Games in support of Ecosystem Crisis Management (gecm)

Welcome to our student project "Games in support of Ecosystem Crisis Management". This project was created in Autumn 2020 as part of Foundations of Ecosystem Management, a graduate-level course offered at ETH Zurich. The team members were Lena Wunderlin, Martina Buck, Marco Barandun, Ella-Mona Chevalley and Felix Zaussinger.

Collaboration

We tried to keep the core of the package rather flexible to leave ample room for the possibility of simultaneously addressing different application domains. However, even better abstraction of core functionalities would be an important prerequisite of enhanced flexibility. Ultimately, one could potentially play such games around the world and across ecosystem management problems: all one needs is a sound conceptual model, and a GIS map to start with. If you share the excitement for such an endeavour and would be interested in taking a lead on further developments of these (or other) aspects of the package, we would be happy and excited if you got in touch with Felix (fzaussinger@ethz.ch).

Implementation paradigm

The common, underlying data structure/interface consists of a set of matrices, which can be combined ("stacked") into a 3D array representation ("data cube"). The following 2D matrices of equal shape (currently only quadratic supported) need to be implemented to play a game. Note that all of them should be numpy masked arrays (np.ma), bto handle cropping out all Land Use Land Cover (LULC) data outside the area of interest (in this case, a biosphere park boundary).

This table summarizes initial parameters needed to describe the size and blocks/plots of the playing field, given that a LULC raster map already exists.

Parameter	Data structure	Data type	Shape	Description
n_pixels	int	np.uint16	(1,)	The size of the quadratic playing field along one dimension, where n_pixels = rows = cols.
n_blocks	int	np.uint8	(1,)	The number of plots/blocks to be created.

From these initial parameters, more parameters are derived:

Parameter	Data structure	Data type	Shape	Description
n_pixels_per_block	int	np.uint8	(1,)	n_pixels_block := n_blocks / n_pixels.

This table summarizes all matrices necessary for fully describing the initial state of the playing field and for updating it after each round:

Matrix	Variable name	Data structure	Data type	Shape	General description	Mapping description
Land-use and land-cover (LULC) types	lulc_matrix	np.ma	np.unit8	(n_pixels, n_pixels)	defines the land-cover and land-use types of the playing field	each integer maps to a unique LULC class.
Plot/Block Definitions	block_definition_matrix	np.ma	np.unit8	(n_blocks, n_blocks) --> (n_pixels, n_pixels)	defines the plots/blocks the players can manipulate	each integer maps to a unique plot/block identifier. this "small" matrix is brought into (n_pixels, n_pixels) shape via the kronecker delta function: (n_blocks, n_blocks) --> (n_pixels, n_pixels).
Cooperation/Teamwork	cooperation_matrix	np.ma	np.bool	(n_blocks, n_blocks) --> (n_pixels, n_pixels)	defines in which block players from a certain stakeholder group are open for cooperation with players from other stakeholder groups.	TRUE for blocks/plots/pixels where stakeholders are open for cooperation, else FALSE. this "small" matrix is brought into (n_pixels, n_pixels) shape via the kronecker delta function: (n_blocks, n_blocks) --> (n_pixels, n_pixels).
Tourism/SSDA	tourism_matrix	np.ma	np.bool	(n_blocks, n_blocks) --> (n_pixels, n_pixels)	defines the plot which SSDA designates as being particularly valuable for tourism based on biodiversity, etc.	TRUE for blocks/plots/pixels which the SSDA designated as particularly valuable for touristic activities, else FALSE. this "small" matrix is brought into (n_pixels, n_pixels) shape via the kronecker delta function: (n_blocks, n_blocks) --> (n_pixels, n_pixels).

Note

This project has been set up using PyScaffold 3.2.3. For details and usage information on PyScaffold see https://pyscaffold.org/.