access.fca.weighted_catchment

access.fca.weighted_catchment(loc_df, cost_df, max_cost=None, cost_source='origin', cost_dest='dest', cost_cost='cost', loc_index='geoid', loc_value=None, weight_fn=None, three_stage_weight=None)

Calculation of the floating catchment (buffered) accessibility sum, from DataFrames with computed distances. This catchment may be either a simple buffer – with cost below a single threshold – or an additional weight may be applied as a function of the access cost.

Parameters:

loc_dfpandas.DataFrame

should contain at _least_ a list of the locations (df_dest) at which facilities are located.

loc_index{bool, str}

is the the name of the df column that holds the facility locations. If it is a bool, then the it the location is already on the index.

loc_valuestr

If this value is None, a count will be used in place of a weight. Use this, for instance, to count restaurants, instead of total doctors in a practice.

cost_dfpandas.DataFrame

This dataframe contains the precomputed costs from an origin/index location to destinations.

cost_sourcestr

The name of the column name of the index locations – this is what will be grouped.

cost_deststr

The name of the column name of the destination locations. This is what will be _in_ each group.

cost_coststr

This is is the name of the cost column.

weight_fnfunction

This function will weight the value of resources/facilities, as a function of the raw cost.

max_costfloat

This is the maximum cost to consider in the weighted sum; note that it applies _along with_ the weight function.

Returns:

resourcespandas.Series

A – potentially weighted – sum of resources, facilities, or consumers.