Category: mapping

Mapping Venues with Flourish

Posted on by jazzman

I recently shared some venue maps created in Carto using MusicBrainz data where we can see all the musical venues (with lat/lng coordinates) listed in the database. Now that I am exploring Flourish as a visualization tool, it is time to test the mapping capabilities to see if it provides a viable alternative for geographic visualization.

As with the Carto map, the first step is to retrieve the data with a query from my local MusicBrainz database:

select p.”name” , p.address , p.coordinates , split_part(trim(p.coordinates ::text, ‘()’), ‘,’, 1)::float AS lat,
split_part(trim(p.coordinates ::text, ‘()’), ‘,’, 2)::float AS lng,
pt.”name” as type, ar.name as locale
from place p
inner join place_type pt
on p.”type” = pt.id
inner join area ar
on p.id = ar.id

where p.coordinates is not null

The results are exported to a .csv file for ingestion by the Flourish map template. In this case we are using a point map to display venues using the latitude (lat) and longitude (lng) data we just created. Before uploading our data, we can see that Flourish provides an easy to use template populated with sample data, allowing us to see the data format we need to deliver:

Sample data for a Flourish point map

Here we can see a name field (City) and latitude and longitude fields for positioning points on a map. Other attributes provide data that might be used for sizing, coloring, or context.

In our case, we can upload our venues .csv file in this general format, and then tell Flourish which columns to use. Here’s what our data looks like after uploading:

Data for venue mapping

Now that the data has been uploaded we can specify which columns to use. Rather than using column names (Name, Address, etc.), Flourish works off the actual column locations (A, B, C, etc.). These are the values we need to input in the options tab. We can use A, B, and F for describing the data with labels and/or popups, use D for latitude and E for longitude, and switch from the Date to the Preview pane.

When we complete that task, Flourish gives us an attractive map, with many templates to choose from, and dozens of options for styling the map and surrounding area:

Venues map zoomed in to Italy

Here’s a quick view of the many available categories for styling your map:

Template options for mapping with Flourish

We’ll skip the details for now, but it is obvious that we could do an amazing number of things to customize our map.

To answer my earlier question, it appears that Flourish will be more than capable for mapping a variety of data, and I’m looking forward to testing it with much larger data sets. See you soon, and thanks for reading!

Music Venues – Interactive Maps

Posted on by jazzman

As a follow-up to my last post on mapping music venues using the great MusicBrainz database, I am adding interactive versions of the maps for you to explore. First up is the cluster map showing aggregations of venues across the globe. Scroll in and out to view more or less detail:

The second map view uses a categorical approach, coloring each venue by it’s specific type (arena, stadium, etc.) and provides additional information when a venue is selected. As you scroll in the specific venues become more visible:

Have fun exploring, and watch for new maps coming soon!

Music Venues Mapped

Posted on by jazzman

The last two weeks have been quite involved with updating my local database with data from the amazing MusicBrainz site. If you’re not familiar with MusicBrainz, it is a bit of a wiki for musical data, including artists, releases, places, and so much more. It’s a truly impressive data source, and it is, of course, very large from a data perspective.

While this site is skewed toward jazz, it would be a shame not to dabble in some other genres; after all, I have spent my share of time listening to classical, rock, Americana, and a few more styles of music. I’ll begin with a simple mapping of all the places (not just jazz venues) in MusicBrainz with lat/lng coordinates, using my old friend Carto to do the display work.

Let’s start by viewing a set of the raw data from the place table using DBeaver:

Data sample of MusicBrainz places

Here you get an idea of useful information we can pull from the place table; name, address, coordinates, and type, plus new fields created in the query for latitude and longitude. Carto (plus Mapbox and other mapping platforms) requires latitude and longitude attributes in order to map the data. Here’s the simple code used to extract this information:

Code to create lat/lng attributes

After creating a .csv export file, the data is uploaded to Carto, where we can begin mapping the information in a variety of ways. Since the places dataset is quite large (21k records with coordinates), a cluster map might prove useful. Carto allows for setting some options, including bubble and text sizes to optimize the display. Cluster maps aggregate the information at high levels, and then allow us to scroll in on the information at a more localized level. Here is the Carto menu:

Setting cluster options in Carto

Here’s a very high level display using the cluster option:

Top level of the place cluster map

As we scroll in, the bubbles will change into smaller aggregations:

Scrolling in on the place cluster map

At the deepest level of scrolling, every place in the data file will display as a single point at it’s respective lat/lng coordinates:

Lowest level of the place cluster map

This is fun to see how the data is aggregated and ultimately dispersed at lower and lower levels, but it comes with some limitations, including the inability to see any identifying details at the individual place level. To see this information, we’ll need a different Carto visualization.

Let’s investigate the category option, which allows for the addition of labels and the coloring of attributes by a specific category. We select the categorywizard, and choose the type column to be used for coloring markers on the map.

Setting Carto category map options

Here’s what we see at a very high level – lots of individual markers (and colors) that are not consolidated as they were with the cluster approach.

High level view of the place category map with type colors

The positive trade-off comes when we select any individual marker, where we have set up the info window to display the name, type, and address:

An individual place with detailed information

This was a simple overview for how we can visualize the place data; in future posts I look forward to exploring some more interesting uses of this rich geographic data. Thanks for reading!