If you are a fan of the TV show Arrested Development (and you should be!), you are lucky enough to have not one, but two snazzy graphics to help visualize the laughs. The main idea with both of these is to show how individual jokes continue through many episodes. Click on the image to go to the interactive graphic. Which one do you like best?
A few years ago I had an idea for a smartphone app for homeowners considering installing rooftop solar panels. The homeowner would climb on top of the house and place the smartphone the roof. The app would use the phone’s gyroscope and compass to calculate the orientation of the roof’s plane in space, much like a geologist uses a Brunton compass to measure strike and dip of rock strata. Knowing the orientation and location of the roof, the app could make an estimate of solar insolation and help the homeowner work out the economics of the decision.
If this is such a good idea, why am I telling you all about it? Because I fear that it will soon be obsolete, and high resolution remote sensing data will be used to investigate initial feasibility of rooftop solar.
That is exactly what a website called Mapdwell Solar System is doing, at least in Washington DC. This company uses LiDAR to produce a very high resolution digital elevation model of the urban landscape. They use an algorithm to find all the rooftops, and for each one square meter area, they calculate the solar potential. The LiDAR data is so detailed that they can calculate the slope and orientation of roofs, as well as shading that occurs from buildings and trees. These one square meter pixels (color coded so that bright yellow equals higher solar potential) are overlaid on a satellite image of the city. Here’s what it looks like:
The white circles are existing solar rooftop units. To illustrate how this works, I decided to use Mapdwell to figure out what EPA could do with rooftop solar. I zoomed in and selected the headquarters complex (WJC Building and EPA East and West) in Federal Triangle. Here’s what it looks like:
The yellow areas are where Mapdwell recommends putting solar photovoltaic panels. Bright yellow, the optimal solar locations, are concentrated on the south facing parts of the roof. The most interesting part is the report generated that estimates cost of the system, federal tax credits, power output, payback time, and more. Here’s a summary from the EPA example:
You can see that the system would cost about $10 million, but federal tax credits would reduce that by about $3 million. (Can federal buildings claim this tax credit?). It would have a capacity of 2.4 MW, and generate revenue from solar renewable energy certificates (SRECs) as well as offset electricity purchases from the grid. And the payback time would be 7 years with an internal rate of return of about 14%. That sounds like a good investment. It would also offset enough greenhouse gas emissions to be equivalent to planting 12,000 trees.
This is a very powerful tool. Unfortunately it’s only available in Washington, DC, and Cambridge and Wellfleet, MA, but hopefully your neighborhood will be covered before too long.
Last week I wrote a post about Ukraine’s stark regional differences in language, ethnicity, and politics. There was a fair bit of interest, so I found some more data on regional demographic, ethnic, linguistic, and economic indicators in Ukraine and played around a bit in Tableau. I produced this interactive graphic, which illustrates the results of the 2010 presidential election for each region and presents some indicators that were correlated with support for Viktor Yanukovich (native Russian speakers, ethnic Russians, urban population, and average wage).
I hope to play around some more with these regional data. What sorts of illustrations would you be interested in seeing?
And if you just can’t get enough of Ukrainian regional geography, this guy is the man!
If your own mother does not read your blog, you know it’s time to pack it in. Fortunately, my dear mom is a faithful reader. She works in the geriatric care management industry – basically coordinating medical care for elderly patients – and asked me if I could put together some visualizations of old age in America. I thought it would be a good idea to start with figuring out where exactly older Americans live. This turned out to be a good chance to play with CartoDB and US Census Data. See the end of the post for more details on how I put these maps together, and click on the map images to access the zoomable, clickable CartoDB versions. So here you go mom, this one’s for you.
About 14 percent of Americans are over age 65. But, as you can see from the map above, they are not evenly distributed across the county. At the county level the percent of residents over 65 varies from 10 percent to almost 50 percent. You can see that the Great Plains, parts of the west, northern Michigan and Wisconsin, and Florida all have concentrations of these older counties.
But if we are interested in where the most elderly people live, the percentage of population over 65 is really not the most instructive. After all, many of the counties with high proportions of elderly people are rural counties with low population densities. A more instructive metric would be the number of people over 65 per county per square mile, or the population density of elderly. We can get this by multiplying, in each county, the percent of people over 65 by the total population density. That’s what the map above shows. It looks a lot different than the map of elderly population by percent. You can see that even though areas like the northeast corridor (Washington DC – Boston) do not have an especially large share of elderly residents, the elderly population density is high simply because the total population density is high. Perhaps the most obvious conclusion we can draw from this map is that there are an awful lot of elderly people in the state of Florida.
Finally, as an illustration of the sort of things you can do with CartoDB, I made a map (above) that shows counties with relatively high densities of elderly residents and below average median household incomes (click on the image and zoom in to see the smaller urban counties). This might be useful if you were interested in areas where social services for the elderly might be best directed.
