Tag Archives: maps

“Beyond the Hundredth Meridian” by Wallace Stegner


I put this book on my to-read list after seeing the Powell monument at the Grand Canyon a few years back.  Kind of a dated book in terms of some of the Western policy discussion (but a lot is still relevant) but at least it begins with a good adventure.

A surprisingly unimportant fact about John Wesley Powell, given all that he later achieved, was that he lost an arm during the Civil War.  Thereafter, his initial claim to fame was leading the first expedition which ran the Colorado River, from the Green River in Wyoming all the way to about present-day Las Vegas. (the river had been explored up from the mouth to that point already by others)  Each rapid was a plunge into the unknown.  When possible, they emptied the boats, carrying their supplies by foot to be reloaded at a safer place downriver, and gently lowered the boats by ropes.  But there were other times when no landings were available and they had to run the rapid blind.  At one such point near the end of the trip, a couple of Powell’s companions called it quits rather than face the rapid.  They were later killed by Indians while trying to hike out to Mormon settlements.

Afterwards, Powell led up a geographical survey which returned him to the same part of the country.  He often used Indian guides (sometimes through intermediaries like Jacob Hamblin) to find accessible paths to certain points along the river.   He became interested in Indian culture and ethnology, and eventually convinced enough Senators in Washington to set him up as the first head of the Bureau of Ethnology, under the Smithsonian.

Powell’s political wheeling and dealing was not nearly finished.  He must have been a very shrewd persuader because he frequently got funding through unusual means, like as an add-on rider to some larger appropriations bill.  Like the geological systems he studied, he figured out how Congress and government worked, and used that to his advantage.  He eventually became the second head of the US Geological Survey (after largely instigating its creation), leading the noble effort to map the country.

Powell became very influential on Western policy issues.  Powell’s firm belief was that the West was fundamentally different from the rest of the country due to aridity and thus should be settled differently and much more sparsely.  This was at odds with a view of a land of milk and honey (and gold, copper, and silver) espoused by Gilpin and Stewart.   Powell was eventually forced out of his position at the USGS by pro-settlement factions, but I believe he has been vindicated by history as many current policies seem to follow his initial vision.  He saw the most danger in the “in-between” lands — those places where, to the East, there is plenty of rainfall; to the far West is the desert, where there is no way to survive but by irrigation, but once established agriculture is fairly secure — but in the middle, a string of good rainfall years trick settlers into thinking the land is good, but there can just as easily be a string of bad years which induce famine and hardship.

“The Ghost Map” by Steven Johnson

In 1854, the working class Soho district of London experienced a particularly virulent outbreak of cholera.  The leading theory of the day was that cholera, and indeed most diseases, were caused by “miasma” or bad, stinky air.  Not too bad of a guess.  But some doctors noticed that for cholera (actually a waterborne bacteria) the infection patterns didn’t seem to support the miasma theory.  Data collection on disease and mortality rates and characteristics was in its infancy; physician John Snow was looking for data that would prove his theory of waterborne cholera.  He found it by mapping the cholera deaths and the victims’ nearest source of pump water.  By doing so it became quite clear that using a single water source, the Broad Street pump, was the biggest factor correlated with contracting and dying of cholera.

Kind of a cool story about one of the first examples of modern data-driven analysis.  Not many authorities believed Snow’s analysis; it took several years (and another outbreak) for him to be vindicated by history.  I admire Snow’s dogged determination, his sticking to the observable facts, and looking at things in a different way than anyone had ever done before.  The author mentioned Snow’s key to a bulletproof analysis: identify the trend, explain outliers (unexpected manifestations) and (equally important) explain the lack of expected manifestations.

Now, the history of this whole thing is cool and interesting.  But I had some problems with “The Ghost Map.”  For one thing, it is too long.  It’s actually a quick read, but there seemed be a lot of beating the same drum over and over about Snow and others discovering the source of the Broad Street contagion.  Then there is the book’s conclusion, where the author asserts that we should all move to cities, fund genetic research, and get rid of nuclear weapons.  Huh?  Some admirable goals, I suppose, but I didn’t quite make the author’s leap from 19th century epidemiology to these other topics.  My impression was that he now had the reader’s attention and wished to promulgate his personal opinions from that soapbox.  Kind of dubious; if he wanted to write on those other subjects then I think he should have stuck them in a different book.

City-Data Screen Scraper and Maps

I wrote a screen-scraper that extracts data from the City-Data website’s county pages.  Luckily, the format of the URLs and county pages themselves are mostly consistent, so it was easy to extract desired bits of data with regular expressions … well, easy after a bit of trial and error.  The input to the script is a .csv with county names, states, and FIPS codes.  The output is the same as the input file but with additional columns of data extracted from the webpage.  I extracted bits of data that I thought would be interesting and that were easy to get.
There are 3,141 counties or county equivalents in the US.  (At least according the the list I used, which is modified from the US Poverty Rate data from the last post).  The script took about 23 minutes to run – for each county it downloaded the webpage from the City-Data website, parsed it for the desired info, and added the info to the output file.  There were a few hiccups along the way due to the following:
  • Rather than counties, Alaska has Boroughs, Municipalities, and Census Areas.  Crazy Alaska!
  • Skagway-Hoonah-Angoon Census Area is now split up between the Hoonah-Angoon Census Area and the Skagway Municipality.
  • Broomfield County, Colorado was incorporated 2001 and City-Data doesn’t have a county page for it yet.
  • Counties that begins with “De”,  “La”, “Mc” – some discrepancies about spacing and capitalization.

