Last updated: 2021-03-12
Checks: 6 1
Knit directory: mapme.protectedareas/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 86caefa | Johannes Schielein | 2021-03-11 | small changes to link the readme html (only test) |
| html | 516d799 | Johannes Schielein | 2021-03-05 | create workflow r website |
| Rmd | e410b62 | Johannes Schielein | 2021-03-05 | new init commit with new datastructure for improved reproduciblity |
This file contains information on the general goals of the project. For details on the repository, open-source code and information on how to contribute please have a look at our contribution description (currently in contributions.md). If you want to use the source code of this repository to create your own analysis, please have a look at our documentation pages (currently in contributions.md). If you are interested in the results of our portfolio analysis and impact evaluation please visit our reporting sites (insert link here later).
Protected Areas (short PAs) are essential to conserve endangered natural habitats with high biodiversity and ecosystem functions. KfW assists other countries with financial cooperation projects to establish new PAs and increase conservation efficiency in existing PA networks worldwide.
The overarching goal of this repository is to create reproducible processing and analysis routines with open source tools such as R (and/or other languages, if necessary) that can serve as a software stack to enables us (and third parties) to leverage the potential of open (geo-) data to assist in planning monitoring and evaluation of projects to support PAs worldwide.
Variables to be processed and analyzed can be grouped into five broad categories:
We rely heavily on external pre-processed datasources to derive these variables. Furthermore, our analysis is based on the IUCN/WDPA database, i.e. KfW portfolio was matched with WDPA to identify the relevant areas.
As such this repository has two broader spheres of action 1. Create reproducible pre-processing routines to derive the relevant geodatabase(s) for analysis 2. Create reproducible analysis routines consisting in a) a simple portfolio analysis and b) a quasi-experimental impact evaluations (IE) for the KfW portfolio.
The specific outputs that should be produced by this repository regarding the geodatabase(s) consists in: 1. A database for the portfolio analysis (vector data): This database will use PAs as units of observations. The output is expected to be a Geopackage (gpkg) with PAs as rows and all processed variables for the portfolio analysis as columns. 2. A database for the quasi-experimental IE (raster data): this database will create rasterstacks for all relevant variables regarding the IE. We need raster stacks for this exercise in order to be able to analyze also what is happening outside of protected areas.
The specific outputs that should be produced by this repository regarding the analysis consist:
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 rstudioapi_0.13 whisker_0.4 knitr_1.30
[5] magrittr_2.0.1 R6_2.5.0 rlang_0.4.10 stringr_1.4.0
[9] tools_4.0.3 xfun_0.19 git2r_0.27.1 htmltools_0.5.0
[13] ellipsis_0.3.1 rprojroot_2.0.2 yaml_2.2.1 digest_0.6.27
[17] tibble_3.0.4 lifecycle_1.0.0 crayon_1.3.4 later_1.1.0.1
[21] vctrs_0.3.6 promises_1.1.1 fs_1.5.0 glue_1.4.2
[25] evaluate_0.14 rmarkdown_2.5 stringi_1.5.3 compiler_4.0.3
[29] pillar_1.4.7 httpuv_1.5.4 renv_0.13.0 pkgconfig_2.0.3