Blame it on the Voices covered a venn diagram published by reddit user prateekmi2 today which shows the search terms that people use for different religions on Google search. It exposes the different words associated with different religions and the common words are equally interesting.
Web Seer is another visualization tool built specifically for comparing and contrasting google search suggestions for two different search terms. I decided to augment the venn diagram above with the web seer visualization – it’s just another way to present the same information.
As you can see from the visualization, “stupid” is the most frequent word used on Google to complete the sentence “Why are muslims so …” that is also used to complete the sentence “Why are christians so …”. Other common words for both religions are “intolerant” and “hateful”. On the extremes,the words associated with Muslims are “angry” and “violent” while those associated with Christians are “judgmental” and “mean”.
Contrasted with the unique words associated with Muslims, those associated with Jews are “cheap”, “successful” and “smart”. However, the interesting common word for both Muslims and Jews implies “Why are Muslims and Jews so hated?”
According to this visualization, there is nothing in common between words associated with Jews and Christians, however, the original Venn diagram above found the word “annoying” to be common enough.
India is a democratic country with separation of church and state. However, though it’s dominantly Hindu it still hosts one of the largest Muslim populations in the world in terms of absolute numbers. I was curious to see how the two religions compare on Google. There were zero search suggestions for the term “Why are Hindus so …” so for this case I shortened the search terms to “Why are Hindu” and “Why are Muslim”. Unfortunately, there were no words commonly associated with the two religions but it was interesting to see Hindus’ discontent with the movie Avatar and the color of the depictions of their gods.
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?
Puget Sound Energy uses Energy Guide to provide very interesting analytical tools to see your energy consumption. It’s similar to tools by Google and Microsoft, both of which do not work with my PSE account.
I hadn’t noticed this visual before which compares our energy consumption with other similar dwellings in the area:
I am not quite sure what we are doing better. When I compare it to the same month last year, my total YoY consumption has gone down:
If we assume no other factors have changed then the main difference seems that we had two more people living with us that month. I remember reading somewhere that hot water consumption is one of the main variable factor in energy bills. Less people means less hot water consumption. Maybe that’s all that makes us “better” than others in our area. If this is true, then just by looking at anyone’s bill in our area one could predict how many people live in that household
Maybe it is due to some other changes we have made e.g. I took down my 24×7 FON hotspot last month. I could be wrong, but I remember from my previous calculations that a 24×7 router adds up to around the same energry consumption as a stove-top used twice a day. Time to put the rusty Kill-a-Watt to some use.
If you have been following my tweets, I am a big Roomba fan. I have been quite fascinated by the way the Roomba seems to get every part of the room, detects corners to spend more time and energy there etc. The user manual that comes with it tries to explain that the seemingly random motion is actually a concerted exercise in discovering, maximizing power use and efficiency. However, it’s easiest to understand if you look at the long-exposure shot taken by signaltheorist.
The above image shows the entire path taken by a Roomba over 30 minutes. I would really like to see how this looks in a bigger room.
[Doobybrain via Gizmodo]
We have a 2004 Saturn Ion. Check out the awesome MPG we get on it:
This is real data. From actual data points my wife and I have painstakingly recorded each time we fill up. The spikes in the chart above correspond to road-trips. You can tell by the nature of the spikes that we have not taken more than one-tank road trips lately
Predictably, there is a lower MPG in the winter months where you have more stops and slow-downs.
Corresponding to the above, this is how much we have been using our car:
If you want to record and view data about your car in a fun way like this, head over to http://www.mymilemarker.com
If you have always wondered if you are extracting your money’s worth from your Netflix subscription, head on over to FeedFlix. Just “connect with Netflix” and it will fetch your data using Netflix’s APIs and quickly give you graphs like below:
I am paying an average of $0.44 per movie – this includes movies I get as DVDs and those I stream online through my Xbox or Windows Media Center PC. Not bad at all!