{"id":624,"date":"2020-12-21T19:40:03","date_gmt":"2020-12-21T19:40:03","guid":{"rendered":"https:\/\/emmettz.com\/?p=624"},"modified":"2020-12-22T20:49:41","modified_gmt":"2020-12-22T20:49:41","slug":"putting-data-in-its-place-for-transit-equity","status":"publish","type":"post","link":"https:\/\/emmettz.com\/?p=624","title":{"rendered":"Putting Data in Its Place for Transit Equity"},"content":{"rendered":"\n

\u201cSmart\u201d and \u201cequitable\u201d appear in the mission statement of nearly every public agency, consultancy, and start-up seeking to improve transit systems. However, as Ruha Benjamin writes in Race After Technology,<\/em> “The road to inequality is paved with technical fixes.”<\/p>\n\n\n\n

How, then, can transportation planners use data for equity?

It was my privilege to convene a discussion on this topic at the Hindsight Conference, an equity-focused urban planning summit hosted by the APA-NY\u2019s Diversity Committee<\/a>. The panel included Ambar Johnson of the LivableStreets Alliance<\/a>, Jascha Franklin-Hodge of the Open Mobility Foundation<\/a>, Lilly Shoup of Los Angeles\u2019s Urban Movement Labs<\/a>, and Sarah Williams, of MIT\u2019s Civic Data Design Lab<\/a>.<\/p>\n\n\n\n

Our discussion underscored that transportation equity has never just <\/em>been about mobility infrastructure. Data can, at best, capture only part of the story in complex cities. At worst, it can exacerbate the problem by giving planners and politicians a false sense of impact, without having addressed the root of transit inequity.<\/p>\n\n\n\n

To advance equity through data, we must keep the focus on people.

Transit Equity is Complex<\/em><\/strong><\/p>\n\n\n\n

Graphics like this one suggest that smart mobility technologies \u2014 and, by extension, the data streams and algorithms that power them \u2014 will primarily benefit Black, indigenous, and people of color.  <\/p>\n\n\n\n

\"\"
Image: Center for Neighborhood Technology<\/figcaption><\/figure><\/div>\n\n\n\n

Some planners cautiously endorse this view, and suggested that “data is a tool for equity and inclusion<\/a>.” Others go further, suggesting that comprehensive data<\/a> can measure transit equity. As smart city tech gains traction in transportation and other sectors \u2014 observers increasingly ask, \u201cWho wins when a city gets smart?\u201d<\/a><\/p>\n\n\n\n

The most optimistic view suggests that data can help overcome the social, political, and financial forces that have produced today\u2019s inequitable transit systems. If so, planners must understand that transportation systems have always been political.<\/p>\n\n\n\n

Consider the destruction of Black neighborhoods to construct the interstate highway system, or the Montgomery Bus Boycotts which were cued off by Rosa Parks\u2019 refusal to cede her seat. Another landmark event \u2014 the voting rights march in Selma \u2014 traversed a bridge that was itself named for a white supremacist<\/a>.<\/p>\n\n\n\n

To use a more recent example, the Movement for Black Lives has built political power by occupying roadways. These protests have provoked shows of solidarity from some transit agencies, who refused to let sheriffs use public buses to transport protestors \u2014  as well as resistance from others, who offered their services to law enforcement<\/a>, and even closed stations<\/a> that demonstrators depend on. <\/p>\n\n\n\n

To wit, transit systems are infused with social history. This history, in turn, shapes the data they produce and which inform algorithms used for transit planning. We must therefore understand data, and the way we use them, not just as a neutral descriptors of physical infrastructure \u2014 but as artifacts of political processes.<\/p>\n\n\n\n

How, then, might planners use data to amend past and current injustices?<\/p>\n\n\n\n

Engaging with historical inequities<\/em><\/strong><\/em><\/p>\n\n\n\n

A 2016 visualization<\/a> by Massachusetts Bay Transportation Authority (MBTA) showed the bus routes with the most hours of passenger delay tend to serve Black and brown neighborhoods such as Roxbury and Mattapan. The same trend for a map of sidewalk quality conducted by the City of Boston: areas with crumbling infrastructure tend to be low-income and non-white.<\/p>\n\n\n\n

Taken alone, these data can show which pieces of infrastructure are due for an upgrade. Viewing data through a historical lens reveals truths about power that purely quantitative analysis would not.<\/p>\n\n\n\n

Ambar Johnson noted that many of these areas were subjected to the practice of \u2018red-lining<\/a>\u2019 whereby federally-backed mortgage lenders would refuse fair financing to Black and brown residents who were deemed \u2018high risk.\u2019<\/p>\n\n\n\n

