However, there is one group with which I’ve worked and written about before for Nightingale—the Yurok Tribe in far Northern California—where there is no well of data from which they can draw. For many reasons, overdose data are not available for them to understand the deep impact of overdose on Native American tribal members. I remember Yurok Tribe members telling me that they were flying blind with no access to useful data and that they often don’t know about an overdose until one of their tribe members dies from it, when it’s obviously too late to provide supportive and life-saving services.

I fear we may be entering an era when many more communities across California and the country will be flying blind without access to data–and on a range of issues, not just the devastating impact of overdose. Among the swirl of changes taking place within the federal government these last few months, you may not have noticed that the availability of meaningful, community-level data is under serious threat. As staff across U.S. data-collecting agencies are let go (and with it, institutional knowledge is lost); budgets for data work shrink; and federal data advisory boards are disbanded, the capacity for the federal government to collect data, conduct surveys, and publish community-level findings could greatly diminish. 

We won’t notice these impacts immediately. After all, the Census Bureau, Centers for Disease Control and Prevention along with other federal agencies often take a few years to publish community-level data on poverty, crowded housing, nutrition, smoking, domestic violence, and suicide prevention, among many other topics. One person with deep experience managing federal data described to me recent developments with the U.S. data infrastructure as a slow rot, as if termites are, bit by bit, eating away at the foundation of federal data. So it may be years before we truly see the extent of this damage, and by then, it won’t be easy to simply reinforce the foundation with minor repairs.

Few would likely argue with the concept that we need detailed data—including from federal data systems—for the U.S. to compete effectively in a worldwide marketplace where companies, and countries, increasingly rely on data to get ahead. From the perspective of global competitiveness, deconstructing our federal data systems seems short-sighted. After all, to compete globally, we need current and reliable data, better breakdowns, and a greater capacity to interpret, visualize, and communicate meaningful findings.

But that’s the world stage. And for those of us in the visualization community, we soon may have less social good data to visualize, and innovation with public sector visualization could slow. Why, however, are data important to communities across America? There are countless ways in which individuals harness these data to save lives, build safer communities, and improve local well being:

• Schools use data on reading and math proficiency, for example, to improve curriculum for our children
  
• Local hospitals and county health departments examine government data about service delivery and health care conditions impacting the community, in order to improve medical care and provide preventative services
  
• Adult kids seek Medicare data on the quality of nearby nursing homes for aging parents
  
• Realtors increasingly share public data with clients about crime and the quality of life in neighborhoods to help people make informed decisions about where to buy a home
  
• Many of us consult the local weather forecast each day—the federal government is a key source for this information, especially for tornadoes, hurricanes, heat waves, and other weather emergencies
  
• And, as noted above, data are used by coalitions to help communities save lives by addressing the threat of overdose

These data are not bound by political lines. They benefit Republicans, Democrats, and independents alike. People of all political persuasions can, and do, make use of the treasure trove of data that the U.S. government publishes, often thanks to data translators that participate in the Data Visualization Society. 

Our local elected officials—county supervisors, the school board, town council members—rely on these data, too, for good governance and effective policymaking. And, of course, access to quality data helps us evaluate our politicians’ policy choices and keep them honest. 

In short, these data are vital to help communities thrive, and lives hang in the balance with decisions we make using these data. These data are not just numbers. They represent each of us and the communities in which we live, and we have every right to the high quality, detailed data for which we pay as taxpayers.

Actions you can take

So, what can you do as someone focused on data visualization about the threats to federal data? 

Be aware of the slow rot that we’re beginning to see in our federal data infrastructure. 

Use the data we have now while encouraging your community leaders to do the same. Maybe such usage will make it harder to take away these valuable resources. There are an array of data tools that leverage what’s available now from federal sources to provide summaries of how your community is faring on wide-ranging topics (I maintain a listing of roughly 100 such data websites). 

Join efforts to do something. National groups taking a lead role include the Association of Public Data Users, the Data Rescue Project, and the newly launched Federal Data Forum, sponsored by the Population Reference Bureau. For anyone in California who’s concerned, there’s a group of us now meeting to address the threats to federal data on our state’s communities, so you can join us. And other states could be, and maybe are, taking similar action. 

