19 Nov 2016
Let’s map the unmapped voters. Let’s not show the winner of each state, but the percentage of people and voters that voted for a specific candidate: The popular vote. I talked and wrote about the reasons to do so. The following graphics and maps are dedicated to the popular vote of the US Election 2016 (you know, the one last week. The one where Trump won).
Let’s start with an explanation of the electoral college:
If we know the percentage of people who voted for the winning candidate in each state, we can look at the votes that we normally don’t find on a map:
This map prove once again that it was a close race. There were lots of blue votes in red states – and lots of red votes in blue states.
If we go one step further, we can take non-voters into account: People who are not allowed to vote (because they are under 18 years old, in prison etc.) and people who didn’t want to vote. And this time, we don’t look at the electoral college votes, but at the population. The states in the following graphic are sorted by the share of the population that voted for Trump. We can see that a greater share of people voted for Trump in red states like Texas or Utah than in blue states like Delaware or Oregon:
In fact, thanks to the immense population in blue states, there were more Trump voters in Illinois, New York and California than in 12 of the most red states:
Do you have comments, better ways to map the popular vote or found mistakes? Let me know on Twitter or write to email@example.com. Thank you!
02 Nov 2016
What does scraping actually mean? Why would I need databases and what the heck is MongoDB? What’s the difference between SQL and SQLite? And can I truncate my y-axis or will the data vis community kill me? Even if we’ve worked with data for years, we might have simple questions like that. And now it’s too late to ask. Waaaay too late. Nobody can ever know that you still don’t know what Pandas is. Or why people use PostgreSQL.
Last weekend, hundreds of people came together to celebrate the Mozilla Festival in London, “the world’s leading festival for the open internet movement.” My fellow OpenNews fellows and I attended – that’s me and two of them in Hyde Park:
I decided to host a session that would tackle all the knowledge gaps in the sphere of data. The idea: During one hour, all attendees anonymously submit data questions (which we gathered in an Etherpad). Then everybody reads one question and tries to answer it. If she or he needs help, the whole group helps out.
Why I wanted such a session to happen? Because self-taught as many of us are, we have gaps in our knowledge and skills that other people can fill. And we all can fill other people’s knowledge gaps.
The whole session was one big experiment. I’ve never hosted such a question round before. Here is a write-up of what went well, and what could be improved the next time. Let’s start with what didn’t go well, and how to improve it:
What didn’t go well
My aim was to close knowledge gaps. That definitely happened. But it happened far more for beginners than for advanced people. During the session, we answered questions that were on hugely different levels. Questions like “What does scraping mean?” as well as “How can I more reliably bin data for choropleth maps?”. The advanced people were the ones answering the basic questions to beginners AND the advanced questions to other advanced people. It might be a good idea to divide beginners and advanced people in different groups.
If I was hosting such a session again, I’d try to get more un-googlable questions. Google got you covered when you ask “What is an API?”, but not if you want to know if “your audience really interacts with your interactive dataviz”. Answering the latter question challenges the advanced people more than answering the former one. Also, un-googlable questions require opinions more than explanations. That’s good, because explaining things is hard. People think for hours how to explain an API best – it’s unlikely that my session attendees and I can come up with perfect explanations in seconds. The result were sometimes confusing answers (which could demotivate to learn more about it), and not enough time to go in-depth and ask clarifying questions.
Third improvement: Better defining the question space. “Data” is an extremely big field: It includes Business Intelligence and stats, maps and interactivity. I appreciate about the field of data visualization that every new project challenges us to learn about new tools, methods, workflows. And most answers made me learn something relevant. But a dashboard person won’t gain anything out of hearing an answer to the question: “Are there any good/clear resources for better understanding of “overpass turbo” queries in OSM?” It would be helpful to at least define the industry for such a session: Will it be about data questions in journalism? Or in science, or in business?
What went well
I was positively surprised especially about how open people were with their questions and with what they didn’t know. I tried to create a safe space by
a) talking about how we are all students and teachers at the same time and that nobody can know anything
b) doing a fun short exercise in the beginning where people needed to interact with each other
c) splitting the attendees in two groups to create more intimacy
d) letting people submit questions anonymously
e) stressing it a lot when I didn’t know something.
And it was great to see how quickly people came up with questions. I prepared some questions beforehand, but we didn’t even get to them.
I was also happy with the engagement of the attendees. I hope that everybody got something out of it: The beginners got answers, and the advanced people got the feeling of helping somebody; of explaining things that are relevant for at least one person in the room.
