D3x10/Brainstorming - Revision history

Admin: /* D3 + MongoDB */

2016-01-29T19:25:27Z

D3 + MongoDB

← Older revision		Revision as of 19:25, 29 January 2016
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	* great descr of geoids: http://censusreporter.org/topics/geography/		* great descr of geoids: http://censusreporter.org/topics/geography/
	* census metro ids: http://www.census.gov/population/metro/		* census metro ids: http://www.census.gov/population/metro/

	~~===D3 + MongoDB===~~

	~~Implementing some kind of chunked data, implemented into MongoDB~~

	==Alluvial Diagrams for Reaction Datasets==		==Alluvial Diagrams for Reaction Datasets==

Admin at 05:25, 25 March 2015

2015-03-25T05:25:14Z

← Older revision		Revision as of 05:25, 25 March 2015
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	==Cluster/Stat Analysis of 10 Cities==		==15 Metro==

			===Python Preprocessing + D3 Viz for Large Datasets===

			Step 1: Python hooks up to large data set (census), iterates through, implements multithreading, The Cloud, MapReduce, Amazon, etc., dumps to json files in a bucket

			Step 2: D3 accesses those buckets - accesses LARGE data sets - by segmenting, making buttons, dividing and conquering

			Step 3: how you visualize and grok spatial and/or other data

			===Cluster/Stat Analysis of 10 Cities===

	Look at 10 metropolitan areas		Look at 10 metropolitan areas
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	* census metro ids: http://www.census.gov/population/metro/		* census metro ids: http://www.census.gov/population/metro/

	==D3 + MongoDB==		===D3 + MongoDB===

	Implementing some kind of chunked data, implemented into MongoDB		Implementing some kind of chunked data, implemented into MongoDB
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	That would be a cool way to visualize reaction rate set... evolution of network over time		That would be a cool way to visualize reaction rate set... evolution of network over time




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	and visualizing with some D3 charts.		and visualizing with some D3 charts.

	~~==Python Preprocessing + D3 Viz for Large Datasets==~~

	~~Step 1: Python hooks up to large data set (census), iterates through, implements multithreading, The Cloud, MapReduce, Amazon, etc., dumps to json files in a bucket~~

	~~Step 2: D3 accesses those buckets - accesses LARGE data sets - by segmenting, making buttons, dividing and conquering~~		==Map + Scatterplot (DONE)==

	~~Step 3: how you visualize and grok spatial and/or other data~~
	==Map + Scatterplot==

	D3 chart: scatterplot of data; circles display some multivariate information (x by y, etc), and clicking on particular points highlights them on a map. In this way, the data, and not the map, drive the discovery process.		<s>D3 chart: scatterplot of data; circles display some multivariate information (x by y, etc), and clicking on particular points highlights them on a map. In this way, the data, and not the map, drive the discovery process.</s>

	==Map-to-Map Data (DONE)==		==Map-to-Map Data (DONE)==

	<s>Want to be able to use the state-level county map to control the county-level census map, AND control quantities contained in the census map layers.</s>		<s>Want to be able to use the state-level county map to control the county-level census map, AND control quantities contained in the census map layers.</s>

Admin: /* Cluster/Stat Analysis of 10 Cities */

2015-03-14T16:20:39Z

Cluster/Stat Analysis of 10 Cities

← Older revision		Revision as of 16:20, 14 March 2015
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	By moving Python processing out of the loop and doing it on the front end as a pre-processing step, you can start to use different strategies/technologies (parallelization, cloud, S3 buckets, etc) for Python code.		By moving Python processing out of the loop and doing it on the front end as a pre-processing step, you can start to use different strategies/technologies (parallelization, cloud, S3 buckets, etc) for Python code.

			For obtaining info abt metropolitan areas:
			* great descr of geoids: http://censusreporter.org/topics/geography/
			* census metro ids: http://www.census.gov/population/metro/

	==D3 + MongoDB==		==D3 + MongoDB==

Admin: /* Cluster/Stat Analysis of 10 Cities */

2015-03-14T16:10:57Z

Cluster/Stat Analysis of 10 Cities

← Older revision		Revision as of 16:10, 14 March 2015
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	* with Python: it may take a loooong time, but can parallelize it and turn it loose for a bigger project		* with Python: it may take a loooong time, but can parallelize it and turn it loose for a bigger project

	By moving Python processing out of the loop and doing it on the front end as a pre-processing step, you can start to use different strategies for Python code.		By moving Python processing out of the loop and doing it on the front end as a pre-processing step, you can start to use different strategies/technologies (parallelization, cloud, S3 buckets, etc) for Python code.

	==D3 + MongoDB==		==D3 + MongoDB==

Admin: /* Campaign Finance */

2015-03-14T06:40:34Z

Campaign Finance

← Older revision		Revision as of 06:40, 14 March 2015
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	OpenStates API		OpenStates API

			Another list of more data sets for campaign finance: https://sites.google.com/site/bicoastaldatafest/data

			Visualization of industries by location/Congressional district?

			Click on a district, see two senators and representative, see major industries in that area, see what industries in that area contribute to whom, see what major contributors to each politician are

	==Log File Visualization==		==Log File Visualization==

Admin at 06:34, 14 March 2015

2015-03-14T06:34:54Z

@@ Line 1: / Line 1: @@
-=Maps=
+==Cluster/Stat Analysis of 10 Cities==
-==Scatterplot==
+Look at 10 metropolitan areas
 D3 chart: scatterplot of data; circles display some multivariate information (x by y, etc), and clicking on particular points highlights them on a map. In this way, the data, and not the map, drive the discovery process.
-==Map-to-Map Data==
+==Map-to-Map Data (DONE)==
-Want to be able to use the state-level county map to control the county-level census map, AND control quantities contained in the census map layers.
+<s>Want to be able to use the state-level county map to control the county-level census map, AND control quantities contained in the census map layers.</s>

Admin: /* Maps */

2015-02-23T03:55:44Z

Maps

← Older revision		Revision as of 03:55, 23 February 2015
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	D3 chart: scatterplot of data; circles display some multivariate information (x by y, etc), and clicking on particular points highlights them on a map. In this way, the data, and not the map, drive the discovery process.		D3 chart: scatterplot of data; circles display some multivariate information (x by y, etc), and clicking on particular points highlights them on a map. In this way, the data, and not the map, drive the discovery process.

			==Map-to-Map Data==

			Want to be able to use the state-level county map to control the county-level census map, AND control quantities contained in the census map layers.

Admin: Created page with "=Maps= ==Scatterplot== D3 chart: scatterplot of data; circles display some multivariate information (x by y, etc), and clicking on particular points highlights them on a map. I..."

2015-02-23T03:44:40Z

Created page with "=Maps= ==Scatterplot== D3 chart: scatterplot of data; circles display some multivariate information (x by y, etc), and clicking on particular points highlights them on a map. I..."

New page

=Maps=

==Scatterplot==

D3 chart: scatterplot of data; circles display some multivariate information (x by y, etc), and clicking on particular points highlights them on a map. In this way, the data, and not the map, drive the discovery process.