And let politicians on both sides of the aisle know that federal data are under threat in ways that harm all of us and could have lasting negative impacts for the communities our children and grandchildren will inherit.

The post Data About America’s Communities Are In Jeopardy, and Lives May Hang in the Balance appeared first on Nightingale.

I can conjure up another pleasant daydream: I imagine two family members on opposite sides of the political spectrum looking at data visualizations on a host of issues—poverty, educational attainment, immigration trends. Then, they each speak from the heart and consider what can be done to address issues exposed through the data. It’s hard work, but they begin to understand each other’s perspective and see a middle path.

I know these sound like flights of fancy. After all, who among us actually uses public data to decide how to vote? Very few of us, I’m sure. I can’t help but wonder, however, if we could all benefit by nurturing this capacity in us. 

Why try to rebuild political dialogue through data? 

These days, it seems, we crave more and more data in so many facets of our lives, yet we disregard its power in political discourse by not seeking out unbiased facts on issues or community well being. Data, for example, is central to a well-functioning business, with real-time indicators and sophisticated dashboards regularly leveraged to plot next steps. Many of us, too, now measure our sleep, steps, and daily exercise. We view the results as progress graphs on our phones and watches and take action accordingly. And in my realm of work, where I run a data storytelling consultancy to serve organizations working to improve the social good, I see that universities, government agencies, and foundations increasingly want to learn how they can better leverage and communicate data to champion their work. 

I don’t mean to suggest we willingly keep data at arms’ length in political discourse. Many of us, I’m sure, would invite data into our lives to help us perform civic duties like voting. It’s just that we’re collectively overwhelmed by the volume of unhelpful political messaging that permeates our minds—social media feeds that influence our thinking; political advertising that’s often built to make us feel tense; and rhetoric from candidates that may exaggerate actual conditions. It’s not easy for the everyday voter to think about available data as a tool for education when there’s already a cacophony of seemingly urgent messages drowning out everything else.

Despite these challenges, we should actively try to insert data into discourse, because of the power of data to bridge divides and help us get beyond angry rhetoric. The common post-election refrain I keep hearing from friends, family, and colleagues is that they just can’t fathom why someone voted for the person in the other party. Yet the election results are pretty clear that votes were roughly evenly split between the two presidential candidates, which means that many of us simply don’t understand the perspective of the other half. How could we when we’re battered by political messaging that may only confirm our own political biases? 

Relaying data about issues impacting our everyday lives, in essence, would allow us to find common ground and help us read from the same script—and such data would counter the foreboding images of doom in political discourse and social media feeds that taint our understanding of the world.

How can data help combat polarization and unhelpful rhetoric?

The question of why to do this work to leverage data to inform the electorate is easier to answer than the how to do this. After all, the media channels noted above—political advertising, social media content, candidate rhetoric—are strong forces that can’t easily be tamed. However, there’s reason for hope if we start by lifting up data about, in particular, local community. You see, local work offers the most potential to bridge divides. CivicPulse, in newly published research with the Carnegie Corporation, found that local government leaders say that polarization is not likely to impact their work. The smaller the community, in fact, the less polarization plays a pivotal role, according to their survey. 

Our local communities can indeed be safe harbors from polarization and a means by which we stitch back together a sense of mutual understanding. Local community may well have played a more vital role in understanding our world, say, 100 years ago. These days, our outlook is much broader as our horizons have expanded. Yes, we’re more worldly, which is good, but we’re also more susceptible to fear-inducing stories we hear happening in other regions of the country. 

The evolution away from community as a way to understand our world perhaps began to take hold as national media, both TV and newspapers, started to drown out local reporting. Then came the Internet, which further erased geographic distance, and, along with cable news, enabled us to focus only on digesting media related to our interests. And finally came social media, which allowed people to find common outlets for their anger in places distant from home. As a result, that sense of local community that helped keep us sane and less polarized lost its influence. 

