The Rise of the Wearables

WIRED’s article on Wearable Computers being the next big thing got us talking about whether  wearables were indeed what the future had ordered. When I was researching for the MSR Design Expo submission last year, I came across the fact that life-logging has been present since the early 1980’s. Pioneered by Steve Mann, who went on to found the Wearable Computing group in the MIT Media Lab, this was the beginning of wearable computing.

Steve Mann and his Wearable Device.
Steve Mann and his Wearable Device.

The start was almost 30 years ago and an idea ahead of its time. Today social media is brimming with people posting their day to day lives. Some do so with caution and a slew of new networks like Snapchat have risen to the occasion. Facebook has replaced my regular emails and Twitter my newspaper. To me, this is prime time for the rise of the wearable device. Smartphones with their multipurpose apps own the current era but they have that one fatal flaw; as Thad Starner, the technical lead of Google Glass and who spent nearly two decades wearing a wearable device, points out: “If you can’t get to a tool within two seconds your use of it goes down exponentially.” Wearables will be able to fix this gap. Already people are at work hacking this readily available tech for their advantage. Use cases like Patrick Jackson‘s fire fighting apps or Recon Instruments‘ sports training enabling devices are where I see the potential of these devices. Just the other day, I was part of a conversation where we were discussing which fitness device is a better buy. I think this should signal that wearable devices are very much a part of our future. This said, I don’t really like the idea behind Google Glass for “everybody”. Although designed with the core idea of reducing the time between their intention to do a task and their ability to perform that task, it does give the impression of not being there in the moment. I think the problem here is the obsession that people have with devices and services. Very often the human becomes the slave. Even though you seem to have access to capture every moment of your life today, how much of you remember living it?

Image Credit: Wikipedia and Avi Solomon

Swissnex Gaming Jam

This is the story of the origin of the card game, Glutton and my experience at the Swissnex Game Jam held last Sunday.

This is the story of the origin of the card game, Glutton.

It all started, when I signed up for the Gaming Jam to be held as part of Swissnex’s Game Gazer Exhibition. Feeling very n00bish, I still decided to attend the session on Sunday at Swissnex India. After an Introduction, the theme was released; Indian Culture in Game Design.

Team Stark

I was in a 5 member team which we named as ‘Team Stark’ and it consisted of me, Murali, Debashish, Rufael and Simon. After an initial brainstorming, where we dealt with traffic(not really indian) and gods(too controversial), we narrowed down to food etiquette. This was because Simon pointed out that Indian foods would be something that a foreigner would want to learn about and there was no easy way of going about it. We brainstormed some more where we started out thinking about Game etiquette and ended up trying to conquer cities based on food knowledge. Narrowing down, we decided to make a card game and tried to figure out the game play and the game mechanics of the game.

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Prototyping and Playtesting the game helped us realize the problems in the game play and also how to scale the game. Overall it was a fun experience and I will definitely try to organize one in college in the near future.

What:
A Card Game to introduce Indian Foods and Food Habits to Foreigners.

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How to Play:
One set of cards is designed for a game with 2-4 players and it’s encouraged to have atleast one Indian who could help moderate the game. Each card will have information regarding the food like a photo and a calorie count. The cards would be separated into Breakfast, Lunch and Dinner cards. Each player is dealt 9 cards and this forms his ‘plate’. He needs to maximize the value of each 3 card combo before the game ends. There are special combos that can be formed which would create added value for the player. At the end of the game, the points are tallied up to judge who wins which meal.

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Hope to make a better prototype when I get some free time.

Building ‘Meteorites: Earth Impact’

It all started with this tweet. Time was scarce indeed and I had not created an interactive visualization till now. I was supposed to have been doing some d3 this holidays but I never got round to it.

The dataset provided for the contest has 45k+ rows and that was slightly intimidating for a beginner like me.

So I initially decided to take a small set of the data and use it. Like the top 10 found and fell meteorites size wise. Using Tilemill to geotag these was simple anyway.MeteoritesAs you can clearly see above, the size of the red and yellow dots represent the found and fallen meteorites respectively. The relative size of theirs is because of their actual size varies that much with the biggest weighing in at 60 tons. Problem with this graphic was that the size was too big for you to pinpoint the targeted coordinate. Certain impact points hid/overlapped with others. No additional information about the meteorites could be presented.

This led me to the second iteration. Importing a slightly better map into Illustrator(via Export as PDF from Tilemill, SVG didn’t work for me), I played around with labelling the meteorites and this resulted with the map below.

