https://github.com/NINAnor/oneimpact
Tools for the assessment of the cumulative impacts of anthropogenic features in ecological studies
https://github.com/NINAnor/oneimpact
biodiversity cumulative-impacts grass-gis r r-package
Last synced: 5 months ago
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Tools for the assessment of the cumulative impacts of anthropogenic features in ecological studies
- Host: GitHub
- URL: https://github.com/NINAnor/oneimpact
- Owner: NINAnor
- Created: 2022-01-28T14:38:29.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-04-25T11:28:56.000Z (6 months ago)
- Last Synced: 2025-04-25T12:28:05.930Z (6 months ago)
- Topics: biodiversity, cumulative-impacts, grass-gis, r, r-package
- Language: R
- Homepage: https://ninanor.github.io/oneimpact/
- Size: 82.8 MB
- Stars: 4
- Watchers: 5
- Forks: 5
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- open-sustainable-technology - oneimpact - Provides tools for the assessment of cumulative impacts of multiple infrastructure and land use modifications in ecological studies. (Biosphere / Biodiversity Analysis and Metrics)
README
# oneimpact 
[](https://zenodo.org/badge/latestdoi/453101311)
`oneimpact` provides tools for the assessment of cumulative impacts of multiple infrastructure and land use modifications in ecological studies.
This includes tools to calculate the zone of influence (ZOI) of anthropogenic variables as well as tools for model fitting, estimation of the
effect size and ZOI, and ancillary functions. The functions dealing with spatial data processing can
be run in both R and GRASS GIS, using R as an interface. The tools available so far are:
## Compute spatial layers representing zones of influence
The first set of functions in `oneimpact` are aimed at computing the (potential) ZOI of infrastructure or other
spatial covariates. This means we use spatial information on where they are located to compute
the density of features in space (i.e. the cumulative ZOI) and/or the (decay) distance to the nearest
feature (i.e. the ZOI of the nearest), given an expected ZOI radius (i.e. the distance up to which
a given feature is expected to affect a certain species or process). These functions do not estimate
the ZOI, though (which is context and process dependent); for that see more functions further down.
Here are the main functions in `oneimpact` to compute spatial layers representing zones of influence.
### Zone of influence (ZOI) decay functions
- zoi_functions: a set of decay zone of influence functions to characterize different shapes of the ZOI around infrastructure,
parameterized based on the zone of influence radius. The functions implemented so far are: threshold (`threshold_decay()` or `step_decay()`),
linear decay (`linear_decay()` or `bartlett_decay()` or `tent_decay()`), exponential decay (`exp_decay()`), or Gaussian decay
(`gaussian_decay()` or `half_norm_decay()`).
- `plot_zoi1d()`: plot ZOI in 1 dimensional space for multiple points infrastructure, using both the ZOI of the nearest
feature and the cumulative ZOI metric.
### Compute zones of influence (ZOI)
- `calc_zoi_nearest()`: Calculate the zone of influence from the nearest infrastructure, according to multiple possible
decay functions and zones of influence radii.
- `calc_zoi_cumulative()`: Calculate the cumulative zone of influence of multiple features, according to multiple possible
decay functions and zones of influence radii.
- `calc_zoi()`: Calculate both the the ZOI of the nearest infrastructure and the cumulative ZOI, at multiple
scales or zones of influence radii.
### Spatial filters
- `create_filter()`: Create filters or weight matrices for neighborhood analysis, according to different decay functions
and parameterized using the zone of influence radius.
- `save_filter()`: Saves filters/weight matrices outside R for use within GRASS GIS modules.
## Estimate the cumulative impact and the ZOI of features on a certain species or process
The `oneimpact` package also allows us to, given a set of potential candidate ZOIs (with possibly
different types, shapes, and radii; computed with the functions above), estimate the actual
effect and ZOI of the variables on a certain species or process. This is done combining three elements:
- Bootstrap aggregation (bagging), a multi-model bootstrap procedure that allows us to estimate the
uncertainty in the effect sizes and ZOI radii;
- Penalized regression, an approach that allows us to penalize estimated coefficients and possibly
remove the least likely covariates from a model, i.e., it allows us to perform model fitting together
with variable section;
- Nested cross-validation, which allows is to consider hierarchical, spatial, or temporally cross-validation
schemes in model and variable/feature selection.
