Difference between revisions of "DataVis2012/Projects/Schapiro"

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=== Step 1: Acquiring Data ===
=== Step 1: Acquiring Data ===


The band tour data will be loaded dynamically, using the following datasource:
The band tour data will be loaded dynamically, using one of the following datasources:
http://www.songkick.com/developer/upcoming-events-for-artist
Last.fm
Eventful
Songkick
JamBase


I think my best bet is to scrape the data myself. I will set up a server script to ping the Last.fm API. I will grab the weekly hyped artists charts for 46 US cities (http://bit.ly/wBWTk7). The two API calls will be geo.getMetroArtistChart and geo.getMetroHypeArtistChart (http://bit.ly/yoYlvF). The results will be stored in a mySQL database.
I'll need to test and see which produces the most accurate and extensive data.
 
I think my best bet for geo hyped music data is to scrape the data myself. I have set up a server script to scrape from the Last.fm API. I grab the weekly hyped artists charts for 46 US cities (http://bit.ly/wBWTk7). The two API calls will be geo.getMetroArtistChart and geo.getMetroHypeArtistChart (http://bit.ly/yoYlvF). The results will be stored in a mySQL database.


=== Step 2: Filtering Data ===
=== Step 2: Filtering Data ===

Revision as of 20:03, 26 February 2012

My Projects

Visualizing Music Hype and Music Tour Correlations

I will be mapping out how many people listen to an artist in various metros compared to the most recent tour route.

Step 1: Acquiring Data

The band tour data will be loaded dynamically, using one of the following datasources: Last.fm Eventful Songkick JamBase

I'll need to test and see which produces the most accurate and extensive data.

I think my best bet for geo hyped music data is to scrape the data myself. I have set up a server script to scrape from the Last.fm API. I grab the weekly hyped artists charts for 46 US cities (http://bit.ly/wBWTk7). The two API calls will be geo.getMetroArtistChart and geo.getMetroHypeArtistChart (http://bit.ly/yoYlvF). The results will be stored in a mySQL database.

Step 2: Filtering Data

The subset of the artists I use will be decided based on the richness of the data that is scraped. Hopefully we'll see something interesting here!

Step 3: Rendering Data

My current design idea is a map of the US (or world) with circles of differing sizes compared to how popular an artist is, overlaced with a connecting line betweens cities traveled to (in order) in the upcoming/previous tour