the individual searching for the information.
What was good about MapMe was that it allowed me to input the addresses in one form. I did not have to separate by street or zip code and the mapping tool is straight forward for beginners and efficient. MapMe also allowed me to input information in the description box which allows the person viewing the map see the number of offenses in different police departments when the computer mouse is hovered over the point on the map. What is good about BatchGeo is that there is a box to drag/copy data from a spreadsheet, generates map within seconds, and is simple. As a result, the best mapping tool to use for police data would be MapMe because MapMe can handle a lot of information, does not require the address to be separated, allows the user to input additional information, and is simple enough for beginners to understand.