pylandstats

Computing landscape metrics in the Python ecosystem

https://github.com/martibosch/pylandstats

Science Score: 49.0%

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codemeta.json file
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DOI references
Found 4 DOI reference(s) in README
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2 of 9 committers (22.2%) from academic institutions
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Scientific vocabulary similarity
Low similarity (9.9%) to scientific vocabulary

Keywords

land-change-analysis landscape-ecology landscape-metrics python raster

Last synced: 6 months ago · JSON representation

Repository

Computing landscape metrics in the Python ecosystem

Basic Info

Host: GitHub
Owner: martibosch
License: gpl-3.0
Language: Python
Default Branch: main
Homepage: https://doi.org/10.1371/journal.pone.0225734
Size: 903 KB

Statistics

Stars: 98
Watchers: 3
Forks: 16
Open Issues: 5
Releases: 17

Topics

land-change-analysis landscape-ecology landscape-metrics python raster

Created over 7 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License

PyLandStats

Open-source library to compute landscape metrics in the Python ecosystem (NumPy, pandas, matplotlib...)

Citation: Bosch M. 2019. "PyLandStats: An open-source Pythonic library to compute landscape metrics". PLOS ONE, 14(12), 1-19. doi.org/10.1371/journal.pone.0225734

Features

Read GeoTiff files of land use/cover:

```python import pylandstats as pls

ls = pls.Landscape("../data/processed/veveyse-AS184.tif") ls.plotlandscape(legend=True) ```

landscape-veveyse

Compute pandas data frames of landscape metrics at the patch, class and landscape level:

python class_metrics_df = ls.compute_class_metrics_df( metrics=["proportion_of_landscape", "edge_density", "euclidean_nearest_neighbor_mn"] ) class_metrics_df

| classval | proportionoflandscape | edgedensity | euclideannearestneighbor_mn | | --------: | ----------------------: | -----------: | ----------------------------: | | 1 | 7.749572 | 19.102211 | 309.244705 | | 2 | 56.271868 | 50.599270 | 229.079970 | | 3 | 33.946252 | 38.167200 | 253.299859 | | 4 | 2.032308 | 3.722177 | 552.835154 |

Analyze the spatio-temporal evolution of landscapes:

```python import matplotlib.pyplot as plt

inputfilepaths = [ "../data/processed/veveyse-AS97R4.tif", "../data/processed/veveyse-AS09R4.tif", "../data/processed/veveyse-AS184.tif", ]

sta = pls.SpatioTemporalAnalysis(inputfilepaths, dates=["1992", "2004", "2012"]) sta.plotmetric("contagion") ```

spatiotemporal-analysis

Zonal analysis of landscapes

See the documentation and the pylandstats-notebooks repository for a more complete overview.

Installation

The easiest way to install PyLandStats is with conda:

$ conda install -c conda-forge pylandstats

which will install PyLandStats and all of its dependencies. Alternatively, you can install PyLandStats using pip:

$ pip install pylandstats

Nevertheless, note that in order to define zones by vector geometries in ZonalAnalysis, or in order to use the the BufferAnalysis and SpatioTemporalBufferAnalysis classes, PyLandStats requires geopandas, which cannot be installed with pip. If you already have the dependencies for geopandas installed in your system, you might then install PyLandStats with the geo extras as in:

$ pip install pylandstats[geo]

and you will be able to use the aforementioned features (without having to use conda).

Development install

To install a development version of PyLandStats, you can first use conda to create an environment with all the dependencies and activate it as in:

$ conda create -n pylandstats -c conda-forge geopandas matplotlib-base rasterio scipy openblas $ conda activate pylandstats

and then clone the repository and use pip to install it in development mode

$ git clone https://github.com/martibosch/pylandstats.git $ cd pylandstats/ $ pip install -e .

Acknowledgments

The computation of the adjacency matrix in transonic has been implemented by Pierre Augier (paugier)
Several information theory-based metrics from Nowosad and Stepinski [1] were added by achennu
With the support of the École Polytechnique Fédérale de Lausanne (EPFL)
The Corine Land Cover datasets used for the test datasets were produced with funding by the European Union

References

Nowosad, J., & Stepinski, T. F. (2019). Information theory as a consistent framework for quantification and classification of landscape patterns. Landscape Ecology, 34(9), 2091-2101.

