The basic premise was to create a data visualization for Oakland homicide crime data that made the victims and, more importantly, the people in their lives real participants in the story rather than pure statistics (or just plain ignored entirely).
It’s a very powerful site and a model for all local newspapers to follow. It’s disappointing but no surprise the media creates these kinds of community services before local governments do. At least we’re getting more access to crime data.
Wayne also points to a crime data visualization from the Los Angeles Times called The Homicide Map that I wasn’t aware of.
They have a nice map mashup that takes a more statistical approach, yet they also include things like images of the victims.
Unfortunately, as Oakland Tribune producers Katy Newton and Sean Connelley point out, a mug shot is not a fair image to use for a violent crime victim in a statistical map. But I’m glad to see them exposing data that needs to be shared.
732 homicides in Los Angeles so far in 2007! Unbelievable.
One of the more interesting sciences, in my mind, is how information relevance is both determined, surfaced and then evolved.
In Fred Wilson’s recent Cautionary Techmeme Tale he argues that making news popular takes away its social context and therefore becomes meaningless. He found Techmeme more useful when its sources more closely resembled his network of friends:
“For years, I’ve been using curators to filter my web experience…Techmeme has been the killer social media curator for my world of tech blogs. Lore has it that it was created using Scoble’s OPML file. It doesn’t matter to me if that’s true or not, I love that story. Because my OPML file was unusable until I found Techmeme and after that I stopped reading feeds and started reading curated feeds.”
This feeds into a larger argument about why pop culture and the art of being or becoming popular can be a bad thing. Not long ago I was inspired by the movie “Good Night and Good Luck” to dive into this idea myself:
“The real problem with popularity-driven models is that they reduce both the breadth and depth of the sources, topics and viewpoints being expressed across a community. Popularity-driven models water down the value in those hard-to-find nuggets. They normalize coverage and create new power structures that interesting things have to fight through.”
This is exactly why personalization, recommendations and social media technologies really matter. They can solve this problem of creating conformist media consumption practices by creating relevance through networks of people rather than through networks of commercial institutions.
I haven’t used My Yahoo! as much as I’d like, but there is a simple function in it that I love which could ultimately create amazing benefits for people who want a human filter for the Internet. It’s called “Top Picks”.
“The Top Picks module automatically highlights stories from your page, based on the articles you have recently read on My Yahoo! The more stories you click on, the more you will see this module reflect your interests.”
Actually, the technology beneath it is not so ‘simple’ but the application of it here makes so much sense that it feels like it’s simple when you watch it work. It works by using implicit behaviors. I don’t have to tell it what I like. It learns.
If it could also show me what my social network is tapped into right now, then the experience would feel nearly complete.
Media researchers will note here that people need pop culture to feel connected to a greater whole. I believe that’s true, too. Television is an amazingly powerful community builder.
But I would gladly trade a powerful singular social voice tied together by networks of distribution ownership for a less unified but still loosely connected network of pop culture tied together by my personal activities and my social connections.
Today it’s easy to store and share my pictures, my favorite URLs, my thoughts and lots of other things online. There are a range of data repositories that allow me to do this kind of thing in different ways.
What still needs work is how I give trusted services access to much more private data — things like my current location, my spending behavior, access to my friends and family, etc.
To date, most services follow the premise that the looser the controls, the more fluidly data will travel. And that’s all that mattered when it was still hard to get data flowing.
Data flow is no longer an issue. Perhaps data flow has actually become too easy now. And therein lies the problem.
Clearly, blogging, RSS and feed readers drove a lot of the early thinking about syndication. Blogging enabled people to post content in a publicly accessible data repository somewhere for anyone to pull out without any privacy or permissioning controls. The further your content then syndicated, the better.
Wikis and community sites like Slashdot created a slightly more complex read/write dynamic against the central content repository that lots of people could access together. The permissioning model was essentially hierarchical where controls were kept in the hands of a smaller community.
