I've been a fan of using maps since I learned about their potential in 1993 while trying to show where non-school tutor/mentor programs were located in Chicago and where more were needed. I've built a library with links to data visualizations such as yours. We need more people looking at this information.
What I have not yet seen is mapping of resources being distributed, down to the neighborhood level. Massive levels of funds from philanthropy, government, business and private donors have been generated annually to fight poverty, yet as some of your maps show, there still are many areas that don't seem to have been reached. Or reached consistently for many years.
Are you (or anyone else) doing any visualizations showing funding flows?
It would be great to see that library of links to data visualizations! We'd be happy to include some of those in the next post too if you'd like?
USA Spending (https://www.usaspending.gov/) has some interesting data on federal funding flows by region, by program, and by industry. The site can sometimes be a bit hard to use but I find it to be a good first step
I'll check out the site you sent. However, what I like about your work is that you're using data maps in stories and offering solutions. I don't see enough of this on other platforms, and I don't see a consistent effort to teach others to embed data maps in on-going stories. Doing so would increase the number of people looking at this information and the frequency in which they see it.
Surveys of how low-income households spend their money could be particularly helpful here. There are also areas where it can be quite difficult to get new data on at-risk populations, like for homeless Americans or incarcerated individuals.
We agree! We are creating OER learning resources at New American History to encourage more K16 educators to build data literacy into their courses, using maps like yours, the Snow map, and those from our Digital Scholarship Lab here at the University of Richmond. Here’s how we incorporated the Snow map: https://resources.newamericanhistory.org/a-public-calamity
Many more examples for migration, climate change, elections, etc in other lessons.
Thanks for all your efforts-we share your maps alongside ours and others when we provide free professional learning to educators across the country. This is a great Back to School piece!
These are great resources! I particularly liked the videos/pictures that accompany the data to help build the full storytelling narrative.
And that's great to hearr that you are sharing the maps in the professional learning. Can you share any of those examples or highlight where specifically it's being used? I'd be happy to add that to our website for testimonials/work-in-the-wild https://www.americaninequality.io/
Thanks, Jeremy! We work with many school divisions across the country sharing our work, and examples from like-minded folks like yourself. With your permission, we can start adding them into our OER learning resources directly, or perhaps even team up at some point. We’d love to host a webinar for teachers and kids on this topic—let’s connect! I will be sharing this blog next week at a statewide training in VA, as we recently embedded data literacy into a new Data Science course for all public high schools. We also support HS GIS educators, so I will also share it with them. We spend a LOT of time working “in the wild!”
I agree that data vis *has* changed *some* things. But, in my experience, this is rare. And when charts do change things, it is almost always as a tool of much larger effort.
Take the icons of data visualization:
Much of what makes Snow, Nightingale, Rosling etc so memorable is precisely the fact they are so anomalous. There are so few other instances.
But we aren't just misrepresenting the frequency of effective data vis beyond these stories: we are confusing correlation for causality in the stories themselves.
The core element of these data visualization icons was never the chart. It was the person's deeper, direct knowledge of the respective social system and the ways it helped them challenge the current paradigms... that lead to the chart:
- Snow treated cholera patients on the ground. He challenged the scientific paradigm that it was spread through the air. He collected better & more relevant data than anyone else had. And then we got the chart.
- Nightingale's insight came from being placed in a wartime healthcare system and then directly caring for young boys dying in ways unrelated to battle. Because she saw the flaws of the medical paradigms directly, she then knew what hospital data to retrieve & how to present it.
- Hans Rosling, ironically held up as a master of "data-driven insight", actually derived his insights from decades of serving as a doctor & health researcher. He spent years in parts of the world most "educated" Westerners had never even set foot in. Then he went out & created the datasets to show convey what he'd learned. In his writing & lectures, Rosling reflects constantly on how useless data is if the system is not understood or if our paradigms are not updated.
The unequivocal takeaway from our data vis icons isn't that data changes things. It's that *after* we've experienced systems directly and challenged social paradigms on topics, then can data help us crystallize & communicate that understanding.
This is literally the opposite of being "data driven" about social systems -- it's an argument that data largely reinforce beliefs.
Think about it: if a contemporary doctor to Snow had collected the pump data instead, they would have likely said, "Ahh, there we go: the cause is all the in people close-contact while in line for a sip. Social distancing must be enforced!"
Data alone, even at it's best, typically shows symptoms, and in doing so helps reinforce the symptom-focused paradigms of those who collected the data in the first place. Data, as ethnographer Michelle Jia once put it, is a form of attention, not objective understanding.
