DataVis2012/Projects/Schapiro - Revision history

Jschap01: /* Step 1: Acquiring Data */

2012-05-07T23:27:32Z

Step 1: Acquiring Data

← Older revision		Revision as of 23:27, 7 May 2012
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	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.		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.

			UPDATE: Full scraped database at: http://theitt.com/InternalProjects/DataViz/lastfm.sql

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

Jschap01: /* Version 2: Google Maps */

2012-05-05T18:21:55Z

Version 2: Google Maps

← Older revision		Revision as of 18:21, 5 May 2012
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	FINISHED: http://theitt.com/InternalProjects/DataViz/tour/		FINISHED: http://theitt.com/InternalProjects/DataViz/tour/
			Source code: https://github.com/yono38/Listeners-Concerts-Corr

Jschap01: /* Version 1: Timecloud */ fixed link

2012-05-05T18:21:11Z

Version 1: Timecloud: fixed link

← Older revision		Revision as of 18:21, 5 May 2012
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	I may be able to do this by modifying https://github.com/stef/timecloud to place tag clouds atop a static image of the United States or a dynamic image such as: http://www.openstreetmap.org/		I may be able to do this by modifying https://github.com/stef/timecloud to place tag clouds atop a static image of the United States or a dynamic image such as: http://www.openstreetmap.org/

	Update: MOCKUP at http://theitt.com/InternalProjects/DataViz/		Update: MOCKUP at http://theitt.com/InternalProjects/DataViz/tagcloud

	'''Conclusion''': This doesn't give very much useful information. Time to go back to my original idea		'''Conclusion''': This doesn't give very much useful information. Time to go back to my original idea

Jschap01: /* Version 2: Google Maps */

2012-05-03T00:10:11Z

Version 2: Google Maps

← Older revision		Revision as of 00:10, 3 May 2012
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	// circle overlays (for # of listeners in city)		// circle overlays (for # of listeners in city)
	https://developers.google.com/maps/documentation/javascript/reference#MarkerShape		https://developers.google.com/maps/documentation/javascript/reference#MarkerShape

			FINISHED: http://theitt.com/InternalProjects/DataViz/tour/

Jschap01: /* Step 3: Rendering Data */

2012-04-30T22:46:00Z

Step 3: Rendering Data

← Older revision		Revision as of 22:46, 30 April 2012
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	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		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

			==== Version 1: Timecloud ====
	UPDATE: After discussing with Silva, the new plan to a slider to deal with time, and as you slide along, tag clouds appear over the various cities of the most popular artists. There is also the option to filter these artists by genre, etc.		UPDATE: After discussing with Silva, the new plan to a slider to deal with time, and as you slide along, tag clouds appear over the various cities of the most popular artists. There is also the option to filter these artists by genre, etc.

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	Update: MOCKUP at http://theitt.com/InternalProjects/DataViz/		Update: MOCKUP at http://theitt.com/InternalProjects/DataViz/

			'''Conclusion''': This doesn't give very much useful information. Time to go back to my original idea

			==== Version 2: Google Maps ====

			// rewrite API to take band name and year and find out total number of listeners in each city

			// getting past events
			// then put them on the map
			http://ws.audioscrobbler.com/2.0/?method=artist.getpastevents&format=json&artist=phish&api_key=46d8e5af527082f81cd3e592d5d832a8&limit=300

			// numbered icons
			http://code.google.com/p/google-maps-icons/wiki/NumericIcons

			// circle overlays (for # of listeners in city)
			https://developers.google.com/maps/documentation/javascript/reference#MarkerShape

Jschap01: /* Step 2: Filtering Data */

2012-04-16T23:50:51Z

Step 2: Filtering Data

← Older revision		Revision as of 23:50, 16 April 2012
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	Rank_data = { // obj containing cities		Rank_data = { // obj containing cities
	Boston: [ // each city is array of weeks		Boston: [ // each city is array of weeks
	[ // each week has array of artist rank objs		[ // each week has array of artist rank objs
	{		{
	artist: 'Beyonce',		artist: 'Beyonce',
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	},		},
	...		...
	],		],
	[		[
	...		...
	]		]
	]		]
	}		}
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	*Miami		*Miami
	*New York		*New York


	=== Step 3: Rendering Data ===		=== Step 3: Rendering Data ===

Jschap01 at 23:50, 16 April 2012

2012-04-16T23:50:08Z

← Older revision		Revision as of 23:50, 16 April 2012
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	]		]
	</pre>		</pre>

			To keep clutter minimum, I think I'll use this subset of cities:
			*Boston
			*Austin
			*Philadelphia
			*Nashville
			*San Francisco
			*Miami
			*New York


	=== Step 3: Rendering Data ===		=== Step 3: Rendering Data ===
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	Update: MOCKUP at http://theitt.com/InternalProjects/DataViz/		Update: MOCKUP at http://theitt.com/InternalProjects/DataViz/

	~~To keep clutter minimum, I think I'll use this subset of cities:~~
	*Boston
	*Austin
	*Philadelphia
	*Nashville
	*San Francisco
	*Miami
	*New York

Jschap01: /* Step 2: Filtering Data */

2012-04-16T23:49:40Z

Step 2: Filtering Data

← Older revision		Revision as of 23:49, 16 April 2012
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	I will take the top 5 artists for each city from each week and put into JSON as such:		I will take the top 5 artists for each city from each week and put into JSON as such:
			<pre>
	Rank_data = { // obj containing cities		Rank_data = { // obj containing cities
	Boston: [ // each city is array of weeks		Boston: [ // each city is array of weeks
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	...		...
	]		]
			</pre>

	=== Step 3: Rendering Data ===		=== Step 3: Rendering Data ===

Jschap01: /* Step 2: Filtering Data */

2012-04-16T23:48:40Z

Step 2: Filtering Data

← Older revision		Revision as of 23:48, 16 April 2012
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	=== Step 2: Filtering Data ===		=== 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!		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!

			I will take the top 5 artists for each city from each week and put into JSON as such:

			Rank_data = { // obj containing cities
			Boston: [ // each city is array of weeks
			[ // each week has array of artist rank objs
			{
			artist: 'Beyonce',
			listeners: 501
			},
			{
			artist: 'Arcade Fire',
			listeners: 200
			},
			...
			],
			[
			...
			]
			]
			}

			Weeks = [ // use FROM_UNIXTIME over full set of weeks
			'2009-02-01',
			'2009-02-08',
			...
			]

	=== Step 3: Rendering Data ===		=== Step 3: Rendering Data ===

Jschap01: /* Step 3: Rendering Data */

2012-04-16T23:36:48Z

Step 3: Rendering Data

← Older revision		Revision as of 23:36, 16 April 2012
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	Update: MOCKUP at http://theitt.com/InternalProjects/DataViz/		Update: MOCKUP at http://theitt.com/InternalProjects/DataViz/

			To keep clutter minimum, I think I'll use this subset of cities:
			*Boston
			*Austin
			*Philadelphia
			*Nashville
			*San Francisco
			*Miami
			*New York