DamageScanner
A python toolkit for direct damage assessments for natural hazards.
https://github.com/vu-ivm/damagescanner
Category: Climate Change
Sub Category: Natural Hazard and Storm
Keywords from Contributors
transforms measur archiving animals conversion observation optimize projection compose generic
Last synced: about 11 hours ago
JSON representation
Repository metadata
Python toolkit to do direct damage assessments for natural hazards
- Host: GitHub
- URL: https://github.com/vu-ivm/damagescanner
- Owner: VU-IVM
- License: mit
- Created: 2019-01-25T10:28:33.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2025-04-03T12:52:39.000Z (27 days ago)
- Last Synced: 2025-04-17T04:46:24.533Z (14 days ago)
- Language: Python
- Homepage: https://vu-ivm.github.io/DamageScanner/
- Size: 88.8 MB
- Stars: 21
- Watchers: 2
- Forks: 9
- Open Issues: 10
- Releases: 23
-
Metadata Files:
- Readme: README.md
- License: LICENSE
README.md
DamageScanner: direct damage assessments for natural hazards
A python toolkit for direct damage assessments for natural hazards. Even though the method is initially developed for flood damage assessments, it can calculate damages for any hazard for which you just require a vulnerability curve (i.e. a one-dimensional relation).
Please note: This package is still in development phase. In case of any problems, or if you have any suggestions for improvements, please raise an issue.
Background
This package is (loosely) based on the original DamageScanner, which calculated potential flood damages based on inundation depth and land use using depth-damage curves in the Netherlands. The DamageScanner was originally developed for the 'Netherlands Later' project (Klijn et al., 2007). The original land-use classes were based on the Land-Use Scanner in order to evaluate the effect of future land-use change on flood damages.
Installation
Requirements: NumPy, pandas, geopandas, matplotlib, rasterio, tqdm,
xarray, pyproj
- Open the python environment in your command prompt or bash in which you want to install this package.
- Type
pip install damagescanner
and it should install itself into your python environment. - Now you can import the package like any other package!
OR:
- Clone the repository or download the package on your computer and extract the folder.
- Go to the DamageScanner folder in your command prompt or bash.
- Type
python setup.py install
and it should install itself into your python environment. - Now you can import the package like any other package!
Create testing environment
Recommended option is to use a miniconda
environment to work in for this project, relying on conda to handle some of the
trickier library dependencies.
# Add conda-forge channel for extra packages
conda config --add channels conda-forge
# Create a conda environment for the project and install packages
conda env create -f environment.yml
activate ds_env
Documentation
Please refer to the ReadTheDocs of this project for the full documentation of all functions.
How to cite:
If you use the DamageScanner in your work, please cite the package directly:
- Koks. E.E. (2022). DamageScanner: Python tool for natural hazard damage assessments. Zenodo. http://doi.org/10.5281/zenodo.2551015
Here's an example BibTeX entry:
@misc{damagescannerPython,
author = {Koks, E.E.},
title = {DamageScanner: Python tool for natural hazard damage assessments},
year = 2022,
doi = {10.5281/zenodo.2551015},
url = {http://doi.org/10.5281/zenodo.2551015}
}
License
Copyright (C) 2022 Elco Koks. All versions released under the MIT license.
Owner metadata
- Name: VU-IVM
- Login: VU-IVM
- Email: s.p.vijverberg@vu.nl
- Kind: organization
- Description: VU-IVM Github page
- Website: https://www.ivm.vu.nl/en/index.aspx
- Location: Amsterdam
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/56406868?v=4
- Repositories: 2
- Last ynced at: 2023-03-10T04:48:25.349Z
- Profile URL: https://github.com/VU-IVM
GitHub Events
Total
- Create event: 16
- Release event: 13
- Issues event: 7
- Watch event: 2
- Issue comment event: 7
- Push event: 35
- Pull request event: 1
- Fork event: 1
Last Year
- Create event: 16
- Release event: 13
- Issues event: 7
- Watch event: 2
- Issue comment event: 7
- Push event: 35
- Pull request event: 1
- Fork event: 1
Committers metadata
Last synced: 9 days ago
Total Commits: 211
Total Committers: 7
Avg Commits per committer: 30.143
Development Distribution Score (DDS): 0.128
Commits in past year: 11
Committers in past year: 1
Avg Commits per committer in past year: 11.0
Development Distribution Score (DDS) in past year: 0.0
Name | Commits | |
---|---|---|
elco.k | e****s@g****m | 184 |
Jens de Bruijn | j****n@o****m | 11 |
dependabot[bot] | 4****] | 5 |
Takuya Iwanaga | t****i@g****m | 4 |
Couasnon | a****n@v****l | 3 |
Anais Couasnon | c****s@g****m | 2 |
BenDickens | b****s@z****m | 2 |
Committer domains:
Issue and Pull Request metadata
Last synced: about 11 hours ago
Total issues: 26
Total pull requests: 21
Average time to close issues: about 1 year
Average time to close pull requests: about 1 month
Total issue authors: 6
Total pull request authors: 5
Average comments per issue: 0.42
Average comments per pull request: 0.29
Merged pull request: 14
Bot issues: 0
Bot pull requests: 9
Past year issues: 1
Past year pull requests: 3
Past year average time to close issues: 2 months
Past year average time to close pull requests: 6 days
Past year issue authors: 1
Past year pull request authors: 1
Past year average comments per issue: 3.0
Past year average comments per pull request: 0.0
Past year merged pull request: 1
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- ElcoK (21)
- czor847 (1)
- jensdebruijn (1)
- rmmilewi (1)
- TBuskop (1)
- couasnonanais (1)
Top Pull Request Authors
- dependabot[bot] (9)
- jensdebruijn (5)
- couasnonanais (3)
- ElcoK (3)
- ConnectedSystems (1)
Top Issue Labels
- enhancement (7)
- good first issue (1)
Top Pull Request Labels
- dependencies (9)
Dependencies
- actions/checkout v3 composite
- mamba-org/setup-micromamba v1 composite
- actions/checkout v3 composite
- actions/setup-python v4 composite
- pypa/gh-action-pypi-publish release/v1 composite
- Sphinx >=5,<6
- sphinx_rtd_theme *
- alabaster ==0.7.12
- babel ==2.10.3
- certifi ==2023.7.22
- charset-normalizer ==2.1.0
- docutils ==0.17.1
- idna ==3.3
- imagesize ==1.4.1
- jinja2 ==3.1.2
- markupsafe ==2.1.1
- packaging ==21.3
- pygments ==2.15.0
- pyparsing ==3.0.9
- pytz ==2022.1
- requests ==2.31.0
- snowballstemmer ==2.2.0
- sphinx ==5.0.2
- sphinx-rtd-theme ==1.0.0
- sphinxcontrib-applehelp ==1.0.2
- sphinxcontrib-devhelp ==1.0.2
- sphinxcontrib-htmlhelp ==2.0.0
- sphinxcontrib-jsmath ==1.0.1
- sphinxcontrib-qthelp ==1.0.3
- sphinxcontrib-serializinghtml ==1.1.5
- urllib3 ==1.26.18
- osm-flex *
- geopandas *
- matplotlib *
- numpy *
- osm-flex *
- packaging *
- pandas *
- pyproj *
- rasterio *
- rioxarray *
- shapely >= 2.0
- tqdm *
- xarray *
Score: 5.37989735354046