NYC Data Science Academy Workshop <05.17.2017>

README.md

NYC Data Science Academy Workshop <05.17.2017>

Grab this page here: http://bit.ly/nyc-dsa

Me, Map Scientist at Carto

• Mehak Sachdeva, mailto:mehak@carto.com, @MehakSachdeva_

Intro

• Hi!
• Some Maps I like
  • UrbanReviewer
  • Colors of Reno
  • CARTO map gallery
• Some maps I made
  • Shift in Margin election map
  • Star Map
  • Immigration Visas
• Blogposts
  • H-1B visa reforms
• Misc
  • QGIS connector

Using CARTO you can:

• easily overlay data onto a map
• analyze the data to reveal spatial patterns
• tell stories by creating beautiful visualizations
• and then share them with the world!

CARTO combines

• SQL and PostGIS
• map styling with CartoCSS
• JavaScript for overlay and interactivity
• A tileserver turning all that data and styles to TILES

Add it up and you roughly get CARTO.

Diagram of CARTO's architecture

Open source

Look at our code! Find the errors bugging you, improve it, be a part of a community.

http://github.com/CartoDB/cartodb

Intro to Builder

Welcome to Carto Builder - let's make a map!

This is map I recently demo-ed in the CartoCamp meetup last month.

The problem statement: Randomly select a citibike and follow it through a random day! (Involved, CARTO, SQL, Google Maps API in Python) Check out the complete workshop here!

Final map:

Start with the datasets here:

Citibike Routing data: http://mehak-carto.carto.com/api/v2/sql?q=select%20*%20from%20citibike_routing&format=csv&filename=citibike_routing

Citibike Stations: http://mehak-carto.carto.com/api/v2/sql?q=select%20*%20from%20citibike_stations&format=csv&filename=citibike_stations

• Upload these datasets and use them to create a map

• On the citibike_routing layer perform the Connect with Lines analysis:

• Pull out the source from the analysis as a separate layer:

• Style the point layer using Animated styling and categorical variables:

• Style the routing lines with categorical variables too:

The final CartoCSS for the line layer looks like this:

#layer {
  line-width: 1.5;
  [category = 1]{line-color:#5F4690; }
  [category = 2]{line-color:#1D6996; }
  [category = 3]{line-color:#38A6A5; }
  [category = 4]{line-color:#0F8554; }
  [category = 5]{line-color:#73AF48; }
  [category = 6]{line-color:#EDAD08; }
  [category = 7]{line-color:#E17C05; }
  [category = 8]{line-color:#CC503E; }
  [category = 9]{line-color:#94346E; }
  [category = 10]{line-color:#6F4070; }
  [category = 11]{line-color:#666666; }
  [category = 12]{line-color:#5effd4; }
  [category = 13]{line-color:#ff3535; }
}

• Styling for the stations:

• Tweaking the CartoCSS to enhance styling:

The final CartoCSS for the torque layer looks like this:

Map {
  -torque-frame-count: 256;
  -torque-animation-duration: 30;
  -torque-time-attribute: "duration";
  -torque-aggregation-function: "CDB_Math_Mode(trip_id)";
  -torque-resolution: 2;
  -torque-data-aggregation: linear;
}
#layer {
  marker-width: 4;
  marker-fill: ramp([value], (#5F4690, #1D6996, #38A6A5, #0F8554, #73AF48, #EDAD08, #E17C05, #CC503E, #94346E, #6F4070, #666666), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10), "=");
  marker-fill-opacity: 1;
  marker-line-width: 0;
  marker-line-color: #FFF;
  marker-line-opacity: 1;
  comp-op: lighter;
}
#layer[frame-offset=1] {
  marker-width: 6;
  marker-fill-opacity: 0.5;
}

Notice the CDB_Math_Mode function which creates predictable values for styling.

• Finally publishing the map!

Let's dive into Cartoframes!

Follow the notebook here:

https://cartodb.github.io/cartoframes/

http://nbviewer.jupyter.org/gist/mehak-sachdeva/e3db643e2f9b0af4346a4c46a70ca773

https://cartodb.github.io/bigmetadata/

Data Science Use-Cases with CARTO:

https://docs.google.com/presentation/d/1fbowTcJfhIahtrDBeOdtGvRSQS2xki0D4wGOuyv09oc/edit#slide=id.p4