Data Files: Investigating Effects of TNCs on Parking Demand and Revenues

Authors

Ann Brown, University of Oregon
Benjamin Y. Clark, University of OregonFollow

Sponsor

This project was funded by the National Institute for Transportation and Communities (NITC2016-UO-15 (2106DR-39972)), a U.S. DOT University Transportation Center.

Document Type

Dataset

Publication Date

8-2020

Subjects

Transportation -- Technological innovations, Uber (Firm), Ridesharing, Automated vehicles -- Effect on parking revenue, Urban transportation -- Planning, Automobile parking -- Economic aspects

Abstract

These data support a final report published on NITC’s website “Investigating Effects of TNCs on Parking Demand and Revenues".Which can be found online at https://nitc.trec.pdx.edu/research/project/1215.

The final report for this project can be found at: https://dx.doi.org/10.15760/trec.252.

Description

See additional files below:

Parking Citation Data: a public records request was made to the City of Seattle for all available data on parking citations. These data were only available for two years—2016 and 2017. We geolocated each citation by Census Tract, then calculated the average number of citations occurring for both years combined.

Data file: parking_citations2016_2017.csv

Off-Street Parking Lots & Garages Data: The City of Seattle posts a datafile with the location and capacity of all parking lots and garages in the city. This data file is periodically updated on the city’s Open Data portal. Data downloaded from: https://data-seattlecitygis.opendata.arcgis.com/datasets/8e52dfde6d5d45948f7a90654c8d50cd_0/data

Data file: Public_Garages_or_Parking_Lots.csv

Land Use (Zoning) Data: The City of Seattle provided land use shapefiles for each year of our study. We then created aggregate categories by Census Tract that calculates land use by commercial, residential, industrial, mixed-use, and other land uses—by year.

Data file: seattle_zoning.csv

King County Transit Stops/Sq Mile Data: King County Transit provided the research team with shapefiles that mark each public transit stop. We then aggregated these data to a number of stops per square mile calculation using ArcGIS.

Data file: Stops_Per_Square_Mile.xls

Rights

This work is marked with CC0 1.0 Universal

DOI

10.15760/TREC_datasets.08

Persistent Identifier

https://archives.pdx.edu/ds/psu/33470

Recommended Citation

Brown, Anne and Benjamin Y. Clark. Data From: What Makes Cents? How Uber Shapes Municipal On-Street Parking Revenue. NITC-2016-UO-15. Portland, OR: Transportation Research and Education Center (TREC), 2020. https://doi.org/10.15760/TREC_datasets.08