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CartoDB: How Working With Geospatial Data Can Be A Joy and Not A Pain

715398eb5eeb2760aabec19bd862d9c5?s=47 jatorre
April 03, 2012

CartoDB: How Working With Geospatial Data Can Be A Joy and Not A Pain

CartoDB is a new Open Source Geospatial Database in the cloud. It leverages the full power of PostGIS and Mapnik to effortlessly stand up the services needed to create maps and develop location aware applications. It has been built with a love for design and interaction so that playing with geospatial data is a joy and not a pain.

The software has been developed by Vizzuality, a data visualization company focus on geospatial analysis and visualization. CartoDB is offered as a free service, on dedicated cloud instances or as an elastic service enabling users to process large amounts of data and only pay for what they use.

Importing geospatial data is as simple as dragging files into it, editing the data is just the way it is supposed to be, the maps are fast and responsive, it just works! Plus set the tables to real time and you will be able to have real time visualizations on your data changes.

Participants on this workshop will learn how to do beautiful and speedy maps using CartoDB. We’ll start with a file of 600,000 “stop and frisks” carried out by the NYPD, and make sense of the data using CartoDB’s analysis and mapping tools, including thematic mapping by block.

715398eb5eeb2760aabec19bd862d9c5?s=128

jatorre

April 03, 2012
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Transcript

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  27. Demo 1 Amalyze & Visualize

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  35. Demo 2 Building applications

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  40. Demo 3 Using the SQL API

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  49. level0 (full extent on 24x30 pixel) avg: 10070.ms <— SimpTP

    46390 v 8094/8094 g avg: 3190.5ms <— vanilla 4845240 v 8094/8094 g avg: 645.24ms <— Snap 4164 v 697/8094 g avg: 640.53ms <— Simp 27279 v 8094/8094 g level1 (full extent on 295x400 pixels) avg: 10185.ms <— SimpTP 47498 v 8094/8094 g avg: 3233.2ms <— vanilla 4845240 v 8094/8094 g avg: 741.77ms <— Snap 106232 v 7889/8094 g [crowded] avg: 707.78ms <— Simp 34438 v 8094/8094 g *** level2 (1/3 of extent on 600x400 pixels) avg: 3335.9ms <— SimpTP,Snap 14004 v 2183/2183 g avg: 945.04ms <— vanilla 1462892 v 2183/2183 g avg: 476.34ms <— Snap,SimpTP 18282 v 2179/2183 g *** avg: 230.86ms <— Snap 60761 v 2179/2183 g [crowded] avg: 216.49ms <— Simp 13299 v 2183/2183 g *** level4 (1/10 of extent on 600x400 pixels) avg: 853.96ms <— SimpTP 10476 v 547/547 g avg: 218.95ms <— vanilla 327660 v 547/547 g avg: 195.80ms <— Snap,SimpTP 14094 v 547/547 g avg: 74.150ms <— Simp 10287 v 547/547 g *** avg: 70.242ms <— Snap 67041 v 547/547 g [spiky] level4 (1/10 of extent on 600x400 pixels - PRESIMPLIFIED!) avg: 54.777ms <— Simp 13459 v 545/545 g avg: 53.419ms <— Snap 13459 v 545/545 g avg: 52.430ms <— vanilla 13459 v 545/545 g avg: 49.978ms <— SimpNCD 13459 v 545/545 g "MNPTUBTSFTPMVUJPOBSZBTUIF*QBE http://blog.cartodb.com/
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  60. Demo 4 Querying Rasters

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  62. Demo 5 HTML5 lab

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