In general, it was definitely an experiment in which I’ve learned a lot. The concept needs improvement, but I look forward to trying it out the next time.
After splitting the attendees into two groups, the great Simon Jockers lead the other group and gave valuable feedback afterwards. Thanks, Simon!
And the photo and GIF are by the great Drew Wilson. Thanks, Drew! All questions can be found here.
21 Oct 2016
This is the transcript of a talk I gave at NACIS 2016 in Colorado Springs. I didn’t include all of the slides, to make it more readable.
Wyoming is boring. Nobody cares about Wyoming. I don’t know that because I asked people or because I was there, but because I looked at the maps that people drew of the United States. I found 48 of these maps, and 17 people forgot to draw Wyoming in the first place. (Nobody forgot California or Florida.) Well, this person included Wyoming:
Here we can see the shapes of all the drawn Wyoming’s. A few people remember that Wyoming looks like a simple rectangle. Most of them don’t:
Hey, my name is Lisa and when I don’t enjoy NACIS, I visualize data. But today I want to talk about Map Poetry: Why we need it, what it is and how it can change the way we see the world.
When we look at a map of the world, we can’t see it as it is. We all lay an internal map of the world on top of the geographical one and only see what’s important to us. This internal map consists of memories & experience, associations, values, beliefs. It is incredible individual. There is no internal map that matches a second one. We all look at the same world differently. And we all look at the same maps differently, and sees different things; according to our internal map of the world.
What do you see here? Where do your eyes get drawn to? Where you live? Where you grew up? Or, when you’re not from the US, the places that Hollywood told you are important?
Hand-drawn maps can show us what people actually notice in their environments. For some of you, Wymoning might be incredible important, because of beautiful memories (or terrible memories). And then of course you’d know that Wyoming is of rectangular shape, because that’s the place you notice when you look at a map of the US. Most people don’t notice Wyoming when they look at the same map. For most people, Wyoming is not important.
For me, Berlin is important. I lived in that city for 2 years.
This is a map of Berlin as we all see it on OpenStreetMap:
This is a map of Berlin how I see it. The brighter an area, the more comfortable I feel navigating. The white buildings and streets are the places I know by name. This is my internal Berlin map:
If somebody asks me: “Have you heard of that new restaurant x? It’s between “Do you read me?” and Rosenthalerplatz on the Kleine Augustusstraße”, then I think: Aaah, great, I know exactly where that is. Well, ok, that’s not how it works, most often. Most often somebody asks me: “Do you know street a? In district b?” and I say: “No, is that close to shop c?” Then they say “Nah, it’s close to park d…do you know this one?” And it takes some time until we find streets and locations that are in the internal maps of both of us.
Here is a map of Dennis Wood that has a similar idea: It’s also cutting away things that he can’t see.
But in contrast to my internal Berlin map, we all share that view, because it’s an actual view. It’s not an internal map. He actually stood on a hill in his neighborhood and drew away parts of the map that were not in his view field. I like that map a lot, because it’s so paradoxical: Maps were supposed to give us an absolute overview. And he takes the maps and says: “What I can’t see from here, nobody should be allowed to see on the map. There is no world beyond my perception of it.” And that’s true for internal maps as well: For our internal maps, what we can recall, exists. What we don’t remember doesn’t exist. We cut away huge chunks of the territory to build our internal maps.
An internal map that many of us share are stereotype maps. Many Berlin people can agree that this is how Berlin works. It seems to add a layer of meaning to the city. But what it really does is cutting away SO, SO much of the meaning. It’s a map that simplifies extremely.
Of course, each map simplifies. That’s what maps are made for. The map can’t be the territory. We can’t deal with the chaos out there. Both the geographical and the internal map must simplify to be useful.
A geographical map, for example, cuts away people. This map of Berlin looks like there don’t live any people. It’s a ghost town. Of course, we understand that simplification. Geographical maps are mostly built for navigation, and we don’t need to know things like the number of people on the street to navigate.
Internal maps have a similar goal: They too are there to make navigating the world possible and efficient. I don’t have the time and motivation to actually poll every single person who lives in these Berlin neighborhoods, so I choose to put them all in one big basket and label it with the stereotype that seems socially accepted.
But with geographical and internal maps, it’s important that we know THAT we simplify. And that we are aware HOW we simplify, and what the limitations are.
