After the jump is a new infographic that was suggested as a follow up to my post highlighting that three Detroit neighborhoods had topped the list of most dangerous places to live. Just as depressingly, this map pulls data from various sources to rank all 50 states based on the terrible things that could happen to you there. You can check out the original webpage here. Notably for midwesterners, Illinois and Michigan are ranked 40th and 44th respectively for murders per 100,000 people (with rank 51 being the worst and going to D.C.), and Illinois has the highest percentage of adults reporting poor mental health (according to a Kaiser Family Foundation analysis of CDC data). Obnoxiously, as you’ll see if you interact with the map, the states are color-coded by total number of murders and traffic fatalities, even though the title for those sections says “per 100,000.” To clarify the accurate info click “see the data behind the rankings.” Continue reading
A recent study by Dr. John Miller at Central Connecticut State University has been getting buzz lately for comparing census poverty data to his study of literacy rates in 75 metro areas. As reported in The Atlantic Cities, he found no correlation:
Using US Census data for income in the relevant cities, I learned that wealthier cites are no more likely to rank highly in literacy than poorer cities. For example, Cleveland ranks second lowest for median family income (among the AMLC cities) and yet, thanks to its great library system (ranked #1 in the AMLC) and strong newspaper (#6) and magazine (#5) circulations, it is ranked 13th most literate in the survey. On the other hand, Anchorage, AK is ranked 5th in median family income and only 61st in literacy.
Other notable cities that exemplify this finding are St. Louis, which ranks 70th in median family income but #8 in literacy; Henderson, NV (#7 in wealth and #53 in literacy), San Diego (#8 in wealth and #33.5 in literacy. While poverty has a strong impact on educational attainment, its impact on literacy is much weaker.
I noticed that one of Dr. Miller’s criteria for evaluating a city’s literacy was Internet Resources. This made me wonder: Does the same trend hold true for Internet resources? Is wealth irrelevant to Internet readership? I could only find the Internet-readership data for 2010 on his website, so I looked at that.
First, I looked at the two cities Miller mentions as outperforming their poverty level, Cleveland and St. Louis: Cleveland ranks 13 overall but 36 in Internet Resources, and St. Louis ranks 11 overall but 38 in Internet Resources. This data seems to support a stronger correlation between Internet literacy and wealth than overall literacy and wealth.
But what about the cities he mentions as underperforming in literacy compared to their wealth? Anchorage ranks 49 overall (in 2010) but 66.5 in Internet Resources, and Henderson ranks 64 overall but 66.5 in Internet Resources. Even these wealthy cities had worse Internet literacy than overall literacy, so it seems to me that wealth is not the driving factor in Internet literacy either. San Diego, however, the third wealthy city he mentions, ranks 10 in Internet Resources and 33.5 overall.
Here are the top 14 cities in terms of Internet Resources (14, so I could include Chicago):
|6||San Francisco, CA||12|
|6||San Jose, CA||56|
|8||New York City, NY||29|
|10||San Diego, CA||33.5|
|12||Kansas City, MO||14|
I recently used open data from HUD about public-housing inventory and Census population data to examine which states had the most Section 8 housing units per person in 2010. (Section 8 is a federally subsidized program for low-income renters.) Here’s the top 6 and bottom 6:
I’m not surprised that DC is number one, since it has the nation’s highest poverty rate. Nor am I surprised that NYC, with its relatively well-organized housing initiatives, is number two. But what about Massachusetts and North Dakota? Any ideas? Here’s a visualization of this data:
In honor of Martin Luther King Day (albeit belatedly), I want to highlight some Census Bureau data about the U.S. Black population. This information comes from a 2010 Census Brief, published in September 2011, entitled “The Black Population: 2010.”
To me, the most noteworthy data in the report shows that Black populations in the North are concentrated in metro areas, especially in Michigan. In some cases, a county has a relatively low percentage of self-reported Black or African-American people, but a metro area within that county has a large percentage. This trend may not be surprising to some people, but I think the numbers are striking.
Apparently, so did the Census Bureau, which noted this trend in the report’s conclusion:
The Black population continued to be concentrated in the South and the proportion increased from 2000 to 2010. Additionally, the Black population that lived outside of the South tended to be more concentrated in metro areas. Other interesting geographic patterns include, for the largest 20 metro areas, the non-Hispanic Black alone population was more likely to live in a largest principal city relative to the non-Hispanic White alone, Hispanic, and other race group populations in 2010. The non-Hispanic Black alone population also experienced the greatest declines in the proportion living in a largest principal city from 2000 to 2010.
As shown by the Bureau’s charts, many Southern counties have large percentages of Black Americans, but in the North the percentages are relatively low.
Let’s use Michigan as an example. There, the percentage of Black population for any given county never reaches above 50%. In fact, outside of the Detroit area, it never gets above 25%.
Yet a few pages later, the report includes a fascinating table ranking the places with the highest percentages of Black Americans. According to this table, Detroit’s percentage of Black Americans (including those in “combination”) is 84.3%. And in Flint, Michigan, is 59.5%. The differences between these metro percentages and the percentages for the counties they are located in are huge!
So what’s this mean for city managers? I don’t have all the answers to that question. But I know one thing: Many counties are facing drastically differing demographics within a relatively compact geographic area. For example, Genesee County, Michigan—where Flint is located—has an estimated population of only about 425,000 and a size of 649.34 sq. miles (the mean size of a county in Michigan is estimated to be 1178.19 sq. miles on Wikipedia), yet Flint is nearly 60% Black, and the county is less than 25%.
To me, this data also shows that, in Michigan at least, there remains a color-line, and we need to remain vigilant about realizing Martin Luther King Jr.’s dream. For another interesting article on the history and current state of race in Michigan cities see Craig Ruff’s recent article in Dome Magazine.
The U.S. Census Bureau website is one place cities can find data about their populations. Every Tuesday, I plan to highlight some aspect of Census data that may be particularly helpful to local communities. This week, I’m going to lay out some basic information provided by the
- There is an impressive mapping feature that showcases data about population, race, age, sex, and housing status—all the way down to the “census block” level (and also at larger divisions, such as townships, congressional districts, or the state as a whole).
- There is another helpful map (go down the page to “Redistricting Data”) that charts population change for 2000-2010 by race for states and counties, and another map (same page under “Apportionment Data” that lets you see changes in population and population density every ten years since 1910.
- Although not on the U.S. Census Bureau website, the New York Times has used Census data (from the Census Bureau’s American Community Survey) to create beautiful infographics covering race, income, housing/families, and education. I particularly liked the data on median monthly rent, changes in mortgages consuming over 30% of income, and percentages of high school/college graduates.
This data provides a treasure trove for analyzing national and local trends. I hope to provide more in depth analysis in future posts.