access.fca.three_stage_fca

access.fca.three_stage_fca(demand_df, supply_df, cost_df, max_cost, demand_index='geoid', demand_name='demand', supply_index='geoid', supply_name='supply', cost_origin='origin', cost_dest='dest', cost_name='cost', weight_fn=None, normalize=False)

Calculation of the three-stage floating catchment accessibility ratio, from DataFrames with precomputed distances. This is accomplished through a single call of the access.access.weighted_catchment() method, to retrieve the patients using each provider. The ratio of providers per patient is then calculated at each care destination, and that ratio is weighted and summed at each corresponding demand site. The only difference weight respect to the 2SFCA method is that, in addition to a distance-dependent weight (weight_fn), a preference weight G is calculated. That calculation uses the value \(\beta\). See the original paper by Wan, Zou, and Sternberg. [Wan et al., 2012]

Parameters:

demand_dfpandas.DataFrame

The origins dataframe, containing a location index and a total demand.

demand_originstr

is the name of the column of demand that holds the origin ID.

demand_valuestr

is the name of the column of demand that holds the aggregate demand at a location.

supply_dfpandas.DataFrame

The origins dataframe, containing a location index and level of supply

supply_dfpandas.DataFrame

The origins dataframe, containing a location index and level of supply

cost_dfpandas.DataFrame

This dataframe contains a link between neighboring demand locations, and a cost between them.

cost_originstr

The column name of the locations of users or consumers.

cost_deststr

The column name of the supply or resource locations.

cost_namestr

The column name of the travel cost between origins and destinations

weight_fnfunction

This fucntion 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.

preference_weight_betafloat

Parameter scaling with the gaussian weights, used to generate preference weights.

Returns:

accesspandas.Series

A – potentially-weighted – three-stage access ratio.