Eviatar Zerubavel says: “Examining how we draw lines will […] reveal how we give meaning to our environment as well as to ourselves.” And here’s where Map Poetry gets important. I’d argue that Map Poetry can make us aware of the lines we draw, and can bring back the cut-away junks.
Let’s start with a definition. We all have a mental image of what Maps are in the sense we talk about it here at that conference: They are geographical representations of the world out there.
But what’s Poetry?
This, for example. It’s an extract from Edgar Allan Poe’s “A Dream Within A Dream”. He talks about a very specific experience: Standing on the shore, holding sand, weeping:
This is the first trait of a poem: Most often, they describe a very small subset of the world – quite in contrast to a map, which tries to give you an overview. Poe describes only a tiny experience in his life.
And it’s important to note that it’s a very personal experience he talks about. What he describes is highly subjective (not as objective as most maps try to appear).
A third virtue of poetry is its ambiguity. Poems can be interpreted in hundred different ways. Everybody reads something different out of the it.
And in the best case, poetry doesn’t just result in a rational interpretation, but evokes an emotion in the reader. As a piece of art, a poem is sublime.
Map Poetry (how I define it) has all these traits, especially the first two ones. Map Poetry doesn’t give us the overview, how maps usually do it. Map Poetry focuses on a subset of the world and looks at it from a subjective standpoint rather than a seemingly objective god-like perspective. Map Poetry makes us aware that the territory out there is, indeed, complicated, and complex and entangled. And that we all perceive it differently.
Before I start showing examples; two remarks: First, Map Poetry doesn’t equal Map Art, but is a subset of it. Map Art can include beautiful designed maps that still show an objective overview. But Map Poetry is related to lots of other fields. It seems like a general term for this idea is not found yet. If you want to google for Map Poetry pieces, I recommend googling for any of these terms.
Now I want to show two examples of people bringing the cut-away junks back into geographical maps.
First, Dennis Wood again – and his maps that some of you might be aware of. He and his students mapped a small neighborhood in North Carolina (Boylan Heights in Raleigh). But instead of mapping buildings and streets, they mapped wind chimes, street signs and Jack-O-Laterns – objects that we normally think of as “unworthy” for mapping. His maps point out all the things we cut off when we map the territory:
But Dennis Wood didn’t seek out to understand how people perceived this town in North Carolina. Becky Cooper did – for Manhattan. On her blog “Map your memories”, she let’s New Yorkers draw their internal map over a blank map of Manhattan. It shows us that the city doesn’t look the same for everybody, but that everybody associates different memories with it.
However, it gets really interesting when we change geographical maps to show our internal map. Hand-drawn maps like the ones from the United States I showed at the beginning are excellent for that.
More than 3 years ago, when I visited Oxford for the first time, I drew this tiny map. I didn’t plan to use it in a talk one day when I scribbled it, so please excuse that it’s a surprisingly ugly map for a design student:
I show it to point out one detail: This white spot there. Oxford doesn’t have rollercoasters. But this part of town felt like a maze to me, in which I walked in circles. So the map shows how I felt like rather than how it is.
But it only shows how I felt in that moment. Half a year later, I returned to Oxford for an exchange semester, and I learnt to navigate that maze. I would have not drawn that loop in my Oxford map after spending a semester there.
And if we get one step further, we don’t just map the world as it feels – but re-imagine the world based on how we feel. One example for that is Sarah Michael Levine’s “San Hoboyorkesey”, which I really like:
She combines streets from all the cities she has lived in: Hoboken, Jersey City, Manhattan, San Francisco. Sarah created her own personal “Sarah city”, in which she has the best of all the worlds she lived in. “All maps are fictional and sentimental, this one just a little more.”, she writes about her map.
All these Map Poetries are not useful for navigating, which is the main reason that maps are made.
You can’t use Dennis Wood’s maps to find an address in this town in North Carolina.
You can’t use Becky Cooper’s map to find Times Square.
You can’t use my Oxford map to navigate through the tiny alleys.
And you can’t use Sarah Michael Levine’s map to find your way from San Francisco to Manhattan.
But in my opinion, watching ourselves exploring these maps and comparing our own experience with the experience of the map maker is the real use:
These are just some questions that Map Poetry asks. It can make us more away of all the things that geographical and internal maps cut away. I think we need more of that. I’ll try to create more Map Poetry – and I’d be happy if you’d did the same. So that we can compare them. And then alter our internal maps.
