Congressional District maps for every state!

It has been almost six weeks since I've had something substantive to add. I wanted to share the complete outputs from the campaign contribution work I spoke about earlier. Please read that post for a full explanation of what the data is and how I put the files together.

The following two ZIP files contain a KMZ for every single state. I used the TimeSpan element, all of the states can be loaded at once.

Without further ado:

• 109th Congressional Districts


• 110th Congressional Districts

If you have any problems with these files, please let me know.

I hope to be back up and running soon, so please stay tuned.

Fixed KMZ Downloads

An alert reader pointed out that the KMZs I posted yesterday were not properly downloading as KMZ files - the ended up as ZIP files on the desktop. This was because I was using Amazon's S3 service to host the files which apparently doesn't have the KMZ mime type set. I've moved the KMZs to BingoDisk which fixes the problem in my testing. Please let me know if you have difficulty getting these new files.

As a side note, the Google Maps links don't work with the BingoDisk files, but do with S3, so I've left a copy there as well.

FEC Data KMZs

To follow up on my last post I wanted to share some of the actual KMZ files I generated to create the screenshots I showed. Please read that post for a full explanation of what these KMZs contain. The KMZ document descriptions also contain a brief explanation.

Please note that I do not warrant in any way, the accuracy of these maps. Use at your own risk. That being said, I have done spot checking for 3 random districts in each of the attached KMZs using the FEC query tool, which can be found here. The numbers I calculated are pretty close, and sometimes match exactly, to what the FEC tool returns. In the cases they do not, I suspect is is because of the data file I choose to use, with the FEC explicitly states might contain some inaccuracies given their attempt to make them as up-to-date as possible.

I've also used the TimeSpan element, so you can load both the 109th and 110th Congress files into Google Earth at one time. Using the time slider you can move between the two Congresses - the effect is pretty cool. I've also included my StyleMap trick, so that when you mouseover the point near the center of each district a label with the district's name and the total receipts in dollars appears.

I'm posting California, New York, and Texas - all states that have many districts. If you are interested in others, please feel free to e-mail me at censuskml [at gmail].

Total Receipts for House of Representative Candidates by Congressional District KMZs:

• California 109th Congress


• California 110th Congress


• New York 109th Congress


• New York 110th Congress


• Texas 109th Congress


• Texas 110th Congress

California and Texas are too large to load into Google Maps, but New York works:

• New York 109th Congress in Google Maps


• New York 110th Congress in Google Maps

There are many interesting insights that can be gleaned from the data, in particular when contrasting the 109th and 110th election cycles. I'll save interpretation for another post. For now, enjoy.

Federal Election Commission Campaign Contribution Data

I am stretching my wings beyond Census data - and what could be more interesting then campaign contribution data? I have an interest in politics and thought it would be fascinating to see if there were ways to make the data about the amount spent on elections available. Using the same basic framework I created to read in Census data, I decided that the Federal Election Commission (FEC) would be a good source of interesting data about elections.

The Census provides boundary files for the last eight Congresses. The FEC provides a wealth of data and I have just started to explore the full extent of what is possible. I started by using the Candidate Financial Summary Without PAC Breakdown data. This data, of course, is in a rather complex format, so writing a Ruby module to read it out was the first order of business.

The Census boundary files for the 110th and 109th are in a relatively similar format (the 108th and before begin to deviate substantially) so I used these as my polygon files. I used the Candidate Financial Summary Without PAC Breakdown (CFS) files from the FEC. These files are the most current but do have some potential accounting issues. As the FEC states:

The cost of this timelines, though, is that some of the information available here is less precise than for "cansum". For example, in "cansum" you can see how much a campaign received from Corporate PACs or Labor PACs, while here there is only one value for the total received from "other political committees." This includes all PAC contributions, but it may also contain contributions from other candidates, and some other types of committees we don't typically think of as PACs. We can't do the full breakdowns until all the information about specific contributions has been entered into the database.

When using these summary files you need to be aware of some possible double counting of activity. Some candidates have more then one committee authorized to raise and spend funds on their behalf. The activity reflected in this file represents the sum of those committees. If they transfer funds back and forth among each other, this activity would be counted twice. Information about "transfers from authorized committees" and "transfers to authorized committees" is included in the file and if there are values in both of these fields it is necessary to subtract these from total receipts and total disbursements to obtain a more accurate value for actual activity.