Meteorites-vizThings seemed a little clearer in the above version, but there were still some problems. The scale was skewed a lot if I considered only these values. The yellow dots were nearly invisible and the red ones overshadowed everything. About that time I came across this visualization. That prompted me to play with the impact sizes a bit more. Iterations were made to get the size right. It was a slow process because most of the dots overlapped and made sections obscure. Finally I came up with this before I slept just past midnight.

reddotsPlaying with the opacity above gave me an idea about the denser areas. At around this time, when I was discussing these visualizations with @Rasagy, @Hashnuke asked if I wanted the reverse geotagged locations so that I could perhaps map the Countries in a sort of choropleth. He said he would write a script and run it using a reverse geotag service like Google. So we set Sunday as the day to do this.

I continued to work on Tilemill and decided to export the map and host it on Mapbox. Decided to learn Wax so that I could build an interactive visualization. At that moment, most of the visualization that you see at the end was forming in my head. With 2 days left to go for the submissions at Visualizing.org. I decided to give it a go.

Playing around with Wax led me to a bug when the map was fullscreen and led me to file my first issue at Github. After that I got stuck while trying to figure out pivot tables in MS Excel. @Sevenaces helped me realise my stupid mistake and I got the data I need to plot the Column charts.

Choosing a charting library was the next thing. I wanted something simple that did not need too much work, offered interactivity out of the box, etc. I went through my bookmarks to check dviz. I had been meaning to use this sometime soon and decided to for this project. The other option I had was dviz but the simplest examples looked like a lot of work. That tipped the scales in dviz’s favour. dviz is the simplest charting solution for people who don’t know javascript and wont be bothered to learn javascript. So the column charts worked wonderfully. Stacked column charts were my first option but that showed me the fact the the fallen meteorites were much lesser and were easily hidden by the found meteorite data. Hence I decided to separate the two charts and show the data separately. Poking around Google’s Visualization api, I figured how to customize the dviz charts some more. I used Flatuicolors for the colours. That done, I turned to Foundation 3 to build something simple.

Next, Akash walked me through setting up a github account, hosting a .io repo there. I installed Github for Windows and everything was simple and intuitive. Git incidentally was something I was meaning to learn from a long time, this project gave me and opportunity to do that. The prototype visualization was up and online on Saturday night but it was quite a long way from finishing.

There were obviously problems with the data set. The section at 0,0 seemed to be awfully dense for a point in the middle of the ocean. This led me to review the dataset. I found that more than 10k rows didnt have coordinate data and some of them had 0,0 instead. I decided to clean these rows out of the geotagging. They were bad locations and did not contribute of anything. The dataset was now slightly above 32k rows. More Tilemill followed. Tilemill kept hanging every now and then and I had to close it every few minutes. Frustrating indeed. The huge dataset could be a possible reason. Figuring out the legend and tooltip design took me some more time. Finally the map was done. More hangups followed and I was finally able to export and host the final map on Mapbox.

The next problem came due to the 32k row .csv file. The big file was throwing errors. We then split the dataset into 3 sections and Akash ran the script on Geonames via nitrous.io. He should really write a post on how he did all that. Here’s the scripts and the processed data. There were about 40 bad locations in the dataset which were removed.

The output of the reverse geocoding was the country code. I wanted the country names. This is how I learnt about vlookup in MS Excel. I also learnt how to fill all the blanks in a table and how to divide a column by a number. These are not as straightforward as you think. Excel hung up on me as well. Lot of times. Remember making everything a table helps a lot when doing Excel operations. I used the country name list from here(It’s missing SS=South Sudan). Finally everything seemed ready. Now all I needed was a good scatterplot example to borrow 😉

A quick search of mbostock’s d3 gallery and I located a scatterplot that I could use. It was simple to understand. I promptly hacked the example to meet my demands. I learnt a bit of d3 along the way.
With the final changes all done, I was done with ‘Meteorites: Earth Impact’. In 3 days I learnt such a lot. It was indeed a wicked journey.

Without anymore delay, Ladies and Gentlemen I present to you ‘Meteorites: Earth Impact’.

Update: The visualization got ‘Staff Picked’ on Visually. #proud

Pincoding India

I was running through the book, Visualizing Data by Ben Fry and came across his pincode example for USA. I decided to replicate the example for India. Thus began my search for an geo-tagged dataset of indian pincodes. Sadly it does not exist. The best set of easily available data is hosted by datameet at http://pincode.datameet.org/

I ran the set through Tilemill and found large parts of India still untagged especially almost all of Maharashtra and Bihar. I asked Arun and he told me that ” there are no official public datasets available. But there is reasonably good coverage in the openstreetmap data. The simplest way to view the data is to probably use http://maperitive.net” which I will try to explore for now.

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You could help by mapping your own pincode on this website. Go on, it only takes a minute.