Saturday, August 11, 2012

2000 US census

2000 US census,Clearwater County was one of eight counties in the state of Idaho that lost population from 2000 to 2010.
According to new figures released by the U.S. Census Bureau, Clearwater County’s population fell 1.9 percent during the ten year period, losing 169 residents. Other counties that lost population were Butte, Minidoka, Clark, Caribou, Bear Lake, Elmore and Shoshone.
Idaho County increased 4.9 percent, Lewis County increased 2 percent, and Nez Perce County was up 5 percent.

The migration from rural to urban Idaho continued unabated, as 80 percent of the state’s population growth over the decade occurred in the 11 metropolitan counties.

The Boise, Idaho Falls and Coeur d’Alene metropolitan areas posted growth rates above the statewide rate of 21.1 percent. Lewiston and Pocatello, which showed little growth in the expansion but did not seem to be hit as hard during the recession, recorded single-digit percentage increases. Lewiston gained 3.2 percent, with a 2010 population of 31,894 residents.Glancing at that map, we’d think California, Texas, Florida, and New York were horribly dangerous places to live, and that there was hardly any crime at all committed in New Mexico. The problem is that we’re basically looking at a map of population. Of course, the more people live in a place, the more crimes (all else being equal) will be committed there. Here’s a population map for comparison:That’s why when you see data on crime, it’s typically crime rates — crimes committed per 100,000 people, for example. This map shows crime rate by state.Now, a distinctly regional pattern emerges — crime rates are actually lowest in the heavily populated Northeast. This is data worth mapping, but it shows the importance of choosing the right data to map — and of asking, when we’re presented with a map, whether the data behind it were well-chosen.
You might also note that I used red — blood-red, in fact — to indicate a high crime rate. It’s always worth noting the colors used on a map. You’re going to respond to those colors viscerally whether you mean to or not! Once you’ve recognized what the colors are and what they imply, you can, if necessary, dismiss them.
Another way maps can distort data is by choosing the wrong scale. In the maps above, I used darker shades of a single color to indicate more of something. That’s a common convention, and it’s easy to read. The question is, how much more does a darker shade represent? It’s important to read the key, and it’s important to keep telling students always to read the key, because that’s where the answers are. Here again, though, a map can make a powerful visual impression that we may have to work hard to correct.
In drawing my map of crimes committed, I had a choice of scales. I could choose a linear scale, in which each darker shade of color represented an equal number more crimes (say, 1,000 more). Or, I could choose a logarithmic scale, in which each shade darker represented an equal number times as many crimes (e.g. twice as many).
It’s usually best to choose a linear scale, because logarithmic scales confuse most people. For example, the Richter Scale, which measures the intensity of earthquakes, is a logarithmic scale, so that 6 on the Richter Scale means a quake 10 times as powerful as one measuring a 5, and so on. Logarithmic scales are great for distinguishing among very small things and among very big things. Richter 3 isn’t noticeable, while Richter 9 — just six steps up the scale, but a million times more powerful — will destroy a city. But logarithmic scales are not as intuitive as linear ones.
Try this example. The January 2010 earthquake in Haiti measured a 7.0; the quake six weeks later in Chile measured an 8.8. How much more powerful was Chile’s earthquake? See the footnote for the answer.1
In mapping crime rates, I used a linear scale with a break in the middle. Because state-by-state crime rates fell into a fairly narrow range, I needed only six shades of red for a linear scale. But absolute numbers of crimes ranged from 1,000 to more than 200,000. I wanted to distinguish between states having 5,000 crimes and states having 10,000 crimes — twice as many is a big difference — but a linear scale with such fine distinctions would have required more than forty shades of red. So I used a logarithmic scale, roughly doubling the number of crimes at each shade darker.Scales are rarely chosen with an intent to confuse; in fact the mapmaker often has little practical choice. But they can, nevertheless, cause confusion if the reader isn’t paying close attention.
Trouble arises quickly when you pair maps with different scales. I’ll pick on myself this time.
In compiling these maps of North Carolina’s changing demographics, I and LEARN NC’s graphic designer used shades of blue to represent population characteristics of North Carolina counties over time. Most of what we were representing was percentages, so we were nearly always able to use a linear scale. Here’s a map showing percent urban population of each county in 2000:When it came to mapping Latino population, though, we faced a problem. In 1980, there were very few Latinos living in North Carolina, and so to show geographical variation, we needed a scale that would show the difference between, say, 1 percent and 2 percent. But by 2000, Latino population had grown tremendously, and so our scale needed to go as high as 20 percent. The only way to show big numbers without losing fine distinctions among small numbers was to use a logarithmic scale.

2000 US census Rating: 4.5 Diposkan Oleh: Arm Aritn


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