In creating a data structure to store the CFS data, I created a somewhat flexible way to aggregate the data using some of the interesting features of Ruby. In particular the eval statement make it quite easy to pass in free text to allow the caller of the aggregation function to specify which field to aggregate quite easily.

I encountered two main challenges mapping the CFS data back to the Census Congressional District polygons. First, the FEC uses the two-letter acronym to identify the state and the Census uses FIPS codes to identify states. Using regular expressions in TextMate, I converted the list from the Census website to a couple of different Ruby hash tables so that I could convert back and forth from two-letter acronym to two-digit code.

Second, the FEC files are somewhat inconsistent (at least based on how I am reading it) about how they handle states with only one Congressional District. The Census bureau is pretty clear that it uses "00" to identify districts that are the sole district for a given state. The FEC seems to follow this convention for the most part, except in a couple of states. For example, in Wyoming there are candidates for the House of Representatives listed in Congressional Districts "00" and "01". Wyoming has only one seat in the 110th congress. Here is a snippet from the webl06.zip file which contains the CFS data for the 2005-2006 election cycle (the ellipses represent where I have cut from the line for the sake of readability):

  H4WY00055CUBIN, BARBARA L                      I2REP...WY01 W W48...
  H6WY01025TRAUNER, GARY S                        1DEM...WY01 W L47...
  H6WY00118WINNEY, JUSTIN WILLIAM JR              2REP...WY00     0...


According to the documentation for the file, the two digits following the state acronym represent the district. In this case, it would appear to suggest that there are candidates for House in district "00" and "01", when in fact there is only one district in Wyoming. To handle this, I simply rolled up all of the House candidates, regardless of the district in the FEC file for one district states. This may be the wrong thing to do, so I've got an e-mail into the FEC to find out the actual answer.

Alright, enough with that background, let's see some pictures. In the following screenshots, I am mapping the total amount received by House of Representative candidates in each Congressional District for a given election cycle. For each $1,000 received, the district gets one meter in height. The districts with higher amounts are more green, those with lower amounts are more red. Missouri is missing from the 109th Congress because the polygon metadata file is missing from the Census website.

109th Congress from above:


110th Congress from above:


109th looking North:


110th looking North:


109th looking West:


110th looking West:


109th looking East:


110th looking East:


109th looking South:


110th looking South:


That is it for now. I'm close to sharing the KMZs for these, so be on the look out for a post soon.

"Dynamic" Labels & StyleMaps

I've been unhappy with how Google Earth handles labeling of shapes - the Placemark KML object is pretty good at containing a single shape, but breaks down when handling multiple shapes. Placemark elements contain the name and description elements, but only Point elements pick up on these. As I have talked about before, you can't label a Polygon without using a Point. I started to use the MultiGeometry to group together Polygon elements that belong to one geography (i.e. when a County has a couple of noncontiguous shapes). The annoying part is that I needed to add a Point for labels to show up.

When dealing with complex geographies, like Block Groups, having the labels show up the entire time doesn't work very well. For example in the last map I shared, there are ~4,300 Block Groups in WA and having the labels all show up doesn't work very well because they overlap and make it quite confusing. You can play with breaking out the labels into a different Placemark and perhaps a folder structure at the County/County Subdivision level might help, but it still isn't perfect because you would have to hunt and peck for what you were looking. In looking through the KML spec, I was excited to find the StyleMap element. The StyleMap element provides a mechanism to have a Placemark respond to mouseover/click/highlight events. You can define a Style element for both normal and highlight classes. I thought this would be a great way to provide a label: when a user moves their mouse over a geography (e.g. Block Group, County Subdivision, County) the label could show up - the normal style would have it transparent and the normal would have it opaque.

Well, turns out it doesn't quite work that way. Unfortunately, the only thing that sparks the transformation is the user moving their mouse over the icon of a Point: the style doesn't change if they mouseover the label or any of the Polygon elements in the Placemark. What is odd, however, is that all of the elements of the Placemark do respond to the new style when you mouseover the icon.

