onstove.MCA#

class onstove.MCA(**kwargs)[source]#

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 MCA class inherits all functionalities from the DataProcessor class.

Parameters:

**kwargs: dictionary 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 Assistance Need Index measures where demand and supply are simultaneously higher.

Methods

`add_layer`(path, layer_type[, category, ...])	Adds a new layer (type VectorLayer or RasterLayer) to the DataProcessor class
`add_mask_layer`(path[, category, name, ...])	Adds a vector layer to self.mask_layer.
`align_layers`([datasets, save_layers])	Ensures that the coordinate system and resolution of the raster is the same as the base layer
`get_distance_rasters`([datasets, save_layers])	Calls the .distance_raster method of all the layers entered.
`get_index`([datasets, buffer, name])	Computes a standard index based on the `datasets` provided.
`index`(layers)	Computes a standard index based on the `layers` provided.
`mask_layers`([datasets, crop, save_layers])	Uses the mask layer in `self.mask_layer` to mask layers to its boundaries.
`normalize_rasters`([datasets, buffer, ...])	Calls the .normalize method of all the layers entered.
`plot_share`([index, layer, title, output_file])	Creates a pie chart showing five different classes of the index categorized from low to high.
`read_model`(path)	Reads a model from a pickle
`reproject_layers`([datasets, save_layers])	Reprojects all layers entered.
`save_datasets`([datasets])	Saves layers.
`set_assistance_need_index`([datasets, buffer])	Computes the `assistance_need_index` based on the `datasets` provided.
`set_clean_cooking_index`([demand_weight, ...])	Computes the `clean_cooking_index` using the `demand_index` and the `supply_index`.
`set_demand_index`([datasets, buffer])	Computes the `demand_index` based on the `datasets` provided.
`set_postgres`(dbname, user, password)	Wrapper function to set a connection to a PostgreSQL database using the psycopg2.connect class.
`set_supply_index`([datasets, buffer])	Computes the `supply_index` based on the `datasets` provided.
`to_pickle`(name)	Saves the model as a pickle.