Lying with Maps

Like lying with statistics and charts, one can also lie with maps. While we can manipulate charts to tell a different story, maps can do the same thing, depending on the way one manipulates it.

These maps below show the percent minority in the Hartford region. However, they show it much differently and one can assume different things by looking at them both.













These maps above, using the gradient feature on google fusion, show where minority rates are concentrated more in the Hartford region. It shows the contrast and one is able to understand that looking at this, where more minority rates live.

These maps below, on the other hand, show a different story and are much harder to understand. A gradient feature shows the differences within the areas through different shades of one color, while using the bucket feature, you manipulate the graph with completely different colors, and makes it harder to understand which area has more amounts of minority. One can see that different areas have different concentrations, but people could think that oh, a color is darker, which means there’s more; however, this is not true necessarily. Also, one can also change the percentage scale, as I did.











The way I scaled the percentages is not even in any sense, so it is harder to understand and grasp which areas really have more minority. In addition, the colors I used also make it more confusing, because 90%-100% minority is a lighter shade of color. This exercise shows how important it is for one to really look at the legend and keys of maps to make sure they really are understanding what the map is supposed to show.

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How to Lie with Statistics

If you have ever looked at a chart, you probably made assumptions about what exactly it was telling you, according to what it looked like. By manipulating the scales of the x and y axis, charts that display the same data can be perceived very different. This data, of the progress of Sheff I can look two different ways; it can look like there was a ton of progress, and it can look like there was very little progress. The data I am working with is:

Actual and Legal Process toward Sheff I Goal, 2003-2007 Chart – Data Source: Dougherty et al. “Sheff v O’Neill: Weak Desegregation Remedies,” Figure 5.1, p. 111






With this, we can make the data look like this:







Or, look like this:







When looking at these two charts, we see two different stories. By changing the scales, we see that there was a lot of progress towards the goal (chart 1) or that there was little progress towards the goal (chart 2). Those that use charts to show statistics can make the data look different based on the way scales are used. To make the data look different, I changed the Y-scale to 0-100% rather than 0-35%. By doing this, the data looks entirely different and tells two different stories. I created these charts through excel by putting data into a spreadsheet and then creating a chart according to that data. All people have to do with charts showing statistics is change the maximum and minimum values of the Y-scale and the chart tells something different.

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Literature Review

The topic I looked up was zoning and exclusionary zoning. Because zoning is a broad topic, I figured if I had trouble, I’d use exclusionary zoning as a search subject. The book I found for zoning is called Housing America: building out of a crisis. I think this will be a terrific book to look at because it talks about all kinds of housing barriers, not just zoning. This book is a collection of people’s writings about housing problems in America and has a wide range of topics (Holcombe & Powell, 2009) . The journal article I found is about exclusionary zoning and called “The Politics of Exclusionary Zoning in Suburbia” by Michael N. Danielson. I thought this article looked interesting because it deals with the complexities and politics of zoning laws. I found this article on JSTOR (Danielson, 1976). The next article I found was from the Hartford Courant that was about exclusionary zoning in Hartford and racism in the housing market. This article is from 1994 but can still be related today (Kauffman & Writer, 1994). The last thing I found was a website, which happened to be an article from Boston College. This article is about exclusionary zoning and is called “Abolishing Exclusionary Zoning.” It discusses the issues of exclusionary zoning and also a program to abolish it (Liberty, 2003).





Holcombe, R. G., & Powell, B. (2009). Housing America: building out of a crisis. Independent studies in political economy. New Brunswick [N.J.]: Transaction Publishers.

Danielson, M. N. (1976). The Politics of Exclusionary Zoning in Suburbia. Political Science Quarterly, 91(1), 1–18. doi:10.2307/2149156

Kauffman, M., & Writer, C. S. (1994, June 27). A COLORBLIND SOCIETY MAY BE NAIVE VISION DESPITE GAINS, PREJUDICE EXISTS Series: Reacting to Race in the Suburbs: [STATEWIDE Edition]. Hartford Courant, p. A1. Hartford, Conn., United States. Retrieved from

Liberty, R. (2003). Abolishing Exclusionary Zoning: A Natural Policy Alliance forEnvironmentalists and Affordable Housing Advocates. Boston College Environmental Affairs Law Review, 30(3), 581. Retrieved from


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Exclusionary Zoning

According to the West Hartford Zoning, by Robert Whitten, zoning is when a town is divided into different sections, and each section serves a different purpose. In West Hartford, for example, the town is broken into residence districts, business districts, and industrial districts.  In the residence districts, Whitten explicitly writes “all business and industrial uses of property are prohibited” (Whitten 1924, 10).  The business district will be for stores, offices, theaters, and restaurants. And the last district, the industrial district, will be for manufacturing. Zoning is “essential to the securing of a measure of orderliness in the building of the city” and “is the direction of building development along orderly and well-considered lines of city growth” (Whitten 1924). Economically, “zoning means increased industrial efficiency and the prevention of enormous waste” (Whitten 1924). For the people, “zoning means better homes and an increase of health, comfort and happiness for all of the people” (Whitten 1924). In conclusion, zoning is a way the town can prevent chaos and limit and control exactly what happens in the town.