What you'll see below are maps of Median Household Value (variable H85 MEDIAN VALUE (DOLLARS) FOR ALL OWNER-OCCUPIED HOUSING UNITS [1] from Summary File 3) by County Subdivision. I'm not entirely happy with my new labeling, but it works such that when you mouseover the icon at the center of the polygon, the name of the County Subdivision shows up and the border is highlighted in white. I like the effect, but I am not happy with how I have to have an icon show up. In the maps below, the more red an area/shape, the higher the median household value - the greener, the lower the median household value. In the 3D maps, each $1,000 of value adds one meter of height.

Movie:


Screenshot (notice how Leavenworth-Lake Wenatchee is highlighted):

Median Household Value for Washington state by Block Group

Using the new functionality I've discussed in the last two posts, I am pushing forward in creating new maps. Today I'm going to share a few maps of median household value (variable H76 - MEDIAN VALUE (DOLLARS) FOR SPECIFIED OWNER-OCCUPIED HOUSING UNITS [1] from Summary File 3). There is another variable, H85, which might be better for what I want, but I'm going to go ahead and share these maps before going back. These new maps show the median household value by block group for Washington state. There are ~4,300 block groups in Washington and a wide array of values in the data, so this data provides a good test of the new functionality (labels, excluded polygons, logarithmic color scales). Given the large number of block groups, I've found that it takes several different map formats to fully explore the data. I've created maps that have the 3D views I've shared before and maps that are flat with some transparency so that you can see the underlying geography.

One of the challenges I found in creating these maps was gathering the Census data at the block group for the entire state. NHGIS doesn't provide many variables beyond the basic population and economic ones and the Census FactFinder website doesn't make it easy to download all of the block groups for a given state at once - you have to download each county separately. Given these challenges, I looked into download the raw data from Summary File 3. There raw files are available by FTP but, of course, are in a very complex format. The Census Bureau provides an Access database template that contains empty versions of each of the ~80 tables needed to work with the data (including import specs which is quite helpful). Feeling intrepid I downloaded all of the data for Washington state (FTP site) and loaded the tables I needed to into the Access template. This worked pretty well, but is somewhat confusing, particularly because joining the geographic identifiers for each record is not quite as straight forward as the Census documentation would lead you to believe. I finally got it to work and this provided the data for the maps provided below - perhaps you can now understand why I haven't gone back and re-run the maps using the H85 variable yet.

In the maps below, the more red an area/shape, the higher the median household value - the greener, the lower the median household value. In the 3D maps, each $1,000 of value adds one meter of height. You'll note that some areas show up as white, this is because the data provided by the Census for these block groups is 0. This makes sense for places like Mt. Rainier, but not for a region of downtown Seattle that shows up as white. I'm going to look into this next.

Now, some maps (don't forget you can click on each picture to get a larger version)!

Entire state (flat from overhead):

Entire state (flat from overhead, borders around each block group, some transparency):

Entire state (3D from overhead):

Entire State (3D looking North):

Entire State (3D looking East):

Seattle (3D looking Southeast):

Seattle (flat from overhead, borders around each block group, some transparency):

New KMZ!

Today I'm going to share a KMZ of a new variable that I've been working on. I wanted to experiment with more complicated variables, beyond Median Household Income, to really push the flexibility of the code I'm writing. The migration variables of summary file 3 from the Census are fascinating and fit the bill. They allow one to understand how people are moving about the country and include quite a bit of granularity. I'm going to share 7 states worth of data: Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island, and Vermont. Before I share links to the actual file, I want to be sure to share some important definitions, data sources, and notes.

What is mapped?

• There are many migration variables, but for this example I've chosen to use two. I used the Population 5 years and over: Total - P024001 variable which represents the number of people in 2000 over the age of 5 and used it to divide the Population 5 years and over: Different house in 1995 - P024003 variable. The resulting percent should represent the number of people, over the age of 5, who didn't live in the house they lived in during the 2000 Census in 1995. Put it simply, the percent of people who moved in the last 5 years. The migration data provides a much more detailed breakout of where the people that moved came from, which I hope to work further with.
  