Technical Notes: Finally, just a word or two about how I made these maps. First, I downloaded data from the U.S. Census QuickFacts service. With these data you can produce an Excel spreadsheet with county-level attributes on demographics, race, poverty and incomes, and business. I generated another column of population density of residents over 65. Next, I downloaded a shapefile of U.S. counties with state and county FIPS codes. I uploaded both of these into CartoDB and joined the tables by matching the numeric FIPS codes. Finally, I used CartoDBs widgets and filters to produce the chloropleth maps above. To see the maps in CartoDB, click on the images. Click on individual counties to see selected demographic and economic attributes for that county.
There is no shortage of commentary on the Affordable Care Act, and no shortage of maps and charts to visualize all sorts of aspects of the U.S. health care system. But I recently came across this interactive tool, which I think is worth singling out. It comes from a company call Value Penguin, and despite the silly name they have put together a great visualization. You can spend lots of time exploring, zooming in on different regions, and playing with different scenarios. For example, go to your county, enter in your age and income, and see if you are eligible for a subsidy and what the cost might be for one kind of plan.
Here are a few screen shots to illustrate a some broad patterns about health care and the ACA:
The map above shows the percentage of uninsured by county in the U.S. You can see that there are more uninsured in the south and west, and fewer in the north central and northeast. The uninsured rate varies greatly. Presidio County in Texas has an uninsured rate of over 37%, while in Norfolk County in Massachusetts only 3.1% are uninsured. In fact, Massachusetts stands out as having extremely high rates of insurance coverage. They can thank the law signed by then-Governor Mitt Romney for that.
Here we see a map of the number of companies offering insurance plans in the Affordable Care Act exchanges by marketplace. This also varies a lot. The Denver metro area has 10 companies offering plans, while most of Mississippi has just one.
You would expect the number of plans to affect the price of insurance on the exchanges, since, all other things being equal, more companies competing on price would drive down the cost for consumers. And indeed this is somewhat apparent in the above map showing the price of a silver-tier plan in the exchanges by county. Look at southern Arizona for example. The counties that have more companies offering plans have lower costs than the surrounding counties. It would be instructive to view a scatter plot of plan cost versus number of companies, so see how strong this correlation really is. You can also see that there are many other factors impacting cost aside from the number of companies. Vermont stands out as having very high costs relative to the rest of the country, despite having an average number of companies. I would suspect that state-level insurance regulations play a role here. What other factors might impact the cost of plans?
It was another snowy and messy day in the mid-Atlantic. The federal government even decided to close offices in the DC area. Here is an image from earlier showing the storm stretched out along the east coast as it moves off to sea. You can also see some nice lake effect snow to the east of Great Lakes.
I love following the storm forecasts and tracking the weather as it moves through. There’s no shortage of websites where you can do this, but one of my favorites, and certainly the one of the most most visually pleasing is called Forecast. It’s a weather app that works on your computer or smartphone, and, among other things, produces beautiful radar images and animations. You can view and toggle the radar animations to look into the past or at future predictions. For example, here is a video of me playing with Forecast this morning. I start of the present (red line), go back in time a few days, and then let the animation play to show the approaching storm. After the red line the animation shows predicted weather patterns.
The developers of forecast have another tool called Quicksilver, an ambitious project which will be amazing if it works. The idea is to use many sources of weather data, including satellites, ground stations, and weather models and combine it with local topography and microclimates to create an extremely high resolution global temperature model. So you might know that the temperature will be 50 degrees in town, but this tool allows you to predict what the temperature would be 25 miles away in a particular valley in the mountains. The image below shows a visualization of the Quicksilver data in the southwest United States. You can see that it’s 90 in Phoenix and 50 in Flagstaff, but you can also see lots of interesting details like the cold patch on the north rim of the grand canyon and the warm bit that follows the floor of the length of the canyon. You can download this data an enormous raster file and do all sorts of things with it. The big unknown is how well it actually works. I’m not sure if the developers have some sort of ground truthing program, but I think that’s needed.
Finally, the makers of Forecast have an iOS app called Dark Sky. It’s supposed to use their forecast models and your location to send you a warning just before precipitation is expected to hit your area. It’s a great idea, but the value is all in the quality of the execution. Does it really work. It’s a paid app and I have not yet shelled out the money to find out for myself.
Note: I now have a new post on Ukrainian political geography complete with an interactive graphic.
If you’ve been following the news, you know that Ukraine is experiencing mass protests and civic unrest. The situation seems to be coming to a head today, with riot police threatening to break up demonstrations in Kyiv and President Viktor Yanukovych talking about meeting with opposition leaders. The protests were triggered when Yanukovych backed out of signing an agreement with the European Union that would increase trade and political cooperation. Things got worse when police beat some unarmed protesters last week. Ukrainians are generally fed up by lack of economic opportunity as well as pervasive corruption, and many seem happy to take to the streets.