Transforming wide-ranging local facts into community education

On the positive side, however, we’ve also become more adept over the last 20 years at finding, publishing, and visualizing data about our communities. I began working with community-level data in 2002, when I helped launch a data website, kidsdata.org, to raise awareness about children’s issues in California. Back then, it felt like me and my colleagues were on the cutting edge by visualizing and communicating data about local child well being. Since those early days, a range of web tools providing community-level data have proliferated. I keep a catalog on my company’s website listing resources with data for California communities, and there are now about 75 such data tools in this catalog.

Many of these sites—such as County Health Rankings & Roadmaps, the CDC’s Places website and AARP’s Livability Index—even have local data for essentially every community in the United States. In short, the data is there for us; we just need to mobilize it.

I know from the work I do with my clients that they’re eager to harness data about community and transform facts into action. My clients’ aims may be focused on encouraging local business leaders to join a collaborative; providing maps and graphs to elected officials to persuade them to pass a policy; or visualizing data about community conditions in order to obtain funding. 

Those are all worthy goals, but we also need to see local data as an asset to help educate the electorate. Twenty years ago, this wasn’t an option, given the lack of data, but local data is readily available, visualized beautifully through tools that are thoughtfully crafted to help shape our understanding of our communities. Granted, these indicators are not available in one place (and probably never will be). That’s a solvable problem; we can guide people to where to go for data on specific topics. And in all likelihood, the everyday voter may need some hand-holding, too—some kind of curation of the data, perhaps through stories that can add needed color. Here, too, there are workable solutions.

There are numerous other obstacles in our path. Funding for government data sources that fuel these data websites could dry up. Facts could be manipulated by domestic (or even foreign) agents. And some of us may become (or may already be) skeptical of data from public sources. Those are indeed obstacles, but they’re not reasons to give up on this endeavor.

What I’m actually most concerned about is the lack of collective action. It’s striking to me that all of these organizations which build their various data tools work on their own islands, and don’t typically join forces. We need to change that mindset to have any chance of combating the power of advertising and social media in political discourse. 

I, for one, am keenly interested in helping to corral together the vast data resources we’ve assembled about our local communities, then finding ways to marshal these data for the benefit of political discourse. My hope is that, if we can establish a beachhead by using data to inform our understanding of place and local community, we can slowly begin to provide people with clear-headed ways to understand their world—and each other.

Who’s interested in joining this journey? And who knows of similar initiatives focused on leveraging data to support discourse? I’d love to hear from you at andy@hillcrestadvisory.com.

The post Can Community Data Help Heal Public Discourse? appeared first on Nightingale.

I found myself focusing deeply on the word “dashboard,” wondering if that’s indeed what my client had in mind and whether that type of display would truly be persuasive. You see, when someone says dashboard, I think of COVID dashboards like the one above that we’ve all become accustomed to seeing (and, admittedly for some of us, to nervously checking at regular intervals). 

In addition, there are the modular-looking dashboard displays used for internal monitoring purposes  – for example, a business dashboard to summarize KPIs (key performance indicators) for senior management.

In the health field, in particular, dashboards have become commonplace. This very journal, for example, featured a post by Tricia Aung on the “The Global Health Dashboard Epidemic” – and that blog post was published about a year before the pandemic brought to us the proliferation of COVID dashboards. 

So, you may ask: What’s my issue with dashboards? Some may point out that dashboards are a terrific way to display findings, certainly better than showing a singular graph to summarize a complex topic. 

I agree that dashboards have their place, but here are a few things that I find problematic with a typical dashboard display: 

• Dashboards prioritize the graphs, meaning there’s typically no room for context or explanation about the significance of the overall findings.
  
• Dashboards don’t often have enough space to explain the meaning of individual visualizations. It’s as if we’re supposed to be familiar with what each indicator is conveying, including its acronyms and other inside-baseball terminology.
  
• There’s often no sense of hierarchy with dashboards. All visualizations are given equal prominence. 
  
• And some common elements that we leverage in data storytelling to help readers better absorb and find meaning in the information – narratives about individuals, quotes, photos – are foreign to most dashboard displays.

