Transit agencies may feel uneasy about sharing such information. Indeed, media coverage has focused on the inequalities brought to light in LA’s report, rather than the culture change it represents for the agency. “I feel like we have to some degree been punished for collecting this data and making it easy for people to look at,” says Metro spokesperson Brian Haas. “The purpose was to see where there were opportunities for us to make improvements. If we don’t do this, then nothing changes.”
What is not measured is not known, and the world of transit data is still largely blind to women and other vulnerable populations. Getting that data, though, isn’t easy. Traditional sources like national censuses and user surveys provide reliable information that serve as the basis for policies and decisionmaking. But surveys are costly to run, and it can take years for a government to go through the process of adding a question to its national census.
Before pouring resources into costly data collection to find answers about women’s transport needs, cities could first turn to the trove of unconventional gender-disaggregated data that’s already produced. They include data exhaust, or the trail of data we leave behind as a result of our interactions with digital products and services like mobile phones, credit cards, and social media. Last year, researchers in Santiago, Chile, released a report based on their parsing of anonymized call detail records of female mobile phone users, to extract location information and analyze their mobility patterns. They found that women tended to travel to fewer locations than men, and within smaller geographical areas. When researchers cross-referenced location information with census data, they found a higher gender gap among lower-income residents, as poorer women made even shorter trips. And when using data from the local transit agency, they saw that living close to a public transit stop increased mobility for both men and women, but didn’t close the gender gap for poorer residents.
To encourage private companies to share such info, Stefaan Verhulst advocates for data collaboratives, flexible partnerships between data providers and researchers. Verhulst is the head of research and development at GovLab, a research center at New York University that contributed to the research in Santiago. And that’s how GovLab and its local research partner, Universidad del Desarollo, got access to the phone records owned by the Chilean phone company, Telefónica. Data collaboratives can enhance access to private data without exposing companies to competition or privacy concerns. “We need to find ways to access data according to different shades of openness,” Verhulst says.
When that works, governments and transit agencies can fall into another trap. A wealth of data can create the illusion that the entire population is accounted for. “A lot of this discussion about gender and data is always focused on the data on the supply side, but not on what are the kinds of questions that actually matter,” Verhulst says. Transport for London may have found a way to bypass those considerations entirely. Earlier this year, the agency started to track riders over Wi-Fi, boosting its ability to compile proprietary data on mobility patterns.
Big data is, ultimately, political. It’s about asking the right questions and also acting on the answers. Haas, the LA Metro spokesperson, says the agency will follow up on its report with a Gender Action Plan, designed to make tangible changes to enhance women’s access to public transit. More importantly, those initiatives are part of a larger movement spurred by the agency’s CEO, Phillip A. Washington, to address gender inequality within the transit system, starting with the agency’s internal operations: It’s working to achieve a gender-balanced workforce. “These improvements will not just make the system better for women,” Haas says. “This will make the system better for everybody.”
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