Over the past two years, our team at NLC built this into a powerful data storytelling tool with almost 5,000 policies tracked across 800 cities. Through the process, we not only gathered invaluable insights into innovative city policies, but we also discovered the power of data storytelling to democratize shared lessons, improve local advocacy, and evaluate policy interventions.

Democratize shared lessons

The early stages of the COVID-19 pandemic saw a proliferation of data graphics highlighting trend lines that showcased the change in case numbers, death rates, and other relevant public health information. These types of data visualizations were incredibly valuable for the general public and for city leaders to assess the situation within their communities. Building off these baseline statistics, our Local Action Tracker combined data-driven trends on popular policy areas with narrative-oriented examples of how cities were addressing their public health, economic, and social challenges. Rather than reinventing the wheel for good policy initiatives, local elected officials were able to identify relevant insights from peer cities and apply them in their own contexts. For example, many cities, ranging from Nashville, Tennessee to Puyallup, Washington, partnered with local businesses to provide gift cards as an incentive for receiving vaccines last summer.

Data storytelling humanizes aggregated, high-level statistics and roots them in the reality of the lived experiences of the stories’ “characters.” Faced with sharply dropping city revenues and increasing expenditures, we saw mayors, council members and other local leaders navigate uncharted waters. City leaders in Cincinnati, Ohio, took a pay cut to their own salaries amidst revenue shortfalls and prioritized city budget spending on supporting those most impacted by the pandemic, such as local small businesses.  By showcasing their stories using data in real time, we were able to help them navigate these challenges together and democratize their shared lessons.

Improve local advocacy

In addition to humanizing high-level statistics, data storytelling also has the power to connect the dots of individual anecdotes and weave them into a compelling thematic narrative. For example, it was immediately clear from the beginning of the pandemic that cities would face added financial pressure due to an overall decrease in economic activity. However, it was not clear how much this would impact cities. There were stories of some cities whose budgets had been hit exceptionally hard while other cities appeared to have experienced only minor setbacks. So, what was driving these differences?

Our data storytelling efforts uncovered a key storyline demonstrating the critical role of municipal tax revenue structures, as cities heavily reliant on more dynamic sources such as income and sales tax saw more immediate financial impact compared to cities dependent on property tax. Overall, our research showed that cities in 2020 experienced $90 billion in revenue loss. This data-driven insight empowered cities to collectively advocate for federal assistance to ensure that essential city services were maintained. The financial challenges that they faced were not one-off examples, but rather part of a collective story that, when amplified, eventually led to the passage of the American Rescue Plan Act (ARPA) State and Local Fiscal Recovery Funds, which provided more than $65 billion to all municipalities across the country.

Evaluate policy interventions

Data storytelling is an effective tool to both find human stories amidst large datasets and identify data-driven themes woven throughout individual anecdotes. It can also provide us with a lens to reflect and evaluate where we have come from in order to inform best practices in the future. Our COVID-19 Local Action Tracker recently celebrated its two-year anniversary. In celebrating the occasion, we collected secondary data indicators that measured the top five key themes of policies that cities had pursued – public health, city operations, infrastructure, housing, and economic/workforce development. In the graphic below, we examined how local government employment numbers had fluctuated during the past two years and were able to demonstrate the effectiveness of ARPA in significantly increasing those job numbers. We can thus see that there is significant value both in leveraging data storytelling in real-time to shine light on current scenarios and in using it as a reflective tool to identify successful policies.

Our team’s experience building out the COVID-19 Local Action Tracker during the past two years has taught us valuable lessons about how to use data to tell city stories. We empathized with our stories’ heroes recognizing them as more than dots on a scatterplot and instead as public servants fighting for their local residents. We energized cities so that they could see themselves as more than singular entities and instead as part of a broader community collectively experiencing a similar storyline. And with the benefit of hindsight, we evaluated the success of both federal and local policies to identify and promote best practices for cities to adopt moving forward in building back better from this pandemic.

The post Telling the Story of Urban Innovation and Pandemic Response with Data appeared first on Nightingale.

Embracing empathy in data and data visualization is a key dimension for people working with data to help put their work into the hands of policymakers, stakeholders, and community members who can use it to affect change. Inclusive and thoughtful data visualization that respectfully reflects the people and communities of focus can also help researchers build trust with those communities.

We think of empathy as it applies to communicating data across six main themes:

1. Put people first. First and foremost, we need to remember and communicate that the data shown reflect the lives and experiences of real people. Data communicators must help readers understand and recognize the people behind the data.