That’s not to say that all dashboards should be banished. With COVID, for example, the use of dashboards is appropriate, given that we all quickly became familiar with the pandemic’s data terminology. For that same reason, a car dashboard works well; we collectively understand what a fuel gauge or speedometer is communicating.

The same reasoning applies to dashboards used internally in business settings – for example, at the start of monthly meetings to help a division of a company know where to focus efforts. After all, when everyone on the team is well-acquainted with the measures, context isn’t necessary, so dashboards work well.

But what about that new employee who needs to be onboarded? It would be helpful to educate them about the significance of the measures on the company’s dashboard. 

You see, in so many instances, our work as data visualization practitioners require us to explain concepts that are not as familiar as a car’s speed or COVID hospitalizations. In other words, much of the time when we communicate data, we don’t have the luxury of the user knowing what we know, because we’re talking about a topic our audiences don’t understand. There are many occasions, too, when we won’t always be able to control where, or how, our data is shared.

The bottom line is that we want to be able to compel people to take an action with the information we’re sharing, even if we’re not there in the room or on the Zoom call to describe the data, and that means that we need to explain and unpack the concepts for the visualizations we’re sharing. 

It’s in these settings that it would be helpful for us to realize that dashboards may not be the best display format. In fact, I’d go one step further: 

Dashboards ≠ Data Stories

The sooner we recognize that dashboards don’t adequately address the assignment we’re often given as visualization specialists – “create a story from the data” – the more willing we’ll be to consider formats beyond the dashboard. Sure, our colleagues not steeped in dataviz may say they want a dashboard – as my client above did – but that’s often because dashboards have become shorthand for data display these days.

It’s our job as data practitioners to find the right format by interviewing end-users and lifting up the actions our clients want audiences to take with the data. If we always fall back to the familiar dashboard display, our efforts may fail. The end user won’t understand the significance of the findings and won’t be persuaded to take an action, which means our work to find, analyze, and visualize the data will have been for nought.

Fortunately, we have many options for how to display data. To assist with this work of expanding beyond dashboards, I recently catalogued other display formats for a continuing education course I’m teaching through George Washington University, “From Spreadsheet to Story: A Step-by-Step Guide to Communicating Data.”

Here are some data story formats I highlighted for that course that can be more helpful than a dashboard can at driving towards action: 

The Fact Sheet: Often geared for print consumption, this one-pager highlights – and explains – some key findings that can be shared as a print handout or attached to an email.

A Slideshow: The emphasis here is on chunking out data findings into snack-size bites of information, so that you’re providing helpful context without describing too much at once.

An infographic: Sometimes cartoon-like in its presentation of information, an infographic often will dive into a specific topic by providing an abbreviated summary of key data findings.

The Persuasive Narrative: In my mind, the purest form of data storytelling is when you relate a story about an individual and use data to describe how this issue impacts more than just this one person. A persuasive narrative is one such way to humanize the data, telling an individual’s story from problem to potential solution. 

A Q&A: I particularly appreciate how the story linked below uses a combination of questions, then small blurbs + data, to answer them. The questions provide a useful cadence that leads the reader through the material.

A List: People engage with lists, such as the ‘top five things to know’ about a topic. Perhaps when we know the end point (e.g., you are on fact, seven out of 10), we’re more likely to digest the findings by reading a paragraph of explanation.

A Data Game: When we transform data into an intellectual exercise or quiz, it’s more likely that our information will be remembered and shared. Simply put, people like participating; it’s often far better than just talking at people with data.

Long-Form Scrollytell or Data Movie: Sometimes we have a lot to say, and need digital space to let the data breathe. There are some wonderful examples of long-form data stories, including “The Desperate Journeys of Rohingya Refugees.” By leveraging a combination of photos, artwork, audio clips, data maps, and graphs, this data story from kontinentalist educates us about the plight of 2,400 Rohingya who have fled Myanmar and Bangladesh. 

Another one of my favorites is this poignant data movie about World War II.

I’m sure this is an incomplete list. Know of any other data storytelling formats? Please share. I’d love to see what other forms of data stories exist out there in the wild.

