Final Carto Project: SAT Scores

The caption for this map is

State mean SAT scores and SAT participation rates. In general, as a greater proportion of a state's high school graduates take the SAT, the state mean SAT scores fall, and vice versa.

This map was our final project. We were given a link to a table with either ACT scores or SAT scores, and state participation rates. We had to pick SAT or ACT, create the map from the data, and include both scores and state participation rates. I typed the data into an Excel file, joined the Excel file to a state boundary file in ArcMap, and once I had symbolized the mean SAT scores on a choropleth map, exported it as an Adobe Illustrator file so I could position the state participation data as numbers on each state.

Initially I downloaded from the web a free, recent TIGER census state boundary file to join to the SAT data. This worked really well right up until the point in Adobe Illustrator when I was almost finished with adding the participation rates and discovered that Michigan was unrecognizable because the TIGER folk had generalized the state's boundaries to include (i.e. to swallow) Lake Michigan. Concluding that the average newspaper reader would be confused too, I discarded the map and after some unsuccessful web searches, hunted down an ESRI shapefile at my local university's (UNC-Chapel Hill) GIS data depository.

The process for this project was fairly straightforward. I am least pleased with the size of Alaska. I wanted everything to the same scale, and I know it is supposed to be bigger than Texas and California (and a few other states) combined, but I now think the map would have been improved if I had scaled Alaska down a bit to match the size of the rectangle around Hawai'i. I put Hawai'i and Alaska in separate data frames so I could manipulate them into the same layout; I tried to exclude them on the main data frame with a definition query but failed, so I moved the mainland on the screen to remove them instead.

This map is an interesting example of how data left out can mislead. Without the state participation rates, one might understandably assume that for some reason the high school students on the east coast were dullards and those in the midwest and south - except for Texas - were a bright bunch, or at least had better education through high school. Adding participation rates is clearly an improvement, although more information overall would be useful so we might know just who writes the SAT in each state and how they compare with those in other states.

Week 11: Using Google Earth

This week our task was to locate a suitable site for a wind farm somewhere in Ohio and then put a placemark at that spot in Google Earth. We were given links to information about criteria for suitable sites, and links to some maps.

Fundamentally, the wind turbine site has to have a minimal average wind speed. Additional criteria mentioned on the BERR website include landscape and visual impact (related to tourism though not entirely), ornithology (Bird migration patterns), noise (related to distance from dwellings), shadow flicker (ditto), and shipping impact (here, around the Great Lakes).

I started by downloading a wind map. I first found the 80m wind power map of Ohio produced by the US Department of Energy’s National Renewable Energy Laboratory. Later I found a 50m version (better resolution) of the same map, also by the NREL, dated 2004. In addition, the summary report by the Health Assessment Section of the state’s Bureau of Environmental Health (March 2008) notes that the “best areas for utility-scale wind turbines are Lake Erie off-shore, the Lake Erie shoreline, and topographically-elevated portions of….Logan, Champaign, and Hardin Counties” (p. 2).

After looking at maps of the Great Lakes shipping routes and at another map of Ohio state parks, many of which are along the Lake Erie shoreline, I decided that siting wind turbines in Lake Erie was likely to be problematic not only for shipping but for tourism reasons – there would also be considerable recreational boating in the area - and wind turbines here would therefore probably be subject to considerable opposition. I returned to Logan, Champaign and Hardin counties.

Next I pasted together three small printed maps of census 2000 block group population, one for each of these three counties: I got the information from the FactFinder section of the census.gov website, which allows you to map a variety of demographic characteristics, including population, down to the block group level, and which I have found very helpful in the past. This gave me a general idea of areas to avoid: in each of these counties there is a population concentration more or less in the center of the county.
On to the pasted-together county maps I did a sketch of the areas on the NREL map that had the highest wind power classification (6.4 – 7.0 m/s at 50 m).

I consulted the npwrc.usgs.gov website about migratory patterns of birds, but those were so general that all I could tell was that some birds would be migrating across this section of Ohio.

USGS.gov has downloadable topographic maps, which would have helped to determine the hillier areas in the counties, but unfortunately the maps are so tiny for the amount of detail on them that it is almost impossible to determine where the high elevations are. I did conclude that it would be better to have the wind turbines on the west or southwest side of hills, partly because the wind will be coming from that direction and partly because there will be more shade to the north of the turbines; but I am not confident that my final choice is actually on a hill because I couldn’t make out the topography in Google Earth or on the downloadable topo maps - I don't have the experience using Google Earth to make out hills, and I don't think Ohio has many hills that are too steep for growing crops in regularly-shaped fields..

I also checked a map of state parks. Many of these are on the Lake Erie coast, as mentioned, but there is also one in Logan and one in Champaign county, each of them in the northwest portion of the county. The wind power areas from the NREL map were chiefly in eastern and northeastern Champaign county, in eastern Logan county, and in a couple of spots to the south and west of Hardin county.

My final site is in southern Hardin County, a little north of the border with Logan county. It is in a rural area, not close to any parks or any large population centers. I think this section is a bit higher - at least it's supposed to have higher wind speed - but I can't be certain. The problem with dealing with Google Earth and GIS maps - at least in this instance - is that although the resolution of Google Earth is very good, we don't have the other, lower-resolution GIS data superimposed on it, so at least in my case, the final wind farm site may not be in the best spot. I'm hoping the people who actually make these decisions do have integrated data! I do think Google Earth is a fabulous tool and has marvellously specific resolution which I have used on a number of occasions, and I know there will be other interesting things to do with it in the future.

