I somehow missed this: Bertin.js has reached a significant milestone with its 1.0 release.
A world map produced with Bertin.js. Screenshot from @neocartocnrs.
Bertin.js is a JavaScript library that simplifies creating static thematic maps in SVG, primarily for print, online publications or presentations. You can certainly add a level of interactivity, but Bertin.js isn’t designed to replace, or compete with, interactive-mapping libraries like Leaflet, OpenLayers, or MaplibreGL.
Bertin.js provides a comprehensive API to make thematic maps using classification, cartograms and tooling to add map components, such as titles, footers, legends, or graticules. It is a wrapper around D3’s powerful mapping capabilities, providing reasonable defaults so you can focus on designing your map and don’t have sweat intricate details such as creating a legend and specifying individual pixel coordinates to perfectly align symbols and labels.
Following last week’s post explaining the tile-addressing schema in PMTiles version 3, Brandon Liu now discusses its compression approach reducing the disk space required to store a global tile dataset to only 91.6 MB.
Update: Brandon clarified on Twitter, the 91.6 MB mentioned above only holds the data required to map Z,X,Y coordinates to the corresponding Tile ID. The actual global map data set is 80GB. (15 August 2022)
A new version of MapLibre was released just yesterday. Release 2.2.0 is a minor version in semantic-versioning speak, but it adds a major feature: 3D terrain maps, enabling developers to visualise the topography of land surfaces in interactive maps on the Web.
Mapbox introduced a similar feature to Mapbox GL 2.0.0, the first of the library’s releases after moving away from an open-source license. MapLibre is a fork of Mapbox GL that was created to preserve and continue the work under an open-source license. Thanks to the efforts of MapLibre maintainers, developers can now build interactive 3D maps for the Web using open-source technology.
I’ve played around with a small example based on an example by Oliver Wipfli, and the results look really slick. Some minor issues still exist, for example, the hill shade disappearing when you tilt the map close to a horizontal view.
MapLibre's 3D terrain in action; also demonstrating smaller issues when tilting the map close to a horizontal view.
It’s great to see the project under such active development. Mapbox has paved the way for modern web mapping with vector tiles, but it’s good to have more competition in the space — even better when the competition is open-source.
Django 4.1 brings two new features for handling geographic geometries; allowing developers to create valid and canonical geometries more easily:
GEOSGeometry.make_valid() returns a valid representation of a geometry. Unless the geometry is already valid, then the geometry is returned without changes. The function is very similar to ST_MakeValid in PostGIS.
GEOSGeometry.normalize() returns the canonical, or standardised, representation of a geometry. The method now accepts the optional clone keyword indicating that the normalisation must not change the geometry and instead return a new geometry object.
Additionally, support for GDAL 2.1 and PostGIS 2.4 has been removed, and the Django Admin’s OpenLayersWidget is now deprecated.
Check the release notes for all other changes in Django 4.1.
Whether you’re just beginning with Leaflet or you’ve been around when Leaflet 0.7 was the current release, this collection of more than sixty Leaflet examples is a valuable resource for anyone. There are basic examples like setting up a Leaflet map or adding a marker, solutions to more complex problems like fitting bounds with padding, and advanced concepts such as overlaying images or searching across layers.
A new release of the web-mapping library OpenLayers is out. v6.15 includes, among other things, performance improvements, enhanced styling options for symbols, and bug fixes.
mbtiles-s3-server is a new Python library, developed by Michal Charemza, which reads vector geo-data from an MBTiles file stored in S3 and serves it as vector tiles. The library leverages range requests to query an SQLite database in S3.
Very much in line with advancements in file-based, cloud-optimised data storage we’ve been seeing in the last couple of years.
The Google Maps API was never an obvious choice for building advanced cartographic data products. It was always something local businesses use to put a map on their website showing where their shops are located.
Google Maps has now introduced data-driven styling, addressing a new audience outside local businesses. Data-driven styling of Google-maintained administrative boundaries that is. Google maintains and provides a data set of boundaries at varying administrative levels and allows developers to join their thematic data to create choropleth maps.
Looking at the documentation, linking your own data to Google’s boundaries dataset isn’t straightforward. To match the records from your dataset to the features from Google’s boundary dataset, you need to find the corresponding place ID from the Region Lookup API. If you have a hundred records, you need to do a hundred location lookups via the API before your map can be fully rendered. Unless you’re already keeping Google’s place ID in your data, which we all do, don’t we?
What’s the point of this approach is over loading and styling a GeoJSON layer, which has been supported by the Google Maps API before? Sure, you don’t have to maintain an administrative-boundaries dataset. But if the geometry and thematic data come from different sources, can we be sure that both represent the same underlying geographic area. Does this number of Covid cases aggregated by council assume precisely the same boundaries Google provides? We can’t know for sure, and the resulting maps might be unintentional lies.
And obvious use cases for maps based on administrative boundaries include election data, demographic information, or the number of COVID cases within a council. The people producing such data sets will likely maintain or have access to official and accurate boundary data sets. They have no reason to use Google Maps.
Google Maps data-driven styling isn’t a well-designed API of a product that solves a real problem. It’s a marketing stunt.
Mapbox GL JS’ latest release (v2.9.0) adds a new globe projection, which enables presenting web maps in a way that allows viewers to interact with geographic data much like you would interact with a physical globe.
This new projection displays the map as a 3d globe and can be enabled by either passing projection: globe to the map constructor or by calling map.setProjection('globe'). All layers are supported by globe except for Custom Layers and Sky.
The globe projection is a continuation from custom projections, which were introduced a couple of months ago and, for the first time, allowed creating interactive web maps using projections other than Web Mercator. While custom projections were ideal to present data restricted to countries or continents and without the hefty distortions that come with Web Mercator, the globe projection allows to do the same but on a global scale; ideal to present global data sets like visualisations of climate change.
