access.Access.fca_ratio

Access.fca_ratio(name='fca', demand_cost=None, supply_cost=None, supply_values=None, max_cost=None, normalize=False, noise='quiet')

Calculate the floating catchment area (buffer) ratio access score.

Parameters:

namestr

Column name for access values

demand_coststr

Name of demand cost value column in demand_df

supply_coststr

Name of supply cost value column in supply_df

supply_values{str, list}

Name(s) of supply values in supply_df

max_costfloat

Cutoff of cost values

normalizebool

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

noisestr

Default ‘quiet’, otherwise gives messages that indicate potential issues.

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",
                                  neighbor_cost_df = chi_travel_costs, neighbor_cost_origin = "origin",
                                  neighbor_cost_dest = 'dest', neighbor_cost_name = 'cost')

>>> chicago_primary_care.fca_ratio(name='fca',max_cost=30)
              fca_doc  fca_dentist
geoid
17031010100  0.001630     0.000807
17031010201  0.001524     0.000904
17031010202  0.001521     0.000908
...........  ........     ........
17197884101  0.000437     0.000442
17197884103  0.000510     0.000498
17197980100  0.000488     0.000432