Week 10: Contour maps

This week we had to draw contour lines - they are for yearly precipitation for the state of Georgia - on a map in Adobe Illustrator. First we had to draw the contour lines by hand, using a paper map that had point data from dozens of spots, about one per county (Georgia, it turns out, has 159 counties). We had to interpolate the point data manually, eyeballing the lines from point to point, in order to create the contour lines. Then we were to transfer the lines from the printed map to the map in Adobe Illustrator.

I thought the process of creating the contour lines was tremendously interesting, as well as a challenge, especially where the data went down and then up and down again - since rainfall inconveniently does not go smoothly in one direction up (or down) across the state. I found myself resorting to a beautiful 1970s book of maps of British Columbia for a better look at how one deals with the ins and outs of contour lines; this in turn reminded me pleasantly that I've been fascinated by maps for a really long time.

As usual, Adobe Illustrator provided its own set of challenges. I had hoped to color the spaces in between the lines, but my technical skills in Illustrator aren't yet up to the job and I gave up after I got the easy parts (the closed loops) done. The map still has problems. I made a mistake somewhere and couldn't correct it: even after selecting every one of the 159 counties and clicking on the "stroke" icon to correct the county outlines, I was unable to get several counties to match the rest exactly in outline color: you can see this at the south end of the state. But it is recognizably Georgia! and the contour lines are there, so that's how it stands.

Week 9: Flow Maps

For this week's lab we had to take a world map in ArcGIS, decide on its projection, and transfer the map to Adobe Illustrator to add flow arrows showing the number of people gaining legal permanent residence in the U.S. in 2007 from each continent.

After printing and rejecting several projections, I decided on Buckminster Fuller's icosahedral one (similar to gnomonic apparently) because it preserves shape well and the continents weren't tremendously distorted in size, and because I've always been intrigued by Fuller and this was my first opportunity to use an invention of his! It did however complicate my north arrow positioning, with which ArcMap did not help me. I am afraid that the north arrow in Illustrator is the result of me blindly aiming for the North Pole.

This time I found the Illustrator videos were quite helpful - having the narrator not rush the words made a big difference. As for the data, I separated arrows for Canada from the flow from the rest of North America because they were coming from two different directions. I chose not to include the GIS file of all the individual U.S. states, since I thought they would only clutter a map that does not display which states are the particular destinations of immigrants.

Dot Density Maps

This week we had to take some Florida population / housing unit / land area data, in Excel calculate housing density (I used housing units per land area rather than per total area) and create a map of housing density, using Adobe Illustrator again.

I ended up creating two maps because we had a lot of confusion about which we should be doing, raw housing unit (or population) data versus density (units per area). I wanted to see how different the maps would look. In the end I haven't really answered this question well because in the density map for some reason I didn't position the dots as randomly as in the housing units map. The dots in the housing units map are also very slightly larger than those in the density map - 0.8 pixels instead of 0.7 pixels each. I had tried selecting all the dots and then transforming them "Each", but when I did this with large transformations the dots moved as well as expanding/contracting, so I haven't really solved this problem. Overall the maps look quite similar in terms of county-to-county relative amounts of dots, despite differing distributions from map to map within the individual counties.

I feel more comfortable with the housing units map, or at least with the idea of mapping raw data with dots rather than density with dots, but I could see mapping density at the county level if the data were census tracts or something finer than county level. I would like to try this exercise out in ArcMap. I haven't yet learned how to make a template (assuming this is possible) in Illustrator, which may be why not all of my map pieces are identical - the north arrow is in a slightly different place in each, for instance.

Other than that and my gradual lessening of alarm while in Illustrator, I learned how to make a north star a la the cartography text, which pleased me.

Week 7: using proportional symbols

This week we had to take a simple map of European countries in ArcMap and export it to Adobe Illustrator so we could add proportional symbols in Illustrator. The data came from an Excel file of wine consumption for each country. We had to convert the figures for wine consumption using the formula for representing values via circles (it involves square roots), and represent each country's wine consumption with a circle of the appropriate size.

This went pretty well, a bit to my astonishment but now I have a better idea of how to organize my layers and am a bit more likely to be able to undo unintended errors. In Adobe I did have two peculiar unwanted rectangles that refused to be selected (and hence, to go away), but fortunately they disappeared from the final map when I exported it to a .jpeg. Next time we do something in Adobe Illustrator I'll try making a decent north arrow. If I had more time I'd put the Excel info into ArcMap and see how different that map looked, but I feel pressed for time. I still feel a bit at sea in Illustrator, although I think I can spy land.

Week 6: Using Adobe Illustrator to make cloropleth maps

For this week's assignment we first had to create a simple color cloropleth map of population change state by state in the U.S., open the map in Adobe Illustrator and make a few more changes as needed. This went reasonably well until the Illustrator part, although I also had problems with projection because we had to include Hawaii and Alaska on the map. Eventually I put Alaska in a different data frame (and different projection). The map ended up having about three different data frames and I had trouble arranging them all.

For the second, grayscale, map, we had to open an Excel file of the 1990 and 2000 population figures for each state, calculate the percent change in population for each state, and aggregate the data into averages for each of the regional census divisions (there are 9 of these). The range of values for this second map had to be categorized on an equal interval scale, unlike the color map (map #1), the data for which was categorized according to natural breaks. Then we had to open the first map in Adobe Illustrator, convert the colors for each state to grayscale, and assign the correct color on the grayscale scale to each state, according to where it sat in the census division region. This tinkering also included redoing the numbers for the legend. This wasn't quite as bad as I thought it wuold be, as I am now figuring out how to organize layers and objects in Illustrator, more or less.

Only map #2 (grayscale) is in landscape format, but #1 will have to stay in portrait orientation for now, as I only managed to save the file for #1 in the right format when I was halfway through the changes to the map to make it into #2!