2. Use personal stories to help readers and users better connect with the material. Pairing data-driven charts with personal stories centered on individual experiences can help readers understand and identify with the people represented in the research and data visualizations. Techniques that can be used in tandem with data visualizations to help lift up personal stories include photography, illustrations, pull quotes, and oral histories.

3. Use a mix of quantitative and qualitative approaches to telling a story. Most charts and graphs are built on top of spreadsheets or databases of quantitative data. However, focusing on numbers alone without any context can overlook important aspects of a story including the “why” and the “how.”

4. Create a platform for engagement. This can take the form of interactivity in which users are able to manipulate buttons, sliders, tooltips, and other elements to make selections, filter the dataset, or create customized views of a chart. Such engagement can be leveraged as a way to allow users to find themselves in the data or discover the stories that most interest them. Another form of engagement is offering audiences a means of providing feedback about a data tool or visualization.

5. Consider how your framing of an issue can create a biased emotional response. Carefully consider how the data you visualize presents a particular perspective on the content. Take the examples ProPublica journalist Lena Groeger discusses in this post on different ways to visualize the impact of crime on local communities. Maps that show the locations of where crimes occurred versus maps that show the percentage of residents in a neighborhood who were in prisons are two different ways to visualize data related to the criminal justice system. What data we choose to focus on and what we choose to ignore can bias our audiences’ perceptions of the issues about which we are communicating.

6. Recognize the needs of your audience. Taking an empathetic view of the readers’ needs as they read or perceive information is an important step to better data communication. This kind of empathy can also be couched in terms of producing visualizations that are accessible by people with vision, physical, or intellectual impairments; reducing overly technical or jargon-laden language; and translating your work into languages most used by your target audiences.

Being empathetic to the people and communities of focus does not imply sacrificing the data and methods used in responsible, in-depth, sophisticated research. In fact, the opposite is true: high-quality research and empathy for people and communities can be complementary. Effective research necessarily means understanding someone else’s point of view nonjudgmentally and recording that perspective as accurately and truthfully as possible. Empathy underlies research and data visualizations that uphold diversity, equity, and inclusion, so data communicators should seek to find ways to help their audiences understand and connect with the people that the data represent.

 Read the full Do No Harm guide here.

The post Six Ways to Bring Empathy into your Data appeared first on Nightingale.

Even when well-articulated, the private sector applications of data science can sound quite alien to public servants. This is understandable, as the problems that Netflix and Google strive to solve are very different than those government agencies, think tanks, and nonprofit service providers are focused on.

Alex Engler, Brookings Institute

Against this backdrop, Sarah Williams, an associate professor at MIT’s Department of Urban Studies and Planning, has built a portfolio from civic empowerment. In December 2020, Sarah will release her first book, Data Action: Using Data for Public Good. She considers it “a manifesto for those who want to use data to generate civic change.” Recently, I interviewed her about her interdisciplinary expertise and the project work that informed the book. The following transcript has been lightly edited for clarity.

When did you start to think of yourself as a “data person?” Or do you?

Sarah Williams: I worked a lot on remote sensing and GIS (geo-informatics systems) very early on in my career trajectory. But I don’t think I ever thought of myself as a “data person” specifically. I always thought that I used data to answer questions that I was interested in. I think of myself as a landscape architect. I have a lot of interest in environment, climate change, and racial equity — that is, applying my skills as a data scientist on environmental and racial equity issues that shape our public landscape. I do think that I’ve been branded more as a data person because of my work at MIT and really trying to emphasize the need to use data to create policy change.

I also felt like there was a missing area, where when we talk about the ethics of data, or we talk about the use of data for both elevating certain positions but also oppressing, that there was perhaps this real hole in the current literature, so I became interested in this stuff. Maybe that’s how I became more of a data person as well.

You’ve worked in all different locations. There are various disciplines involved. Your projects have a variety of applications. I’m curious about the path from one to the next.

SW: I came into data science through geography. I was a computer science and geography major during undergrad. I think my projects have just been a combination of people reaching out to me and me reaching out to them. For example, I have a lot of work in the African continent, and that has to do with somebody very early on in my career asking me to get involved in a project with Nairobi. I developed a commitment to that region. At first, the goal was to improve the condition of the city of Nairobi, but then there was this realization that what we were doing in Nairobi could not only be applied to other cities in the African continent, but also in the global South in general. A lot of the work that I’ve done with informal transit really started there.

[Author’s note: The Digital Matatus Project is an open data effort that collects transit data from cellphones for use in mobile routing applications.]

