Miles and miles of squares

Fifteen years ago I edited a short video about my life during the M.Sc. Back then, the memory in my camera was limited and I could only shoot about 30secs of very grainy video before it was full. So I had to come back to the computer, download the contents of the camera and liberate some space to continue filming. Tools were different, but it was great fun to play with them:

• I had an IBM Thinkpad T22 and the rendering took ages.
• I used Sonic Foundry Video Factory 2.0 to edit the video.
• Goldwave 4.25 was used to record some of the audio and to edit all of it.
• All the pictures were taken with my beloved Olympus D-490 camera.

Just Like Heaven

10 years ago we organized a big birthday party.

When I was 30 I thought I could sing... I try to remain foolish.

Planning a trip

On my last post I talked about Google Maps... Today I'm going to share a couple of holiday itineraries that I've put together using Google Maps:

London 2015
Vienna-Prague-Berlin 2016
Portugal & Spain 2018

This is the process that I've followed for planning these schedules:

• Research (browse the web, read guides, listen to podcasts).
• Enlist the interesting places you want to visit.
• Place the points in a map.
• Group the places into areas that are close to each other.
• Consider opening times and special events (exhibitions, concerts, festivals, etc). 
• Investigate restaurants and bars (check reviews on Foursquare and Trip Advisor).
• Choose an area for your hotel (the maps in Airbnb and Trip Advisor are very useful to make sure your selection is easily accessible and not too far from where you want to be).
• Take everything into account and plan your days.

I enjoy traveling well prepared to make the most of my time and to know a little about the places I visit. Sometimes you definitely need to improvise and sorprises can be great, but I've always found much truth in this phrase: "if you fail to prepare, you prepare to fail".

Google Maps for traveling

I love Google Maps and I've used them to create my own collection of interesting places when visiting a city or planning a trip:

• Bangkok (2013)
• Montreal (2014)
• London (2015)
• Grece-Turkey (2015)
• Vienna-Prague-Berlin (2016)

I have also found them useful for explaining a route and defining guiding points on a map. This is an example I built to present a recommended itinerary from Mexico City to Acapulco (originally posted in my wedding blog):

O-D Animation

I just found a cool feature to animate dashboards!

It is possible to define a filter on a slide that then lets you explore the data as some kind of evolution. This works pretty neatly when observing a variable through time.

Remember the Ecobici dashboard? Check how this kind of animation can be applied on that data:

On Tableau Desktop you can automate the transitions and just hit the play button when examining your data, but this functionality doesn't work when you publish your file to the web. Anyways, the visualization looks something like this when it is automated:

Ousterhout's dichotomy

On a previous post about efficiency I shared my experience when I optimized a dashboard that was running slowly.

However, I didn't measure the performance improvement and the comparison, although very obviously noticeable, was not quantified. It wasn't important then, but in many other cases it is necessary to know more accurately. As it is said, what isn't measured can't be managed.

Measuring efficiency

Tableau's help guide includes an article on Recording and Analyzing Workbook Performance that can lead you through the step by step process on how to evaluate the performance of a workbook. I ran this analysis for the Original Ecobici file and then for the Optimized version.

When filtering the heatmap table by a certain time and day in the week, it took an average of 22 seconds for the original file to run:

While it only took 0.35 seconds for the optimized file to execute the queries implicated.

This signified that the same query ran 60 times faster on the latest workbook, but it also implied a certain tradeoff in the simplicity that the tool offers since it was a little laborious to adapt the data. In this example, it was definitely worth investing some effort in better organizing the information for Tableau to be swifter. However, in some occasions this may not be the case.

The pretentious name for this dilemma is called the Ousterhout's dichotomy:

Easy for humans to program  vs  Easy for machines to run

(Real) Data Ownwership

I'm still trapped on Nike+ since I don't want to give up more than 6 years of data. However, if I started again I'd probably use Strava. It is a very cool mobile app and the desktop version fully allows the import and export of your data. It has pre-built widgets as the one on the left, but you can also extract the second by second readings of your training sessions. You can download your information as a GPX file (a simple XML structure) where each reading is recorded as follows:   <trkpt lat="19.4226360" lon="-99.1724980">     <ele>2244.5</ele>     <time>2017-02-11T22:42:08Z</time> </trkpt> You can also extract the information as a JSON file that I found a little harder to parse, but includes a few extra measures for the accumulated distance, the instant speed and the course (as a value between 0 and 360, I guess it is measured in degrees).

I downloaded the data for a bicycle ride, I translated the JSON into a table and replicated some of the graphs that are shown in the app: