从大型 GeoJSON 特征集中构建向量图像集。
Note: there is an active fork of this project over at https://github.com/felt/tippecanoe
Builds vector tilesets from large (or small) collections of GeoJSON, Geobuf, or CSV features, like these.
Mapbox Tiling Service (MTS) is a hosted, data processing service that allows you to integrate custom datasets of any scale into your maps faster, cheaper, and with more flexibility and control than previously possible.
MTS is the same service we use internally to create our global, daily updating basemap product Mapbox Streets, which serves over 650 million monthly active users and customers such as Facebook, Snap, the Weather Channel, Tableau, and Shopify.
MTS creates and updates data using distributed and parallelized processing, meaning data is processed much more quickly than is possible with a standard, single server setup with comparable tools. For example, a global basemap at 30cm precision (max zoom of 16) can be processed in under 2 hours with MTS, whereas a comparable workload would take multiple days to process on a single server.
Customers like AllTrails, Plume Labs, and Ookla have noted that MTS helps them:
Learn more about MTS.
The goal of Tippecanoe is to enable making a scale-independent view of your data, so that at any level from the entire world to a single building, you can see the density and texture of the data rather than a simplification from dropping supposedly unimportant features or clustering or aggregating them.
If you give it all of OpenStreetMap and zoom out, it should give you back something that looks like "All Streets" rather than something that looks like an Interstate road atlas.
If you give it all the building footprints in Los Angeles and zoom out far enough that most individual buildings are no longer discernable, you should still be able to see the extent and variety of development in every neighborhood, not just the largest downtown buildings.
If you give it a collection of years of tweet locations, you should be able to see the shape and relative popularity of every point of interest and every significant travel corridor.
The easiest way to install tippecanoe on OSX is with Homebrew:
$ brew install tippecanoeOn Ubuntu it will usually be easiest to build from the source repository:
$ git clone https://github.com/mapbox/tippecanoe.git
$ cd tippecanoe
$ make -j
$ make installSee Development below for how to upgrade your C++ compiler or install prerequisite packages if you get compiler errors.
$ tippecanoe -o file.mbtiles [options] [file.json file.json.gz file.geobuf ...]If no files are specified, it reads GeoJSON from the standard input. If multiple files are specified, each is placed in its own layer.
The GeoJSON features need not be wrapped in a FeatureCollection. You can concatenate multiple GeoJSON features or files together, and it will parse out the features and ignore whatever other objects it encounters.
If you aren't sure what options to use, try this:
$ tippecanoe -zg -o out.mbtiles --drop-densest-as-needed in.geojsonThe -zg option will make Tippecanoe choose a maximum zoom level that should be
high enough to reflect the precision of the original data. (If it turns out still
not to be as detailed as you want, use -z manually with a higher number.)
If the tiles come out too big, the --drop-densest-as-needed option will make
Tippecanoe try dropping what should be the least visible features at each zoom level.
(If it drops too many features, use -x to leave out some feature attributes that
you didn't really need.)
Create a tileset of TIGER roads for Alameda County, to zoom level 13, with a custom layer name and description:
$ tippecanoe -o alameda.mbtiles -l alameda -n "Alameda County from TIGER" -z13 tl_2014_06001_roads.jsonCreate a tileset of all TIGER roads, at only zoom level 12, but with higher detail than normal,
with a custom layer name and description, and leaving out the LINEARID and RTTYP attributes:
$ cat tiger/tl_2014_*_roads.json | tippecanoe -o tiger.mbtiles -l roads -n "All TIGER roads, one zoom" -z12 -Z12 -d14 -x LINEARID -x RTTYPcurl -L -O https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_railroads.zip
unzip ne_10m_railroads.zip
ogr2ogr -f GeoJSON ne_10m_railroads.geojson ne_10m_railroads.shp
tippecanoe -zg -o ne_10m_railroads.mbtiles --drop-densest-as-needed --extend-zooms-if-still-dropping ne_10m_railroads.geojson-zg: Automatically choose a maxzoom that should be sufficient to clearly distinguish the features and the detail within each feature--drop-densest-as-needed: If the tiles are too big at low zoom levels, drop the least-visible features to allow tiles to be created with those features that remain--extend-zooms-if-still-dropping: If even the tiles at high zoom levels are too big, keep adding zoom levels until one is reached that can represent all the featurescurl -L -O https://usbuildingdata.blob.core.windows.net/usbuildings-v1-1/RhodeIsland.zip
unzip RhodeIsland.zip