The good news for those of us attached to our dashboards is that it actually doesn’t need to be an either/or choice between data stories like the examples above and dashboards. I often advise my clients that they can start with a data story to educate and explain. Then, you can let readers explore the findings on their own through an interactive dashboard that allows them to find their own narrative.

So rather than: 

Dashboards ≠ Data Stories

Let’s think of it as:

Dashboards & Data Stories

What did I end up doing for that community health collaborative project, you may ask? Well, the project is still very much in progress, but the template is taking shape as a fact sheet, one that leaves space for quotes, poignant photos, and other forms of content that help paint the data on a broader canvas than a dashboard can and help steer audiences to take an action that’s important to the health collaboratives.

The post Dashboards Are Not Data Stories appeared first on Nightingale.

While California collects racial and ethnic data on a host of issues, from opioid overdose to COVID prevalence to academic outcomes for students, data for Native Americans is reported less frequently, or unreported due to small sample numbers and policies that hinder collection.

The problems with data collection facing the Yurok Tribe are not unique to California, nor are they specific to Native American populations. What the Yurok Tribe experiences exemplifies a broader issue that data analysts and visualization practitioners should confront. How can we analyze findings and visualize results when data for important communities are simply not reported?

This is the question we, a data storyteller and an epidemiologist, posed to ourselves as we set out last summer to work with the Yurok Tribe Wellness Coalition as part of a technical assistance program sponsored by the California Overdose Prevention Network, a program of the Center for Health Leadership and Practice.

The issues the Yurok Tribe face helped us better appreciate what so many groups contend with and allowed us to puzzle through what can be done to help a community aiming to confront modern challenges by leveraging data. Beyond simply obtaining broad data about Native American status, the Yurok Tribe also needs more specific tribal affiliation identification, which represents a political designation of the tribe’s sovereignty. Alas, this information, which can help the tribe provide necessary services while preserving important traditions, is rarely available.

Although the Yurok Tribe is California’s largest, at about 6,300 enrolled members, it simply can’t access crucial indicators of how members are faring. As Lori Nesbitt, the opioid program manager for the Tribe’s Wellness Coalition, observes, they often don’t get any data until a member dies from an opioid overdose, when it’s obviously too late to provide supportive and life-saving services.

Those of us who work frequently with data understand what’s at issue here: epidemiologists, statisticians, and analysts reporting racial and ethnic information are trained to suppress populations with small numbers, or aggregate several smaller groups together. Although these are accepted as good statistical practices, these approaches often fail to articulate trends at the micro-level, which challenge an array of communities in the U.S., including tribal populations, Native Hawaiians and Other Pacific Islanders, Middle Eastern and North African populations, and other ethnographic groups.

In short, the aggregate means we aren’t looking at the full story. As California Governor Gavin Newsom often observes, “We don’t live in the aggregate.” Disaggregating smaller populations (whether they are racial and ethnic groups, by tribal membership, or some other important feature) is a technique that analysts and data storytellers should include in their toolbox to advance health and equity, even if it bumps up against statistical practices. There are strategies for disaggregating data (combining multiple years of data, oversampling smaller populations) while maintaining statistical rigor.

But if we can’t disaggregate, we’re left with incomplete information, and these blank cells in tables and empty spaces on graphs are often visualized by an asterisk, indicating suppressed data. The National Congress of American Indians’ Policy Research Center says it well:

“American Indians and Alaska Natives may be described as the ‘Asterisk Nation’ because an asterisk, instead of a data point, is often used in data displays when reporting racial and ethnic data due to various data collection and reporting issues, such as small sample size, large margins of errors, or other issues related to the validity and statistical significance of data on American Indians and Alaska Natives.”

However, beyond issues of data analysis, there are important historical and contemporary factors at play — namely the genocide and oppression of Native Americans which pre-dates the founding of the United States of America. During our project, we learned from our Yurok Tribe partners how this history and its legacy plays out today, even through our data systems. The U.S. Census, for example, did not count Native Americans until 70 years after the inaugural census in 1790 (to learn more, check out this timeline from the U.S. Census Bureau and this commentary from the Pew Research Center). And in a more modern example, CNN’s election coverage this past November reported out results from Native Americans as a group they termed, “something else,” which was offensive to people of all racial/ethnic backgrounds, particularly Native Americans and other communities of color.