What data and charts alone often cannot do, despite our claims, is help us address the systemic forces & world views that create the symptoms in the first place.
Jeremy, so many people need your work & I'm myself am grateful you're doing it. Good, usable data is a necessary component to understanding and & evolving social systems. You know this better than most.
But I don't believe the reason "Why I use data to evaluate inequality" is because it is often directly effective in bringing about a more equitable world. We use data because is the best option available to us when we're sitting at our laptops or at our government meeting room tables.
Yes, mapping inequality is an important place to start. But the question we need to answer is far harder than "How do we make things more equitable?" -- they are, "How do we contribute to an environment where forms of thriving that we can't predict might emerge? And how do we see & feel the systems deeply enough to even know where to start?"
I think data can play a role in that. But, from my experiences in non-profits and Silicon Valley startups alike, we're largely deluding ourselves about what data is doing for us.
You write, "As leaders in the private, public, and nonprofit sectors continue to use data visualizations to make decisions, the key will be to observe both what is not working as well as what is working well."
Data can inform systemically-minded decisions.
But in my experience, it's rare: leaders will look at charts and most often see their own flawed paradigms & over-simplifications of the system. They will see the symptoms and will reach for quick fixes. Those leaders are, after all, the ones who helped decide what data should be collected at all. Is it surprising that it ends up reinforcing their beliefs? Data is a form of attention, and it was their original attention that guided the data collection.
Ok. But it's easy to critique.
How do we practically go about understanding systems rather than symptoms? How do we use data to challenge, not reinforce, paradigms?
I don't know.
I think the answers will be diverse & emergent, as with the social systems themselves.
But I am experiencing in my own work, bit by bit, better ways of using data. One approach is simply: "Work with ethnographers."
Recently I partnered with ethnographers researching the push for underrepresented students to attend college, and it's impact on economic mobility. I was pulled in to help provide quant to interact with their qualitative research.
They had spent months deep within the educational systems they were studying, collecting stories & direct observations -- and loads of contradictions. The results were challenging and uncomfortable: "college makes poor kids poorer, often", "an adult who brings you food is more predictive of college success than a counselor who tells you how to study better", "students will actively choose educational options that clearly disadvantage themselves economically", and on and on.
They contradicted many of the slogans & feel-good ideas pushed by progressive thinkers & policymakers.
And when we used that research to help interrogate the data (or just simply did a comprehensive survey of the data that exists), the complexity & contradictions grew:
- first, detailed & quality data largely didn't exist. What did exist, merely reinforced existing assumptions (or left them unchallenged)
- most reports published shed very little light on underlying causes of economic & educational inequity, instead dropping phrases like, "College is correlated with increased lifetime earnings, on average", which the broader media then used to assert things like, "college helps you climb out of poverty." In reality, the implications of the data in the reports were usually the opposite, closer to, "From this aggregate data, at best we might assert that college is a system that largely allows those with power to pass it on to their offspring."
Sure, I didn't spend 10 years teaching. But I got to place data within system-wide ethnographic research.
The result wasn't a simple insight, or a clear policy. But it was a better, more nuanced picture of the system.
---
I'm going to keep exploring ways of doing this, as I learn from you & others in our field.
A final thought:
I think Anscombe's Quartet, which you highlight here & which I was shown on day 1 of my intro viz course, represents the first step in data vis's short evolution: the idea that stats can be misleading, and vis can provide a truer picture.
But I hope the next generation of those using data to understand social issues will develop a new version of Anscombe's Quartet: one that shows how data visualization alone can be misleading, and how a systems-deep understanding can provide a truer picture.
Thanks for the thoughtful note David and yes definitely agreed that data viz alone is not enough and 100% true that we need to make sure it doesn’t embed or reinforce biases. This type of detailed insight and personal experience
Is really helpful for us at American Inequality to understand as well what practitioners are seeing so thank you for that.
I've been a fan of using maps since I learned about their potential in 1993 while trying to show where non-school tutor/mentor programs were located in Chicago and where more were needed. I've built a library with links to data visualizations such as yours. We need more people looking at this information.
What I have not yet seen is mapping of resources being distributed, down to the neighborhood level. Massive levels of funds from philanthropy, government, business and private donors have been generated annually to fight poverty, yet as some of your maps show, there still are many areas that don't seem to have been reached. Or reached consistently for many years.