tippecanoe -zg -o RhodeIsland.mbtiles --drop-densest-as-needed --extend-zooms-if-still-dropping RhodeIsland.geojson-zg: Automatically choose a maxzoom that should be sufficient to clearly distinguish the features and the detail within each feature--drop-densest-as-needed: If the tiles are too big at low or medium zoom levels, drop the least-visible features to allow tiles to be created with those features that remain--extend-zooms-if-still-dropping: If even the tiles at high zoom levels are too big, keep adding zoom levels until one is reached that can represent all the featurescurl -L -O https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_1_states_provinces.zip
unzip -o ne_10m_admin_1_states_provinces.zip
ogr2ogr -f GeoJSON ne_10m_admin_1_states_provinces.geojson ne_10m_admin_1_states_provinces.shp
tippecanoe -zg -o ne_10m_admin_1_states_provinces.mbtiles --coalesce-densest-as-needed --extend-zooms-if-still-dropping ne_10m_admin_1_states_provinces.geojson-zg: Automatically choose a maxzoom that should be sufficient to clearly distinguish the features and the detail within each feature--coalesce-densest-as-needed: If the tiles are too big at low or medium zoom levels, merge as many features together as are necessary to allow tiles to be created with those features that are still distinguished--extend-zooms-if-still-dropping: If even the tiles at high zoom levels are too big, keep adding zoom levels until one is reached that can represent all the featurescurl -L -O ftp://avl-data.sfmta.com/avl_data/avl_raw/sfmtaAVLRawData01012013.csv
sed 's/PREDICTABLE.*/PREDICTABLE/' sfmtaAVLRawData01012013.csv > sfmta.csv
tippecanoe -zg -o sfmta.mbtiles --drop-densest-as-needed --extend-zooms-if-still-dropping sfmta.csv(The sed line is to clean the corrupt CSV header, which contains the wrong number of fields.)
-zg: Automatically choose a maxzoom that should be sufficient to clearly distinguish the features and the detail within each feature--drop-densest-as-needed: If the tiles are too big at low or medium zoom levels, drop the least-visible features to allow tiles to be created with those features that remain--extend-zooms-if-still-dropping: If even the tiles at high zoom levels are too big, keep adding zoom levels until one is reached that can represent all the featurescurl -L -O https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_populated_places.zip
unzip -o ne_10m_populated_places.zip
ogr2ogr -f GeoJSON ne_10m_populated_places.geojson ne_10m_populated_places.shp
tippecanoe -zg -o ne_10m_populated_places.mbtiles -r1 --cluster-distance=10 --accumulate-attribute=POP_MAX:sum ne_10m_populated_places.geojson-zg: Automatically choose a maxzoom that should be sufficient to clearly distinguish the features and the detail within each feature-r1: Do not automatically drop a fraction of points at low zoom levels, since clustering will be used instead--cluster-distance=10: Cluster together features that are closer than about 10 pixels from each other--accumulate-attribute=POP_MAX:sum: Sum the POP_MAX (population) attribute in features that are clustered together. Other attributes will be arbitrarily taken from the first feature in the cluster.…Countries:
-z3: Only generate zoom levels 0 through 3--coalesce-densest-as-needed: If the tiles are too big at low or medium zoom levels, merge as many features together as are necessary to allow tiles to be created with those features that are still distinguishedStates and Provinces:
-Z4: Only generate zoom levels 4 and beyond-zg: Automatically choose a maxzoom that should be sufficient to clearly distinguish the features and the detail within each feature--coalesce-densest-as-needed: If the tiles are too big at low or medium zoom levels, merge as many features together as are necessary to allow tiles to be created with those features that are still distinguished--extend-zooms-if-still-dropping: If even the tiles at high zoom levels are too big, keep adding zoom levels until one is reached that can represent all the featurescurl -L -O https://www2.census.gov/geo/tiger/TIGER2010/COUNTY/2010/tl_2010_17_county10.zip
unzip tl_2010_17_county10.zip
ogr2ogr -f GeoJSON tl_2010_17_county10.geojson tl_2010_17_county10.shp
curl -L -O https://www2.census.gov/geo/tiger/TIGER2010/COUNTY/2010/tl_2010_18_county10.zip
unzip tl_2010_18_county10.zip
ogr2ogr -f GeoJSON tl_2010_18_county10.geojson tl_2010_18_county10.shp
tippecanoe -zg -o counties-separate.mbtiles --coalesce-densest-as-needed --extend-zooms-if-still-dropping tl_2010_17_county10.geojson tl_2010_18_county10.geojson-zg: Automatically choose a maxzoom that should be sufficient to clearly distinguish the features and the detail within each feature--coalesce-densest-as-needed: If the tiles are too big at low or medium zoom levels, merge as many features together as are necessary to allow tiles to be created with those features that are still distinguished--extend-zooms-if-still-dropping: If even the tiles at high zoom levels are too big, keep adding zoom levels until one is reached that can represent all the featurescurl -暂无开放 Issues，或尚未同步最近议题。