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Inauguration (commercial opening) dates for São Paulo metro stations, covering stations whose opening falls within or near the station_daily / station_averages window. Used to flag ramp-up periods in which monthly ridership is still climbing toward steady-state and should generally be excluded from year-on-year or baseline comparisons.

Usage

station_inauguration

Format

A data frame with one row per (line, station):

line_number

Metro line number (integer).

station_name

Full station name (character).

inauguration_date

Date of commercial opening (Date). NA for stations whose opening predates the dataset window (i.e., they were already operating when the data record begins).

phase

Short label identifying the expansion phase, e.g. "L15 Fase 4" (character).

verified

Whether the inauguration date has been cross-checked against the operator's announcement or an equivalently reliable source (logical). Stations with verified = FALSE carry best-effort dates and should not be relied on for legal or publication purposes without re-checking.

notes

Free-text annotations about the source or any caveats (character, possibly NA).

pre_data_window

TRUE when inauguration_date is NA because the station opened before the data starts (logical).

ramp_up_end

inauguration_date + 180 days — a heuristic end of the initial ramp-up period (Date). NA when pre_data_window is TRUE.

Source

Compiled from operator announcements (Companhia do Metropolitano de São Paulo, ViaQuatro, ViaMobilidade).

Details

The table is assembled by data-raw/build_station_inauguration.R from data-raw/station_inauguration.csv. To extend the table or verify uncertain dates, edit the CSV (setting verified = TRUE once cross-checked) and re-run the build script.

Suggested use: when computing pre/post comparisons (e.g.\ 12m-vs-prior-12m or recovery-vs-2019), exclude stations where either window overlaps ramp_up_end to avoid mistaking ramp-up growth for organic demand change.

See also

stations for spatial point locations, station_averages for monthly weekday averages.