for Election 2008 maps.
for Election 2006 map.
for Election 2005 maps.
for Election 2000 map.
To license high resolution versions of these maps, contact the author.
Using County-by-County election return data from
with County boundary data from the US Census'
the following graphic depicting the results. Of course, blue is
for the democrats, red is for the republicans, and green is for all other.
Each county's color is a mix of these three color components in proportion
to the results for that county.
Counties shown in black represent either missing election data or
a mismatch between the US Census data and the USA Today data.
For example, the New England states' election return data is given
for each municipality and/or district rather than for each county.
Hence, it couldn't be easily matched with the county boundaries.
Click anywhere on the map for a larger version.
Finally, I made a 3D version. Height is voter density (voters per
square mile) so that volume represents total number of voters.
If you have a 3D (vrml) plugin installed with your browser and
a high-bandwidth connection, you can dynamically view the 3D world
or click on either of the images below.
The dynamic model allows you to zoom in/out, rotate, pan, etc.
But, beware, the file is 7 Mbytes.
You can download a VRML plugin from
Here are two screen shots from the 3D model.
There is a similar map by ESRI here:
In my opinion, it has a two serious flaws: (a) counties are painted either red
or blue and (b) heights are proportional to total votes rather than votes per
area. This latter makes it look like there are lots more people on the west
coast than on the east coast, which is not the case.
Finally added Alaska and Hawaii:
Here's a population density map from the US Census:
- There are now some T-shirt selections available at
Check out the artwork on the back side.
- A large poster is available now.
to buy a small poster. I'm still working on a larger version.
Check back tomorrow.
Here's an animated gif that blinks back and forth between 2000 and
My colleague J. Richard Gott, together with his collaborator Wes
Colley, correctly predicted well ahead of the election every state
except Hawaii. They did this by using the median of several poll
Here's another county-by-county purple cartogram (scroll down):
- Purchasing Posters.
I have received several requests for printed versions of the map.
I am looking into it. If you are interested, help me out by emailing
me what size would interest you. You can find my email address on
my home page (link above).
Many thanks to Tom Morris for assembling the county data for Vermont
Here's a map showing Congressional Districts for all 50 states:
If anyone can tell me where to get corresponding voting data, I'd be
happy to paint this map in shades of purple.
Many thanks to Paul Schlichtman for assembling the county data for
Suresh Venkatasubramanian, who work's in Stephen North's department at
AT & T, has made a
My apologies to the many who have written to me in the last few days.
There are simply too many emails for me to respond to them all.
But, I have read each one and considered all suggestions that have been
put forth. Hopefully, my comments in this section address most of the
more common suggestions.
Here's a version with mountains added to indicate where people
Click here for a full-sized
Today I received many emails requesting that I make a version of the
map in which the 50/50 even split point is rendered in a neutral
color, such as white, black, or gray. Such an image would make
it easy to distinguish a 45/55 county
from a 55/45 county---one is slightly red whereas the other is
slightly blue. Modifying the code to make 50/50 appear gray was
trivial. But, I think such a plot distorts the essential point
which is that a 55/45 community is almost the same as a 45/55
community. I'd rather not introduce artificial devices to
facilitate making such distinctions. Maybe tomorrow I will feel
- 11/04/04: I've received a flood of emails today. Let me try to
answer some of the questions here.
Can you make the areas with high population density brighter than
those with low density? Yes, I tried that. The trouble is that
the big cities are so much more densely populated than everywhere else
that the map appears black with just a few small bright counties.
Unfortunately, computer monitors have a dynamic intensity range of just
256 and this is not enough for an intensity differentiated map.
I experimented with some nonlinear transformations (such as a
logarithmic or gamma-power law) and so doing was able to make a map
with darker, but not black, unpopulated areas. But, this seems even
more misleading because the viewer is told that the intensity
represents population density and then thinks more people live in the
unpopulated areas than actually do---the correct luminosity is virtually
So, instead, here is a map from the International Dark Sky Association.
It shows quite dramatically where most people live.
- Where did you find the data?
The NJ data came from this website at USA Today:
http://www.usatoday.com/news/politicselections/vote2004/PresidentialByCounty.aspx?oi=P& rti=G& sp=NJ& tf=l
The other states came from analogous web pages with "sp=NJ" replaced by
"sp=xx" where "xx" is the two-letter code for a particular state.
I used the mouse to copy and paste the data into 51 separate state
files (don't forget DC).
- Will you make source-code available?
Unfortunately, probably not---at least not in the near future.
One reason is that I use this data for a programming assignment in a
computing course that I sometimes teach---if the source code is
available on my webpage, then I won't be able to assign that programming
assignment any more. Also, I used an eclectic mix of UNIX tools and
JAVA utilities. It would be painful to describe how it
all fits together.
- Can you warp the counties so that each county's
area is proportional to its total vote count?
Such warped maps are called cartograms. There are already
these at the state-by-state level on the web. I haven't seen any at the
county-by-county level. A few years ago I collaborated briefly with
David Dobkin and Stephen North on algorithms for producing cartograms.
I can say that making a cartogram with so many individual elements
(counties) would be very difficult.