___ ___ ____ /'\_/`\ __ /\_ \ /\_ \ /\ _`\ /\ \/\_\\//\ \ \//\ \ \ \ \L\ \_ __ __ _____ \ \ \__\ \/\ \ \ \ \ \ \ \ \ \ ,__/\`'__\/'__`\/\ '__`\ \ \ \_/\ \ \ \ \_\ \_ \_\ \_\ \ \/\ \ \//\ __/\ \ \L\ \ \ \_\\ \_\ \_\/\____\/\____\\ \_\ \ \_\\ \____\\ \ ,__/ \/_/ \/_/\/_/\/____/\/____/ \/_/ \/_/ \/____/ \ \ \/ \ \_\ \/_/
MillPrep eats random geodata and poops shapefiles, SQLite databases, or GeoTiffs that have been optimized for rendering with TileMill. It is being actively developed and is mostly untested.
It requires the commandline utilities
shapeindex to be
TileMill optimization means:
- reproject everything to The One And Only Google Mercator
- clip any data that falls outside the TMS zoom-level 0 tile (this is optional)
- index output files appropriately for Mapnik
In addition to these core optimizations, MillPrep also allows you to merge many input files into a single output file. This is useful for data with a single data schema that is distributed as many separate files, such as the U.S. Census Bureau's TIGER shapefiles which are split up by state.
- appropriately handle any input that the OGR/GDAL supports (the current extent of this is untested)
- related to above: handle raster input/output
- test everything in something other than ideal situations
- possibly: geometry simplification
usage: millprep.py [-h] [--sqlite] [--noclip] [-d OUTPUT_DIR] [-m MERGED_FILE] INPUT_FILE [INPUT_FILE ...] Convert geographic datasources to more TileMill-optimized formats. positional arguments: INPUT_FILE A geographic file to optimize for TileMill optional arguments: -h, --help show this help message and exit --sqlite Use SQLite as the output file format instead of the default, ESRI Shapefile --noclip By default files will be clipped so they don't expand outside the bounds of the Google Mercator square. -d OUTPUT_DIR Destination directory to output the processed files. If not specified, output files will be kept in the same directory as their respective input files. -m MERGED_FILE, --merge MERGED_FILE Merge all input files into this single output file.
The simplest case: reproject & shapeindex a single file. Output file would be
Bulk process a number of files at once. Output filenames will be like
Bulk process a number of files at once and put the results in a specified directory:
millprep.py *.shp -d ./processed/
Merge a number of input files into a single output file.
millprep.py --merge all_counties.shp county_*.shp
Convert a number of shapefiles to individual SQLite files.
millprep.py --sqlite *.shp
Merge a number of shapefiles to a single table of an SQLite file.
millprep.py --sqlite --merge merged_file.sqlite *.shp