The end result is a paucity of data, and, put simply, you can’t visualize an asterisk. If the data are not there, how are we to know and visually describe how these populations are faring?

Through our project, the Yurok Tribe Wellness Coalition sought to better understand what data were being collected on Native Americans (and specific tribes) by public agencies across Humboldt and Del Norte counties, where the tribe is located, so that data reporting can be improved to better support the Yurok people and prevent opioid overdoses. We partnered with the Coalition to conduct interviews with public agencies (social services, health, law enforcement, education) to learn more about their data systems and practices.

What did we learn?

1. Tribe-specific data — and data on Native Americans in general — is not regularly collected by the eight public agencies who participated in our assessment.
2. Data sharing policies are in place between non-tribal and tribal entities, but they are underutilized.
3. Despite the challenges, public agencies are interested in partnering with tribes to improve data collection and reporting. All agencies think there would be benefits to the larger community through better data collection and sharing.

So what broader lessons can be drawn from this project? Simply being aware of who’s not measured is an important first step. Next is to talk to the tribes, and other populations, who may be made “invisible” in data about how we can do better. It’s only in partnership that we can start to make data more representative of all groups.

As the census count wraps up in the United States, we’ll soon analyze results and create illuminating visualizations summarizing the findings. As we do, however, it’s important to account for those who are simply not counted, or who are undercounted by federal, state, and local agencies who have no data, or don’t report the data they do have.

In the coming months and years, as census data are compiled, released, analyzed, and visualized, and as we fret over and visualize COVID-19 findings — including now, the need to obtain racial/ethnic breakdowns for vaccination data — let’s keep in mind who we don’t count, or who we undercount. Let’s remember that we’re often not able to visualize information about Native Americans and the hundreds of tribes in the United States, as well as Asian Americans, Native Hawaiians, and Pacific Islanders — such as Hmong, Filipinos, Cambodians, Fujians — and many more groups that we typically combine together into broad racial and ethnic categories. We need to advocate for them and for the release of their data, recognizing that the results from such data activism can catalyze social change and empower these communities to improve issues of dire importance like drug overdose.

The post Asterisk Nation: One Tribe’s Challenge to Find Data About its Population appeared first on Nightingale.

As a result, this past week has been an education for me. But more importantly, as a student of health dataviz it’s been interesting to see how quickly data visualization — sprinkled with some context — brought me up to speed on this important concept. In the process, I’ve been thinking a lot about what makes the visualization on “flattening the curve” so successful — and what else we as data visualization practitioners can do to educate people on why it’s valuable to spread out and “squish down” that curve describing COVID-19 cases, so that the health care system can effectively treat all those impacted. More than that, though, this visualization also taught me about what makes a health data visualization successful, regardless of topic.

I first encountered “flattening the curve” as I was walking my dog around the neighborhood, catching up on tweets. Vox had just published a piece with the provocative headline, “How canceled events and self-quarantines save lives, in one chart.”

Those “in one chart” headlines always grab me, so I clicked. But this one left me wanting.

You see, I was on my phone — as surely many of us are when we encounter information these days — and it wasn’t worth squinting to try and read those annotations that explained the concept. The text was simply too faint. All that I noticed were two different-colored area charts. I mean, the chart looked very nice — great color palette — but it was hardly practical to my on-the-go, mobile use case. Even that important line about “health care system capacity” washed over me. I simply didn’t see it as I clicked out of the article and back into Twitter land.

Then, as I read more about the concept and encountered charts with clearer typography, the concept of flattening the curve crystallized. For example, Stephanie Evergreen shared this tweet, where the chart has easier-to-read annotations and a headline that’s action-oriented, not generic.

Do I think the chart directly above looks as nice as the one by Vox? Not really, but it sure educated me better on the concept, as did this one from Drew Harris, a professor at Thomas Jefferson University.

