Category: maps

The following maps are generated from the same data but were altered in style (purposely) to portray Connecticut school district-level racial data in two very different ways. I sought out to create one map depicting sharp racial divisions between districts in contrast to another map illustrating widespread racial diversity among Hartford school districts. Google Fusion Tables were used to create each map pictured below by merging racial composition of Hartford-area school districts data with Connecticut town boundaries information. After formulating identical maps with Google’s assistance, I was then able to alter the viewing settings (or “map styles”) for each map to achieve my desired outcome as previously stated above.

To communicate sharp racial division, I selected the “buckets” option in the map styles menu to limit the appearance of widespread racial diversity. Specifically, I limited this map to represent only two “buckets,” or categories, of racial diversity. This narrow focus gives off the illusion that not only is there a stark contrast in racial composition between districts, but more specifically that racial minorities are highly concentrated in a cluster of districts central to Hartford.

 

 

 

 

 

 

To represent widespread racial diversity, I followed a similar selection process as detailed above. I chose a multi-faceted gradient map that would give off the effect that racial composition in Hartford school districts is more evenly spread throughout. A total of six gradients allows for the reader to see a softer blend of colors among the school districts thereby representing widespread racial diversity.

 

 

 

 

 

 

 

 

 

 

It is no surprise then that each map above contains the same information represented in an entirely different format, however, would this still be the case if I did not detail my “creative” process?

Both of these maps were generated using the exact same information, however both maps portray two very different interpretations. Surprisingly enough, just like statistical data can be manipulated to show to sharply contrasting graphs, simply manipulating the legend and the number of buckets presented in the map can also skew maps, and produce two very different diagrams. Bucket is a term used to define percentage intervals represented by different colors on the map. These maps were generated by strategically merging two sets of given data, one set was the school district racial composition data used in the Sheff v. O’neil case and the other set was the Connecticut town boundaries based on the 2010 census.

 

 

 

 

 

 

 

 

 

 

 

 

First I entered the data as is and produced a general map. Then using the change the map style option I adjusted the buckets to two separate extremes. First I only used two buckets with a percentage interval from 0 .0 to 0.5 and from 0.5 to 1.0. By using only two buckets I was able to portray sharp racial differences for this specific Connecticut region. To produce my second graph I divided the map into eight percentage intervals (buckets). The eight buckets in this map started at 0.0 and increased by 0.125 until 1 was reached. In doing this I was able to produce a map with more colors thus portraying a more widespread diverse map. It took a matter of a few minutes to manipulate these maps and depict two completely different stories. This makes me think about maps I have analyzed and interpreted in the past and if they were possibly manipulated for what ever the reason maybe.

 

In merging school district racial data from the Sheff v. O’Neil case with Connecticut town boundaries, I was able to create two maps that showed two different understandings of racial diversity with the same data. In order to manipulate the maps to show two distinct racial breakdowns while maintaining the legitimacy of the maps, I altered the colors and gradients. This looks like two different representations of racial diversity in Connecticut with the exact same data.

In order to show a sharp racial divide, I used two categories. This allowed me to pair one against the other and show sharp racial contrast and polarity. By only using black and white, the two categories showed a stark contrast in racial diversity. To show widespread diversity, I included four categories with different gradients of a similar color. This made the map blend more and harder to distinguish between groups. More categories and gradation makes it difficult to see a stark contrast even though the data is exactly the same as the first map.

A:Widespread Racial Diversity and Key:

B: Stark Racial Divide and Key:

In class we used a Google Fusion Table to combine a map of Hartford and its surrounding neighborhoods with data on the demographics of each Connecticut town. We then adjusted the map’s settings to color each town depending on what percentage of the town’s population is a minority. Depending on the limits and scale used, we discovered we could change the map to either portray a racially divided Connecticut or a racially diverse one. Even though both maps contain the same information, they represent this information in different ways, and thus it is easy to see how to lie with maps.

Map 1 shows the racially divided Connecticut. Knowing that the towns on the outer limit of the Hartford area had a small percent of minorities in the population, I changed the range of the lightest color to “0.0 up to 0.5,” so that towns with a population of less than 50% minority were a light yellow color. Not surprisingly, many towns were painted yellow by this wide range. I then increase by 10% for each color up to 80% and assigned the darkest color (a dark red) the range of 80-100% percent, which applied to the three middle towns. The map shows a sharp racial contrast because of the wide range of low percentages combined with the narrow range of middle percentages and a slightly wider range of higher percentages, allowing for an unfair amount of towns to be painted light yellow, and allowing for the middle towns to sharply contrast with the dark red.

 

 

Map 2 tells a very different story. Instead of starting the lightest yellow color with a large range, I made all the ranges for each color equal in size, so that there is a uniform difference between the range of percentages for each color. The result is a less biased map, and a map that shows greater racial diversity in and around the Hartford area.

 

In class we used a Google Fusion Table to combine a map of Hartford and its surrounding neighborhoods with data on the demographics of each Connecticut town. We then adjusted the map’s settings to color each town depending on what percentage of the town’s population is a minority. Depending on the limits and scale used, we discovered we could change the map to either portray a racially divided Connecticut or a racially diverse one. Even though both maps contain the same information, they represent this information in different ways, and thus it is easy to see how to lie with maps.

Map 1 shows the racially divided Connecticut. Knowing that the towns on the outer limit of the Hartford area had a small percent of minorities in the population, I changed the range of the lightest color to “0.0 up to 0.5,” so that towns with a population of less than 50% minority were a light yellow color. Not surprisingly, many towns were painted yellow by this wide range. I then increase by 10% for each color up to 80% and assigned the darkest color (a dark red) the range of 80-100% percent, which applied to the three middle towns. The map shows a sharp racial contrast because of the wide range of low percentages combined with the narrow range of middle percentages and a slightly wider range of higher percentages, allowing for an unfair amount of towns to be painted light yellow, and allowing for the middle towns to sharply contrast with the dark red.

 

 

Map 2 tells a very different story. Instead of starting the lightest yellow color with a large range, I made all the ranges for each color equal in size, so that there is a uniform difference between the range of percentages for each color. The result is a less biased map, and a map that shows greater racial diversity in and around the Hartford area.