Parking Space Wars

This is a story that I wasn’t planning on telling anyone, but I was invited recently to propose a technological project that relates to traffic issues, which are very severe in my city. This brought this to my mind. The story begins with me waking up very early so I could arrive around 6 a.m. every morning to work.

I had a different, but related problem at the same time, due to a lack of parking spots at the office where I worked some time ago. A person bought a new car and decided to start taking my sport, which was conveniently located right next to the office’s entrance.

I was having none of that, so we started this passive-aggresive (but friendly) competition for this parking spot. Soon, a few people followed suit in seeking this parking space, including my boss (who is also one of my best friends). I started then registering data on Twitter to make it a bit more fun. The logic was as follows:

  • A hashtag (#s) to record my departure and arrival times.
  • Another one to register the result (W = win, L = lose).
  • A time modifier in case it was necessary (i.e. I had forgotten to record data on time).

Take this tweet for example, on a day with a loss (they were painful!)

My friend were having a good time with it:

In the end, I used Twitter’s API to fetch and R to analyze the data. Did I learn something? Not really from the data, but from using simple modifiers on Twitter to record activity and results. I think that a useful application can be used to encode situations like those of disaster response.

Mining Facebook data for music recommendations

A group of friends maintains a Facebook group for music recommendations. Every day, someone comes up with a hashtag and the community replies with links to songs, mostly from YouTube. The hard part of the game was, however, to gather all the songs at the end of the day, since on good days we could end up with more than 100 songs recommended by musicians and experts.

My proposed solution to the problem was to use the FacebookR package for R to mine all the information in the group. One thing that I noticed, though, was all the information that you’re actually able to mine by using the Facebook API about publications and reactions. The information was transformed and organized by hashtag. The second part of the project involved using YouTube’s API to retrieve not-so-clean artist and song information. Finally, I “manually” created playlists for each hashtag and put it online on a static web page on a test server for the group. I’ve successfully used the playlists at parties in a few countries around the world, so I’m happy with the result.

You can see the source code here.

Analysis of collaborative project behavior

This past August I presented alongside Isaac Robles a paper that analyzed the work of members of the Latin American Fab Lab Network on regional collaborative projects, through their engagement with Google Drive project folders.

A few months later, I have worked on the solution of some technical dificulties on the mining of data (I am in debt with Mario Gómez and Joksan Alvarado for all thier support in making the script) and I have worked on an R script to clean and graph some of the data, for future analysis. You can see the script here.

Some of the interesting findings of this analysis is the lack of connections between number of participants and levels of engagement for certain periods, and the inconsistence in the participation of some project participants, which may mean that certain types of incentives must be necessary for these types of projects.

Due to the constraints of this study, however, it is difficult to compare the findings with other types of initiatives.

Transantiago Visualization

As a result from the workshop made at Fab13 in Santiago, Chile, I worked with Mario Gómez with the design of a proof of concept for a model of visualization of data for people who mobilize in the city. For this, sample data from the Transantiago system was mined and visualized for further analysis. The concept is the feasibility of implementing Internet of Things devices to measure pollution in the city, in order to have more accurate environmental readings of the city in real time.

This platform was completely developed by Mario Gómez and presented at the #DatosYCerveza event organized by Escuela de Datos and Cadejo, in San Salvador. You can view the platform here.

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