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geofileops

Python toolbox to process large geospatial vector files faster.

https://github.com/geofileops/geofileops

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.9%) to scientific vocabulary

Keywords

gdal geopackage geopandas geoprocessing geospatial-data geospatial-processing gis large-files python vector

Keywords from Contributors

mesh energy-system geoscience optimizer yolov5s pipeline-testing datacleaner data-profilers particles parallel
Last synced: 6 months ago · JSON representation ·

Repository

Python toolbox to process large geospatial vector files faster.

Basic Info
Statistics
  • Stars: 147
  • Watchers: 4
  • Forks: 6
  • Open Issues: 37
  • Releases: 28
Topics
gdal geopackage geopandas geoprocessing geospatial-data geospatial-processing gis large-files python vector
Created over 6 years ago · Last pushed 6 months ago
Metadata Files
Readme Changelog Contributing License Citation

README.md

geofileops

Actions Status Coverage Status PyPI version Conda version DOI

Geofileops is a python toolbox to process large vector files faster.

Most typical GIS operations are available: e.g. buffer, dissolve, difference, intersection, union,...

The spatial operations are tested on geopackage and shapefile input files, but geopackage is recommended as it will give better performance. General layer and file operations can be used on the file formats supported by GDAL.

The full documentation is available on readthedocs.

Different techniques are used under the hood to be able to process large files as fast as possible:

  • process data in batches
  • subdivide/merge complex geometries on the fly
  • process data in different passes
  • use all available CPUs

The following chart gives an impression of the speed improvement that can be expected when processing larger files. The benchmarks typically use input file(s) with 500K polygons, ran on a Windows PC with 12 cores and include I/O.

Geo benchmark

Owner

  • Name: geofileops
  • Login: geofileops
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 1.2.0
message: If you use this software, please cite it as below.
type: software
title: geofileops
version: 0.9.0
date-released: 2024-05-26
doi: 10.5281/zenodo.10340100
abstract: GeofileOps is Python toolbox to process large vector files faster.
url: https://github.com/geofileops/geofileops
repository-code: https://github.com/geofileops/geofileops
license: BSD-3-Clause
authors:
  - given-names: Pieter
    family-names: Roggemans
    orcid: https://orcid.org/0009-0009-2046-3284

keywords:
  - geofileops
  - geospatial
  - vector
  - processing

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 1,063
  • Total Committers: 8
  • Avg Commits per committer: 132.875
  • Development Distribution Score (DDS): 0.033
Past Year
  • Commits: 127
  • Committers: 5
  • Avg Commits per committer: 25.4
  • Development Distribution Score (DDS): 0.173
Top Committers
Name Email Commits
Pieter Roggemans p****s@g****m 1,028
dependabot[bot] 4****] 19
Kris Van Wayenberge 3****V 9
RichardScottOZ 7****Z 2
JoeriBroeckx j****x@g****m 2
pre-commit-ci[bot] 6****] 1
jutoth 9****h 1
Kris Van Wayenberge 3****y 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 107
  • Total pull requests: 668
  • Average time to close issues: 4 months
  • Average time to close pull requests: 5 days
  • Total issue authors: 6
  • Total pull request authors: 7
  • Average comments per issue: 0.41
  • Average comments per pull request: 0.69
  • Merged pull requests: 595
  • Bot issues: 0
  • Bot pull requests: 35
Past Year
  • Issues: 33
  • Pull requests: 235
  • Average time to close issues: 12 days
  • Average time to close pull requests: 4 days
  • Issue authors: 3
  • Pull request authors: 5
  • Average comments per issue: 0.45
  • Average comments per pull request: 0.67
  • Merged pull requests: 187
  • Bot issues: 0
  • Bot pull requests: 16
View more stats
Top Authors
Issue Authors
  • theroggy (101)
  • irm-codebase (2)
  • RichardScottOZ (1)
  • david-morris (1)
  • jorisvandenbossche (1)
  • sehHeiden (1)
Pull Request Authors
  • theroggy (609)
  • dependabot[bot] (31)
  • KriWay-LV (17)
  • pre-commit-ci[bot] (4)
  • RichardScottOZ (4)
  • KriWay (2)
  • jutoth (1)
Top Labels
Issue Labels
enhancement (42) bug (11) upstream (4) dependencies (1) question (1) good first issue (1)
Pull Request Labels
dependencies (31) bug (1) upstream (1) github_actions (1)

Dependencies

setup.py pypi
  • cloudpickle *
  • fiona *
  • gdal *
  • geopandas >=0.10
  • numpy *
  • pandas *
  • psutil *
  • pygeos *
  • pyproj *
  • shapely *
.github/workflows/tests.yml actions
  • actions/checkout v3 composite
  • codecov/codecov-action v3 composite
  • conda-incubator/setup-miniconda v2 composite
.github/workflows/release_tag_to_pypi.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
  • pypa/gh-action-pypi-publish 27b31702a0e7fc50959f5ad993c78deac1bdfc29 composite
pyproject.toml pypi
.github/workflows/tests_installed.yml actions
  • actions/checkout v4 composite
  • mamba-org/setup-micromamba v1 composite