Member Spotlight: Cynthia Albright, FAICP CUD, GISP

HCD Member Spotlight: Cynthia Albright, FAICP CUD, GISP

Mapping Mobility and Asking Questions

Cynthia Albright, FAICP, interviewed by Deborah Myerson, AICP.

Responses have been edited and condensed for clarity.

Spatial Data Goes Big

I started in this field over 30 years ago, using ArcGIS in a command line V5!  I stay adept and agile in an environment that’s constantly changing. Stantec is a global company. I work with teams in both in the U.S. and around the world, from Canada to New Zealand and India.

Spatial data was formerly purely cellular data with an accuracy of 100 feet or more.  Over the last six years, analyzing data extracted from the geolocation services on smart devices exploded as an industry with reductions in purchase price and increased spatial accuracy to 10 feet. Vendors anonymize and aggregate the data to a buyer’s geographic specifications. It’s important to understand that we’re not able to see individual device movements, but aggregated devices in time and space.

Data collection methodologies, sources, and applications are not uniform across vendors. Evolving from cellular pings, vendors aggregate our geolocation from a variety of apps. The data also includes a few social demographic attributes such as age group, income level and gender.  You can’t buy the same data from all vendors, and not all telecom companies or app providers make their data available for sale. For instance, AT&T does not sell aggregated data to independent third parties.

Where Are People Going, and How Do They Get There?

All location-based services (LBS) data vendors provide invaluable information for mobility planning, public infrastructure improvements and equity, improving the public realm, and more.  It’s possible to map how people move and then look at physical locations for where people spend most of their time.  As transportation planners and urban planners, we can improve the quality of experience for visitors, and quality of life for residents, by creating the necessary linkages between modes.

In Winnipeg [Manitoba, Canada], my team created a web-based dynamic dashboard to illustrates the roadway network with filters to overly transit routes, stops, land use/zoning, locations of medical services, schools, grocery stores and bike lanes. We then created desire lines from the LBS data to identify the magnitude of population movements along the roadway. Using heat mapping tools, it was clear where transit services and ridership were robust and where they were misaligned with population travel patterns. The LBS data coupled with employment data from Environics allowed us to visualize where high concentrations of people live and work and correlate those locations with demands for services and mobility infrastructure.

The Natural Curiosity of Planners

The great thing about this data is being able to go back in time and look at trends, and how things have changed. LBS data vendors sell historical data going back to 2017. Data collected today can be available in approximately two weeks. This type of information enables planners to explore pre-COVID, COVID, and today for planning studies aggregated to the same geography.

I believe as planners we have a natural curiosity to understand the context of our environment.

The value for professional planners is to learn how to use big data (aka millions and millions of data points and/or rows), buy it, work with it, and apply it. The application of the data is only as limited as our ability.  To remain relevant and really effectuate change, planner needs to embrace data, in all its forms.

Using Data to Drive Equity

Rather than asking “How can every neighborhood have access?” the appropriate question is, “How can professional planners help cities and their supporting transit agencies ensure a collaborative approach to understanding how people want to move within their environment for access to employment, medical services, grocery stores, and other services?”

To measure access to employment, we merge employment destinations with residential uses and look closely at the available mobility services and infrastructure. Are there transit services, bike lanes and sidewalks? The data tells us low-income earners are less likely to use a car and more likely to rely on transit for work.

With this information, we can build more transportation connections between residential and employment uses, such as transit services with more stops, improved walkability, safer bicycle paths, or a combination. 

Local Governments Realizing the Potential

Understanding how people move is hugely applicable in housing and community development. We are only as limited in its application as our minds permit. And, as far as our profession is concerned, this type of data has never been available before—really, just in the last five years.

Local governments are adapting to use spatial data at a large scale. In larger geographic areas with several different jurisdictions, everyone can share in the cost of the data and use it for their own specific needs. The beauty of it is how data brings agencies together, and each can then fine-tune the analysis for their needs.

Every Day is "Day 1"

I’m inspired by learning new things, and make it a point to teach myself new skills, often through YouTube and online Coursera classes. I’m a firm believer in “Day 1.”  What does that mean? Every day is my new Day 1. What am I going to accomplish today that has meaning? Oftentimes, it’s as small as a big, bright smile to stranger.