We hit an awesome milestone over the Presidents’s Day weekend – 50,000 unique datasets uploaded to GeoCommons by the community. It has been great to see the community grow, and the diversity of data that has been contributed. The most exciting part to me is all the potential ways this data can be remixed. From the 50,000 data sets there are 400,000 attributes that you can map – that is 1,599,999,600,000 possible map combinations.
In the spirit of encouraging more remixing of data across the community I combined an eclectic mix of data for the Mission in San Francisco. First I grabbed a data set of all the current start ups in the Mission District from Jessica Lum. Then looked for important start up infrastructure – bars – and found a cool data set from Hunter Holcombe. Then for a bit of fun I grabbed all the vermin reports for the Mission from Ashwin. Just plotting these out was cool, but I wanted to go the next step and answer a question. Which start up in the Mission had the best ratio of bars to rats? With a quick mix of buffers, aggregation and subtraction I got the answer.
The hands down winner is Crowdflower with 5 bars within 200 meters of their office and 0 rat reports to the city! Check out the map below to play with data and filter through it (just click the gray triangle in the layers palette under “Bar to Rat Ratio”)
You can see the map on GeoCommons here and click “details” to grab it as KML, download the raw data or snag your own embed. Many thanks to the GeoCommons community for getting us 50,000 data sets. We are stoked and can’t wait to get to 100,000 data sets. Keep tuned for more cool tools to make it easier to map and analyze your data.
2 Responses to 50,000 Unique Datasets on GeoCommons – Woot!!
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[...] because I think it means more cross data analysis similar to Sean’s recent analysis into San Francisco start-ups and his other analysis on DC Restaurant Inspections and Yelp [...]
[...] find myself pointing people to GeoCommons for data more often these days. With over 50,000 data sets, there’s a lot there. The people I work with seem to usually be able to find data of value [...]