Again: clear, cogent, and to the point. Which is just what we need from a visualization in a time of crisis when we need to quickly educate a public hungry for accurate information.

I’m learning, too, how you can use visualizations to expand an initial concept. That dotted line above about “healthcare system capacity” is so crucial, and it hadn’t occurred to me that there is another key reason to flatten the curve and spread out cases: The healthcare community will learn over time how to respond to COVID-19 and, with more time, research will bring treatments and therapies into play. A few squiggles of a line, including by showing that providers getting sick will negatively affect outcomes in the short term, made all this clear through this version of flattening the curve:

Finally, I realize that this concept of flattening the curve cries out for animation, so that people can see “flattening the curve” unfolding, preferably as someone explains the concept in real-time, just as ABC News did toward the beginning of this piece.

Not many of us have easy access to tools to animate visualizations, especially those on the front lines of providing necessary information (such as local public health officers). But there’s no need to go high-tech here. That local public health officer could just as easily have taken a white board and drawn out the concept, whether in front of local news cameras or presenting at a community meeting. Alternatively, here’s an animated gif that’s licensed for public distribution.

So here’s what I’ve learned about health data visualization as I’ve been educating myself, too, on flattening the curve:

• Headlines matter. Don’t go generic when we need to tell people that they need to pay attention. Use a chart headline that helps orient the reader.
• Annotations are good. I mean, with flattening the curve, you don’t even need numbers. Just make sure your explanations have clear messages in easy-to-read type.
• Know your users. For example, many of us encounter visualizations on COVID-19 while we’re on our phones — which can make it difficult to digest what we see. Keep in mind that mobile experience.
• Animations are great, but if you’re in a face-to-face setting, so is drawing out the concept live on a white board or a sheet of paper.

And, of course, when all else fails, realize that cats are key to clarification on the internet, as this redrawn version of the Vox graphic attests to, posted on Twitter by Anne Marie Darling:

My list above is just a start. I’d love to learn from others on what else we need to do to marshal visualization that make this concept of flattening the curve actionable to all.

The post Flattening the Curve and Expanding My Understanding appeared first on Nightingale.

We’re often told that stories, not stats, sell a point. In my data storytelling consultancy, Hillcrest Advisory, I remind clients regularly that our brains are wired for stories. When we hear a story, not only are the language-processing parts of our brain activated, but so too are other areas of our brain that we use when experiencing events from the story. A story, in other words, puts our whole mind to work.

But if storytelling is so important, where does that leave us with respect to data? Typically, stories tell us about an individual experience, but data help us generalize that to a broader phenomenon. Can data visualization, on its own, make a persuasive case — for example, for more funding to support a cause; to encourage people to change a health behavior; or to encourage city council members to vote for a policy?

In a discussion forum, we posed this age-old question to the thousands of members of the international Data Visualization Society, asking them to focus, in particular, on the role that data play in local, in-the-trenches work around the globe to improve living conditions. It’s in these community settings where we wanted to know how people leverage data and what specific tactics work.

Far from data visualizations being antiseptic, abstract representations, we learned that graphs and maps, when thoughtfully done, can be visceral, eloquent, and yes, impactful.

In Colombia, for example, Juan Pablo Marin Diaz from Datasketch described how data helped raise awareness, and funds, to combat forced disappearances among young and poor Colombians. In the map above, red shows where people went missing, and green signifies where their bodies were found, often in mass graves.

On a personal level, Bridget Cogley expressed how a data dashboard brought comfort to her friend, Kelly Martin, who was in hospice care and recently passed away. By using a dashboard, they would try out new medications to find the optimal balance between symptom management and alert time. You can read about Kelly’s experience in her blog post, “So long and thanks for all the dashboards.” Bridget, too, blogged about using data as a tool to support hospice care.

Chris Henrick of Google shared an MFA thesis project in which renters in New York City can visit a convenient, step-by-step data tool that helps them find out if their apartment is rent stabilized to prevent high rent increases. Some have used the tool to confront landlords for reimbursement of back rent and to lower future rent.