I’ve also done quite a bit with criminal justice and criminal justice policy — looking at issues of equity and race. In fact, one of the chapters of my book covers the ways in which we can use data to help highlight some of the injustices that exist within our criminal justice system. That started as an area of interest and after I left grad school when I got involved in the Million Dollar Blocks Project and just kind of kept going.

[Author’s note: The Spatial Information Design Lab and the Justice Mapping Center sourced inmate residential addresses from Bureau of Justice statistics data and census data to show blocks where more than one million dollars is spent annually to incarcerate residents.]

Recently, I’ve been reinvesting in restorative justice work. Right now, we’re looking at a visualization project that examines prisoners rights, especially related to workforce — how much they get paid and some of the injustice involved in the way those jobs are created. There has been recent coverage related to prisoners fighting fires on the West Coast, but then they can’t actually become firemen after their incarceration.

I’ve also been involved in “data literacy” projects. Data literacy needs to be included in our school curriculum. We use data all the time, and it should be a skill that we learn, just like we learn math. City Digits focused on using data as a way to teach youth about issues in their community, while learning math at the same time. We relied on the kinds of data points that were most relevant to the particular community with whom we were working, tying the teaching to a real-world subject. We worked with the Bushwick School for Social Justice and we embedded data literacy within the math curriculum.

We used maps quite a bit because maps are oftentimes fractions, right? And, we taught ratios and percentages — for example, the percentage of African Americans in a community. We also decided to pick a topic related to a particular issue that the students wanted to investigate. With one particular class, we examined lottery tickets, which also involves math. We could look at the percentage of people who buy lottery tickets and cover not only how much money they spend on lotteries, but also the probability that they’ll win. That way, we could demonstrate how to collect data or where data comes from, but then actually take it to the end and show them, using math skills, how that plays out.

How do you get non-data people over the relevance barrier? How do you get them to engage?

SW: So you mean like how do we move them from, “This is a stat or a number” to something where they can take action? It’s absolutely about visualizations. I’ll say it over and over again, the communication strategy is the number one way that I get people to understand the power of data. In almost every project that has been the case. Consider the Digital Matatus Project where we collected data on informal transit systems in Nairobi. Everyone knew that data was important because they’d heard data was important. They knew the hype around smart cities meant you must have data. The city and the officials were kind of loosely interested in the project until we created visualizations of the concept and showed it as one comprehensive system that they could use to make decisions.

It just transformed that project and really created something that the government, NGOs — everybody — could use because now they could understand what that data meant and what they could do with it.

Visualization is the number one way that you communicate the power of data.

Sarah williams

I talk about this idea in Data Action. Very early on, statisticians knew that they needed visualization as a skill to communicate their efforts. Building interdisciplinary teams is critical to making powerful visualizations. You need policy experts in the field who help contextualize the problem. You need data scientists who can help process that information. And then the designers and the communicators who can transform and translate those insights to the broader public. One further team member of critical importance, though, is the community represented by the data itself. The community feedback is absolutely essential. I don’t know how many times I’ve been in a meeting where the stats are wrong, and somebody from the community could have told them that right away, had they just asked.

Tell me about your experience with the networks that develop and evolve to continue to support some of this community work?

SW: In Nairobi, we have a center for the development of open data for transport. We additionally have one in Latin America, called DATUM, which is also focused on development of data for informal transit. The Latin American network was informed by the work that we did in Africa. To step back for a second, these are the main bus systems in most of the world. It’s only Europe and the US and some parts of Asia that have more formal systems where data are collected and can be analyzed. In these informal systems, the data just do not exist. So when we did the project in Nairobi, we sparked interest from a lot of people who wanted to do their own data collection. We started to help them use our tools for their projects. Then, those people started teaching other people. And, through that, we built this network. Then, we actually raised funds to keep that network going. As a result, now on DATUM, there are tutorials, links to resources, and connections with other groups that have done the work.

This kind of data collection is hard during COVID, but we recently finished a project in the Dominican Republic, that was informed by what we learned on a project in Mexico City. Now, I don’t have to be personally involved — the network can be teaching other parts of the network and people from the Latin American context can be teaching each other.

Part of what we recommend as critical to this network is connecting to local universities — having the local communities do all the data collection and work with students or others in that process. We’ve created training materials that go through how to get started and who to connect with in your community. For example, on the transit work, getting buy-in from the local transit system owner was a major first step. Typically, they have a union. It’s not just the government that you need to talk to, but it’s also making sure that you talk to the drivers, the actual workers, as you get started.

Where are some opportunities you see for addressing inequity with data?

