access.Access.three_stage_fca

Access.three_stage_fca(name='3sfca', cost=None, supply_values=None, max_cost=None, weight_fn=None, normalize=False)

Calculate the three-stage floating catchment area access score.

Parameters:

namestr

Column name for access values

coststr

Name of cost value column in cost_df (supply-side)

max_costfloat

Cutoff of cost values

weight_fnfunction

Weight to be applied to access values

normalizebool

If True, return normalized access values; otherwise, return raw access values

Returns:

accesspandas Series

Accessibility score for origin locations.

Examples

Import the base Access class and Datasets.

>>> from access import Access, Datasets

Load each of the example datasets:

>>> chi_docs_dents   = Datasets.load_data('chi_doc')
>>> chi_population   = Datasets.load_data('chi_pop')
>>> chi_travel_costs = Datasets.load_data('chi_times')

>>> chi_docs_dents.head()
         geoid  doc  dentist
0  17031010100    1        1
1  17031010201    0        1
2  17031010202    4        1
3  17031010300    4        1
4  17031010400    0        2

>>> chi_population.head()
         geoid   pop
0  17031010100  4854
1  17031010201  6450
2  17031010202  2818
3  17031010300  6236
4  17031010400  5042

>>> chi_travel_costs.head()
        origin         dest   cost
0  17093890101  17031010100  91.20
1  17093890101  17031010201  92.82
2  17093890101  17031010202  92.95
3  17093890101  17031010300  89.40
4  17093890101  17031010400  84.97

Using the example data, create an Access object.

>>> chicago_primary_care = Access(demand_df = chi_population, demand_index = "geoid",
                                  demand_value = "pop",
                                  supply_df = chi_docs_dents, supply_index = "geoid",
                                  supply_value = ["doc", "dentist"],
                                  cost_df = chi_travel_costs, cost_origin  = "origin",
                                  cost_dest = "destination", cost_name = "cost")

>>> chicago_primary_care.three_stage_fca(name='3sfca')
             3sfca_doc  3sfca_dentist
geoid
17031010100   0.001424       0.000690
17031010201   0.001462       0.000785
17031010202   0.001411       0.000767
...........   ........       ........
17197884101   0.000285       0.000380
17197884103   0.000404       0.000464
17197980100   0.000365       0.000407

The newly calculated 3sfca access measure is added to the access_df attribute of the Access object.

>>> chicago_primary_care.access_df.head()
                     3sfca_doc  3sfca_dentist
geoid
17031010100   0.001447       0.000698
17031010201   0.001487       0.000795
17031010202   0.001420       0.000777
17031010300   0.001479       0.000742
17031010400   0.001274       0.000726