Amanda Makulec, data visualization lead at Excella, shared how data dashboards for health centers in Zimbabwe help district-level decisions-makers understand where they need to take action, such as when supplies are running low at clinics, and to see how communities are fixing issues (e.g., drilling boreholes to improve water supplies at clinics). S. Anand, who runs the data science/storytelling platform Gramener, described how visualizations helped government officials in India pinpoint where beneficiaries were having difficulties filling out necessary forms, which, when corrected, cut down on error rates and the time taken to complete forms.

And in a county in West Texas, Kate McKerlie described how data visualization helped community organizations catalog what child abuse prevention services were being offered and referred — and where there were gaps. In so doing, the project helped advance local collaboration.

Finally, the Institute for Health Metrics and Evaluation (IHME) at the University of Washington published a data story this month titled, “Precision mapping to end child deaths.” In this visualization, they show how granular data and visualization can work in tandem to “reveal trends and patterns that can help us better understand where to focus efforts to prevent child deaths.” As IHME also says in their story, “Every region has different challenges, and by knowing what the obstacles are at a local level, decision-makers can better strategize how to overcome them.”

To help make that point crystal-clear, IHME showed how visualizing local data in Nigeria, Ethiopia, and Peru can help target interventions in those countries.

The catalog of what works was indeed inspiring, but we also wanted to know what tactics data visualization experts employed to successfully help people leverage data and transform numbers into local change.

These days, there’s a digital arms race in the data visualization world, with news outlets and digital agencies building jaw-droppingly beautiful web creations with which people can interact on computers, phones, and tablets.

But when conducting targeted work at a local level, we learned that such interactives sometimes provide more firepower than is needed. In fact, it can sometimes be awkward in face-to-face meetings to haul out a tablet or a computer to help make a point with a digital display. As Tricia Aung from Johns Hopkins School of Public Health observed, “In my global health experience, data visualizations that are non-digital (printed) can have more weight than digital” tools. She noted that people have “appreciated handouts that they can take back to their office and show others.”

Unisse Chua of De La Salle University in the Philippines commented that “for places that are not fully connected to the Internet, I’d say using non digital would be the best way to get the message out. Some provinces here in the Philippines do not have Internet access.”

Elijah Meeks was advised when presenting to local elected officials to make a map that would be “suitable to be held up on a poster during the city council meeting.” I’ve also seen health departments in California successfully employ data posters at convenings to help elicit input. Attendees are encouraged to add post-it notes to large-scale data displays, in order to share their questions and insights.

Similarly, people talked about printed fact sheets, street murals, data placemat settings at convenings, and stroll-through data galleries that, unlike a linear PowerPoint presentation, allow you to walk to where your interests take you.

Overall, when marshaling data to achieve local change, Data Visualization Society members advised a design thinking approach of walking in the shoes of audiencies you need to reach and integrating their use cases in what you build. Amanda Makulec described this as understanding end-users “needs and experiences.” She said, “Ultimately, if the goal is for the local organization or audience to own the product (as in, use it in their day-to-day work), customizing and tailoring to make it personal to them is so important.”

In other words, if you don’t ask what users need, you won’t know, and you’ll be flying blind with the data product you’re building — perhaps, for example, by overbuilding with an interactive that doesn’t match local use cases. It’s no surprise then that, when marshaling data to advance local change, data visualization practitioners were strong advocates of talking to end-users, so that you build durable data tools that respond to real-life scenarios.

Finally, a number of respondents noted how it’s not a question of data or stories; the two elements needn’t be siloed from each other when imparting information to make progress on social issues. Chris Henrick advocated for adding what he termed “the human element” to data visualizations — such as photos, personal narratives, and audio. By painting data on this broader canvas, he said, we’ll make numbers “more relatable, and engage audiences who aren’t as familiar or comfortable with abstract data, charts, and fancy visualizations.”

So in the end, we learned from Data Visualization Society members that there isn’t an either/or choice to be made between data and stories. In the social sector, we need to leverage both to make compelling cases that can advance local change.

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