SW: We live in a world where we think data is everywhere. One of the things I talk about in the last chapter of my book is missing data. We have so much missing data, and that missing data tells you so much about what we’re interested in, what we care about, but also it can really lead to inequity. As practitioners, we talk a lot about showing observations in data as being inequitable, but what’s missing can be just as inequitable.

Ghost Cities was a project a funder brought to us. The guiding question was: how can we create socially-equitable real estate in China? These ghost cities that have been manufactured in China are going all over the world and they’re not equitable. They create huge risk in the real estate margins. We set out to explore how we could we address it with data.

[Author’s note: Researchers scraped data from from Chinese social media open access API’s, including Dianping (Chinese Yelp), Amap (Chinese MapQuest), Fang (Chinese Zillow), and Baidu (Chinese Google Maps) to evaluate community viability and score foreclosure risk on the Ghost Cities project. The model identified areas without amenities and allowed the team to map these over-developed locations.]

How have you been able to open policy dialogues or get invited to those tables?

SW: A lot of my projects are kind of bottom up. On the Digital Matatus Project, we didn’t have the data we needed to answer the questions. But, we were constantly building dialogue on transportation. And, on the Ghost Cities work, which was also bottom up, I really had to go after it, really leverage my connections and start talking to people in China. But, the biggest door opener was when we made a website where people could visualize our analytics and play around with them.

After trying to get a sit-down with academics and real estate agents and getting nowhere, the visualizations helped a ton to allow for that dialogue to happen. It was absolutely a marketing vehicle. In all of the data visualizations that I’m doing, I’m advocating for something. My bias is all over it. It’s fine to just say it’s a marketing thing. It’s a communication device. It is also a transparency device. It can build trust. In Ghost Cities, I allowed the Chinese government to explore the data and the model behind my work. There was instant trust — that doesn’t always happen when dealing with government bodies.

Alright, now tell me a bit about the book; let’s give Nightingale readers a preview.

SW: I frame the book with a historical perspective – examples of how we use data for good and bad, so that when we talk about good and bad uses of data, the meanings are clear. I hope that people use the book as a methodology for how they can create change using data.

There are three really important components to that:

1. I advocate for collecting your own data and using data that’s out there creatively, bringing both qualitative and quantitative data together.
2. Sharing and visualizing are critical.
3. I also emphasize that building interdisciplinary teams is the most effective way to create data for policy change.

I end the book with a discussion on the future of data and society, asking some larger questions such as: “Are we data colonialists?” Data access is being consolidated, and not just by the government anymore, making regulation more difficult. Private companies play a large role in decisions that are being made with data. I hope the book challenges people to consider the ways in which they can use data for action in their own communities.

[Author’s note: Data colonialism refers to the process of appropriating data for the purposes of extracting value rather than to, as a government might, establish societal safeguards.]

Data Action: Using Data for Public Good will be available from the MIT Press in December, 2020, and can be purchased from a variety of retailers.

For more information about Sarah Williams’ projects such as Million Dollar Blocks: background on the architecture and justice and the pattern, as well as details about the scenario planning used by the project team. Here is an online visualization of Chicago’s Million Dollar Blocks.

Here is some additional detail about City Digits: Local Lotto.

More about the Ghost Cities project can be found here and here is a video explanation of their amenity model.

Derek Poppert provides a useful primer on public tech here. And, WIRED has a recent take on anticipated industry growth here.

The post Generating Data Action: How an MIT Professor Hopes Data Can Empower Civic Change appeared first on Nightingale.

In late 2016, I read an article by Michael Brennan, principal of Detroit-based, nonprofit design firm Civilla, in the Harvard Business Review. I was haunted by this line:

“I cannot recollect, in 30 years of work, a single PowerPoint presentation I saw or gave that altered the course of anything.”

I am a qualitative researcher and strategist. It is my job to talk to people. Over the years, I’ve spoken with all types of people: school cafeteria workers to women who lost pregnancies late in their terms. When this exploration goes well, what happens is termed “developing empathy.” In other words, I get a tiny sense of what it’s like to walk in another person’s shoes. The challenge is what happens next. I have to transmit that new understanding to the rest of the project team, most of whom have not been as “in the weeds” with the participants. We call this “socializing the research insights,” and no matter how integrated and cross-functional the project team, somewhere along the way, in order to achieve alignment or obtain buy-in, other people will need to be persuaded, but the farther away they are from the participant, the harder it is to pass that same degree of empathy along to them. Over the years, I have experimented with a variety of approaches, relying on video, audio, verbatim excerpts, photography, PowerPoint, and data visualization.