• The data is presented broken out by county: the taller, the more blue a county is in the file the higher a percent that moved - the shorter, the more green, the smaller percent that moved. The actual percent can be found in the description for each polygon, however it is multiplied by 1,000 there so the actual value is what is found in the description divided by 1,000.
• In New England, the region in the provided KMZ, Tompkins County, New York has the max value at ~ 58%. This means that ~ 58% of people in Tompkins County moved since 1995. There are many counties at the low end: Hamilton County, New York is quite low at around ~30%, but so are Aroostook County, Maine and Orange County, Vermont, both around ~33%.
• You'll also note how the counties are organized into folders within the KMZ file. This is a recent improvement to the KMZ generation process. I've also modified the code to follow the best practice of referencing repeated styles by ID (each polygon references a base style and only overrides what it needs to) - I thought this would save a lot on file size, but it didn't because ZIP was quite efficient at compressing the bits that were repeated again and again.
  

How about a quick tour of the map?
[YouTube seems to like to cut down the length of videos, so this may feel a bit choppy - not sure why it is doing this]


Where is the data from?

• I downloaded the data from the FactFinder Download Center.
• The County boundary files came from a Census website: 2000 County Cartographic Boundary Files.
• The Census boundary files include data the denotes cut outs when a polygon should not cover an area. These are denoted with a -99999 ID in the Census boundary files. While I read these in, I have not decided the best way to handle them so that data is not represented here: in other words, some of the polygons may inappropriately cover an area.

Notes/Disclaimers:

• This is a preliminary release.
• Turn off Terrain for best viewing.
• Rotate, fly around, change the viewing angle to get a real sense of the visualization!
• Commercial use of this file is prohibited. If you are interested in using this file commercially, please drop an e-mail to censuskml [at] [Gmail].
• I do not warrant in any way, the accuracy of these maps. Use at your own risk.

Files:

• 2000 Census New England Migration Data.kmz

Please pass along any feedback/thoughts/inquires via comments!

Get your real, live examples!

After only posting screenshots for the past few days, it is time to share some real, live KMZ files. I'm going to share 3 states worth of data: California, Massachusetts, and Wyoming. Before I share links to the actual files, I want to be sure to share some important definitions, data sources, and notes.

What is mapped?

• I wanted to start simple: the following KMZs map the "Total Population" variable from the 2000 Census, broken out at the County Subdivision level. The height of each subdivision is equal to 1 meter for every 10 people. The colors also represent population, but use the state's max county subdivision population as the denominator, meaning they are only relevant within the state, not comparable across states. The largest subdivision will be bright red and the small ones will be white. I'm experimenting with this to try and get data on two levels - national and state.

Where is the data from?

• While I could have gotten the data from the FactFinder, I got the actual Total Population variable data from National Historical Geographic Information System (NHGIS). This system is powerful because, among other things, it lets you download the data for the entire US at once. The citation for this data is as follows:
• John S. Adams, William C. Block, Mark Lindberg, Robert McMaster, Steven Ruggles, and Wendy Thomas, National Historical Geographic Information System: Pre-release Version 0.1 Minneapolis: Minnesota Population Center University of Minnesota, 2004.
• The County Subdivsion boundary files came from a Census website: 2000 County Subdivisions Cartographic Boundary Files.
• The Census boundary files include data the denotes cut outs when a polygon should not cover an area. These are denoted with a -99999 ID in the Census boundary files. While I read these in, I have not decided the best way to handle them so that data is not represented here: in other words, some of the polygons may inappropriately cover an area.

Notes/Disclaimers:

• The actual population (in people) is listed for each County Subdivision in the name for each polygon. The number listed here is the actually population divided by 10, which is the height of the polygon in meters.
• This is a preliminary release.
• I license the use of these files under Creative Commons and Commercial use of the data is prohibited by NHGIS. The data can be gotten for other ways, so Commercial use is not out of the question in the future.
  
• Turn off Terrain for best viewing.
• Rotate, fly around, change the viewing angle to get a real sense of the visualization!
  
• I do not warrant in any way, the accuracy of these maps. Use at your own risk.

Maps of Total Population (people) from the 2000 Census by County Subdivsion:

• KMZs:
• California
• Massachusetts
• Wyoming
• Google Maps (viewing the KMZ files in the Google Maps interface - may only work in some browsers):
• California - won't work - too large
• Massachusetts
• Wyoming

Please pass along any feedback/thoughts/inquires via comments!