shapes-to-poly

python3 program that converts a each polygon/multipolygon in an input shapefile into a .poly file

Where is a .poly used?
Mainly by osmconvert to clip out custom OSM .pbf data, and then to use the .pbf in routing applications like OSRM / OpenTripPlanner. See: https://wiki.openstreetmap.org/wiki/Osmconvert#Clipping_based_on_a_Polygon

Usage

You can run python3 shapes-to-poly.py -h to display the same options documentation as below:

usage: shapes-to-poly.py [-h] [-n NAMINGCOLUMN] [-b BUFFER]
                         [-s SIMPLIFICATION] [-c CRS] [-l LAYER] [-g]
                         shape

positional arguments:
  shape                 filename or URL of shapefile to convert to .poly(s).
                        Can be .geojson, .shp, .gpkg or any format supported
                        by geopandas / fiona packages.

optional arguments:
  -h, --help            show this help message and exit
  -n NAMINGCOLUMN, --namingColumn NAMINGCOLUMN
                        If there are multiple shapes, what is the unique
                        field/column in the shapefile to name each .poly file
                        by (default:will just name them shape_1.poly
                        shape_2.poly etc
  -b BUFFER, --buffer BUFFER
                        buffer each shape by this many kms
  -s SIMPLIFICATION, --simplification SIMPLIFICATION
                        simplification factor to reduce output filesize. Best
                        used in conjunction with buffer. Typical values: for
                        country-level: 5000
  -c CRS, --crs CRS     CRS projection to use for kms calculations. Default:
                        EPSG:7755 for India
  -l LAYER, --layer LAYER
                        specify name of layer if loaded shapefile is multi-
                        layered
  -g, --geojson         also create .geojson to preview

Sample commands

If you have a shape saved as a shape.geojson file:

python3 shapes-to-poly.py shape.geojson

Buffer by 50 km:

python3 shapes-to-poly.py -b 50 shape.geojson

Buffer by 50km, and simplify by a factor of 5000 (ok at country level):

python3 shapes-to-poly.py -b 50 -s 5000 shape.geojson

If you have a ESRI shapefile (sample.shp +) with multiple shapes and a column like "S_NAME" holding the name for each shape,

python3 shapes-to-poly.py -b 50 -s 5000 -n "S_NAME" sample.shp

If your shape is not in India and you want to apply a diffrent CRS for 100km buffering, for example: EPSG:1324 for USA:

python3 shapes-to-poly.py -c "EPSG:1324" -b 100 shape.geojson

Features

• buffering : If you want to apply ex: 100 kms buffer to your shape before converting to .poly, use -b 100
• handles multi-polygons : The .poly output will also be multi-polygon. Just make sure to keep the shape(s) saved as a multi-polygon in your input file.
• simplification of geometry : Esp recommended in confunjction with buffering: It cuts down complexity for other processes downstream like osmconvert

Installing requirements before running:

pip3 install geopandas

Windows

Note: If on windows and using anaconda / miniconda, then conda is better than pip as it takes care of some GDAL dependencies:

conda insall geopandas

Linux / Ubuntu

In linux/ubuntu, there can be extra dependencies of geopandas on GDAL. Here's one way to install them which worked for me:

sudo apt-get install python3-pip libgdal-dev locales build-essential python3-dev python3-setuptools python3-wheel
pip3 install GDAL==2.4.2

If the last line errored out, try this:

pip3 install GDAL=="`gdal-config --version`.*"

Alternative ways

• See https://github.com/nextgis/pygdal

Using Docker

Ensure that your shapefile is present in the same folder (or in a folder beneath)

Open a terminal/command prompt, navigate it to current folder, and run these commands if for the first time:

docker build -t shapes-to-poly1 .
docker run -v $(pwd):/app -it shapes-to-poly1

This gets you into a dockerised ubuntu shell with all dependencies installed, your current directory mounted in it. Practically speaking you haven't gone anywhere; the system with the dependencies installed has just momentarily come above. You can type the command ls -l to see and confirm that all your files are there. Check out the Dockerfile in this repo to see what's happening under the hood.

You can now run the same python commands as given above. The outputs will be saved in a output/ folder in your current folder, and will stay even after you've exited the dockerised ubuntu shell.

Once done, you can get out with the exit command.

To run this again, you won't need the "build" command unless you purge your local docker images:

docker run -v $(pwd):/app -it shapes-to-poly1

Shout-out to https://github.com/thinkWhere/GDAL-Docker for making a gdal-friendly docker container readily available.