Recently, I have encountered examples of physical visualization — data presented as an interactive exhibit — that inspire my client work. These installations make aspects of the intangible, tangible — something you can touch and experience in real life. In my work with nonprofits, I have become an advocate for data fluency, encouraging community-focused clients to exercise agency and control their own narratives by learning the language of data. To illustrate the potential for visualizing data to inspire empathy, consider the Undocumented Migration Project.

Their Hostile Terrain installation will occur in 150 locations worldwide this fall, timed to coincide with the US Presidential Election. The exhibit is a map of the Arizona border with Mexico. Data were collected between 1999–2019 and the resulting visualization blends ethnography, archaeology, visual anthropology, and forensic science methodologies. Color-coded toe tags are placed to precisely mark the remains of people who died attempting migration. Orange tags represent unidentified remains and manilla tags represent the bodies of those who have been identified. Volunteers at participating organizations will write in the details of the deceased on each tag and hang them. The project describes its interpretive goals as:

• Raising awareness of the human toll extracted by the United States’ current Prevention Through Deterrence Border Control policy.
• Memorializing the dead.
• Developing an installation that can be accessibly replicated in multiple locations to ensure community participation.
• Amplifying local programming, reflective of local immigration issues.

The power of this visualization is its simple design and stark symbolism. Toe tags, typically associated with the sterility of a morgue are juxtaposed with handwritten details. It would have been logistically more efficient for the team to print the details digitally on each set of toe tags. But, this way, each life represented will be considered 150 times by volunteers as their details are hand printed. As the exhibit’s executive director, Jason De Leon explained, “In the first prototype, we took GIS data and made a giant sticker graphic using red dots to show migrant death. That visualization, where the red dots represented a person, just didn’t have the impact. So, in the second version, we swapped out the dots for the handwritten toe tags. Students did the handwriting. They shared that it was emotionally difficult for them. I wanted to put names to the dead. This is an act of witnessing. The data come from a Microsoft database. We’re asking volunteers to breathe life into the data by writing out the details. That was the closest way we could think of to get someone to feel the human cost. I’ve filled out hundreds of toe tags. It doesn’t get any easier.”

Concentrating on a specific geography, the Sonoran desert of Arizona, serves both an instructive and a functional purpose. The map quantifies the degree to which the migration deterrent policy has failed by humanitarian standards. The stacked tags from ~3,200 bodies recovered produces an inescapable emotional and physical reaction — a palpable reality. And, limiting the geography allows for a practical installation package that makes replication in 150 locations by non-data scientists possible. There were other reasons influencing the choice of geography. De Leon explained, “I knew it the best and it was the only place where we have data that fine grained. If we’d tried to also map other geographies, the wall visualization would have been unmanageable.” An additional benefit of this focused perspective is that it may pique curiosity about other border geographies; curiosity has been identified as foundational to developing empathy and changing personal perspective.

I particularly admire the exhibit’s replicability. It can be cost and resource prohibitive to develop and install custom visualizations. The Undocumented Migration Project cleverly addresses this limitation with grid sheets inspired by archeology sites. Additionally, their material selection signifies thoughtfulness regarding affordability, capping the necessary investment for partner organizations at $2,070 and providing partners with fundraising support if necessary. De Leon shared that — in light of its subject matter — accessibility is a foundational value for Hostile Terrain 94, “I had done previous exhibition work that was prohibitively expensive, costing upwards of $20,000 to install … I wanted this to be something that anyone could host.” In addition to being financially accessible, the exhibit is intellectually and emotionally accessible. Though it’s the product of interdisciplinary academic training, De Leon says the goal was for the meaning to be “very accessible so people working in policy can easily digest it.” No matter the medium, this is the key measure of a data visualization’s success.

Finally, I appreciate the potential for this installation to amplify local partner programming around their own immigration context. For De Leon, “Success is in seeing our installation partners take ownership. The radical differences in their installation events will show it works — doing something unique in their communities.” In Detroit, I am working with several nonprofit organizations to achieve a complete and accurate count of historically undercounted populations for the 2020 Census. Many of these organizations are also supporting communities experiencing immigration crisis, including one group that was collecting gifts for 150 families whose children had one or both parents facing deportation this past holiday season. No PowerPoint presentation could as compellingly convey the feeling of helplessness I experienced looking at those presents and what they meant for the children receiving them. Visualizations like the Hostile Terrain 94 installation have the potential to move us to meaningful action and inspire systemic change.

Visit the Undocumented Migration Project support page to lend your support to their work.

Listen to podcast episode 183 of Armchair Expert to hear more from Jason De Leon.

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