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We love Google Earth because it puts the power to explore data in the hands of average folks. We’ve been exploring uses for census data and wanted to share some of this data with the world.

What you’re seeing here is a map of the counties in the United States colorized by median age. Lighter colors are older.

Median age in United States by county (lighter is older)

Census data is also available at the block group level which is much, much more detailed.Median age around Detroit, Michigan by census block group (lighter is older)

Without further ado, what follows are three sets of links for each state which allow you to explore population density, median age, and male/female ratio in each state at two levels of detail. Google Earth is required. We did have some ftp issues when uploading these files, so if you have any problems, let me know and I”ll re-upload the file.

Population Density

Lighter is higher population density (white is 800+ people per square mile), Dark is lower population density (black is 2 or fewer people per square mile)

by County (overview) by Census Block Group (fine detail)
Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

Median Age

Lighter is older median age (white is 46.0 years median age), Dark is younger median age (black is 29.0 years median age)

by County (overview) by Census Block Group (fine detail)
Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

Male/Female Ratio

Lighter means more men than women (white is 55% men), Dark means more women than men (black is 45% men)

by County (overview) by Census Block Group (fine detail)
Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

If you want to know more about Google Earth, check out our Absolutely Google Earth a collection of tools and resources to get you started.

We’re working on a project to make this and other simple mapping applications more widely available. If you’re a python guru who is interested in building great mapping applications like Chicago Crime give me a jingle.

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  • http://sbutler.com/blog Shane

    I found this really interesting, thanks guys. It would be nice if there were some translucency or something, so that you can see the satellite picture also, is this possible?

    Also one other suggestion maybe you could use the population data you already have to improve the other maps. For example, Central Park in New York has misleading median age and m/f ratio information, maybe sections like this could be transparent or some to colour off your scale, eg green.

  • Chris

    Thanks Shane. We’re on top of that translucency thing. Look for a screencast very soon. All the census block groups are supposed to have roughly the same population base, so there _shouldn’t_ be population-based problems. I’ll take a look at it, though, because there are definitely some outliers.

  • Josh

    Very cool. I’m just getting into ArcView at work… I’m definitely going to have to figure out how to export some data to Google Earth. It just makes it so much more fun! I work with political data, and it seems like I should be able to do the same thing with precincts and electoral data that you did with block groups and census data.

    BTW, it seems like you might need to set a mime-type on your server for .kmz files.

    http://www.keyhole.com/kml/kml_tut.html#kml_server

    Safari downloaded your files as text.

  • Babak

    Shane,

    You should be able to change layer transparency without waiting for Chris’ screencast. In google Earth, select the census overlay layer, and there should be a horizontal scroll bar (in between ‘Places’ and ‘Layers’). Drag it to the left and you’ll get what you want.

    Let me know if you need any more help.

  • Rob Mosley

    Is there a quick way to download all the files in one go? Or do I need to click on every link?

  • Chris

    Rob,

    There’s no quick way to grab all the files. We’ll look into creating an archive containing all the files. Thanks.

    Chris

  • Brian

    Thanks for the great data!
    How about a more detailed legend? For example, you state that in the pop. density maps, white is 800+ / square mile and black is such and such, but what about the other colors?

  • adaptive_tech

    Hi guys — great work, thanks for sharing this.

    One quick question: Do you plan on releasing a version that has the actual census block group numbers in there? It would be great to relate data back to block groups as described here:

    http://ftp2.census.gov/plmap/pl_blk/st06_California/

  • Chris

    Thanks, err, adaptive.

    We are considering some more releases, one of which is to be just the labeled outlines. Anything else you want to see?

    - Chris

  • Brad

    Is there a way to look at geocoded census information by county.

    I need to look at census tracts, street addresses, road networks for the city of Tulsa, OK county is Tulsa.

    I have low income census tract numbers and need to know where these neighborhoods are located.

