[This is one of those moments when you realise you haven’t been seeing the big picture. Digging around the edges of a new concept you suddenly see the foundations are much deeper than you thought. So – hats off to our wonderful dev team for being several steps ahead…]

I finally had a few moments of spare time the other day, so I got to watching some internal training videos for Tableau 10.2. These particular videos are what we call WINK (what I need to know) training and are deep dive sessions on new features we have released. One of them immediately caught my eye with the following abstract:

“Extract API supports geospatial data”

Wait… what!?!

Sure enough – when I went looking I found one of the new features in 10.2 is that the extract API now supports the spatial data type. You can find more about this feature in the Tableau SDK Reference. The really cool part of this is that it’s super simple to use – all you have to do is insert spatial data in WKT format. This means you can easily fabricate your own spatial data or import it from a spatial database using a function like ST_AsText().

It’s been a long time since I flexed my coding muscles but my Google-fu is mighty, so without too much hassle I was able to install Python, install our Tableau API and fiddle with our SDK sample. The code was easy (once I realised that indenting is apparently important in Python J) and the relevant lines are highlighted:

# Insert Data
row = Row( schema )
row.setDateTime( 0, 2012, 7, 3, 11, 40, 12, 4550 )    # Purchased
row.setCharString( 1, 'Beans' )                # Product
row.setString( 2, u'uniBeans'    )            # Unicode Product
row.setDouble( 3, 1.08 )                 # Price
row.setDate( 6, 2029, 1, 1 )                 # Expiration Date
row.setCharString( 7, 'Bohnen' )            # Produkt
for i in range( 10 ):
    row.setInteger( 4, i * 10 )                # Quantity
    row.setBoolean( 5, i % 2 == 1 )             # Taxed
    inner = str(i * 3)
    outer = str(i * 5)
    row.setSpatial( 8, "POLYGON ((" + inner + " " + inner + ", " + inner + " " +
outer + ", " + outer + " " + outer + ", " + outer + " " + inner + ", " + inner +
" " + outer + "))"
    table.insert( row )

The result was this:

I was also able to load the file with mixed spatial types:

# Insert Data
row = Row( schema )
row.setDateTime( 0, 2012, 7, 3, 11, 40, 12, 4550 )  # Purchased
row.setCharString( 1, 'Beans' )                     # Product
row.setString( 2, u'uniBeans'    )                  # Unicode Product
row.setDouble( 3, 1.08 )                            # Price
row.setDate( 6, 2029, 1, 1 )                        # Expiration Date
row.setCharString( 7, 'Bohnen' )                    # Produkt
for i in range( 10 ):
    row.setInteger( 4, i * 10 )                     # Quantity
    row.setBoolean( 5, i % 2 == 1 )                 # Taxed
    inner = str(i * 3)
    outer = str(i * 5)
    if ( i % 2 == 0 ):
           row.setSpatial( 8, 'POINT(' + inner + ' ' + inner + ')' )
    else:
           row.setSpatial( 8, 'POLYGON ((' + inner + ' ' + inner + ', ' + inner +
' ' + outer + ', ' + outer + ' ' + outer + ', ' + outer + ' ' + inner + ', ' +
inner + ' ' + outer + '))' )
    table.insert( row )

Note that this isn’t fully supported in Tableau but you can use them if you are careful not to mix them in the same mark. Get it wrong and you’ll see this:

Get it right and you’ll see this:

The result of all this is that we are not limited to just bringing in spatial data from spatial files – we can bring it from anywhere with a little bit of effort. This is very exciting and I look forward to seeing what you all create.