If you haven’t yet checked out Wind Map by Hint.fm, now might be the most dramatic view you might see in a while. Using hourly data from the National Digital Forecast Database, it shows the organic flow of wind over terrestrial US. It uses HTML5 Canvas so you do need a modern browser to see it. Here’s a live GIF snapshot of it from Monday night (Oct 29, 2012) of the Hurricane Sandy. Click through the image for a higher quality version. And if you like it enough, you can even buy a high resolution print.
One of the biggest technology news this week has been the announcement made by Alasdair Allan and Pete Warden, researchers at O’Reilly, that theiPhone keeps a log of every location you have been to over the past one year and more. One could argue that it isn’t really news but it definitely is a rude surprise to most people. More so because the researchers also made a tool which makes it super easy for anyone to easily parse the contents of the file their own iPhone has been keeping on them.
Though I agree that saving an indefinite history of sensitive location data without explicit user notification is a terrible oversight at the least, I was also tempted to see what my own data held. So I went ahead and here’s what it looks like.
My iPhone faithfully recorded my road trip halfway across the country, my SXSW visit to Austin, Bay Area and LA trips and also my trip to Michigan and Ohio. I think it makes a very interesting sharing object at this level of zoom. Especially because I have been voluntarily giving that data to Foursquare anyway. Foursquare is a lot sparser than the iPhone data but it has more explicit knowledge of the exact business/venue I went to as opposed to the iPhone data that can only be used to make a reasonable guess. However, overall the data that the iPhone has been accumulating is obviously more exhaustive.
I am curious to run more detailed analysis on my own data, and possibly compare it with other people I know and other data sources I have to see what interesting stuff I can find. For example, it would be cool to see how much time my wife and I spend with each other and how it correlates to how many steps I took that day, what I ate, or what music I listened to.
Are we really as unique and different as we like to believe or are we just predictable dots on the map? At a higher aggregate level, data from cellphone carriers has already been used to find that we actually are quite predictable!
Forbes recently published a visualization based on IRS data which shows where Americans moved between 2008 and 2009. You click on the city name that you are interested in and it shows you a dense sets of lines showing migration paths. Red lines show that there was a net number of people moving out whereas black lines show a net number of people moving in. It’s interesting to look at, but really really hard to read, and almost useless due to that. But that’s a different story.
I found it interesting to compare this data with the temperature heatmap of that day. We have had an awfully cold spring but this heatmap really drives in the point. That week, the Pacific Northwest was the coldest region in the country! Makes one wonder, if people really are moving to Seattle in hordes, do they really know what they are getting into?
I wrote a small Facebook application that scans your friends list and finds how they are related to each other. It prepares the data in a format compatible with Many Eyes. You can visualize this data in Many Eyes to see the connections between your friends.
About the visualization: The people on the left are my high school friends, the dense nodes on the right are my current grad school friends and people on the fringes are college friends and other common friends.
Please head over to http://apps.facebook.com/manyeyes/ and visualize your data. I would love to see what other’s people’s data looks like!
This also opens up another interesting dilemma: I accessed information about my friends since I am their friend and they know that I can access it. But I am sure they do not expect me to make a text dump of it and visualize it on a publicly viewable website. I have been thinking hard about my right to do so or their incorrect expectation in this regard. I trust my friends with my information. But if they are not trustworthy, the only thing I can do about that is remove them from my list – this the advice Facebook itself gives to get rid of stalkers and bad wall-posters. I am very interested in hearing from my friends (it’s your data in this visualization) about what they think about this.
[Edit: You might find the following applications interesting as well:
Do you want to see how chatty you have been in the blogosphere? Is there a trend in your periods of lull and heavy activity?
I wrote a plugin that helps convert information about your blog posts into data compatible with Many Eyes. You can install this plugin onto your WordPress installation and then you are just one click away from creating a visualization that looks like the one below:
According to my stats, I’ve been very active in the August of 2005! How about you?
You can download the Many Eyes Data Exctractor plugin from here.
- Unzip to your wp-contents/plugins directory
- Login to to your WordPress admin area – usually <your-blog-uri>/wp-admin/
- Click on Plugins and activate the Many Eyes Data Exctractor plugin
- Click on Manage
- Click on the Visualize tab
Bollywood’s famous lyrics writer, Sameer , uses the words “gaya”, “main”, “dil”, “pyaar”, “sanam” and “mehbooba” the most! Why am I not surprised!
- gaya = went (used as an auxillary verb)
- main = “I”
- dil = heart
- pyaar = love
- sanam, mehbooba = sweetheart