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InVEST urban cooling model calibration
===============================
Overview
--------
Automated calibration of the InVEST urban cooling model with simulated annealing
**Citation**: Bosch, M., Locatelli, M., Hamel, P., Remme, R. P., Chenal, J., and Joost, S. 2020. "A spatially-explicit approach to simulate urban heat islands in complex urban landscapes". Under review in *Geoscientific Model Development*. [10.5194/gmd-2020-174](https://doi.org/10.5194/gmd-2020-174)
See [the user guide](https://invest-ucm-calibration.readthedocs.io/en/latest/user-guide.html) for more information, or [the `lausanne-heat-islands` repository](https://github.com/martibosch/lausanne-heat-islands) for an example use of this library in an academic article.
Installation
------------
This library requires specific versions of the `gdal` and `rtree` libraries, which can easily be installed with conda as in:
$ conda install -c conda-forge 'gdal<3.0' rtree 'shapely<1.7.0'
Then, this library can be installed as in:
$ pip install invest-ucm-calibration
An alternative for the last step is to clone the repository and install it as in:
$ git clone https://github.com/martibosch/invest-ucm-calibration.git
$ python setup.py install
TODO
----
* Allow a sequence of LULC rasters (although this would require an explicit mapping of each LULC/evapotranspiration/temperature raster or station measurement to a specific date)
* Test calibration based on `cc_method='intensity'`
* Support spatio-temporal datasets with [xarray](http://xarray.pydata.org) to avoid passing many separate rasters (and map each raster to a date more consistently)
* Read both station measurements and station locations as a single geo-data frame
Acknowledgments
---------------
* The calibration procedure is based simulated annealing implementation of [perrygeo/simanneal](https://github.com/perrygeo/simanneal)
* With the support of the École Polytechnique Fédérale de Lausanne (EPFL)
PyPI 官网下载 | invest-ucm-calibration-0.3.3.tar.gz
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