Getting the real numbers out of 311 phone calls
When municipalities across North America began setting up 311 call centres to handle requests and complaints, the centres weren’t positioned as “smart city” systems.
Rather, proponents saw 311 as a means of improving citizen engagement and bureaucratic accountability. Over the years, 311 services, including Toronto’s, have become increasingly tech- enabled, with social- media accounts, apps and the release of machine- readable complaint- tracking records through open data portals.
Municipalities now sit on vast troves of data from 311 calls — hundreds of thousands or even millions per year — that can be mined and analyzed, and then used to inform municipal planning and budgeting.
Aproliferation of calls about basement floods, missed garbage pickups or dubious odours from factories can provide important clues, both about what’s happening in a neighbourhood as well as the performance of city departments. If scanned carefully for longer- term patterns, 311 calls can also offer predictions about future problems.
These digital mountains of call records certainly qualify as “big data.” But the ways in which this information is or can be used also offers important lessons, both positive and negative, about applications for other large urban data sets that might be generated by smart- city technologies.
The most obvious application is how municipal agencies respond to residents’ requests for service. New York University urban analytics expert Constantine Kontokosta observes that many municipalities tend to make such decisions in a “black box,” with little transparency as to whose needs take first priority ( first- come- first- serve, a triage system, etc.).
He and other 311 researchers say that these data sets also contain important signals that could assist in making service delivery either more efficient or more equitable ( which aren’t necessarily the same thing).
One pattern, noted by a New York state Health Foundation/ Harvard research team in a 2020 study, found that spikes in calls about a particular problem may be orchestrated community campaigns. The study described the practice as a “misuse” that could lead city officials to “erroneously” conclude that an area was experiencing some kind of decline.
Another evaluation, published by Kontokosta in 2017, looked at New Yorkers’ complaints about hotwater problems in their buildings. Drawing on 311 data, inspection reports, census tract information and other records, the study found that neighbourhoods with high rents and incomes, better educated residents and larger non- Hispanic white populations “tend to overreport”: “Based on these results, we find that socioeconomic status, householder characteristics and language proficiency have a nontrivial effect on the propensity to use 311 across the city.”
Still other analysts have mined 311 data sets to show how they correlate to broader trends, such as the spread of urban “blight.” Those patterns, according to a 2016 analysis by NYU and the Center for Urban Science and Progress, could theoretically be used to predict future real- estate prices.
In 2017, a team of geographers and artificial intelligence scholars at the University of Illinois Urbana used six years of Chicago 311 sanitation service requests ( e. g., overflowing garbage cans) to develop what they said was the first algorithm capable of generating predictions to help guide decisions about scheduling and routes.
Kontokosta, whose work focuses more on fairness and equity than efficient management, contends that such algorithms will eventually be available commercially, but notes that one limiting factor is that many local governments still use older mainframe computers that don’t have the chops to process so much data.
The other is a dearth of data scientists and mathematicians on municipal payrolls. “People with these skills,” he says, “aren’t working for cities.”