Are you (or anyone else) doing any visualizations showing funding flows?
It would be great to see that library of links to data visualizations! We'd be happy to include some of those in the next post too if you'd like?
USA Spending (https://www.usaspending.gov/) has some interesting data on federal funding flows by region, by program, and by industry. The site can sometimes be a bit hard to use but I find it to be a good first step
Fascinating!
Fantastic, Jeremy—so glad you and your team are creating this "portal" for data visualization!
Absolutely and we’re excited to hear it’s been so helpful
Here's a page showing six sub sections of my library. Two are focused on GIS maps and visualization. https://tutormentorexchange.net/resource-links/collaboration-process-improvement
And here's a page on my website where I focus on "distribution" of needed youth programs and resources. https://tutormentorexchange.net/mapping-the-programs
I embed maps and visualizations in my http://tutormentor.blogspot.com blog and focus on uses of maps in the http://mappingforjustice.blogspot.com site. Both of these blogs date back to 2008 and earlier.
And here's a concept map where I point to many data mapping platforms. http://tinyurl.com/TMI-MappingData
I'll check out the site you sent. However, what I like about your work is that you're using data maps in stories and offering solutions. I don't see enough of this on other platforms, and I don't see a consistent effort to teach others to embed data maps in on-going stories. Doing so would increase the number of people looking at this information and the frequency in which they see it.
What additional research would provide insight into what data should be collected on federal spending projects and their impact on equity?
Surveys of how low-income households spend their money could be particularly helpful here. There are also areas where it can be quite difficult to get new data on at-risk populations, like for homeless Americans or incarcerated individuals.
We agree! We are creating OER learning resources at New American History to encourage more K16 educators to build data literacy into their courses, using maps like yours, the Snow map, and those from our Digital Scholarship Lab here at the University of Richmond. Here’s how we incorporated the Snow map: https://resources.newamericanhistory.org/a-public-calamity
Many more examples for migration, climate change, elections, etc in other lessons.
Thanks for all your efforts-we share your maps alongside ours and others when we provide free professional learning to educators across the country. This is a great Back to School piece!
These are great resources! I particularly liked the videos/pictures that accompany the data to help build the full storytelling narrative.
And that's great to hearr that you are sharing the maps in the professional learning. Can you share any of those examples or highlight where specifically it's being used? I'd be happy to add that to our website for testimonials/work-in-the-wild https://www.americaninequality.io/
Thanks, Jeremy! We work with many school divisions across the country sharing our work, and examples from like-minded folks like yourself. With your permission, we can start adding them into our OER learning resources directly, or perhaps even team up at some point. We’d love to host a webinar for teachers and kids on this topic—let’s connect! I will be sharing this blog next week at a statewide training in VA, as we recently embedded data literacy into a new Data Science course for all public high schools. We also support HS GIS educators, so I will also share it with them. We spend a LOT of time working “in the wild!”
Yes let's definitely connect - if you want to shoot me an email at americaninequality@substack.com that would be great.
And that's awesome to hear about the VA training. Thanks so much for sharing the blog there!
I agree that data vis *has* changed *some* things. But, in my experience, this is rare. And when charts do change things, it is almost always as a tool of much larger effort.
Take the icons of data visualization:
Much of what makes Snow, Nightingale, Rosling etc so memorable is precisely the fact they are so anomalous. There are so few other instances.
But we aren't just misrepresenting the frequency of effective data vis beyond these stories: we are confusing correlation for causality in the stories themselves.
The core element of these data visualization icons was never the chart. It was the person's deeper, direct knowledge of the respective social system and the ways it helped them challenge the current paradigms... that lead to the chart:
- Snow treated cholera patients on the ground. He challenged the scientific paradigm that it was spread through the air. He collected better & more relevant data than anyone else had. And then we got the chart.
- Nightingale's insight came from being placed in a wartime healthcare system and then directly caring for young boys dying in ways unrelated to battle. Because she saw the flaws of the medical paradigms directly, she then knew what hospital data to retrieve & how to present it.
- Hans Rosling, ironically held up as a master of "data-driven insight", actually derived his insights from decades of serving as a doctor & health researcher. He spent years in parts of the world most "educated" Westerners had never even set foot in. Then he went out & created the datasets to show convey what he'd learned. In his writing & lectures, Rosling reflects constantly on how useless data is if the system is not understood or if our paradigms are not updated.
The unequivocal takeaway from our data vis icons isn't that data changes things. It's that *after* we've experienced systems directly and challenged social paradigms on topics, then can data help us crystallize & communicate that understanding.
