Author: Fionnuala Darby-Hudgens

Honestly, the creation of this map was more difficult than I imagined it would be. I will first layout a descriptive table and then I will give explanation as to why I made the choices I did.

The Red Areas: indicate that no data was available for zoning restriction for this area.

The Darkest Areas: indicate the largest amount of land required by the zoning laws, specific to geographic area, for the allowance of multifamily housing construction. (Think most prohibitive for land developers to build affordable housing in these communities.)

The Lighter to Lightest Areas: indicate smaller amounts of land needed for the allowance of multifamily constructive. (Think least restrictive for developers.)

Explanation:

Although, it seems I may have wanted to remove town that did not have any data to avoid the red blocks, it an inconclusive map when I did. There appeared to be questionable ‘holes’ in the map and at quick glance, it was not apparent as to why these holes were there. Leaving them red allows me the ability to quickly alert the view that no data is available for these locations.  I also thought that examination of the “no data areas” might also be interesting to some.

I then chose to use the gradient colors between black and white because I am using Google fusion. The span between black and white allows for the largest number of shade possibilities. It is also a good contrast to the red, no data areas.

The next parts are confusing. I honestly believe that this map should be viewed along side the spreadsheet table of data because the variance of allowable square footage is so wide (0 sq ft – 653400 sq ft).

So, my goal was to show discrepancy “at a glance”. I am hoping the Fair Housing Authority will be able to use the map as a teaching tool, so by showing discrepancy easily I choose to include five gradients, rather than multiple gradients that only differed slightly from town to town. This seemed most effective to show extremes, i.e.; Bloomfield in comparison to Hartford.

* In addition: I attempted to change the representation of data values several times in a hope to get a closer margin from 0 sq ft to 653400 sq ft. I attempted using percentages of 653400, and scientific notation. These did not help. Perhaps an arbitrary ratio assigned to specific data would work. 6000sq ft allowable land is = to 1 whereas 300,000 sq ft of land is equal to 5.

As the exercise is designed to illustrate both of these charts are the same data set. At first glance though, they appear to be polar opposites. In an era of flash media, and “shocking” statistics, simply changing the order the data is presented in could easily sway a reader to look at it differently.

I played with the ‘reverse’ tool on the x axis. So, although the data is the same as chart 1 it appears as if percent is going down by flipping the years. I am unsure if this a statistically correct way to present years on a line graph – but, something tells me that those who ‘lie’ with stats. are not always following all the “rules”. In addition by allowing the y-axis to illustrate a larger span of percentages in the second chart the line does not appear to be drastic by starting at the bottom of a chart and raising. instead it appears to illustrate less drastic change by remaining in the middle of the chart.

Jack, asked in class if we recognized anything about his racial change chart that may have been done on purpose to present a viewer with “guided” interpretation of the results. What I noticed after further examining the chart is:

1. the percentage values associated with a color in the middle of the data from 90% white residence to 10% white residence are in 15% increments. So, for a color to change with in those figures it would have to be a substantial difference of a 15% racial population change.

2. In contrast – the highest and lowest percentages have color changes on the map at just 2% racial population change, 100% to 98% white reflects a color change, as well as 0% white population to 2% white population results in color change.

3. Data sets between 90% to 98% white population and 2% to 10% white population are alone in a reflecting a color change at an 8% racial population shift.

I can only speculate why this was done, but I would not have noticed it had I not been told to look closely at the map.

Here is my practice post!