To create a standard base map that anyone can contribute to and benefit from, the TomTom Maps Platform is bringing together a pool of map content from map users around the world. The resulting geolocation database – the largest available today – feeds continuous improvements back to the map, helping it keep up with reality.
The pool is filled with an array of sources, including OpenStreetMap, sensor-derived observations (SDO) from millions of vehicles, probe data and shared points of interest (POI). It’s a dizzying amount of data that we quickly make sense of, validate and act on.
There’s a marketing page and a video featuring dramatic music and Steve Coast saying very little: “Maps are used way more than people think but it’s invisible to us. As things get quicker, we have to change the way we think about maps.”
Is this new product primarily OpenStreetMap data and some additional data sprinkled on top? Does it have an API? Map tiles? An SDK? It’s hard to tell as the material was written by marketing people for people in suits.
We will have to wait for further announcements to understand what TomTom’s new map platform can do.
A design proposal for the modularization of the map rendering architecture can be found at #547. This modularization will allow new rendering architectures to be implemented quickly and more easily, and we anticipate that the modularization will give us a concrete framework to better interrogate various migration strategies.
I love this approach, gathering feedback from the community before starting the work to make actual changes to the code and architecture.
The program from this year’s Pacific Geospatial Conference in Fiji has been released. The focus of the 2022 editing is less on technology but on applications to problems specific to the Pacific region. For a pleasant change, the list of presenters doesn’t include the usual suspects from the industry.
NACIS, the North American Cartographic Information Society, have uploaded recordings of this year’s annual meeting in Minneapolis. The playlist contains over 100 videos covering all sessions from the meeting.
Greg Miller, writing for Wired Magazine, in a portrait of Cynthia Brewer, of Colorbrewer fame:
Brewer’s influence on cartography is far-ranging. Others have imitated her approach, developing a TypeBrewer and a Map Symbol Brewer. She’s seen her color schemes in everything from financial charts to brain imaging studies.
It’s a portrait in one of the most renowned technology publications of a university professor working on a rather niche subject — goes to show how much influence Brewer’s work has on our craft.
The fine folks at Crunchy Data have lined up a great set of talks for a one-day conference celebrating this year’s PostGIS day on 17 November. It’s an online event, with sessions scheduled for over twelve hours; wherever you in the world and whenever you’re awake that day, you can drop in at any time. The event is free and registration is now open.
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.”
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:
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.
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.