Why would Yanukovych refuse to sign the EU agreement after previously promoting it, knowing that it would make a lot of Ukrainians unhappy? Well, the standard answer is that Russia put enormous pressure on Ukraine, including threatening economic retribution. And that’s true. But to grasp why Yakukovych felt comfortable making this decision, and why Russia has such an outsized influence on the country, you have to understand how Ukraine is ethnically, culturally, and linguistically divided by geography.
The map above shows the percent of ethnic Russians in each of Ukraine’s oblasts (regional administrative units. About 17 percent of Ukrainians identify as ethnic Russian (2001 census), but they are clustered in the east and south of the country. There is a very sharp drop off in the number of Russians to the north and west of this dividing line, for example from 25.6% in Kharkov Oblast to 7.2% in Poltava Oblast. There are many historic reasons for this ethnic divide, including migration from Russia in Soviet times to industrial regions in eastern Ukraine, but we won’t get into that now.
But ethnicity is really only a minor part of the story. The map above shows the percentages of Ukrainians whose native language is Russian. Again you can see the stark divide separating south and east from the rest of the country. Comparing with the ethnicity map you can also see that many Ukrainians who are not ethnic Russians speak Russian as their native language.
When you look closer, at the sub-regional level, you can actually see that Russian language is concentrated in Crimea and in the large cities and industrial areas of the south and east. Rural areas in the east are predominately Ukrainian speaking. This reminds me of election maps in the United States where Democratic votes are concentrated in dense urban areas, meaning that the map might be awash in a sea of Republican red even if the Democrats won.
This ethnic and linguistic divide coincides with a cultural and political divide. The map above shows how much of the vote Yanukovich got in each region in 2010. Even though the election was decided by only about 3.5%, Yanukovych didn’t even get 10% in some areas of western Ukraine while he carried over 90% in Donestk Oblast (where he is from) in the east. That’s a geographically divided electorate!
Eastern and southern Ukraine, especially urban areas, are ethnically, linguistically, and culturally closer to Russia than the other parts of the country. This divide is stark. In my experience as a Peace Corps volunteer working in all-Ukraine summer camps, it was not uncommon for many of the young people to have never met someone from the “other” region. Yanukovych and his Party of Regions have their power base in the east and south, and their supporters are much less likely to be upset at forgoing closer relations with the EU, and much more likely to favor closer relations with Russia. Moreover, the economic threats allegedly made by Putin would have affected the pro-Yanukovich regions more because they are industrial areas that sell lot of goods to Russia.
So Yanukovych choose a course of action that paralleled the wishes of his power base and his geographic region of support. It remains to be seen whether this was a wise political decision for him, but at this point it does not look good.
Perhaps Yanukovych overestimated the cultural and linguistic divisions in Ukraine, and did not account for the fact people all over the country are unhappy with the regime, generally perceived as corrupt and ineffectual, and with the economic situation in the country as a whole.
Update: Just an couple hours after I posted this, the Washington Post WorldViews blog published this article. It makes many of the same points regarding the ethnic and liguistic divides in Ukraine and includes some interesting recent polling on the EU integration agreement.
It’s snowing here in the Washington DC area. Well, it’s actually spitting down freezing rain after a few flakes this morning, but that’s about you can expect in this area. But it was also the first time my young son had ever seen snow, and he thought it was pretty darn cool. And snow is pretty darn cool! If you’re a jaded adult perhaps it helps to take a closer look. Check out this image of a snowflake taken by an amateur photographer in Moscow named Alexey Klyatov. It’s stunning (and of course totally unique), and it was taken with a clever jerry-rigged system on his balcony. This Atlantic Magazine article explains how he did it. So enjoy the snow!
One of the great things about Switzerland is its passenger rail system. Service is frequent and convenient, and the network is dense so you can get almost anywhere in the country by train. And you can even ride the rails high into the Alps on the many cog wheel railways and funiculars. But Swiss railways really outdid themselves with this rail service diagram. If you download the full sized version (and you should) and zoom in, you will see that the diagram contains train arrival times for every station pictured (for those train services that run daily). With only a copy of this diagram you’d have all the information you needed to navigate the entire Swiss rail network, although you might need a magnifying glass to read the small print. The Human Transit blog explains how to read the notation.
Lawrence Livermore National Laboratory produces fantastic annual diagrams that show energy flow in the U.S. from source to end use. Here’s the latest one, from 2012. You can learn a lot from studying this, but here are a few insights that were interesting to me:
- Almost 2/3 of total energy is not used for productive work (energy services). Most of this is probably from waste heat. Our transportation sector is particularly wasteful, which I guess is what happens when most people have their own internal combustion engine to propel them at high speeds over vast distances every day.
- Petroleum is used mostly for transportation (i.e. gasoline), with some going to industry (not sure what this would be).
- Coal is still the biggest source of electricity, although if you look at diagrams from earlier years you can see that it is decreasing at the expense of natural gas, and to a less extent, wind and solar.