I previously posted a list of updates to Geojson.io. Chris Whong, one of Geojson.io’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.
Darren Wiens with a short and sweet example of how to enhance web maps using SVG filters. Check out the codepen to see an approach in action.
The beauty of the technique is that it’s independent of any mapping libraries. You can use SVG filters with Leaflet, OpenLayers, or MabLibre.
Christopher Beddow reported it first (at least in my timeline); the small-scale GeoJSON editor Geojson.io 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:
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:
A new book by Ryan Lambert teaches Postgres and PostGIS using real-world data from OpenStreetMap.
This book provides a practical guide to introduce readers to PostGIS, OpenStreetMap data, and spatial querying. Queries used for examples are written against real OpenStreetMap data (included) to help you learn how to navigate and explore complex spatial data. The examples start simple and quickly progress through a variety of clever spatial queries and powerful techniques.
Looking at the sample chapter, Mastering PostGIS and OpenStreetMap is very hands-on and very technical.
You can purchase the book for $99 from the website; it’s available in three formats: HTML, PDF and ePub.
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.
During a recent workshop on the fringe of this year’s SatSummit, participants discussed how to design APIs that simplify ordering satellite data. Matthew Hanson wrote a summary of the workshop, noting the complexity of decision-making that goes into ordering data and tasking a satellite; arguably one reason why we haven’t seen a production-ready ordering API so far:
It turned out the most interesting discussions were centered around tasking as a process, rather than the details of a transactional API with a data provider. Tasking is really about the negotiation, as Phil Varner (Element 84) put it: a user says “This is what I want” and the provider responds with “This is what I can offer”. The questions that arose were less about detail and more about how users should interact with the provider. How do users want to discover what is feasible? How do they evaluate multiple possible options and request one or more of those options?
And consequently, how the ordering APIs could be designed:
There was a general consensus that users start by making a “feasibility request. Included in the request is usually a spatial Area Of Interest (AOI) and a date/time range, Time of Interest (TOI), and possibly some additional parameters constraining the options. What is returned by the provider is a list of possible results that may vary by total area of coverage, time of acquisition, price, resolution, sun angle, or by virtually any collection parameter.
Rather than the provider trying to make a decision of what the user wants from the available options, this choice should be pushed back to the user.
The user then gets to pick their preferred options and places the order for the product best suited for their needs.
Detailed notes of the event are on GitHub, providing some early and still rough outlines of potential API states and parameters, amongst insights on more high-level discussions.
There’s a new Carpentries-style lesson teaching the fundamentals of processing geospatial raster and vector data with Python. It teaches the basics of vector and raster data, how to access raster data via STAC, how to do calculations on raster data, and parallelisation with Dask.
The course is designed for in-person workshops, but you can easily follow the instructions at home.
Steven Feldman, analysing What3Words’ 2021 accounts:
The business is powering ahead despite a small fall in sales to £444,382 (no I have not missed out a couple of zeros), they have managed to increase losses from just under £17m last year to just under £44m this year.
To earn £1, What3Words spent almost £100 in 2021. My understanding of economics and running a business is minimal, but that doesn’t look like a healthy business. But the investors keep the money tap open, so everything is fine.
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.
BellTopo Sans works best in medium font sizes and with a bit of character spacing.