Apps for Californians and Where We Need To Go

Initially we were all excited by the opening of the Apps for Californians Data Gold Rush app challenge, but personally, I'm pretty disappointed with the data available.

Looking through the datasets at data.ca.gov what we have is generalized data in formats that are not easy to use or keep current. At the time of this writing, thirty-four of the forty-eight datasets are available only in XLS, TXT or CSV format - usually from a technical standpoint that means the data is a pain to translate and only manually updateable. This means that any kind of up-to-date information is out of the question.

However, up-to-date information doesn't matter as much considering that most of that data is only based on year and county - which brings us to the second point. Average users of an online or mobile app aren't looking for generalized data. Trends point to an increasing need for micro localized information. For example, users want to know which DMV has the shortest wait time, in a 30 mile area, so they can go there, RIGHT NOW (an excellent suggestion from someone in the "ideas" area at appsforcalifornians.ideascale.com).

When I looked at the information on traffic accidents at the CHP site I was hoping to find data that could be morphed into something that might look like a "Dangerous Intersection Alert." Imagine driving along and your GPS is able to warn you that you are approaching a dangerous intersection - a geographical point that has a high incidence of traffic accidents or fatalities, especially given a particular time of day or type of weather. If you've ever been in a traffic accident you know that any report contains the nearest intersection, as well as time and possible cause. Sadly, this data is simply not available. The data available is generalized by county or sometimes not at all. We can see that most traffic accidents happen on Fridays between 5:00 - 5:59 P.M. - interesting, but where? No doubt screeching out of the parking garage at work, somewhere, anywhere in California, as it is a statistic for the whole state. The most recent information is from 2008 which is also problematic, since intersections could be tracked in on at least a weekly or monthly basis and help point to problems that need to be addressed (are drivers swerving to avoid a pothole? Is a stop sign obstructed by spring foliage?).

Overall, data availability in any form is a step in the right direction, however city and state data initiatives have a long way to go. I'm interested to see what comes out of the Apps for Californians Data Gold Rush app challenge, but as of right now, I'm stumped for ways to use what is currently available there.

Visualizing Data: Snapshots of America

The major thing missing in government community health data are the human faces and on-the-ground visual information that we can get from walking around in a particular community. This is a data visualization project that seeks to make all these numbers and percentages make sense at a glance, by merging photography and Community Health Status Indicators data from the USDA.

Here is my proposal for the Sunlight Foundation's Design for America Challenge:

Using Community Health Status Indicators and a library of people & product photos, we can dynamically build a snapshot representation of a typical family in given county, anywhere in the US. What does a family look like in Southern Arizona? How is it different from a family in Maine? Are they drinking soda? Eating vegetables? Are they overweight? Are they active? What color is their skin? Do they own a car? Is the environment urban? rural?

I envision a single photo - similar to the "Groceries Around the World" photos - to represent each county in the US. The photos could be dynamically retrieved based on a number of variables, similar to those available in the USDA food atlas. A layering system (either in photoshop or on-the-fly) would add items based on statistics.

For example:

Pima County Arizona is represented by these statistics in the USDA Atlas:

FIPS Code 04019 NAME Pima STATE_NAME Arizona

1. % Households no car & > 1 mi to store 1.85
2. Grocery stores per 1,000 pop 0.122
3. Supercenters and club stores per 1,000pop 0.009
4. Fast-food restaurants per 1,000 pop 0.596
5. Full-service restaurants per 1,000 pop 0.617
6. Avg monthly # School-Lunch participants* 656017
7. % Students free-lunch eligible 29.1
8. % Students reduced-price-lunch eligible 7
9. Lbs per capita fruit&veg 157
10. Lbs per capita pkg sweetsnacks 109
11. Gals per capita soft drinks 60
12. Lbs per capita meat&poultry 55
13. Lbs per capita prepared foods 299
14. Relative price of low-fat milk 0.89
15. Relative price of sodas 0.99
16. Adult diabetes rate 7.8
17. Adult obesity rate 21.2
18. Low-income preschool obesity rate 15.2
19. Farmers' markets per 1,000 pop 0.015
20. % Adults meeting activity guidelines* 66.5
21. % Highschoolers physically active* 32
22. Recreation & fitness facilities per 1000p 0.1
23. % White 57.3
24. % Black 3.1
25. % Hispanic 33.1
26. % Asian 2.4
27. % Amer. Indian or Alaska Native 2.6
28. % Hawaiian or Pacific Islander 0.1
29. Poverty rate 15.4
30. Metro-nonmetro counties 1

I believe we can represent all of these statistics (and more) in one dynamically generated photo. For the example statistics above:

The background: A driveway with a car (low Stat #1) in an urban area (Metro #30) in front of a lower income house (high #29), filled with bags from a superstore (low #2, high #3). There is a basketball hoop on the driveway (med #22)

The People: Hispanic Family (high Stat #25) with 2 adults of healthy weight (low #17), and 2 slightly overweight children (high #18). One adult is holding a soccer ball (high #20), one child is holding a video game controller (low #21).

The Groceries on a table in front of them: A small bag from a fast food restaurant (med #4), a plate of food from a full service restaurant (med #5), a school lunch tray (med #6, 7, 8), only a few fruits & vegetables (low #9), a medium amount of boxed sweet snacks (med #10), one can of soda (low #11), a small amount of packaged meat or poultry (low #12), a few medium amount of frozen dinners (med #13), a gallon of milk (low #14, high #15), no glucometer/ medical products (low #16), no farm fresh produce (low #19)

Source: USDA Food Environment Atlas

The Impotence of Proofreading

Take a minute to figure it out. This is in the DC Metro.

This seems like an easy thing to overlook. You had a hard time seeing it too, didn't you? But from an insider perspective, there had to be at lease 10-20 eyes on this before something this size could make it's way to the Chinatown Metro Station. From the design intern to the big boss to the guy who pulls this out of the vinyl printer, your voice is important. When you don't speak up we all get a little bit dumber.

AfterSchoolSF.org Launched!

AfterSchoolSF.org (aka. After School Special) was created in two days during California Data Camp: Exploring State Data and DataSF App Contest by Zap Squeak's main man, This e-mail address is being protected from spambots. You need JavaScript enabled to view it . California Watch, Spot.Us, and some notable others sponsored the event so that computer nerds, data geeks, public servants and reporters could come together to "learn and discuss issues around public data in the State of California."

The goal here was to do something insightful with the datasets provided by datasf.org in the allotted time period: 9-5 on a Saturday, November 7th, 2009 (we also put in a day of prep time, but that was allowed). See it in action at www.afterschoolsf.org! There were some other great ones in there including a web app for tracking, maintaining and requesting city trees, a water usage tracker, and a map of San Francisco's oft-misused handicapped parking spaces. There was also the announcement of the release of more MUNI Bus data, which I'm sure will be very exciting to someone soon.

The site uses school sets from datasf.org and combines them with library and food information from GeoCommons. It is a PHP application with a simple admin area and a MySQL database behind it. Ultimately I'd love to expand this out so that more discreet data can be shown. If have a relevant dataset, have suggestions or happen to have a lot of time to enter address data into the database please let me know.

And of course there was someone liveblogging.