This is literally the opposite of being "data driven" about social systems -- it's an argument that data largely reinforce beliefs.
Think about it: if a contemporary doctor to Snow had collected the pump data instead, they would have likely said, "Ahh, there we go: the cause is all the in people close-contact while in line for a sip. Social distancing must be enforced!"
Data alone, even at it's best, typically shows symptoms, and in doing so helps reinforce the symptom-focused paradigms of those who collected the data in the first place. Data, as ethnographer Michelle Jia once put it, is a form of attention, not objective understanding.
What data and charts alone often cannot do, despite our claims, is help us address the systemic forces & world views that create the symptoms in the first place.
Jeremy, so many people need your work & I'm myself am grateful you're doing it. Good, usable data is a necessary component to understanding and & evolving social systems. You know this better than most.
But I don't believe the reason "Why I use data to evaluate inequality" is because it is often directly effective in bringing about a more equitable world. We use data because is the best option available to us when we're sitting at our laptops or at our government meeting room tables.
Yes, mapping inequality is an important place to start. But the question we need to answer is far harder than "How do we make things more equitable?" -- they are, "How do we contribute to an environment where forms of thriving that we can't predict might emerge? And how do we see & feel the systems deeply enough to even know where to start?"
I think data can play a role in that. But, from my experiences in non-profits and Silicon Valley startups alike, we're largely deluding ourselves about what data is doing for us.
You write, "As leaders in the private, public, and nonprofit sectors continue to use data visualizations to make decisions, the key will be to observe both what is not working as well as what is working well."
Data can inform systemically-minded decisions.
But in my experience, it's rare: leaders will look at charts and most often see their own flawed paradigms & over-simplifications of the system. They will see the symptoms and will reach for quick fixes. Those leaders are, after all, the ones who helped decide what data should be collected at all. Is it surprising that it ends up reinforcing their beliefs? Data is a form of attention, and it was their original attention that guided the data collection.
Ok. But it's easy to critique.
How do we practically go about understanding systems rather than symptoms? How do we use data to challenge, not reinforce, paradigms?
I don't know.
I think the answers will be diverse & emergent, as with the social systems themselves.
But I am experiencing in my own work, bit by bit, better ways of using data. One approach is simply: "Work with ethnographers."
Recently I partnered with ethnographers researching the push for underrepresented students to attend college, and it's impact on economic mobility. I was pulled in to help provide quant to interact with their qualitative research.
They had spent months deep within the educational systems they were studying, collecting stories & direct observations -- and loads of contradictions. The results were challenging and uncomfortable: "college makes poor kids poorer, often", "an adult who brings you food is more predictive of college success than a counselor who tells you how to study better", "students will actively choose educational options that clearly disadvantage themselves economically", and on and on.
They contradicted many of the slogans & feel-good ideas pushed by progressive thinkers & policymakers.
And when we used that research to help interrogate the data (or just simply did a comprehensive survey of the data that exists), the complexity & contradictions grew:
- first, detailed & quality data largely didn't exist. What did exist, merely reinforced existing assumptions (or left them unchallenged)
- most reports published shed very little light on underlying causes of economic & educational inequity, instead dropping phrases like, "College is correlated with increased lifetime earnings, on average", which the broader media then used to assert things like, "college helps you climb out of poverty." In reality, the implications of the data in the reports were usually the opposite, closer to, "From this aggregate data, at best we might assert that college is a system that largely allows those with power to pass it on to their offspring."
Sure, I didn't spend 10 years teaching. But I got to place data within system-wide ethnographic research.
The result wasn't a simple insight, or a clear policy. But it was a better, more nuanced picture of the system.
---
I'm going to keep exploring ways of doing this, as I learn from you & others in our field.
A final thought:
I think Anscombe's Quartet, which you highlight here & which I was shown on day 1 of my intro viz course, represents the first step in data vis's short evolution: the idea that stats can be misleading, and vis can provide a truer picture.
But I hope the next generation of those using data to understand social issues will develop a new version of Anscombe's Quartet: one that shows how data visualization alone can be misleading, and how a systems-deep understanding can provide a truer picture.
Thanks for the thoughtful note David and yes definitely agreed that data viz alone is not enough and 100% true that we need to make sure it doesn’t embed or reinforce biases. This type of detailed insight and personal experience
Is really helpful for us at American Inequality to understand as well what practitioners are seeing so thank you for that.