Frequently Asked Questions

The cities in this report were selected because they represent a broad geographic range, growing populations, and also had political momentum toward improving sustainable transportation at the time the study was started. Chicago, Washington, D.C., and New York City were added later as prominent reference points in the online tool.

The indicators developed as part of this research endeavor were calculated primarily using ArcGIS, and the majority of them were calculated using the ArcGIS network analyst extension. The road data was collected from OpenStreetMaps, and the General Transit Feed Specification (GTFS) data was collected from TransitFeeds and from various transit agencies. United States population and income data was from the United States Census American Community Survey 2015 three-year estimates. US job data was from the 2015 Longitudinal Employer Household Dynamics survey, also from the United States Census. Canadian population data was from the 2011 Canadian Census, and Mexico population data was from the 2010 Mexican Census. In addition to the open-source data, city boundaries were used to define the extent of the city.  Bicycle lanes were collected through city agencies, as well as through manual collection with Google Maps. Only protected bicycle lanes were included in the study, as those have been shown to be used by more people due to the greater sense of safety they provide.

GTFS stands for General Transit Feed Specification and is a standard data format that many transit agencies use, which allows transit to be used in applications like Google Maps. It includes information about all of the stops, trips, and routes in the system.

Accessibility measures a person’s ability to reach destinations. This is in contrast to mobility, which measures a person’s ability to move (speed). Access to Jobs by Sustainable Transit measures the average of the number of jobs that can be reached within 30 minutes and 60 minutes for each census tract within the area by walking, cycling (on protected bike lanes), and public transit. The number of jobs that each census tract can reach is weighted by the number of people living in the census tract. This gives a better understanding of the experience on the ground than an unweighted average would. This indicator serves as an actual measure of a person’s ability to reach potential destinations via sustainable modes of transportation. It is measured assuming a start time of 8 a.m. on a weekday morning, as that is typically at the commuting peak.

For Access to Low-Skill Jobs, we found the average of the number of jobs requiring less than a high school education that can be reached within 30 minutes and 60 minutes for each census tract within the city by walking, cycling (on protected bike lanes), and public transit.  The number that could be reached was weighted by the number of workers in the census tract that have less than a high school education. This measure was done at 8am on a Wednesday for the 30 minute indicator and 8am on a Sunday for the 60 minute indicator.

While these indicators do measure a lot of aspects of sustainable transport, they do not measure everything. The indicators do not measure emissions, ridership, vehicle preference, or safety on transit. They also don’t measure the quality of transit vehicles, stations, or stops, nor do they measure the quality of the walking and cycling environment, beyond key features, such as physical separation for bicycle paths.  Finally, the indicators presented online look specifically at the city proper, and not at the metropolitan region as a whole. This is largely due to concerns about data consistency at that scale.

Moving forward with this work, ITDP hopes to apply these indicators to more cities in and countries. We think there is a lot of potential for the Access to People indicator to be used as proxy measure for Access to Jobs in cities around the world, since jobs data is often unavailable, incomplete, and very difficult to standardize across countries and regions. As such, ITDP plans to continue testing the indicator to gain a better understanding of how factors such as urban form, land use segregation, and population distribution impact the viability of the indicator as a proxy.