Workflows, a new workflow builder introduced by Carto, allows people to build geo-data-processing workflows without writing code. It simplifies the creation of nested SQL queries. It provides means to import data from an external service or send the processing result via email.

The full extent of capabilities is pretty sparse at the moment. Workflows is currently in private beta; the public beta will be released in the “coming weeks.”

I previously posted a list of updates to Chris Whong, one of’s maintainers, pointed out on Twitter that most of the functionality I reported as new existed before the update. I have corrected the post based on Chris’ correction and the updated changelog.

The post was the result of sloppy work on my part. I know how to read a commit history, and I should have done that to verify my assumptions. Quietly Receives an Update

Christopher Beddow reported it first (at least in my timeline); the small-scale GeoJSON editor received an update after development had lied dormant for a while. 

There are no recent releases, the changelog hasn’t been updated in over four years, and the Mapbox blog is quiet on the topic. It’s hard to precisely summarise what has changed. But based on my memory of the feature set before the update, newly added features include the following:

  • Project the data using Mapbox’s recently released globe projection.
  • New base maps, including Outdoors, Light, and Dark styles.
  • Load XYZ tile layers from external sources.
  • Create a set of points, ideal if you want to quickly create an artificial dataset for testing.
  • Enhance existing geometries by automatically adding bounding boxes to each feature.
  • Import data from text and binary formats, including:
    • Encoded polylines
    • Well-know Binary (WKB)
    • Well-known Text (WKT)

Update: Chris Whong pointed out on Twitter that most of the functionality outlined above was already existing prior to last week’s update. Chris has also updated the changelog. I missed a couple of new features, including:

  • The underlying mapping library was upgraded to MapboxGL, which enables the globe projection.
  • Automatic formatting of GeoJSON when pasted.
  • Code-folding, ideal for working with long GeoJSON documents.

Martijn van Exel wrote a review of Every Door, the OpenStreetMap editor for mobile phones:

We had a lot of fun mapping with Every Door, and I think we were more productive adding and updating POIs than we could have been with any other app! There’s lots of little things that make your life easier. […] I would encourage anyone who likes to get out and survey to try it!! Huge thanks to Ilya for making Every Door available to the community.

I have reported on Every Door before; you should read Martin’s review for an opinion from someone who edits OpenStreetMap much more than I do.

QField, is an open-source app for collecting and managing geographic data in the field that integrates tightly with QGIS, the poster child of open-source desktop GIS. Until recently, the app was only available for Android phones, but since the release 2.4 you can also use it on iPhone devices.

We all love a bit of retro flair on our maps, don’t we? If you agree, then BellTopo Sans might be what you’re looking for. Designed by Sarah Bell, it’s a sans-serif typeface for map labels, inspired by old USGS maps:

When you see this typeface that I’m referring to on these old beautiful maps, you may think it is nothing special. It’s simple. It might even be very similar to a common font that you already know. Perhaps you’re thinking, “Why didn’t she use that font?” But for me, the beauty of this typeface that I see on old USGS maps exists within its subtle differences.

I like BellTopo Sans because, unlike many modern fonts, it is a little rough around the edges — it has character. Look at that upper-case R and that lower-case g; just look at them.

The words 'Thüringen', 'Thüringer Wald', and 'Grosser Inselsberg (916m) displayed using different variations of the font BellTopo Sans.

BellTopo Sans works best in medium font sizes and with a bit of character spacing.

JSON documents can be challenging to read, especially GeoJSON, with complex geometries and many feature properties. JSONCrack visualises JSON documents in a graph, making them easier to parse visually and to understand their structure and content.

GeoJSON features as visualised by JSONCrack. It shows the properties object, and the geometry object further broken down into type and coordinates.
GeoJSON features visualised in a graph using JSONCrack.

JSONCrack works best with small(ish) files. I initially tested with a 17.7MB GeoJSON file that contains about 16,000 records. While it parses and formats the file without issues, it can’t produce the visualisation. Only at about 500 records did JSONCrack render the visual representation.

Headway, a Batteries-Included Geocoding and Routing Stack

Instead of using an established navigation service and handing over details about where you’re going, how about running the infrastructure required for routing and navigation on a server you control so you know where your location data is going.

That’s the idea behind Headway, a batteries-included software stack including a front-end application, basemap, geocoder and routing engine. With just a few commands, You can spin up a Headway instance on your local machine within minutes. You can build Headway using data from over 200 preconfigured metropolitan areas, a custom OpenStreetMap extract, or the whole planet.

Headway bundles many well-known open-source software, such as MapLibre for its map client, Pelias for geocoding, Valhalla for routing, Planetiler to prepare vector tiles from OpenStreetMap, and many more.

For most people, even the nerdy folks out there, running and maintaining a personal Headway instance for your navigation needs is still likely too much effort and cost. But for anyone trying to build a business that needs navigation, Headway is a fantastic starting point to make a product.

To test Headway without lifting a finger, you can try instance.

Nick Herr, usually writing about Apple-related topics, writes about What3Words:

But I stumbled across this amazing catalogue of how What3Words is insufficient for emergency use. This comes by way of a Twitter thread where the queue to see Queen Elizabeth’s coffin has apparently stretched as far away as North Carolina and California.

