Census Data in Google Earth

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 CarolinaNorth Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South CarolinaSouth 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 CarolinaNorth 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 CarolinaNorth Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South CarolinaSouth 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 CarolinaNorth 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 CarolinaNorth Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South CarolinaSouth 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 CarolinaNorth 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.

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License. All source code is released under a BSD License unless otherwise specified.

33 comments | Show all comments only the last 5 are shown


September 25, 2007
A munir said:

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


October 2, 2007
Chris M. said:

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.


November 28, 2007
Andrew C said:

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.


June 23, 2009
Wai Lee said:

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!


June 25, 2009
Chris Gemignani said:

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.

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