onstove.MCA#

class onstove.MCA(**kwargs)#

The MCA class is used to conduct a spatial Multicriteria Analysis in order to prioritize areas of action for clean cooking access.

The MCA model is based in the methods of the Energy Access Explorer (EAE) and the Clean Cooking Explorer (CCE). It focuses on identifying potential areas where clean cooking can be quickly adopted, areas where markets for clean cooking technologies can be expanded or areas in need of financial assistance or lack of infrastructure. In brief, it identifies priority areas of action from the user perspective.

Note

The OnStove class inherits all functionalities from the DataProcessor class.

Parameters:
**kwargs: dict of parameters

Parameters from the DataProcessor parent class.

Attributes:
demand_index

The Demand Index highlights the potential demand for clean cooking in different parts of the study area.

supply_index

The Supply Index highlights the potential for clean cooking supply in different parts of the study area.

clean_cooking_index

The Clean Cooking Index measures where demand and supply are simultaneously higher.

assistance_need_index

The Clean Cooking Index measures where demand and supply are simultaneously higher.

Methods

add_layer(category, name, path, layer_type)

Adds a new layer (type VectorLayer or RasterLayer) to the MCA class

add_mask_layer(category, name, path[, ...])

Adds a vector layer to self.mask_layer, which will be used to mask all other layers into is boundaries

align_layers([datasets])

Ensures that the coordinate system and resolution of the raster is the same as the base layer

get_distance_rasters([datasets])

Goes through all layer and call their .distance_raster method

mask_layers([datasets])

Uses the a mask layer in self.mask_layer to mask all other layers to its boundaries.

normalize_rasters([datasets, buffer])

Goes through all layer and call their .normalize method

read_model(path)

Reads a model from a pickle

reproject_layers([datasets])

Reprojects the layers specified by the user.

save_datasets([datasets])

Saves all layers that have not been previously saved

set_postgres(dbname, user, password)

Wrapper function to set a connection to a PostgreSQL database using the psycopg2.connect class.

to_pickle(name)

Saves the model as a pickle.

autopct_format

get_assistance_need_index

get_clean_cooking_index

get_demand_index

get_supply_index

index

plot_share