Once the data was all extracted into the output .csv, I used the modified mapmaker script from the last post to make some maps.  Without further ado….

Population density per square mile

<1 <2 <10 <50 <100 <200 <500 <1000 >1000

This is a pretty familiar type of map.  Kind of can use this as a sanity check on City-Data’s figures and my plotting script.  Do a Google Image Search for “US Population Density” and compare.  Example: http://en.wikipedia.org/wiki/File:USA-2000-population-density.gif

Cost of living (100 = US average)

<75 <85 <95 <105 <115 <125 <135 <145 >145

I wondered how this map would compare to the poverty rate map from the last post.  Answer: not really.  Places with a higher cost of living probably have higher salaries.  (Hey, how about we plot that next??)

Lowest cost of living: King County, TX (68.4)

Highest cost of living: Kings County, NY (194.1)

And, no, not every county name is a variation of the word “king.”  🙂

Median household income ($)

<25000 <35000 <45000 <55000 <65000 <75000 <85000 <95000 >95000

This map in conjunction with the preceding cost of living map does show some similarities to the poverty rate map.  Areas with low median incomes, but mid-level or high cost of living, generally have higher poverty rates.  Kind of “no duh,” I know.  Notable examples which stand out: eastern Kentucky, along the lower Mississippi River, the Navajo reservation in the Four Corners area.

Lowest median household income: Kalawao County, HI ($12,591).  This is the former leper colony of Molokai.

Highest median household income: Loudoun County, VA ($111,925)  The DC area – your tax dollars at work!

Federal Government Expenditure per Capita ($)

<5000 <7500 <10000 <12500 <15000 <17500 <20000 <22500 >22500

So, how well are those Washington fat cats at sharing the wealth with the voters back home?

Top 5:  Falls Church, VA ($145,164), Fairfax County, VA ($138,060), Los Alamos, NM ($105,868), District of Columbia ($67,982), Arlington County, VA ($52,254).  DC area once again….

Percent Foreign Born Residents

<1% <3% <5% <7% <9% <11% <13% <15% >15%

The map of the percent of foreign born residents looks pretty much as you would expect – higher percentages along the Mexican border and in cities – NYC, DC, Chicago.  And pretty much all of California.  The “finger” stretching up the Texas panhandle into Kansas is kind of interesting.  Also surprising figures in eastern Washington.

Gender (Im)balance

<-5% <-3% <-2% <-1% <1% <2% <3% <5% >5%

The gender balance of a county is calculated as the (#females-#males)/(#females+#males).  So, a positive percentage indicates more females than males, and a negative one indicates more males.

The South appears to be female-heavy, while the West is loaded with males.  Maybe those two should get together…

Lowest: Crowley County, CO (-34.5%).  Apparently one-third of the county residents are inmates at the state prison.  This is likely skewing the numbers toward males.

Highest: Pulaski County, GA (14.9%)

Mean travel time to work (minutes)

<10 <15 <20 <25 <30 <35 <40 <45 >45

Low: Aleutians East Borough, AK (6.3 minutes).  Maybe they all live on their fishing boats?

High: Elliott County, KY (48.7 minutes).  Ouch.  I’m guessing it’s just as long on the way back home … that’s almost 2 hours per day!

Percent affiliated with religious congregation

<20% <30% <40% <50% <60% <70% <80% <90% >90%

There is a pretty surprisingly wide swing here, and it is definitely regional.  Definite concentrations in the Midwest and Texas, as well as the Mormons in Utah and Idaho.  The percentages tend to diminish towards either coast.

Low: Camas County, ID (1.8%).  This figure is kind of suspect if you ask me.  About 1000 residents, and only 18 attend church?

High: Falls Church, VA (164.5%).  Once again the DC area!  Hey, wait a sec City-Data…how come this is above 100%?  People going to more than one church?  There are about 40 counties total with the figure > 100%.

Caveat Emptor…

I have no idea how the data presented on the City-Data website was collected.  Could be correct or it could be wildly off.  In any case the interpretation heavily depends on the data collection method, as with anything of a statistical nature.  (Something that the general news media, and media consuming populace, fails to consider many, many times IMHO).

Related to the above – I found I could skew the look of the maps in different ways in the selection of the color scale.  If I wanted to “drown out” a few high outlier counties, for instance, I could set my high threshold low enough and presto, they’re gone and no one is the wiser.  Not that I have knowingly done that here … I tried to get the best scale that shows the overall trend.  But the reader should be wary of maps, statistics and the like – always ask yourself what the author is trying to get you to believe.

TastyCakes Map Maker

In the Wikipedia “Poverty in the United States” entry, there is a map of the US shaded with the overall poverty rates of each county.  It was made by user TastyCakes (thanks!) using this script, this blank .svg map of US counties, and data from the census bureau.

I modified the script a bit to include functionality for creating an HTML legend rather than a wikipedia one.  Here’s the census data reformatted to work with the script.  (Got rid of extraneous data and merged the state FIPS and county FIPS columns into one.)

Here’s the same map as on the wikipedia article, but in green.  (TastyCakes script has a green color spectrum option.)

<5% <10% <15% <20% <25%
<30% <35% <40% >40%


I converted the .svg to a .jpg using this online converter.

Cool, huh?  Now all I need is some interesting data per county in a .csv file, and I can make nifty maps!