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.
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.
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.
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.
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.
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.
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.
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.
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.
Some early-internet household names are still going. Amongst them is a familiar face I didn’t think still existed: MapQuest, still used by a surprising amount of people today:
More than 17 million Americans regularly use MapQuest, one of the first digital mapping websites that was long ago overtaken by Google and Apple, according to data from the research firm Comscore.
What reminded me of MapQuest the other day: The Azure-Maps tiles that GitHub now uses to display GeoJSON files. Add a button on each side of the map to navigate towards north, east, south or west, and it will look like a MapQuest map from 2002.
Not surprising at all. The impact of the change is marginal, maps aren’t exactly a core feature of GitHub.
As part of the transition, custom icons and formatting of features in geojson and topojson files will no longer be supported.
Not just icons and formatting, it seems GitHub chose the nuclear option. There are lines and polygons alongside points in this dataset, but only points are visible on the map. Plus, it doesn’t show anything in Safari.
Is it latitude, longitude or longitude, latitude? Everybody has an opinion, no one knows. It doesn’t matter — as long as you know what the application or library you’re working with requires.
FlipCoords flips coordinates in the appropriate order and provides the result copy-paste ready in various formats, such as arrays, tuples, URL parameters or JSON objects. Very handy if you ever need to hard-code coordinates.
Felt, a new web-based map editor, launched in public beta last week.
Felt isn’t just another web GIS; it’s a tool for collaborative map-making. You can drop pins (even using emojis as markers), plot routes and highlight areas on the map and can annotate all this with text, notes and images. But there aren’t any features typical for professional GIS software, such as editing attribute tables or capturing complex geometries and valid topologies. However, Felt supports importing data from various formats (KML, KMZ, GPX, and GeoJSON) and exporting maps to GeoJSON.
This is a tool for anyone to create maps, whether they have prior knowledge in GIS or not. It’s designed for citizen engagement and participatory mapping; it’s for communities, not professional surveyors. Quite similar to the work around participatory mapping that groups like UCL’s ExCiteS and Mapping for Change do.
I like the simplicity of Felt. It focuses on a well-defined use case and is well executed. Much thought went into Felt’s design; the routing tool is a great example. Wherever you click, it snaps to the closest road and automatically calculates the route between two points, so you don’t have to add nodes to follow bends or turns at every intersection. By holding the Shift key while drawing, you can also draw segments that don’t align with the road network.
The team behind Felt found a gap in the current product landscape and is addressing the need nicely. I’m curious where they will take the $15M Series A funding.
Placemark have released a neat map-data conversation tool that transforms data between pretty much any geo-data file format. Upload files or paste text, convert and then download the converted data in no time.
In the course of implementing lots and lots of file formats in Placemark, we’ve ended up with some great, reusable tools. I figured it’d be pretty useful to just let anyone use those things, on a convenient drag & drop (or click, or paste) page. I hope it’s useful. Happy Friday!
This list will go out of date, but right now - you can convert: