Step 3 - Summarize Access

This script reads modal impedances from skim files, specifies decay rates by purpose, and calculates purpose-specific OD decay factors. It then summarizes access scores using zonal activity data and the calculated decay factors.

Workflow:

  • Read data - Travel cost skims - Zonal activity data

  • Specify decay rates

  • Calculate decay factors (using emma.Decay methods)

  • Summarize access scores (using emma.od.summarizeAccess)

  • Export results

Functions

The following functions are referenced in this script, from the wsa.summarize_access (or access) submodule:

wsa.access.loadInputZones(lu_config, taz_table='MAPC_TAZ_data.xlsx', taz_sheet='Zdata', block_table_hh='Household_Types_by_Block.csv', block_table_emp='Jobs_Enroll_by_Block.csv', taz_id='TAZ', block_id='block_id')

Reads zone input tables from default locations.

Parameters

  • lu_config (String) –

  • taz_table (String, default="MAPC_TAZ_data.xlsx") –

  • taz_sheet (String, default="Zdata") –

  • block_table_hh (String, default="Household_Types_by_Block.csv") –

  • block_table_emp (String, default="Jobs_Enroll_by_Block.csv") –

  • taz_id (String, default="TAZ") –

  • block_id (String, default="block_id") –

Returns

  • taz_df (pd.DataFrame)

  • block_hh_df (pd.DataFrame)

  • block_emp_df (pd.DataFrame)

wsa.access.decaysFromTable(decay_table, **selection_criteria)

Create Decay objects based on parameters specified in a csv file.

Parameters

  • decay_table (String) – Path to a well-formed csv file with decay curve specifications.

  • selection_criteria – Keyword arguments for selecting rows from the table when constructing decay objects (Mode=”auto” will only construct auto decay curves, e.g.).