Mapping 25 years of CO2

I went on vacation over the weekend, so my content is a little bit behind, but I’ve got a pretty interesting re-blog today from Jeremy Williams at Make Wealth History (another WordPress blog). I’m posting this for two reasons: 1. the graphic from ESRI summarizes a pretty important and complex issue (CO2 emissions) in a simple and illustrative way. 2. Jeremy’s post at once recognizes the importance of the ESRI graphic and calls our attention to the fact that developed countries, even if they are “in the green” don’t always have much to be proud about.

So, I hope you enjoy this post. More analysis of diversity in U.S. cities will be coming soon!

The Earthbound Report

The mapping and information company ESRI have just released this rather striking image. It shows the change in carbon emissions by country since 1990. Green triangles show the countries that saw fall in emissions over that quarter century, thus meeting their Kyoto agreement targets. Red triangles show a rise.


Yes, that’s China. Hard to miss it. In fact, it’s impossible to see the rest of Asia’s emissions, buried as they are under that whopping red triangle. (If you want to see India’s emissions and China’s neighbours, visit the interactive map and zoom in)

Of course, we’re talking growth in emissions here, not total CO2 emitted – such a map would be less generous to the developed world. China started from a low base here. 60% of its population were living in serious poverty in 1990, a percentage that was driven into single figures in a remarkably short 20 years…

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Top 10 Most and Least Diverse U.S. Census Tracts

Diversity in the Real World, Following Up on the Parable of the Polygons

In December, I introduced the Parable of the Polygons, an incredibly engaging and revealing bit of computer-generated game theory (read: fancy graphics with shapes that move based on laws of social behavior), designed to illustrate how segregation can happen in our cities.

This got me curious about how segregation happens in our cities today, and what it might look like to find a neighborhood that was truly diverse. In this vein, I’ve compiled data from all U.S. Census Tracts with populations larger than 500 and created an index* which ranks each by its level of diversity. Above, you can see a map of the top ten most diverse (green stars) and top ten least diverse (red teardrops) tracts in the U.S.

Here are the most diverse:

Rank Diversity (0-1) State Neighborhood Tract
1 0.9626 AK Mountainview, Anchorage 6
2 0.9552 AK Airport Heights North, Anchorage 9.01
3 0.9128 AK Russian Jack Springs Park North, Anchorage 8.01
4 0.901 WA Portland Avenue Park Area, Tacoma 9400.07
5 0.9081 NY South Ozone Park, Queens 100
6 0.9076 NY South Ozone Park, Queens 96
7 0.9023 NY South Ozone Park, Queens 840
8 0.8973 NY South Ozone Park, Queens 838
9 0.8959 NY South Ozone Park, Queens 846.01
10 0.8898 NY E. Jamaica Estates, Queens 478

Here are the least diverse:

Rank Diversity (0-1) State Neighborhood Tract
1 0.0126 VA N Buchanan County 102
2 0.0132 TX Maverick County, near Eagle Pass 9502.04
3 0.0225 PA Central Cambria County/Beaverdale 131
4 0.0229 OH NE Holmes County 9763.02
5 0.0235 WV Western McDowell County 9539
6 0.0239 KY SW Breathitt County 9205
7 0.0241 VA SW Buchanan County 107
8 0.0256 AZ SW Yuma County, near San Luis 114.06
9 0.0258 TN NW Claiborne County/Clairfield 9704
10 0.0258 NY East Midwood, Brooklyn 754

More analysis will be following, but in the meantime I’ll let you do your own analysis of these results.

*Index is based on an entropy index as described in academic literature. My method grouped U.S. Census data into 6 racial/ethnic groups: White, Black, Native American, Asian/Pacific, Other/Multiple, and Hispanic. The proportion of each of these groups per tract is then put into the following formula:

Where p equals the proportion of each group of the total census tract population

I realize that this is not a perfect representation of diversity, but perfection must be balanced with ease of use.

Lies, Damned Lies, and Statistics

Why you should always be willing to ask questions about what is presented to you.

I’m currently working on a post around Freakonomics’ latest podcast, “How Efficient is Energy Efficency?” I will have that post up sometime in the next few days, but in my research for the post I ran across an insight that I thought was important to share about how we present and challenge information. The podcast centers around the work of Arik Levinson, an Environmental Economist at Georgetown University. Two of his papers in recent years have focused on energy efficiency regulations for housing in California. The standard narrative is that because California enacted energy efficiency standards for all new houses starting in 1978, it has lead the nation in electricity reduction. This narrative is represented most clearly by the following graph:

Residential Electricity Per Capita. From Original Academic Article Here.

Residential Electricity Per Capita. From Original Academic Article Here.

Wow! Look at that difference and how dramatically California separated from the rest of the nation right in the 1970s!

People have gotten really excited about this graph. If you want to see how many groups have referenced it, take a look at the Google Image results for searching “California 1970s energy efficiency.” The National Resources Defense Council, Scientific American, and the World Bank (on page 215) are just a few. Each of these organizations has made California and their energy efficiency standards out as some major hero. I am not going to cast doubt on the accuracy of these numbers themselves and so, I believe, they display some useful truths about the nature of California’s energy consumption profile. However, no matter how much truth is represented in this graph, I believe that it serves to cloud the discussion rather than illuminate it. Continue reading