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smart location database

Better, Stronger, Faster: Inside the New Revolution in Transit Planning


Better, Stronger, Faster: Inside the New Revolution in Transit Planning


This is an adaptation of the material I presented at the US Department of Transportation's Data Palooza event, part the Geospatial Transportation Mapping Association’s Annual Meeting in Arlington, Virginia.  GTFS is the de facto open data standard for transit analyses. Carless in North Tampa

The year was 2004.  I was an undergraduate student at the University of South Florida in Tampa, attending classes full time and working 30 to 35 hours per week.  With rent, food, and transportation, I was just making ends meet.  So I made a bold decision: I gave up my car.  I determined to walk or ride my bike for short trips; and for my regional travel needs, there was always the bus….

Well, sort of.

Bruce B Downs Boulevard at Fletcher Avenue. I walked this intersection fairly regularly while living as a carless student near the University of South Florida. (Google Streetview screenshot)

At the time, there was no Google Transit or any other trip-planning application.  Plotting my bus trips involved staring at a tangle of lines on a map and poring over schedules, and in many cases it was often quite difficult to work out how to get from A to B using the Hillsborough Area Regional Transit (HART) system.  In many cases, trips were impossible or at least felt impossible.

And so it was difficult to meet my travel needs – I often had to bum a ride or just miss out on things. Of course, some of that was to be expected, but I often complained that surely, in a metro of 2.5 million people, I should be able to count on transit to get me and my fellow citizens to more places.  In all my ranting on this subject, I never had any quantitative means for articulating the poverty of my circumstances as a transit-dependent person.

The maps and schedules available on the HART website look much the same as they did 10 years ago, but the agency has used open data products like GTFS to change the way patrons plan trips (HART website).

The Information is Flowing, Even if the Buses Aren’t

Fast forward to today – things look different.  Oh, sure, the trips I could make by transit from my former home adjacent to that sprawling campus set in the auto-oriented wilderness of Tampa's northside neighborhoods are probably not vastly improved, but the information about that setting is.

In the first place, HART now offers a Google Transit trip planning application that makes transit trip-making more intelligible than ever before.  Moreover, they openly share the GTFS files that power the application and inform several emerging data products that essentially analyze the maps and timetables to describe the characteristics of transit service in a particular place in ways that were previously unknowable.

One such data product is the US Environmental Protection Agency’s (EPA) Access to Jobs and Workers Via Transit dataset (AVT).  This dataset is a supplement to the Smart Location Database and is built upon the same data, providing the same level of geographic resolution (census block groups – sometimes referred to as the ‘neighborhood’ scale).  The AVT provides information about how much activity of a given type (jobs, population, housing, low income residents, and low or medium income residents) are reachable by transit from a given place.  These values are then compared to regional totals to articulate what share of a region’s activity is reachable from a specific location within that region by transit.

GTFS data can be extended beyond trip planning applications to create value-added data products that provide at-a-glance analysis.  This example comes from EPA’s “Access to Jobs and Workers via Transit” dataset (AVT screenshot).

The AVT map above provides a lot of information about the place I lived when I took the plunge into the autoless lifestyle.  For example, it tells me that I could only reach about seven percent of the jobs in the Tampa bay region by bus (another layer tells me I could reach about six percent of the area’s population).  Given that I worked across town and had friends in all corners of the city, it was bound to be difficult for me to make this adjustment.

Then I thought, maybe I just lived in a particularly inaccessible area? Sadly, that was not the case. The average value for the entire Tampa-St Petersburg-Clearwater region is about eight percent for jobs (and just four percent for population), so my conditions were pretty close to the mean.

What about my complaint that, for a region of its size, transit in Tampa was underperforming?  I queried the AVT and found that of the 25 CBSAs having populations over two million and sharing at least some GTFS data, Tampa-St Pete ranked 21st in average access to population, 18th in average share of population accessible by transit, 22nd in average access to jobs, and 16th in average share of jobs accessible by transit.   As I thought, Tampa is on the low end of transit accessibility for a region its size.

Tampa’s ranking among 25 CBSAs having populations of 2 million or greater and sharing some GTFS data.

A Few Notes on Interpretation

Despite the statistics cited above, there are some reasons to withhold judgment for the region. The metro level data available in the AVT are difficult to compare across regions due to certain elements of its construction, listed below:

Travel time radius is constant, metro size is variable

In the AVT, activities are considered ‘accessible’ if they are within 45 minutes travel time from the origin.  In a small region, that radius is probably enough to get to a large proportion of the region’s activities, but in a larger area, the urban fabric is just too extensive for a 45 minute trip to cover much of that area.  So we have to take care in interpreting these values, especially looking across different metros areas.

In some places, transit serves multiple CBSAs

According to the AVT, from my current home in Durham, NC, I can reach about 23 percent of the jobs in the Durham-Chapel Hill CBSA.  However, that number includes employment in the adjacent Raleigh CBSA.  This is because these regions are linked by Triangle Transit’s express bus service.  So the numerator for my home block group isn’t consistent with the denominator, and this is something to bear in mind when working in an area that abuts another region having overlapping transit service.

GTFS data is not universally available

Finally, not every agency is using GTFS, and a large number of those that do use it are not sharing that information on the GTFS data exchange.  If the data was not being shared at the time the SLD and AVT were being developed, the accessibility attributes of a region will be incomplete.