Apparently, four of the seven locations announced until yesterday afternoon were incorrect because of minor typos.

What3Words isn’t fit for purpose, not for any purpose, really. The more people realise this, the better, especially the ones outside the usual geo crowd.

Every Door Simplifies Point-of-Interest Mapping for OpenStreetMap

I only occasionally contribute to OpenStreetMap, mainly from the comfort of my desk and rarely on the go. I almost exclusively add and edit Points of Interest when I’m out. I used Go Map!! before, but it didn’t stick. In dense areas like central London, too many features are displayed in the editor. You see points for traffic lights, intersections, crossings, bins, and shops – all at once. Understanding what features exist or need to be added often requires clicking individual points to identify what they are.

Every Door is a new mobile OpenStreetMap editor, built by Ilja Zverev and available for iOS and Android. And it takes a different approach to edit OSM on a mobile phone.

Every Door focuses on fewer things at a time. You edit amenities, street furniture or building entrances and house numbers — but never all at the same time. You pick one group, see what’s already mapped around you and can only edit and add the same feature types. And instead of showing you all of the existing features in the current map view, it downloads just a few closest to your current location. Every Door nicely caters to the way many mappers edit OpenStreetMap. They focus on one goal at a time, say to map all the rubbish bins in a park, and then just work on that until they’re done. And they map the objects closest in proximity.

Screenshot of the Every-Door interface, showing a map on top with features for editing and corrsponding information on the features below.
Every Door shows only a small number of OpenStreetMap data in close proximity available for editing.

A few well-designed features make editing points of interest a breeze. Entering opening hours is a pain in iD, but it’s straightforward in Every Door thanks to a neat interface to select days and times, which doesn’t require composing a long string, hoping it matches the pattern OSM expects. Every Door also caches selected tags for feature types so I can quickly whizz through a park and map all benches that look the same and share the same attributes. All it takes is a brief stop next to one to get a decent GPS signal.

The interface could be more polished, and some interactions aren’t intuitive. But Every Door is a cross-OS app built by one person, presumably in their spare time. I won’t expect this to look like a boutique iOS app that costs 75$ a year. Every Door is a nice app, which takes away much of the complexity of editing OpenStreetMap on the go.

Kelsey Taylor, writing on the Stamen blog:

Chartographer [is] a visualization tool that breaks down different stylesheet properties by layer and zoom level for easy analysis and debugging. Now instead of panning and zooming around the map to find and identify issues, or scrolling through thousands of lines of JSON looking for mismatched zoom numbers, you can visualize how layers are styled at all zoom levels in a single view.

Chartographer looks like a handy tool if you’re hand-crafting map stylesheets.

Ohsome Adds New Filters Feature for Detailed Analysis of OpenStreetMap's History

Ohsome Dashboard, part of a suite of products for exploring OpenStreetMap’s history built at Heidelberg Institute for Geoinformation Technology (HeiGIT), added a handy new feature last week:

We have a new advanced filter feature available in ohsome dashboard. Now you can globally analyze arbitrary combinations of tags and geometry types over the history of OSM.

The Ohsome Dashboard lets you explore the history of OpenStreetMap by looking at arbitrary combinations of tags, OSM object types, periods of time, and areas of interest. Using a more practical description, you see how the length of all ways tagged with highway=primary has developed over the last five years and compare those numbers between Germany, France, and the UK.

Screenshot of a Ohsome graph depicting the development of length of primary highways on OpenStreetMap in Germany, France, and the UK.
Ohsome showing changes of length of primary highways on OpenStreetMap in Germany, France, and the UK.

Amazingly, these results can be produced on the fly. Sure it takes a minute or two to compute, but we’re dealing with vast amounts of data here. The data-exploration products from HeiGIT and the GIScience group at Heidelberg University have come a long way in the last ten years. OSMatrix, which we first released in 2012, was nice to look at, but it wasn’t nearly as helpful in exploring OpenStreetMap’s vast dataset. All of OSMatrix’s data was precomputed into hexagonal bins, and comparisons were only possible for a tiny area.

Now here’s a very cool project: Allmaps, a browser application made by Bert Spaan and Jules Schoonman, lets you geo-reference images from public sources such as libraries and public archives. You provide the URL of an image and set the reference points via the Allmaps interface. Ideal for laying digitised images of old maps over modern-day data and understanding how the geography has changed.

A digitised map from Antwerp overlayed of modern-day data.
A map from 1860s Antwerp from the Boston Public Library layed over a modern-day digital map. Screenshot from

What is unique about Allmaps are the technical underpinnings of the application; relying heavily on open standards to access images and store annotations:

  • Image resources are accessed via International Image Interoperability Framework’s (IIIF) Image API, which doesn’t just return an image; it allows developers to specify region, size, rotation, and format of the returned image — ideal to crop, resize and rotate an image into place on a map.
  • The map’s control points are stored using a format based on W3C’s Web Annotation Data Model. The custom format uses GeoJSON to represent geographic coordinates of control points while placing the corresponding pixel coordinates inside the properties object.

Allmaps combines existing standards in a novel way, designing for interoperability from the start and enabling sharing of geo-referencing data with other applications.