Matt McAlister heard “crackling firearms” in his San Francisco neighborhood and wrote a wonderful essay on a theme that was central to my keynote talk last week at the GOVIS conference: how citizens can and will work with governments to diagnose social problems and develop solutions. When the District of Columbia’s DCStat program rolled out last summer, I was delighted by the forward thinking involved. Publishing the city’s operational data directly to the web, for everyone to see and analyze, with the explicit goal of making the delivery of government services transparent and accountable, was and is an astonishingly bold move. And as Matt found when investigating crime in his neighborhood, it’s still part of the unevenly distributed future:
I then found the official San Francisco Police Department Crime Map. Of course, the data is wrapped in their own heavy-handed user interface and unavailable in common shareable web data formats.
Access to data is good, and access to data in useful formats is better, but these are only the first steps. We need to make interpretations of the data, compare and discuss those interpretations, and use them to inform policy advocacy. The mashups that Matt reviews are a glimpse of what’s to come, but these interactive visualizations have a long way to go.
Here’s another glimpse of what’s to come: I took a snapshot of the DC crime data, uploaded it to Dabble DB, built a view of burglary by district and neighborhood, and published it at this public URL. There are two key points here. First, discussion can attach to (and will be discoverable in relation to) that URL. Second, the data behind the view is also available at that URL, in a variety of useful formats, so alternate views can be produced, pointed to, and discussed.
Still, these are only views of data. There’s no analysis and interpretation, no statistical rigor. Since most ordinary citizens lack the expertise to engage at that level, are governments that publish raw data simply asking for trouble? Will bogus interpretations by unqualified observers wind up doing more harm than good?
That’s a legitimate concern, and while the issue hasn’t yet arisen, because public access to this level of data is a very new phenomenon, it certainly will. To address that concern I’ll reiterate part of another item in which I mentioned John Willinsky’s amazing talk on the future of education:
Willinsky talks about how he, as a reading specialist, would never have predicted what has now become routine. Patients with no ability to read specialized medical literature are, nonetheless, doing so, and then arriving in their doctors’ offices asking well-informed questions. Willinsky (only semi-jokingly) says the Canadian Medical Association decided this shouldn’t be called “patient intimidation” but, rather, “shared decision-making.”
How can level 8 readers absorb level 14 material? There are only two factors that govern reading success, Willinsky says: motivation, and context. When you’re sick, or when a loved one is sick, your motivation is a given. As for context:
They don’t have a context? They build a context. The first time they get a medical article, duh, I don’t know what’s going on here, I can’t read the title. But what happened when I did that search? I got 20 other articles on the same topic. And of those 20, one of them, I got a start on. It was from the New York Times, or the Globe and Mail, and when I take that explanation back to the medical research, I’ve got a context. And then when I go into the doctor’s office…and actually, one of the interesting things…is that a study showed that 65% of the doctors who had had this experience of
patient intimidationshared decision-making said the research was new to them, and they were kind of grateful, because they don’t have time to check every new development.
When your loved one is sick, you’re motivated to engage with primary medical literature, and you’ll build yourself a context in which to do that. Similarly, when your neighborhood is sick, you’ll be motivated to engage with government data, and you’ll build yourself a context for that.
The quest for context could, among other things, lead to a renewed appreciation for a tool that’s widely available but radically underutilized: Excel. Most people don’t earn a living as quants, so Excel, for most people, winds up being a tool for summing columns of numbers and arranging text in tabular format. That may change as more public data surfaces, and as more people realize they want to be able to interpret it. In which case Chris Gemignani and the rest of the Juice Analytics team will emerge as leading resources available to motivated citizens wanting to learn how to make better use of Excel.