\n
\n
\"\"
Image: MBTA Data Blog<\/figcaption><\/figure>\n<\/div>\n\n\n\n
\n
\"\"
Image: Univ. of Richmond, Mapping Inequality<\/figcaption><\/figure>\n<\/div>\n<\/div>\n\n\n\n

\u201cHow many of us have seen the same kind of slides with the same blocks of where Black and brown folks live and where wealthy white communities are?\u201d Johnson queried. \u201cI think a lot of the times, we know exactly what’s going on with these historic implications.\u201d

Indeed, histories of disinvestment have sowed doubt in marginalized communities as to whether cities will respond to their requests for assistance. This dynamic can lead to these communities being underrepresented in data, and cities to focus on communities with the greatest capacity to be seen \u2014  and less on those truly in need. <\/p>\n\n\n\n

When the City of Boston compared the density of reported sidewalk complaints with an independent assessment of sidewalk quality in 2015, they found that complaints mostly came from white, wealthy communities \u2014 while areas with crumbling sidewalks were under-represented.<\/p>\n\n\n\n

\"\"
Images: City of Boston<\/figcaption><\/figure>\n\n\n\n

\u201cIt\u2019s very much a clich\u00e9 to say that what gets measured gets managed,\u201d reflected Jascha Franklin-Hodge, who served as Boston’s Chief Information Officer at the time.<\/p>\n\n\n\n

\u201cBy saying, ‘Hey, are we actually measuring the right thing?\u2019 it gives us an ability to recognize that what we’re treating as objective data is heavily biased towards specific audiences that feel empowered to be able to demand services for their neighborhood.\u201d <\/p>\n\n\n\n

Aggregating data to the neighborhood scale, as in the graphic above, also introduces new pitfalls. Sidewalk quality doesn\u2019t mean the same thing to everyone. Curb cuts may be especially important to people using wheelchairs, while sidewalk width may be more important to people with hearing impairments, who need to walk side by side to sign to one another.<\/p>\n\n\n\n

Heat maps like this one obscure this nuance, and also fail to show how pedestrian networks complement public transit.  Franklin-Hodge noted that a more effective approach would be to ask, \u201cWhat are the key transportation paths and links that exist within specific neighborhoods? And in particular, the ones that facilitate access to public transit, since every transit journey starts and ends with a walking trip. \u201c<\/p>\n\n\n\n

Rethinking outdated models<\/em><\/strong><\/p>\n\n\n\n

In addition to recording injustices, data played a key role in creating them. Apart from racism, the key value underlying the red-lining maps was efficiency \u2014 which in this case facilitated the flow of wealth to federally-backed mortgage lenders and White homeowners, at the expense of Black and brown communities.<\/p>\n\n\n\n

Similarly, well-intentioned efforts to \u201coptimize\u201d a city lead to unintended consequences today. Franklin-Hodge noted that, \u201cinevitably, when you start to think about something as complex as city and the lives of the people who live in it, as something that can be optimized, you’re imparting your bias and your worldview about priorities into whatever system you build.\u201d<\/p>\n\n\n\n

When you start to think about something as complex as city and the lives of the people who live in it, as something that can be optimized, you’re imparting your bias and your worldview about priorities into whatever system you build.<\/p>Jascha Franklin-Hodge<\/cite><\/blockquote><\/figure>\n\n\n\n

Transportation planning\u2019s roots in civil engineering can mean that influential standards, designed in an auto-centric era, often impede innovations for equity. For example, the \u201clevel of service<\/a>\u201d metric used in many transportation models emphasizes free-flow travel speeds, and ultimately encourages the design of high-speed roadways that only serve the need of motorists.<\/p>\n\n\n\n

Validating these models, and the data that serve as inputs, against lived experience is critical. Lilly Shoup noted that a city she had worked with had implemented a travel demand model in the 1960s \u2014\u00a0which went uncalibrated for six decades, while it over-estimated auto travel demand by 5-10% year over year.<\/p>\n\n\n\n

Ambar Johnson likewise suggested that \u201cDOTs, or other city agencies do before and after counts of construction site of certain infrastructure projects, and compare those to whatever the model projected to what the actual reality is.\u201c

Inaction through \u2018smart-washing\u2019

<\/em><\/strong>As Sarah Williams notes, data are people. However, data can often take people out of the picture, and enable planners to avoid painful \u2014 but critical \u2014 conversations about persistent injustice.

