Travel/Drive-Time Maps in Tableau by Marc Schønwandt

 

Today I'm honored to be hosting a guest post by Marc Schonwandt. Marc has been doing some really cool work in Tableau recently, so if you haven’t already done so, go follow him right away. Marc is a Tableau and Alteryx Lead consultant and Tableau Certified trainer with Inviso by Devoteam, located in Copenhagen, Denmark. When Marc saw Tableau for the first time in 2015 he fell in love with the tool instantly and Tableau has helped him grow professionally and creatively every single day since then. You can follow him on Twitter @MarcSchonwandt, on LinkedIn or on Tableau Public.

 

I’ve always felt there was something mesmerising about maps. Don’t you feel the same? Not just because they are relatively easy to interpret and often beautiful nor because they allow us (data practitioners) to play with things such as shadows, excessive layers of data and other features that are usually not “recommended” when working with data visualisations where simplicity is key. Rather, I think the reason I love maps is because they are so “real”—the closest to something physical and tangible that we have in the data viz world. They hold the truest and most honest representation of data of any type of data visualisation. And I do love a good and honest viz!

 

Recently, a colleague of mine asked for input for a spatial analysis in Tableau. The questions he needed to solve were things like “how many schools are within a 15 minute drive from X location?” and “how many supermarkets are within a 30 minute drive from Y location?” I’ve previously seen some awesome drive time/distance maps online, but never had a good use case to dive into it myself. Until now that is, so let's get going! First, we’ll look at how the drive time data was collected and then we pop into Tableau to finish things in a blast.

 

Getting Drive Time Data

To get my hands on the drive time data I used the free HERE Isoline Routing API. To use the API, you first need to sign-up to their Freemium plan (no credit card required) and get an API key. This part is simple and can be done in just a few minutes.

 

The API’s only required parameters are an origin and a destination (given by two pairs of WGS84 coordinates in the form <latitude>,<longitude>), a drive time in seconds, and a transportation mode, which can be any of bicycle, bus, car, pedestrian, scooter, taxi, or truck. Lastly, you can define the routing mode as either fastest or shortest, which specifies the optimization that is applied during isoline calculation.

 

In my case, I used my hometown, Copenhagen (55.671247,12.523785) and requested the isolines for the drive durations from 1 to 90 minutes by car. In other words, I asked the API to draw a line showing how far I could go in 1 minute, 2 minutes, 3 minutes ect. all the way to 90 minutes. Luckily the API lets me bulk all these requests into a single call. As the HERE API allows you to ask for driving distance all the way down to the second level, I had to convert minutes into seconds. The final request looks something like this. All the numbers at the end of the URL (which make it a bit lengthy) are basically asking for 60 seconds, 120 seconds, etc.

 

https://isoline.route.ls.hereapi.com/routing/7.2/calculateisoline.json?apiKey=<APIKEY>&mode=fastest;car;traffic:enabled&rangetype=time&start=geo!55.671247,12.523785&range=60,120,180,240,300,360,420,480,540,600,660,720,780,840,900,960,1020,1080,1140,1200,1260,1320,1380,1440,1500,1560,1620,1680,1740,1800,1860,1920,1980,2040,2100,2160,2220,2280,2340,2400,2460,2520,2580,2640,2700,2760,2820,2880,2940,3000,3060,3120,3180,3240,3300,3360,3420,3480,3540,3600,3660,3720,3780,3840,3900,3960,4020,4080,4140,4200,4260,4320,4380,4440,4500,4560,4620,4680,4740,4800,4860,4920,4980,5040,5100,5160,5220,5280,5340,5400

 

As an avid Alteryx user I naturally popped open Alteryx and used it to fetch the data from using the call above. You can also download the workflow here. But you don’t need Alteryx to make this work. You can simply drop the URL into a browser window and it will return the JSON. Then save the output as a JSON file which we will use in Tableau.

 

The Tableau Magic

Once we are in Tableau, the steps to generate an expanded animated map are relatively simple. My Alteryx workflow generates a spatial file but you may also be using the JSON method, so I’ll show you how to create the map using both options.

 

Spatial File from Alteryx

If you’re using the output from the Alteryx workflow, then follow these steps:

 

1) Connect to the spatial file in Tableau.

2) Drop SpatialObj Built on the detail card. This will add generated Latitude and Longitude pills onto the rows/columns shelves.

3) Add TimeMinutes to the colour card, choose your colour palette, remove the border (this helps to soften the appearance of the map), and adjust the opacity to your liking.

 

 

JSON File

The JSON output has a few extra steps.

 

1) Use the JSON connector to connect to your file. When prompted, click the “Select All” option to select all schemas in the file.

 

 

Note: You don’t technically need all of the schemas, but it’s easier to just select all of them.

 

2) The Shape field contains a comma-separated Latitude/Longitude pair. In order to create the map, we need separate fields for each, so create calculated fields to parse them out.

 

Latitude

// Parse Latitude from the shape.

FLOAT(SPLIT([Shape], ',', 1))

 

Longitude

// Parse Longitude from the shape.

FLOAT(SPLIT([Shape], ',', 2))

 

3) Set the geographic role of the Latitude and Longitude fields.

4) Drag Latitude to the rows shelf and Longitude to the column shelf, then right click each of the pills and change them to dimensions.

5) Create a calculated field to convert seconds to minutes:

 

Minutes

// Convert range to minutes

[Range]/60

 

7) Drag Minutes to the colour card, choose your colour palette, remove the border, and adjust the opacity to your liking. Note: You could also use the Range field here, but using Minutes allows you to create a legend or tooltips showing minutes rather than seconds.

8) Change the mark type to “Polygon” then drag response.isoline.Value.component.Value.shape. Index (generated) to the path card.

 

 

And, of course, with both options, you can add the appropriate field to Pages if you’d like to animate the map.

 

 

More Examples

I was having a lot of fun with this so I created drive-time maps for 15 major cities around the globe. Click the image to see and interact with the full viz.

 

 

And here are a couple of other examples, including an animation of London drive-times and a more styled map of New York, using the free MapBox style, Labelmaker.

 

 

Recap

And there you have it! Using this technique, you should be able to create your own drive-time analysis, whether it be for creating a beautiful map of your own home town or for something more practical for your place of business. Feel free to download my workbook and experiment as desired, such as using different Mapbox background maps to give it some extra flavour. And, if you have any questions, feel free to drop them in the comments or reach out on Twitter or LinkedIn. Thanks for reading!

 

Marc Schønwandt

February 21, 2022