I have created and contributed to a few open source scientific libraries for Python programming language. You can find more information about these libraries below, as well as about what has been my role in them.
Published:
r5py is a Python wrapper for the R5 routing analysis engine. It allows users to generate detailed routing analyses or calculate travel time matrices using parallel computing, and integrates seamlessly with Python/Geopandas workflows. r5py is inspired by r5r, a wrapper for R, and is designed to interact with GeoPandas data frames. Similar to r5r, r5py represents a simple way to run R5 locally.
Published:
pyrosm is an open source Python library for reading OpenStreetMap from Protocolbuffer Binary Format -files (*.osm.pbf) into Geopandas GeoDataFrames. Pyrosm makes it easy to extract various datasets from OpenStreetMap pbf-dumps including e.g. road networks, buildings, Points of Interest (POI), landuse, natural elements, administrative boundaries and much more. Fully customized queries are supported which makes it possible to parse any kind of data from OSM, even with more specific filters.
to_postgis() method Published:
GeoPandas is an open source Python project and one of the core libraries in Python for doing GIS and working with geospatial data. The goal of GeoPandas is to make working with geospatial data in Python easier. I have contributed to geopandas by adding a new feature .to_postgis(), read more below.
sjoin_nearest() method Published:
GeoPandas is an open source Python project and one of the core libraries in Python for doing GIS and working with geospatial data. The goal of GeoPandas is to make working with geospatial data in Python easier. I have contributed to geopandas by prototyping and suggesting a new feature .sjoin_nearest(), read more below.
Published:
osmnx is an open source Python project and one of the core libraries in Python for retrieving and working with OpenStreetMap data.
osmnx lets you download geospatial data from OpenStreetMap and model, project, visualize, and analyze real-world street networks and any other geospatial geometries. I contributed to osmnx by adding features to extract Point of Interest data from the OpenStreetMap data. Since these contributions (in 2018), the library has evolved and there is a new API design to fetch geometries of any type with specific tags.