The point of this post is not to disparage the regional transit providers in Tampa, but to walk through some of the analytical possibilities available through the AVT as well as issue some basic warnings about what to watch for when using that data.  It was neat for me to look at these numbers and relate them to my own experiences, past and present.  Of course, the data can do more than support my anecdotes.

The AVT can help planners and researchers compare places within a given region in terms of the amount of activity (of a given type) that transit can reach from each block group origin. Here’s a comparison of two places in Sacramento that I would otherwise know nothing about (AVT screenshot).

When used properly, the AVT allows us to directly compare the transit accessibility characteristics of various locations in the same region at a glance.  For the reasons noted above, it’s difficult to compare across regions, though it may be possible and useful in some cases.  Beyond this, one of the most exciting prospects for the AVT is analyzing how accessibility characteristics influence travel behaviors and/or land development trends in a region, teasing out new relationships that can help us better understand the value of effective transit service.

Looking Forward

Documentation for the AVT is available here.  In a follow up post I hope to describe some of the other datasets, tools and reports that are leveraging GTFS data to introduce entirely new ways to articulate what transit accomplishes in our communities and how it is performing.

–Alex Bell, Cities That Work Blog

[For regular news and updates, be sure to follow Renaissance on Twitter @CitiesThatWork]



Getting to Know the Smart Location Database


Getting to Know the Smart Location Database


Earlier this fall, the US Environmental Protection Agency released the second version of its Smart Location Database (SLD), a GIS data resource for measuring "location efficiency" across the country.  Location efficiency refers to the idea that the urban context of a development - say, a suburban mall or a mixed use town center - influences the impact that development has on its environment, including things like travel behavior, energy consumption, and stormwater runoff.  Smart locations, then, are places where development impacts are minimized. Charlotte, NC: The SLD provides a consistent means of describing different locations across the city or across the country in terms of their built environment.

Renaissance led the development of the enhanced SLD, consisting of updating EPA’s original pilot version from 2000 to 2010 Census geographies, but also incorporating a number of new measures.  I had the privilege to work on developing many of the indicators included in the SLD update and wanted to share some of the highlights of this exciting data product.

What Data is Available and How Can it Be Accessed?

The SLD includes over 90 variables that describe the built environment at a given location, especially focusing on the growing list of "D's" attributes (density, diversity, design, distance to transit, and destination accessibility are specifically covered) that have garnered much attention in planning research.  All data are compiled at the census block group scale with nationwide coverage.  Casual users can view data online in an interactive map, while GIS practitioners can download data for a region of interest or the entire country and conduct in-depth analysis.

The immediately cool thing here is that the SLD provides one-stop shopping for data that would otherwise need to be cobbled together from several disparate sources.  Moreover, it comes packed with "value-added" data that would entail substantial analytical effort to develop independently, including :

  • assessments of areas that are protected from development;
  • a host of land use diversity measures;
  • detailed network and intersection density metrics; and my favorite:
  • regional accessibility by auto or by transit.  (Although the latter is limited to those places that shared Google Transit feed files on the GTFS data exchange, it still covers over 200 transit providers.)

Uses and Applications

Having these data available at the block group scale with nationwide coverage opens up a ton of analytical possibilities.   Right out of the box, users can compare the D's attributes of various places within a region of interest or aggregate data for comparisons across counties, regions, or states.  The clever cartographer can produce maps that display multiple variables, providing complex analysis at a glance.  Users in rural areas and small towns - places where local data are often scarce - can access a rich array of information quickly.

Access to jobs by transit compared to low wage residents and zero car households for Baltimore, MD.

EPA offers its own list of prospective uses of the SLD:

The Smart Location Database may be appropriate for use in local and regional planning studies when better local data is unavailable. Sample uses of the Database include:

  • Assessing and comparing neighborhood conditions
  • Identifying suitable locations for growth or investment
  • Scenario planning and transportation analysis

However, the SLD can be taken much further.  By providing a uniform set of measures, it introduces an open platform from which GIS developers can create powerful analytical tools.  These tools could measure how things like impervious surface area, vehicle miles of travel, or trips made by walking would be expected to vary based on where development occurs.  By lowering the costs of data development, the SLD would make such tools immediately deployable by planning professionals throughout the country.

The SLD can provide data to drive lightweight applications.  Here's an example of a simple spreadsheet tool I built to quickly predict travel behavior at two alternative development locations.  The example uses SLD data for two locations in the Salt Lake City, UT area.

Demonstrating this potential, EPA is currently using the SLD in a research study of workplace location efficiency, specifically examining how D's attributes at employment locations impact travel to and from work.  I'm excited to see what tools and methods of analysis will emerge from that study and how others will extend the utility of the SLD through custom geoprocessors and lightweight applications.

Accessing More Information Faster

Planners can always benefit from having faster, easier access to more information. The SLD contributes to a growing body of rich, readily available data that can help local and regional planners understand their communities more comprehensively and, in turn, serve those communities more efficiently and effectively.  I hope to share some additional, specific thoughts on how we can do that in the near future and perhaps provide more detail on using GTFS data to assess transit accessibility.  In the meantime, happy mapping.

Those who are interested in more detailed information on the data contained in the SLD, as well as the source data and methods used to develop it, should check out the SLD User Guide.

–Alex Bell, Cities That Work Blog