In the West Hartford Zoning of 1924, each district has sub-districts, which has certain regulations. For example, in the residence district, there are five sub-districts. All of the sub-districts are allotted a certain square foot for each home; if it is a single family home or two family home in sub-district A or B, they are given more square footage than the sub-district C, D, or E is given. Also, certain types of homes are not encouraged to be in built in certain sub-districts, such as a three family home in district A, B. or C because of the lot areas; sub-districts D and E have square footage regulates that are intended for three family homes (Whitten 1924). By doing this, the town is regulating who can live in what area, because three family homes will be cheaper to live in than a one family home. The districts are divided into who will be able to afford what, therefore, zoning and segregating incomes of families.

The residence district of West Hartford in 1924 is an example of exclusionary zoning. Exclusionary zoning is “steering in the residential markets” and creating “restrictions or bans on multi-family development, minimum lot sizes, age-restricted zoning, and low density zoning” (Reece 2009, 14, 20). In each sub-district of the residence district, there is allotted lot sizes such as the height of the buildings, the square footage, etc. There are requirements for each of these sub-districts, and by doing this, they are determining who will be able to live in what area.

Today, exclusionary zoning affects Connecticut. In West Hartford, for example, the exclusionary zoning still exists today. In this picture below, the different colors show the different districts present right now.

MAGIC 2012

The grey zones in West Hartford are the areas where most of the business/industry are located throughout the town. The purple zones, if I am understanding the codes correctly, show the residential multi-family homes. From my knowledge of West Hartford, the areas around business and industry are mainly multi-family homes, especially the streets off of Park Road, therefore my conclusion of the purple zoning being residential multi-family homes shall be accurate. By all of the multi-family homes being close to industry and businesses, this shows exclusionary zoning; multi-family homes are not spread out throughout the town, rather near industry and business, therefore the town is segregating by income levels. If the town was not participating in exclusionary zoning, multi-family dwellings would be across the town; however, this is not the case and the multi-family homes are in little chunks in surrounding shopping and retail centers, close to the Hartford line. This picture is an accurate showing of exclusionary zoning and how it reflects in a town.


University of Connecticut Libraries Map and Geographic Information Center – MAGIC. (2012). Zoning Maps of West Hartford, Connecticut, 1924 to Present.. Retrieved from

Jason Reece, et al., People, Place, and Opportunity: Mapping Communities of Opportunity in Connecticut: A Report Commissioned By the Connecticut Fair Housing Center (Kirwan Institute for the Study of Race and Ethnicity, The Ohio State University, 2009),

Robert Harvey Whitten. West Hartford Zoning: Report to the Zoning Commission on the Zoning of West Hartford. West Hartford, Conn: Zoning Commission, 1924 (courtesy of the Connecticut State Library)

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Web Simulation

For my web simulation, my annual gross income was $24,000 with no savings for a down payment. Fortunately, I had no debt; however, this does not widen my choices in any sense, especially looking for a place to live with two apartments in the suburban area of the Hartford County. When I did the mortgage affordability calculator to estimate the purchase of a home I could afford, I calculated that the maximum house price I could afford was $72,827. Being from Connecticut and living in a suburban town of Hartford County, I knew this was impossible to find, so I automatically went to looking at apartments in suburban districts. I can only afford a monthly rent of about $666. Automatically I went to look at the town I grew up in, Farmington, and found that anything with two bedrooms was going to be easily over $800. Even a studio apartment was $875. I also looked at Avon, and Simsbury and found similar things; everything was going to be too expensive. Although I am searching for the best education in these suburban towns, I will unfortunately not be able to afford were mainly New Britain and Bristol. When I looked at the CMT scores of the towns I could afford, I was also surprised by my findings; Bristol for third grade was 83.1% proficient in math, 65% in reading, and 78% in writing. In sixth grade the proficient was even more promising; 86.6% in math, 82.1% in reading, 83.8% in writing. I actually anticipated lower results so finding this out was happy for me to see that my children would receive better education than they would have in Hartford. Unfortunately, in New Britain, the proficiencies were much lower; in the third grade, 48.6% were proficient in math, 43.1% in reading, and 52.8% in writing. For the sixth grade, the results were similar, 44.6% in math, 45.3% in reading, and 43.7% in writing. Luckily though, there were a few choices of places in Bristol with two bedrooms that met my budget. I was very frustrated at first because I could not find much in towns with a great education in the Hartford County, but it shows how difficult it is for those who want a better education for their children to move out of a place such as Hartford. This really opened my eyes to the reality of how much apartments cost, even ones that are a studio. I could not even afford to live in a studio apartment in a town such as Simsbury so I could have a better education.


My data was from:,0,855259.htmlstory?appSession=994214309191532

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