Import public NYC taxi and Uber trip data into PostgreSQL / PostGIS database, analyze with R
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README.md

Unified New York City Taxi and For-Hire Vehicle Data

Code in support of this post: "Analyzing 1.1 Billion NYC Taxi and Uber Trips, with a Vengeance"

This repo provides scripts to download, process, and analyze data for over 1.8 billion taxi and for-hire vehicle (Uber, Lyft, etc.) trips originating in New York City since 2009. The data is stored in a PostgreSQL database, and uses PostGIS for spatial calculations, in particular mapping latitude/longitude coordinates to census tracts.

Most of the raw data comes from the NYC Taxi & Limousine Commission. The 2014 Uber data comes via FiveThirtyEight, who obtained it via a FOIL request. In August 2016, the TLC began providing for-hire vehicle trip records in addition to taxi trips.

Instructions

Your mileage may vary, but on my MacBook Air, this process took about 3 days to complete. The unindexed database takes up over 300 GB on disk.

1. Install PostgreSQL and PostGIS

Both are available via Homebrew on Mac OS X

2. Download raw data

./download_raw_data.sh

3. Initialize database and set up schema

./initialize_database.sh

4. Import taxi and FHV data

./import_trip_data.sh
./import_fhv_trip_data.sh

5. Optional: download and import 2014 Uber data

The FiveThirtyEight Uber dataset contains Uber trip records from Apr–Sep 2014. Uber and other FHV (Lyft, Juno, Via, etc.) data is available since Jan 2015 in the TLC's data.

./download_raw_2014_uber_data.sh
./import_2014_uber_trip_data.sh

6. Analysis

Additional Postgres and R scripts for analysis are in the analysis/ folder, or you can do your own!

Schema

  • trips table contains all yellow and green taxi trips. Each trip has a cab_type_id, which references the cab_types table and refers to one of yellow or green
  • fhv_trips table contains all for-hire vehicle trip records made available by the TLC, including ride-hailing apps Uber, Lyft, Via, and Juno
  • fhv_bases maps fhv_trips to base names and "doing business as" labels, which include ride-hailing app names
  • nyct2010 table contains NYC census tracts plus the Newark Airport. It also maps census tracts to NYC's official neighborhood tabulation areas
  • taxi_zones table contains the TLC's official taxi zone boundaries. Starting in July 2016, the TLC no longer provides pickup and dropoff coordinates. Instead, each trip comes with taxi zone pickup and dropoff location IDs
  • central_park_weather_observations has summary weather data by date

Other data sources

These are bundled with the repository, so no need to download separately, but:

Data issues encountered

  • Remove carriage returns and empty lines from TLC data before passing to Postgres COPY command
  • Some raw data files have extra columns with empty data, had to create dummy columns junk1 and junk2 to absorb them
  • Two of the yellow taxi raw data files had a small number of rows containing extra columns. I discarded these rows
  • The official NYC neighborhood tabulation areas (NTAs) included in the census tracts shapefile are not exactly what I would have expected. Some of them are bizarrely large and contain more than one neighborhood, e.g. "Hudson Yards-Chelsea-Flat Iron-Union Square", while others are confusingly named, e.g. "North Side-South Side" for what I'd call "Williamsburg", and "Williamsburg" for what I'd call "South Williamsburg". In a few instances I modified NTA names, but I kept the NTA geographic definitions
  • The shapefile includes only NYC census tracts. Trips to New Jersey, Long Island, Westchester, and Connecticut are not mapped to census tracts, with the exception of the Newark Airport

Why not use BigQuery or Redshift?

Google BigQuery and Amazon Redshift would probably provide significant performance improvements over PostgreSQL. A lot of the data is already available on BigQuery, but in scattered tables, and each trip has only latitude and longitude coordinates, not census tracts and neighborhoods. PostGIS seemed like the easiest way to map coordinates to census tracts. Once the mapping is complete, it might make sense to load the data back into BigQuery or Redshift to make the analysis faster. Note that BigQuery and Redshift cost some amount of money, while PostgreSQL and PostGIS are free.

Mark Litwintschik has used the taxi dataset to benchmark performance of many different technology stacks, his summary is here: http://tech.marksblogg.com/benchmarks.html

TLC summary statistics

There's a Ruby script in the tlc_statistics/ folder to import data from the TLC's summary statistics reports:

ruby import_statistics_data.rb

Taxi vs. Citi Bike comparison

Code in support of the post "When Are Citi Bikes Faster Than Taxis in New York City?" lives in the citibike_comparison/ folder

2017 update

Code in support of the 2017 update to the original post lives in the analysis/2017_update/ folder

Questions/issues/contact

todd@toddwschneider.com, or open a GitHub issue