1. Skillz

    Cities often express concern, rightly, that their developers don’t have the skills needed for web/gov 2.0 tech. I say that’s an opportunity to show that you value the professional development of city employees: organize a skill share. Find someone to teach them new skills and they will respect you for it. Don’t know how? Contact me.


  2. "Yet the main theme of Pahlka’s talk is not to talk about apps. Those are just the start. Instead, she implores the audience to engage with government, not give up on it. Sure, we can be frustrated, but we can’t give up."
    — TED blog on Jennifer Pahlka, Founder and Executive Director of Code for America
  3. jedsundwall:

    The U.S. national debt (red) compared to U.S. GDP (blue) from 1940 to 2016 (estimated from 2011 to 2016) – top chart with a linear scale, bottom chart with a logarithmic one.

    Nicolas Bissantz:

    Since the selected 75-year time frame is impressively long, we should take some time to examine this wealth of information more closely. Take a good look at the chart with the linear scale, then the chart with the logarithmic one, and see for yourself:

    1. You can’t get much information on the first 25 years from the linear scale. The large values at the end of the series dominate the small values at the beginning. You think you are seeing the development of the data when in reality you aren’t. Your look at the historical data does not achieve the desired result, and collecting the data was a waste of time. And worst of all, you have a completely wrong impression.
    2. The logarithmic scale shows that the sharpest rise in debt in the past took place in the 1940s. From 1944 through 1948, the debt was larger than the GDP. From 1948 on, GDP growth was always clearly larger than that of the national debt.
    3. If you look at the linear scale between 1965 and 1983, you think you are seeing the “good old days”. GDP growth was apparently much faster than that of the national debt. The logarithmic scale, however, straightens out the story. It shows a development that is almost parallel and, in fact, it is. The GDP and national debt grew by 400 and 326 percent respectively in this period.
    4. The period from 1983 to 1993 is also deceptive but in the opposite way. Although the gap between the data actually decreased, the linear scale shows a seemingly parallel development. While the GDP rose by 90 %, the overall debt on the other hand tripled in this period. This is the reality – and this is what the logarithmic scale shows.

    We wanted to prove that you don’t have to know logarithms in order to understand logarithmic data. However, you need to know the pitfalls of linear scales if you don’t want to misinterpret linear data. In our example, you would have to see the pattern of the logarithmic scale in the linear chart to still be a skeptic regarding logarithms. You would have to be able to identify and interpret all the distortions of the linear chart just by looking at it. My eyes couldn’t do that – could yours?


  4. Relationships

    In a sad, ironic, weird way I’m relieved the hullabaloo in politics and the anger on the streets in America essentially boils down to the relationship between government and business. That this relationship is deeply out of balance is a concern. That this is the relationship mattering most right now is reassuring. It drowns out all that craziness about the relationship between God and Our government, between our bodies and Our government, between science and Our government, between marriage and Our government. To be sure: I’m not suggesting in the least that mastering effective governance boils down to understanding the relationship between government and business. Government - LAW - plays a role in many facets of life. But when I think of government I think of a physical system, a scientific model. I relate government to pressure in a tube, temperature in a boiler. Let us recognize that the relationship of government and business in The United States is broken and focus solely on repairing it. Let us spend the least amount of time required on matters of morality, or privilege or what we think is right for others. Our families, neighbors, communities and places of worship have always upheld our moral fiber and will always continue to do so. As we find our way back to a balance between government and business let us also remember our relationship with the Earth and seek a greater understanding in our relationship with the Universe.


  5. "its fundamental thesis is that the biggest question is not how much to spend and how much to tax, it is how to adapt the state to the information age."

    Remaking Government in a Wiki Age


  6. USDA Commodities Data via ScraperWiki

    (Source: scraperwiki.com)


  7. Read The Bill at govtrack.us!

    I’m not sure I can express in the short time I have to record my thoughts the wonderfully useful, elegant, and poetic qualities of GovTrack.us. Just a few minutes ago my sister (@elcrean on Twitter) tweeted a reference to the GIVE Act. In that tweet she provided that link to the homepage for HR111-1388 and an unlinked ref to S6104b. So I followed that first link and the first wonderful surprise I encountered was that the bill was even posted. But it got so much better so much faster. Not only is the bill posted, but govtrack.us breaks out every individual element of the document. What does this mean? It means four things (in order of WOW factor). First, you can expand and collapse any section of the bill on the page. Second, you can extract any section of the bill to some HTML that you can copy to your clipboard. Third, you can mouse-over references to other bills and get the context in-situ.

    From Flow
    Fourth, you can link directly to any section of the bill!
    From Flow
    These features are immensely powerful for collaborative research. Bravo GovTrack! Bravo Josh Tauberer!

  8. More Reusing Open Government Data


  9. Reusing Open Government Data from OMB

    1. Went toVisualization to Understand Expenditures in Information Technology and got some data.
    2. Opened data in Google Docs
    3. Created a new chart
    4. Selected range of data (easier than what I’m used to)
    5. Configured chart

    One thing I really don’t like about this is the duplication of data away from the source. Many Eyes also asks you to “paste the data” to their site. A big challenge to this problem is that even the best sources need to be “cleaned up a little.” A common thing is the placement of disclaimers and notes on the same worksheet as the raw data.
  10. Intriguing stat from my Input homepage.