diff --git a/data.qmd b/data.qmd
index 2a533b2..c5ab846 100644
--- a/data.qmd
+++ b/data.qmd
@@ -4,37 +4,33 @@
 
 We are basing our analysis on two datasets, a dataset of taxi use in New
 York and historical weather data. For both datasets, we will limit our
-analysis to 2019–August 2024, in the interest of trying to catch about a
+analysis to 2019-01-01 00:00:00 – 2024-06-25 23:00:00, in the interest of trying to catch about a
 year of pre-pandemic norms to help interpret pandemic and post-pandemic use
-of taxis.
+of taxis. The taxi data only go to August, but the weather data only go
+through most of June this year.
 
 The taxi data are provided by New York’s [Taxi and Limousine
 Commission](https://www.nyc.gov/site/tlc/index.page). They [provide taxi
 data](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page), in
 [parquet](https://parquet.apache.org/) format, going back to 2009. Since
-February of 2019 they have also included data on trips serviced by
+February of 2019, they have also included data on trips serviced by
 companies like Lyft and Uber, which are classified as “high-volume for-hire
-vehicle” (HVFHV) trips. The data are updated monthly, with a two-month lag;
-however, at the time of submission, only data through August 2024 were
-available. Our focus is on yellow taxi and HVFHV trips, because our focus
+vehicle” (HVFHV) trips. The data are updated monthly, with a two-month lag.
+Our focus is on yellow taxi and HVFHV trips, because our focus
 is on intra-Manhattan trips. Only yellow cabs can pick up passengers in
 most of Manhattan, so we are ignoring green cabs and regular for-hire
 vehicles (town cars and limousines). Yellow cab data have [19
 columns](https://www.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf),
-of which the pertient columns for us are pickup/dropoff date and time
-(`tpep_pickup_datetime`/`tpep_dropoff_datetime` in a datetime format), the
-pickup/dropoff locations (`PULocationID`/`DOLocationID` in integer format,
-corresponding to [NYC taxi
-zones](https://d37ci6vzurychx.cloudfront.net/misc/taxi_zone_lookup.csv)) in
-order to filter on Manhattan-only trips, passenger count (`Passenger_count`
-in integer format), fare amount (`Fare_amount` in integer format), and tip
-amount (`Tip_amount` in integer format).
+and FHVHV data have [24 columns](https://www.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_hvfhs.pdf).
+With our Python scripts, we consolidate the data to create 
+aggregated hourly statistics on trip duration, trip distance, fare amount,
+and tip amount.
 
 The historical weather data are provided by the [Global Historical Climate
 Network hourly
 (GHCNh)](https://www.ncei.noaa.gov/products/global-historical-climatology-network-hourly),
 which provides hourly weather data going back over two centuries for New
-York City. We will be limiting ourselves to 2019–present, which comes in
+York City. The data come in
 [annual parquet files for download by
 station](https://www.ncei.noaa.gov/oa/global-historical-climatology-network/index.html#hourly/access/by-year/).
 Our station, `KNYC0`, is listed in the GHCNh as `USW00094728`, and it is
@@ -42,15 +38,17 @@ the weather station in Central Park. The data come in over 200 columns to
 account for the variability that can occur in the terse
 [METAR](https://en.wikipedia.org/wiki/METAR) report for airplanes, which is
 also included under `remarks`. The government provides a
-[codebook](https://www.ncei.noaa.gov/oa/global-historical-climatology-network/hourly/doc/ghcnh_DOCUMENTATION.pdf).
-We may simply feed the reports to the
-[python-metar](https://github.com/python-metar/python-metar) library, which
-parses the reports for us. As we are interested in what conditions
+[codebook](https://www.ncei.noaa.gov/oa/global-historical-climatology-network/hourly/doc/ghcnh_DOCUMENTATION.pdf) to describe the remaining data. As we are interested in what conditions
 determine a “nice” day for not using a taxi, we want to keep as much data
 from the weather report as possible, including temperature, precipitation,
 sky cover, snow, humidity, and so on. 
 
 ## Missing value analysis
+
+The taxi data is notoriously (as in, persistently) messy, registering trips
+outside the bounds of the asserted date and giving results that seem
+extremely unlikely, like 
+
 Describe any patterns you discover in missing values. If no values are missing, graphs should still be included showing that.
 
 (suggested: 2 graphs plus commentary)