Discussing the comparison of the MBTA bus delay maps and the HOLC redlining maps, Johnson noted that the \u201cthe quality and the experience and the trauma that’s involved with those things that gets divorced with all the colors that get on the map.\u201d  In truth, cities have known where neighborhoods are in the most need of improved transit infrastructure for a last time. <\/p>\n\n\n\n

\u201cI’m afraid that we’re in a place now where people are more interested in acquiring the information and holding it and knowing what is wrong \u2014 but not doing anything about changing the material realities of the people who we are supposed to be serving.\u201d<\/p>\n\n\n\n

\u201cI’m afraid that we’re in a place now where people are more interested in acquiring the information and holding it and knowing what is wrong \u2014 but not doing anything about changing the material realities of the people who we are supposed to be serving.”<\/p>Ambar Johnson<\/cite><\/blockquote><\/figure>\n\n\n\n

Franklin-Hodge calls this dynamic \u2018smart-washing\u2019 \u2014 a process through which cities avoid thorny issues that are fundamentally about power by using more data. As Chief Information Officer for the City of Boston, he observed \u201cthis tendency to look to technology in an attempt to make what are challenging political decision somehow easier or free of political friction.\u201d<\/p>\n\n\n\n

Painful histories are not likely to be resolved by the simple addition of more data. Rather, it takes concerted efforts to enact real change.

Does Data Lead to Action?<\/em><\/strong><\/p>\n\n\n\n

How, then, do we make sure that data leads to real, structural change in the built environment?<\/p>\n\n\n\n

One way is to engage more people in the creation of data. Too often, Williams noted, \u201cwe don\u2019t think about who or what is missing from the data,\u201d which can lead to inadequate planning outcomes. <\/p>\n\n\n\n

Working with semi-formal transit networks in Nairobi called Matatus, Williams\u2019 team used cell-phone data to display the semi-formal network digitally \u2014 the same way one would view a subway or a bus network in Google Maps. Beyond making the system easier to navigate for people that rely on it, this process can itself engender new community.

Reflecting on the Digital Matatus<\/a> project, Williams recalled how the drivers\u2019 effort  \u201cto create evidence and advocate for their position actually helps bring them together and create that kind of community around a shared idea, or shared vision.\u201d<\/p>\n\n\n\n

\n
\n
\"Digital
Images: Digital Matatus<\/figcaption><\/figure>\n<\/div>\n\n\n\n
\n
\"Stakeholder<\/figure>\n<\/div>\n<\/div>\n\n\n\n

Perhaps the answer is even more simple than that. Franklin-Hodge issued a challenge to transit officials: “If what you want is outcomes that really serve a community, maybe don’t start with data. Start with bringing the community together.\u201c<\/p>\n\n\n\n

Livable Streets embodied this approach by focusing on the busy often-delayed corridors that the MBTA identified. By simply talking with residents, they were able to quickly identify the intersections that posed the biggest challenges. As Johnson noted, \u201cWe were able to deliver those to the city and say, “Hey, this is a short term improvement list that you can get started on today.”<\/p>\n\n\n\n

Indeed, Franklin-Hodge emphasized the \u201cfine-grained knowledge and information that people are able to impart verbally. They can point to that one intersection and that one corner and say, you know, I keep almost getting hit here, because this thing happens, right? That is really useful data. It is not necessarily a quantifiable percentage of time somebody is almost hit, but you capture enough of those insights, and they actually can really serve the process in meaningful ways.\u201d<\/p>\n\n\n\n

Data can be layered in later on \u2014 but using that data should be driven by questions that are informed by ongoing inquiry, centered on lived experience. Data literacy programs such as that organized by the Boston Public Library can support citizens in accessing and making use of data to influence the planning process.<\/p>\n\n\n\n

Cities are complex, which means there is no one-size-fits-all solution to create more equitable transit systems. Data can generate interest, maybe some new insights for equity, but people will always remain central to the planning process.<\/p>\n\n\n\n

As Shoup put it, \u201cwe want to use data to set the table, rather than driving towards a solution.\u201d<\/p>\n\n\n\n

\n\n\n\n

View the session recording here<\/a>, along with the other sessions at Hindsight 2020.<\/p>\n","protected":false},"excerpt":{"rendered":"

To advance equity through data, keep the focus on people.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[14,16],"tags":[9,8,6,5,7],"class_list":["post-624","post","type-post","status-publish","format-standard","hentry","category-conversations","category-technology","tag-algorithms","tag-community","tag-data","tag-equity","tag-transportation"],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/emmettz.com\/index.php?rest_route=\/wp\/v2\/posts\/624","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/emmettz.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/emmettz.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/emmettz.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/emmettz.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=624"}],"version-history":[{"count":5,"href":"https:\/\/emmettz.com\/index.php?rest_route=\/wp\/v2\/posts\/624\/revisions"}],"predecessor-version":[{"id":652,"href":"https:\/\/emmettz.com\/index.php?rest_route=\/wp\/v2\/posts\/624\/revisions\/652"}],"wp:attachment":[{"href":"https:\/\/emmettz.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=624"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/emmettz.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=624"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/emmettz.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=624"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}