I'm trying out Mapbox for the first time, and playing around with drawing some polygons in the dataset editor for export to a tileset. However, the polygons in the resulting tileset are not the same as what I create in the editor. The polygons are only very rough, simplified approximations of the originals.
In dataset editor:
In map layer as tileset export:
I understand that Mapbox does vector simplification at certain zoom levels, but these changes are not zoom-dependent. I zoom in all the way and the shapes are still like this.
Moreover, such extreme degredation of the geometries makes tilesets essentially useless for features that require any sort of accuracy, like property lot lines.
Am I missing something, or is this really the expected behavior? Is there really no way to get accurate geometries into a tileset?
UPDATE: It appears this is only happening with shapes I create by drawing in the Mapbox data editor. So far the geometries that I've uploaded as geojson files have gotten converted to tilesets accurately...
I suspect this is because the maxzoom is too low.
When you create a Mapbox Tileset, either by uploading GeoJSON directly as a new Tileset, or by exporting your your Dataset to a Tileset, Mapbox will try to guess an appropriate minzoom and maxzoom of the Tileset.
Sometimes the min/max zoom's used aren't suitable for the map you're trying to create. Since there is no way to specify a maxzoom in either of the two approaches the only alternative is to create your Tileset locally with https://github.com/mapbox/tippecanoe specifying an appropriate maxzoom for your data and then uploading the resulting .mbtiles as a Mapbox Tileset.
Related
I have data consisting of parts of road segments of a city, with different number of visits. I want to plot the data on a Map and visualise it in the form of a heatmap.
I have two related questions:
I have the data from Open Street Maps (OSM) in the form of pairs of node ID's, where node ID correspond to the unique ID being assigned to a point by OSM. I also have a mapping for each node Id to its corresponding coordinates. Is there any Leaflet or Mapbox utility or plugin, which can plot a trip / highlight the road segment using 2 node ID's. I can always do it manually (by using the coordinate mapping and converting it into GeoJSON), but the problem occurs with the line width -- I have to make it exactly overlap with the width of the road, so that it seems that I am highlighting a road segment.
Is there any plugin / utility for Leaflet or Mapbox, which can be used for plotting polylines or geojson as heatmap efficiently? My current approach is calculating the color for each polyline and encoding that as a geojson property. But the problem is that with the increase in the number of lines (> 1K) the rendering becomes a pain and the method is not feasible. There are some plugins for Leaflet out there for plotting heatmap, but all of them are for points only and not lines. Any approach using WebGL would be really great.
An approach which I thought of could be converting my data into a shape file, upload to Mapbox Studio and use as a layer directly. But I have no idea how to go about doing that i.e. creating a shapes file, encoding the information in such a way that the complete road segment gets highlighted in the correct color.
Is it possible to apply fill-extrusion for a GeoJSON LineString feature?
Basically I'm looking for a way to draw lines (can be 1 line or multiple connected) in a 3d mode with z-offset.
If that's not possible, maybe this can be done with a polygon instead?
Like, converting my lines to polygons (how can i do that?)
What you're asking for isn't yet implemented, but ticketed in Mapbox GL JS at https://github.com/mapbox/mapbox-gl-js/issues/3993.
For now you'll need to opt for your second suggestion of converting the LineString feature to a Polygon. You can do this with turf's buffer function http://turfjs.org/docs#buffer.
The whole line/polygon will be offset at the same height, so depending on your application you could use turf's linkChunk http://turfjs.org/docs#lineChunk to get it broken up into smaller features which you assign different height properties to.
Is there a way to draw a vector layer with CorelDraw for example and place it on napboxgl and use it as geojson layers?
For example https://seatgeek.com/colorado-rapids-at-seattle-sounders-fc-tickets/mls/2017-10-22-1-pm/3700786
Are they using geojson or etc.? Or just some sort of vector format?
I can't use geojson as it is hard to draw with QGIS any straight lines or symmetrical objects. I just want to draw a lot of vector objects and use them as layers with mapboxGL(use mapbox as render method and interact with layers as with geojson)
Any suggestions how to do it? Or is there a way to draw with Corel and then place it on map with QGIS?
Thanks
UPD:
Now I am using Corel -> dxf export and then import it to QGIS, then save it as geojson. But have some glitches with displaying that geojson geometry in mapbox, so I have to draw another in QGIS over the imported(dxf) one.
Here is an example of the bug, should be just a green polygon like the gray one
UPDATE: my fault, I was using lines instead of polygons.
I think you can achieve this as follows:
Convert the Corel output to SVG
Create an HTML element containing the SVG (not necessarily added to DOM)
On your Mapbox map, add a Canvas source containing the canvas: see https://www.mapbox.com/mapbox-gl-js/api/#canvassource
Obviously you will need to determine the lat/lon of the corners of your image somehow.
Trying to create a custom world map texture with tilemill to load into leafletjs. I have downloaded a free .tiff file from natural earth data and loaded it into tilemill.
When i want to export however, i notice alot of jagged edges mainly around greenland/canada on the lowest zoom level.
a few zoom levels down and it seems ok again. After exporting the tiles to png's the jagged edges stay. How can i improve the quality of these images?
How can i improve the quality of these images?
By using more detailed input data.
By the looks of it, you are projecting a raster image in EPSG:4326 projection into the EPSG:3857 "web mercator" projection. In the original data, each pixel spans the same amount of longitude and latitude degrees. In a mercator projection, each pixel spans the same amount of longitude, but a different amount of latitude. The artifacts you are experiencing are akin to a Tissot's indicatrix.
You can try using a different value for the raster-scaling symbolizer option in your tilemill stylesheet, but that's gonna make the artifacts different, not get rid of them.
My map's source file is GeoJSON, but it is very large and very slow. Is there a way to convert this to vector tiles on the fly using MapBox GL JS? (Load the GeoJSON, pre-process the file into vector tiles, and show use the vector tiles as the base map.) It seems that vector tiles are much faster.
I've tried all the GeoJSON-VT tutorials and examples that I could, like the one on MapBox's site, but it just says that GeoJSON-VT works under the hood, so it isn't much help. The others mostly apply to Leaflet, not MapBox GL JS.
Meanwhile, every example I find that uses a large dataset always does so via vector tiles:
map.addSource('x', {
"type": "vector",
"url": "url"
});
For reference, I am loading my file using this method:
map.addSource('x', {
type: 'geojson',
data: 'file.geojson'
});
In case your GeoJSON file is static, you could use mapbox/tippecanoe to convert the GeoJSON to an .mbtiles file. You could then either upload the file to Mapbox as a tileset (about tilesets) or you could serve your own vector tile source from a web server using the .mbtiles file (reference implementation).
If your GeoJSON file is more dynamic, things get a little bit more complicated. I have never used it, but Mapbox Dataset API might be a good solution for that.
To the best of my knowledge, Mapbox-GL-JS uses GeoJSON-VT to automatically convert client-side-loaded GeoJSON files into vector tiles within the browser - so it's already doing what you're asking for.
If this is still "slow", probably the problem is the actual loading and processing - so pre-generating and serving vector tiles is the right answer.