  • Dave Robinder

    Every time I try to open on of these files, Google Earth crashes. The KML looks fine as near as I can tell. I’m using the latest stable version of Google Earth. Any ideas on what I might be doing wrong? It doesn’t seem to matter which file I use.

  • Chris

    Sorry, Dave. I don’t have any idea. I just opened a few on the latest stable Google Earth and had no problems. It’s a cop-out, but maybe a reinstall is in order?

    Chris

  • http://www.mychurch.org/user/?J=48 Joe

    Awesome work here! The marketing data that can be generated from census extrapolation could be very valuable… thanks for sharing your KMZ’s.

    I second Brian’s comment. Can we get more granularity in the population color legend? Or can you simply state what the colors mean?

  • Mathieu

    Awesome, congrats !!

    Do you know how I could get this kind of information from the Canada ?
    Thanks !

  • Chris

    Mathieu,

    I don’t know where to get this info for Canada. If anyone does, please post.

    Thanks,
    Chris

  • Mathieu

    Chris,

    Nevermind!

    I mean, do you have this kind of information (pop. density) of the Canada ?!

  • Chris

    Mathieu,

    Sorry, I don’t have it.

    Chris

  • ytJohn

    Chris,

    A few other people have mentioned this, but I would just like to add my vote/request/begging for a legend of sorts. Even if you provided it as a static web page that I could refer to, having a legend of color to population density would be great.

    Also I like you idea of outlining the block level regions. I noticed expanding out the state name gives you a list “block group 1″, “block group 2″, and so on, and the names repeat themselves, but give a detailed number of people per square mile. Clicking on the name lets me see where it’s referencing, and then I can zoom in and make out the area, but it’s a guessing game to figure out where a block is and how far it extends. Labelling it with the region numbers that the census bureau provides would definately make things more efficient.

    Those two things aside, this is a incredible piece of work and is just about what I was looking for to help me with a grant I am applying for.

  • http://censuskml.blogspot.com/ Aidan

    This is a great post – thanks for sharing the ideas and data files. I’m helping someone out map Census data and found your examples very helpful. I’ve put together a blog to track my experiments. I’ve been playing with the 3rd dimension to show the data.

    One of the things I’ve found is that Google Earth [at least the Mac version] seems to have a bug when it comes to loading 3D polygons from KML files in that it shows very dark colors, regardless of what the actual color is in the KML file. If you edit the object, the color auto-magically brightens, have you guys noticed this?

    Thanks again!

  • http://eagereyes.org/ Robert Kosara

    Wow, this is great stuff! Information wants to be free and such, wonderful!

    BTW, are you aware of Imran Haque’s gCensus project at Stanford? Saying that it is very similar to your project would be an understatement, perhaps you guys could work together?

  • http://www.juiceanalytics.com/weblog Zach

    We did just find out about the gCensus project, and I agree that it covers similar ground. We got pretty far down this path then were distracted by other shiny objects. Even so, it would be good to get some of our capability out there for others to use.

  • http://www.tacomamama.com jennifer

    This is great. I’d love to also see median income, if that information is available.

  • http://blog.internet-briefing.ch/2007/03/15/gisle/ Internet Briefing Blog / GISle

    [...] GIS-Daten grafisch darzustellen war fürher kostspielig. Google Earth bietet hier eine billige Alternative. So geht das. Und es sieht auch noch spannend aus. Noch spannender wäre es, wenn die grafische Darstellung von Vernetzung mit solchen Landkarten-Darstellungen in einem mash-up zu sehen wären. Nur so ein Gedanke, wahrscheinlich ein saublöder. Ist ja schon gut, man wird ja noch… yigg this! — save to del.icio.us [...]

  • http://censuskml.blogspot.com/ Aidan

    Jennifer – I have put together maps of Median HH income at CensusKML in this post. Feel free to drop me a line and I’ll happily share the KMZ files.

  • Nick

    The data you have put together is excellent. Thanks so much.

