How hard can it be to make two simple maps, one showing the location of addresses and one showing sales by US state? James Killick tried products from all the big names—ESRI, Google, Microsoft, and Felt. Turns out, getting started is not straightforward.
Killick went into the experiment pretending he had no prior experience, which I think is unfair. Complex software is a reflection of a complex problem space, great flexibility, or both. Not everything can and should be dumbed down to the level a disinterested teenager can be bothered to understand. Instagram is easier to use than a traditional camera but the photos all look the same. Mapping software, like any design tool, requires domain knowledge: You need to know what you want to achieve. You need to know what kind of maps exists, and which can be used to most effectively represent your data. If you know these things you’re more likely to already know the right tools and where to find them.
And let’s not forget Felt is just over one year old now but they already raised the bar for map-tech user experience and managed to remove a lot of complexity from the process through clever design and impressive software engineering. Give them a little more time and they will further change the way we think about making maps. In a few years time we might ask ourselves why map-making was so difficult in 2023.
However, map tiles are the only way to create a seamless, smooth and multi-scale user experience for large planet-scale geo-data. Even as devices become more powerful, the detail and volume of geo data grows commensurately. The boundless supply of rich data, combined with the demand for smooth, instant-loading maps, means tiling will always remain an essential part of the digital mapmaker’s toolkit.
The dates and location for next year’s FOSSGIS conference have been announced. The 2024 edition of the German-speaking gathering of open-source builders and users in the geospatial space is scheduled for 20 to 23 March 2024 and will be hosted by the TUHH in Hamburg.
The call for papers is usually opens towards the end of the year and tickets usually go on sale early in the new year.
The GeoParquet community is pleased to announce the release of GeoParquet 1.0.0. This is a huge milestone, indicating that the format has been implemented and tested by lots of different users and systems and the core team is confident that it is a stable foundation that won’t change. There are more than 20 different libraries and tools that support the format, and hundreds of gigabytes of public data is available in GeoParquet, from a number of different data providers.
GeoParquet is one of the most promising geo-data formats introduced in the last few years. It adds ability to encode geographic geometries in Apache Parquet.
GeoParquet’s adoption is growing, some big names are already offering data in GeoParquet:
We’re also starting to see data providers like Microsoft, Maxar, Planet, Ordnance Survey and others put new data in GeoParquet. And the community is also converting a number of interesting large scale datasets like the Google Open Buildings and Overture Maps data to GeoParquet on Source Cooperative.
The GeoParquet community will now take the specification through the OGC’s standardisation process, which won’t fundamentally change the current specification but it will add a formal stamp of approval.
DuckDB is a database management system for data analytics that has picked up steam in the recent months within the geospatial data community.
Chris Holmes documents his experiences with DuckDB and explores its potential for work with geospatial data:
I’m not the type who’s constantly jumping to new technologies and generally didn’t think that anything about a database could really impress me. But DuckDB somehow has become one of the pieces of technology – I gush about it to anyone who could possibly benefit.
There’s a lot to love about DuckDB: Its performance, its early support support for geospatial queries and data formats (although not fully mature), and smart extensions to SQL.
What sets it apart is DuckDB’s support for cloud-native operations via the httpfs extension and Parquet:
DuckDB also has the ability to work in a completely Cloud-Native Geospatial manner – you can treat remote files just like they’re on disk, and DuckDB will use range requests to optimize querying them.
In geospatial, we’re often dealing large data sets that are har to store and explore with traditional tools, unless you import the data into a database. But it’s not just going to make data access and processing easier on your computer. There’s also a great potential to move more large-data processing into the browser:
It’s not just that DuckDB is a great command-line and Python tool, but there’s also a brewing revolution with WASM, to run more and more powerful applications in the browser. DuckDB is easily run as WASM, so you can imagine a new class of analytic geospatial applications that are entirely in the browser.
The 8.0 release brings several API simplifications. Some of them are not backwards compatible, so make sure to read the upgrade notes. The new StadiaMaps source replaces the Stamen source, because Stamen no longer hosts map tiles. Several WebGL improvements are also part of this release. And finally, a new loader API for image layers makes working with non-tiled raster data more modular and faster.