An un-opinionted combination of various available and useful geospatial data tools and helper methods.
Based on Jupyter DockerStacks SciPy Notebook With a lot of input and knowledge from our beloved Arnaud Vedy and his datascience-notebook
Copy env.example to .env
Run docker-compose up or docker-compose up -d if you want to run detached.
The PostGIS port is exposed at 5432. Make sure you have copied env.example to .env so the password gets set.
You can connect from a local client or QGIS, but there's an easy interface available at
http://localhost:8082
The mongo port is exposed at 27017, so you should be able to connect from a local client.
However, there's an easy admin interface also available at http://localhost:8081
Username is admin
Pass is pass
Data from Mongo and Postgis are persisted in the db directory.
Your jupyter notebook are saved and synced to the work directory.
In production. No security precautions have been taken outside of making this a local dev environment.
Because installing all this and orchestrating it together is hard. Docker-Compose makes it easy.
- Mongo (you can do some awesome geo stuff in mongo)
- PostGIS (the elephant of the Geo database world.)
- Adminer for PostGIS.
- NodeExpress for Mongo.
- Mapshaper
- Transformation of geo data
- [Mapshaper GUI] -- See Above
- GitHub - mapbox/mapbox-tile-copy: From geodata files to tiles on S3
- GitHub - mapbox/geobuf: A compact binary encoding for geographic data.
- Turf.js | Advanced Geospatial Analysis
- GeoPandas 0.3.0 — GeoPandas 0.3.0 documentation
- Shapely — Shapely 1.2 and 1.3 documentation
- Fiona — Fiona 1.7.0.post2 documentation
- [OSGEO / Gdal Interface]
- Geojson
- Geobuf
- GDAL: GDAL - Geospatial Data Abstraction Library
- GDAL: ogr2ogr
- [Tippecanoe]GitHub - mapbox/tippecanoe: Build vector tilesets from large collections of GeoJSON features.
- Tegola
- Simple vector tile previews