    I have a question. I assume that the data is from the 2000 Census. Do you have data from previous years (i.e. 1990, 1980, etc.)? With multiple years of data it would be possible map the growth of a city over time.

    Thanks again.

  • Patrick

    Related to Nick’s question – can you confirm that these layers are based on the 2000 Census?

    Thanks

  • http://juiceanalytics.com Chris Gemignani

    Nick/Patrick: This data is based on the 2000 census.

  • http://www.bobrow.com Eric Bobrow

    Is there any way (using Google Earth and your data or additional data available online) to draw a polygon over an area of the map, and get a population count?

    We are trying to work out area definitions for sale/representation of our software products, and it would be very helpful to know how many people are in a particular area on the map.

  • A munir

    Hi Chris. I am interested in using Google Earth to find Clutter info. Basically I need to know if a zipcode is in Dense Urban/Urban/Sub-urban or Rural clutter. Do you have any suggestions?

    Any idea will be appreciated

  • Chris M.

    I am ecstatic about the info you have provided!
    How about ethnicity data? Is there a way to access that in this same format?
    Much appreciated and thanks again!!

    Chris M.

  • Andrew C

    I have been looking for good detailed county overlays for a long time… I hate that they disappear when you zoom in. Finally found your website – perfect! Many thanks.

  • Wai Lee

    Where did you guys get your information on geographic boundaries? You seem to have stored the county-boundaries as lists of lat-lng pairs (in the KML document). I can’t imagine you got these lat-lng pairs from a map. Are they published somewhere?

    Thanks!

  • Chris Gemignani

    Hi Wai Lee: I’m sorry to say, I don’t recall exactly where we got the boundary data. There are published government sources that typically include geographies in shapefiles. I believe we extracted those out using Python and generated the KML also using Python. We did a very slight amount of shape simplification (coalescing nearby points) to make things easier on Google Earth.

  • Aaron Coleman

    Thank you for posting these .kmz. I just found .kmz on the EPA website showing air pollution throughout the country. Any idea why Google hasn’t gotten in kahutz with the census bureau and published this info along with their existing data. Guess it’s a lot of info in one software.

  • http://xanalytica.com/blog/?p=65 Mashing up Disaster Assessment « xAnalytica Weblog

    [...] see or edit. The Disaster Assessor is built using public domain software and data (Google maps API; US Census 2000; and FEMA floodplain insurance rate maps). Putting it together required about the same effort as [...]

  • Anonymous

    Hi I’m a long time reader of the Juice Analytics blog, and this thread is a just a little old but I thought I’d just post my take on nation-wise census maps:
    http://overviewecon.com/webmapping.htm

    I used tigerline boundary data for the above:
    http://www.census.gov/geo/www/tiger/

  • David Milliron

    Chris … let me know if you want updated county population data for the United States from the 2010 Census with FIPS codes in place. Here is a snapshot of the data that was released in March: http://dm.caspio.com/census

  • http://www.dixonspatialconsulting.com Dennis C. Dixon

    Chris, et al – I have had this page bookmarked for a couple of years – I love the flame/fire color scheme. I hope it is not “blog war” to mention my discussing “2010 Census Tracts in Google Earth”, and my offering state-level kml files. You are/This is the future of GIS/computer cartography/geo-visualization – and thanks for all your work!

    Dennis
    http://gisdixon.blogspot.com/
    http://dixonspatialconsulting.com/Dixon_Resume.htm

  • Chris Gemignani

    Dennis: Great work on your thinned Census Track files–that’s a terrific service you’ve provided!

    We thinned the geographies before creating the original files you see above, but didn’t make them available, unless you want to hack my KML files. Thanks for pointing to your cool stuff.

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  • Michael Williamson

    Your overlays were very helpful for some of the work my unit does, determining areas we work.  In reviewing all of the US counties, I happened to find one CO county (Broomfield) not represented.  Just FYI.

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