Owner

Name: Martí Bosch
Login: martibosch
Kind: user
Location: Lausanne
Company: EPFL

Website: https://fosstodon.org/@martibosch
Twitter: mortybosch
Repositories: 83
Profile: https://github.com/martibosch

Doctor in civil and environmental engineering. Urban sprawl, Python, and a bit of landscape ecology and complexity

GitHub Events

Total

Create event: 20
Issues event: 8
Release event: 5
Watch event: 11
Delete event: 18
Issue comment event: 19
Push event: 112
Pull request event: 29

Last Year

Create event: 20
Issues event: 8
Release event: 5
Watch event: 11
Delete event: 18
Issue comment event: 19
Push event: 112
Pull request event: 29

Committers

Last synced: 6 months ago

All Time

Total Commits: 323
Total Committers: 9
Avg Commits per committer: 35.889
Development Distribution Score (DDS): 0.316

Past Year

Commits: 35
Committers: 5
Avg Commits per committer: 7.0
Development Distribution Score (DDS): 0.4

Top Committers

Name	Email	Commits
Martí Bosch	m**h@e**h	221
Martí Bosch	m**h@p**m	76
dependabot[bot]	4**]@u**m	9
paugier	p**r@u**r	5
martibosch	m**h@u**m	4
pre-commit-ci[bot]	6**]@u**m	3
Arjun Chennu	a**u@g**m	2
Martí Bosch	m**2@g**m	2
martibosch	5**h@u**m	1

Committer Domains (Top 20 + Academic)

univ-grenoble-alpes.fr: 1 epfl.ch: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 25
Total pull requests: 53
Average time to close issues: about 1 year
Average time to close pull requests: about 2 months
Total issue authors: 22
Total pull request authors: 5
Average comments per issue: 2.96
Average comments per pull request: 1.21
Merged pull requests: 32
Bot issues: 1
Bot pull requests: 29

Past Year

Issues: 2
Pull requests: 27
Average time to close issues: N/A
Average time to close pull requests: 14 days
Issue authors: 2
Pull request authors: 3
Average comments per issue: 0.0
Average comments per pull request: 0.22
Merged pull requests: 12
Bot issues: 1
Bot pull requests: 23

View more stats

Top Authors

Issue Authors

emuise (2)
simon-tarr (2)
ffrosch (2)
gislfzhao (1)
Baharehfa (1)
1810174827 (1)
Momut1 (1)
paulomur (1)
TGrmn (1)
dependabot[bot] (1)
cisluis (1)
kareed1 (1)
mouzui (1)
achennu (1)
JasperSTV (1)

Pull Request Authors

dependabot[bot] (22)
martibosch (19)
pre-commit-ci[bot] (8)
paugier (4)
achennu (1)

Top Labels

Issue Labels

dependencies (1)

Pull Request Labels

dependencies (22) github_actions (5)

Packages

Total packages: 3
Total downloads:
- pypi 852 last-month

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 2
(may contain duplicates)
Total versions: 90
Total maintainers: 1

proxy.golang.org: github.com/martibosch/pylandstats

Documentation: https://pkg.go.dev/github.com/martibosch/pylandstats#section-documentation
License: gpl-3.0
Latest release: v3.1.0+incompatible
published about 1 year ago

Versions: 32
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Stargazers count: 5.3%

Dependent packages count: 5.4%

Average: 5.5%

Dependent repos count: 5.7%

Forks count: 5.7%

Last synced: 6 months ago

pypi.org: pylandstats

Computing landscape metrics in the Python ecosystem.

Documentation: https://pylandstats.readthedocs.io/
License: GPL-3.0
Latest release: 3.1.0
published about 1 year ago

Versions: 41
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 852 Last month

Rankings

Dependent packages count: 7.3%

Stargazers count: 8.4%

Downloads: 8.7%

Forks count: 9.4%

Average: 11.2%

Dependent repos count: 22.1%

Maintainers (1)

martibosch

Last synced: 6 months ago

conda-forge.org: pylandstats

Open-source Pythonic library to compute landscape metrics in the Python ecosystem (NumPy, pandas, matplotlib...)

Homepage: https://github.com/martibosch/pylandstats
License: GPL-3.0-or-later
Latest release: 2.4.2
published about 4 years ago

Versions: 17
Dependent Packages: 0
Dependent Repositories: 1

Rankings

Dependent repos count: 24.1%

Stargazers count: 38.8%

Average: 39.0%

Forks count: 41.7%

Dependent packages count: 51.5%

Last synced: 6 months ago

Dependencies

setup.py pypi

x.strip *

.github/workflows/release.yml actions

actions/checkout v4 composite
actions/download-artifact v3 composite
actions/setup-python v4 composite
actions/upload-artifact v3 composite
heinrichreimer/github-changelog-generator-action v2.1.1 composite
pypa/gh-action-pypi-publish release/v1 composite
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.github/workflows/tests.yml actions

actions/checkout v4 composite
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docs/environment.yml pypi

pydata-sphinx-theme ==0.13.3

environment.yml pypi

pyproject.toml pypi

black *
geopandas *
matplotlib >= 2.2
numba platform_system == 'Windows'
numpy >= 1.15
pandas >= 0.23
rasterio >= 1.0.0
scipy >= 1.0.0
transonic >= 0.4.0

pylandstats

Science Score: 49.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

PyLandStats

Features

Installation

Development install

Acknowledgments

References

Owner

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

proxy.golang.org: github.com/martibosch/pylandstats

Rankings

pypi.org: pylandstats

Rankings

Maintainers (1)

conda-forge.org: pylandstats

Rankings

Dependencies