To help jumpstart some interesting analysis and visualizations built on the PLUTO dataset, we have created a CartoDB instance dedicated to hosting this data for you.

• Current - 13.1
• Available
  • 13.1

• Create maps of NYC data
• Find the average floor size of an NYC neighborhood
• Build a reverse geocoder

Data is stored on CartoDB as individual tables in the same way that the NYC.gov website provides them. The naming schema is as follows

File bk_mappluto_13v1.zip contains two shapefiles, BXMapPLUTO.shp and BX_Dcp_Mappinglot.shp. They becomes two tables on CartoDB in the naming pattern {burrough code}{file root}{version}. For example,

NYC.gov provides 5 files just like bk_mappluto_13v1.zip described above, one for each of the boroughs with codes as follows, qn, bk, si, bx, mn.

You can use the SQL API to quickly query and filter data in any of the tables listed above. The SQL API takes PostgreSQL and PostGIS SQL statements. This means you can query by distance, perform intersections, or full-text searches. A basic query would be as follows,

SELECT * FROM bk_mappluto_13v1 LIMIT 10

To access the result through the SQL API, you would use the URL, https://pluto.cartodb.com/api/v2/sql and pass it the q= parameter with your URL encoded SQL statement. The result of the above example would be,

https://pluto.cartodb.com/api/v2/sql?q=SELECT%20*%20FROM%20bk_mappluto_13v1%20LIMIT%2010

If you want to use the data in a programming language such as Javascript or Python, the default JSON format will be handy. If you want to create client-side maps with Javascript (e.g. D3), you can get the results in GeoJSON by adding a parameter, &format=GeoJSON to your URL. Finally, if you want to filter and then download the results, you can get results formatted as CSV, &format=CSV

Maps are available for each of the latest versions of the PLUTO datasets.

You can build your own maps from these layers quickly and easily using CartoDB.js. The quickest example for creating a new map layer from mn_mappluto_13v1 is as follows

  var map = L.map('map').setView([40.77499462,-73.98909694], 12);

  // set a base layer 
  L.tileLayer('http://a.tile.stamen.com/toner/{z}/{x}/{y}.png', {
    attribution: 'stamen http://maps.stamen.com/'
  }).addTo(map);

  // add the cartodb layer
  var layerUrl = 'http://pluto.cartodb.com/api/v2/viz/e21b7338-fbb7-11e2-a0ed-7128c9850036/viz.json';
  cartodb.createLayer(map, layerUrl).addTo(map);

You can get more details on customizing the maps on the CartoDB.js website. The API documentation will tell you how to customize styling and apply filters on the fly.

See this working example to get started.

You can style the maps on the fly using the Javascript library. Above is an example of the Bronx styled by the ownertype column. Click the image to see the live example. Another example would be to apply the building-height style to create maps similar to the following,

The particular CartoCSS used here is,

 #mn_mappluto_13v1{ 
   building-fill: #084081; 
   building-height: 512; 
   [ numfloors <= 64] { 
     building-fill: #0868AC; 
     building-height: 256; 
     [ numfloors <= 32] { 
       building-fill: #2B8CBE; 
       building-height: 128; 
       [ numfloors <= 16] { 
         building-fill: #7BCCC4; 
         building-height: 64; 
         [ numfloors <= 8] { 
           building-fill: #A8DDB5; 
           building-height: 32; 
           [ numfloors <= 4] { 
             building-fill: #CCEBC5; 
             building-height: 16; 
             [ numfloors <= 2] { 
             building-fill: #E0F3DB; 
             [ numfloors = 1] { 
               building-fill: #F7FCF0; 
               building-fill-opacity: 0.6; 
             }
             [ numfloors = 0] { 
               building-fill: #EEE; 
               building-fill-opacity: 0.6; 
             }
           }
         }
       }
     }
   } 
   [ numfloors= null] { 
     building-fill: #ccc; 
     building-fill-opacity: 0.6; 
     building-height: 0; 
   } 
 }

To change use any of the datasets in the CartoDB.js visualizations, you need to change the layerUrl parameter in the example to the correct one for the desired dataset. Below are the dataset viz.json URLs,

You can use SQL statements to filter the data on your maps or to access JSON or GeoJSON formatted data for building content or visualizations. For examples of each see

• Filtered maps
• JSON request

Some examples of interesting SQL requests.

• Find the nearest building to a LatLng
  • http://pluto.cartodb.com/api/v2/sql?q=SELECT%20*%20FROM%20pluto_reverse_geocode(40.792276,-73.968219,3)

Andrew Hill | @andrewxhill | [email protected]

Hosting provided by CartoDB