This is one of the most useful charts I have found to easily track the trends. Exponential events have to be seen in logarithmic scales and the thing we want to watch is not just the absolute numbers of cases but the growth rates. When the growth rate goes below zero we will start to see everything else flat line.
Y-axis: New Cases Last Week (log scale) X-axis: Total Cases (log scale)
So in this chart below, the dominant line describes the general trajectory that COVID-19 takes in all countries. China & South Korea are the only ones whose growth rate appears to have gone down.
Also notice that Italy has just started to break out but it’s too early to tell. And Japan had slowed down but is climbing back up.
Please remember it takes 2-4 weeks after lockdown for the numbers to start to change.
And here’s the version with death rates plotted this way:
Click here to see the live version with full animation https://aatishb.com/covidtrends/
And here’s a video that explains this visualization in detail. https://m.youtube.com/watch?v=54XLXg4fYsc
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!
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?
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.
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 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!
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.
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:
Friend Wheel – Creates a wheel with connected dots between your friends