Then Flickr broke ground with a new approach. They applied a user-centric friends and family relationship model to permissioning access to personal photos. Flickr opened up what was once considered private data and defaulted it to a public read-only permission status. But each individual still has a great deal of control over the data he or she contributes.
Similarly, del.icio.us made it possible to store and publicly address what had previously been private data. The nice twist here was the easy-to-understand URLs that allowed machines to consume, interpret and redistribute data stored in del.icio.us.
Where services like Facebook and Wesabe are now breaking ground again is in identifying a security model around highly sensitive data. Contact lists are very personal, but there aren’t many data sets more personal than my purchases and spending patterns.
Neat things can happen when I give machines access to my data, both the things I explicitly ‘own’ and my implicit behaviors. I want machines to act on my behalf and make my data more useful to me in a range of different contexts.
For example, I like the fact that Facebook slurps up my Twitter activity and shares it with my friends in the Facebook network. I don’t want to change my ‘status’ on every service that shows status messages. Similarly, I like that Last.fm captures my listening behavior from iTunes and then uses that data to give back personal recommendations on a badge posted to my blog.
Allowing machines to automatically act on personal data on my bahalf is the right direction for things to go. But important questions need to be resolved.
For example, what happens to my data in all the places I’ve allowed it to appear when I change it? How do permissions pass from one service to another? How do I guarantee that a permission type I grant in one service means the same thing in another service? How do changes propagate? How does consent get revoked?
And even trickier than all that will be the methods for enforcing protection of privacy and penalties for breaking those permissions.
Until trust is measurable with explicit consentual triggers, loosely coupled networks that act on the data I wish to protect are going to struggle to talk to each other. Standards need to enable common sharing tactics. Responsibility needs to be clearly defined. And policies need to be enforceable.
Empowering a person to invest in storing and sharing the more sensitive data he or she owns is going to require a lot more than traditional read/write controls. But given the pace of change right now I suspect the answers will happen as the people behind these services work things out together before the industry taskforces, legal entities and blogosphere sort it out for them.
We’ve been playing around with video as a communications mechanism on Yahoo! Developer Network for a while now. Our casual attempts to generate interest in Yahoo! technologies through interviews, screencasts, tech talks, etc. have worked really well.
So, we hired a full time videographer/filmmaker named Ricky Montalvo and got him some decent gear to push the envelope a little further. And today we rolled out YDN Theater on the YDN web site to establish a home for all the work he has been producing.
The journey here started with a pretty lame but surprisingly successful screencast that Dan Theurer and I did to explain how browser-based authentication worked. It was blurry. We made mistakes. The subject matter was pretty abstract. And neither Dan nor I have particularly strong camera presence.
Regardless, it has been viewed over 19,000 times, so far.
We kept pushing with new types of videos such as partner showcases with people like Joyce Park, Adam Rifkin, and Leah Culver. We brought the camera to our various Hack Days and produced a particularly funny recap of the London event. And we recorded tech talks from our own staff at Yahoo! and presentations from guest speakers like Grady Booch, Joe Hewitt and David Weinberger.
By the time we found Ricky, we knew we were building a program that was going to be really interesting. Yet, we hardly spent any money other than a few cheap cameras and some basic editing tools including Camtasia at that point.
The success to date I think has been in large part due to the fact that we haven’t tried to pimp out our videos with any professional plastic gloss or staged demos. We also try to have a little fun with them. Jeremy Zawodny is a really good interviewer. His unassuming yet pointed questions get people to say things they otherwise wouldn’t include on any planned script. And the fact that the videos are raw with few cuts or edits make them feel real, too.
There are some good video program ideas floating around here that could be a lot of fun, but now we’re torn between how much time we want to spend building out the video offering and how much time we want to spend on all the other ways the team can evangelize Yahoo! technologies.
I’m not sure how to measure that decision just yet, but as long as people are consuming these shows we do with such enthusiasm we’ll probably tilt the scale in favor of doing more video whenever possible.