### Estimating ZOI - set up analysis
Functions to set up RSF and SSF analyses using ZOI variables:
- `add_zoi_formula()`: Adds ZOI radii to formula
- `spat_strat()`: Prepares data for spatially stratified cross‐validation schemes
- `explore_blocks_pre()` and `explore_blocks()`: Explore hierarchical blocks before or
after sampling or spatial stratification, respectively
- `create_resamples()`: Create samples for fitting, calibrating, and validating models in
a bootstrap/baggin procedure.
### Estimating ZOI - fit models
Functions to fit RSF and SSF and estimate ZOI using penalized regression
- `bag_fit_net_clogit()`: Fits a a bag of conditional logistic regressions/SSF/iSSF using glmnet.
This function is a wrapper around `fit_net_clogit()` which is the one properly setting up the
model fitting, tunning, and validation. It allows the use of different penalization algorithms,
including Lasso, Ridge, Adaptive Lasso, and different adaptations from Adaptive Lasso. This
function calls the function `net_logit()` which is the one properly calling `glmnet` and fitting
the model.
- `bag_fit_net_logit()` (and `fit_net_logit()`, `net_logit()`): equivalent to the one above, but
performing common logistic regression, with no strata.
- `bag_load_models()`: Load a vector of files with the output of `fit_net_clogit()` or
`fit_net_logit()` and put them on a bag.
- `bag_models()`: Bag a list of loaded/fitted models fitted through `fit_net_clogit()` or
`fit_net_logit()`. This created an object of class `bag` with all information for understanding
and making prediction from the bag of models.
- `AUC()`, `conditionalAUC()`, `coxnet.deviance()`, `Cindex()`, `conditionalSomersD()`: functions
used for model tunning (selecting penalties) and validation.
### Estimating ZOI - interpret and visualize models
Functions to help interpreting parameters and visualizing cumulative impacts from bags
of fitted models:
- `bag_predict()`: Prediction of a bag of models to new data.
- `variable_importance()`, `plot_importance()`: Computes and plots variable importance from a bag of models.
- `plot_coef()`: Plots the coefficients of bags of models.
- `plot_response()`: Plots (partial) response curves from a bag of models.
- `bag_predict_spat()`: Predict bag of models in space.
- `bag_predict_spat_vars()`: Predict reponses of each individual covariate in space according to
a bag of models.
## Installation
To install the development version of the `oneimpact` R package, please use:
```
library(devtools)
devtools::install_github("NINAnor/oneimpact", ref = "HEAD")
```
## Run with Docker
```bash
docker run --rm -p 8787:8787 -e PASSWORD=rstudio -v $PWD/myproject:/home/rstudio/myproject ghcr.io/ninanor/oneimpact:main
```
If you use Compose:
```bash
docker compose run rstudio
```
You can customize `docker-compose.yml` based on your needs.
## See also
For model fitting nad estimation of ZOI, see the pacakage [`glmnet`](https://glmnet.stanford.edu/index.html),
which is the backbone of the modeling approach used in `oneimpact`. For other similar approaches,
check the [`maxnet()`](https://cran.r-project.org/web/packages/maxnet/index.html) for MaxEnt
species distribution models using `glmnet`.
The `oneimpact` functions to compute the ZOI layers are greatly based on neighborhood analyses
made through the [`terra` package](https://rspatial.org/terra/pkg/index.html) in R and on three GRASS GIS modules:
[`r.mfilter`](https://grass.osgeo.org/grass78/manuals/r.mfilter.html),
[`r.resamp.filter`](https://grass.osgeo.org/grass78/manuals/r.resamp.filter.html), and
[`r.neighbors`](https://grass.osgeo.org/grass78/manuals/r.neighbors.html). The connection
between R and GRASS GIS is made through the [`rgrass7`](https://github.com/rsbivand/rgrass) R package.
## Meta
- Please [report any issues or bugs](https://github.com/NINAnor/oneimpact/issues/new/).
- License: GPL3
- Get citation information for `oneimpact` in R running `citation(package = 'oneimpact')`, or check the reference [here](https://ninanor.github.io/oneimpact/authors.html#citation).
- Contributions are mostly welcome!