19 Oct 2016
I updated this blog post with a rough transcript of a talk I gave at the GeoNYC Meetup at the 14th of November, 2016. Please note that most of the displayed data are FiveThirtyEight forecasts from three weeks before the election, NOT the final election results.
Before I start, I want to give a big shout-out to all graphics reporter, visual journalists, data vis designer, news developer and however the call themselves, who have worked on election graphics and maps in the last weeks and months. They barely slept, and it was all worth it. These people did an amazing, innovative job, and seeing their work made me very happy. Thank you to all of you.
Election maps. So many blog posts have been written about the challenges that come with displaying election results on a map of a country with extreme different population density, by the Financial Times, The Washington Post, The New York Times and The National Geographic. They mostly discuss the different ways to display the electoral votes: Cartogram? Physical map? 3D?
But I think we should go one step up. Instead of asking “HOW do we want to display the electoral votes?”, we should first ask “WHAT do we want to map, and WHY?” The electoral college votes and the chance of winning got lots of love in this election. Today I want to argue in favor of mapping the popular vote.
I have four compelling reasons for doing so! Let me start the first one with an embarrassing confession.
Until a month ago, I didn’t understand the US election system. I was hugely invested, sure: I got the NYT Election Forecast newsletter and, like most of you, was on FiveThirtyEight multiple times a day. But I didn’t really understand what I saw there. I would look at the “chance of winning” bar or the cartograms and saw at least three quarter of the space colored in blue. But then I’d scroll down to the popular vote and would be confronted with a really, REALLY close forecast.
So I started to get into it. In fact, I wanted to understand it so well that I build myself an explainer. I learned the following:
That last map shows what I’m arguing for: A map that doesn’t just show the states colored in by the winner, but that shows ALL votes–even the ones that didn’t lead to the win of a candidate in that state.
2. How we see them
That last map really made me think about stereotypes; about red states and blue states. When I look at New York or California on an electoral college map, I see states in which everybody will vote for Clinton. When I look at Wyoming or Alabama, I see states in which everybody will vote for Trump. On the map, these states are bright blue and bright red. And I’m afraid that these maps can have a huge negative influence on our collective stereotypes.
Turns out, the image of the US is not that simplified as we want it to be. We should be aware that there are lots of Trump voters in the most democratic states, and lots of Clinton voters in the most republican ones. We can’t just put a whole state population in one box and put a red or blue label on it.
Red states are not as red as we think. And blue states are not as blue as we think.
3. How we see ourselves
Even more important than working against stereotypes is it that we find ourselves on a map. Imagine being one of the thousands of democratic voters in Utah or one of the millions of Republicans in California. The map ignores you, and has so since decades. “This is blue territory”, cartograms tells me about California. “There is not one Trump voter here. NOT ONE!”
Approximately half of the population can’t find themselves and their vote on an electoral map. Every vote counts, we say. Every vote matters. So why don’t we map every vote?
4. Less surprises
When I showed my popular vote map to a friend a few weeks ago, he said: “Yeah, Lisa, you know, that doesn’t tell me anything.” And maybe that’s a flaw in the design. But maybe it’s also by design. Of course we want to see clear patterns, clear chances of winning, clear dominating colors in cartograms. But US Elections has been traditionally very close, and we should be aware of that. A map of the popular vote can show that there IS no clear predicted winner. And that it’s a really close race, again and again.
Does that mean we should get rid of the electoral college map? Heck no. This map is important – far more important than the popular vote map, because it represents the US election system better than showing the popular vote. In the best case, we should show both: A map with the popular vote, and a map with the electoral votes.
In the best case this can lead to an education about the US election system, can make us aware that stereotypes exist more in our head than in the real world, can let us find ourselves on the map and can show us how close a race really is.
Some disclaimers: First, it’s important to note that I’m coming from a foreigner’s perspective. Maybe I would see things differently if I had grown up in the US, experiencing lots of elections and experiencing far more of the country. Second, my map is not the perfect design. It’s a prototype, that’s built to make a point (also, sorry Maine and Nebraska).
29 Sep 2016
Two weeks ago I started to learn C, as part of the CS50 MOOC I’m taking with some of my fellow OpenNews fellows. And I got reminded of so many feelings I had during all the other code learning experiences before.
I packed all these steps and feelings the one long GIF you can find at the top – and in a more static comic that one can find below. You might need to zoom in a bit to be able to read it. The uncanny resemblance to the Gartner Hype Cycle that some people will notice is no coincidence. Also, in case it’s not clear: These black shapy things are programming concepts.
Let me know what you think: Have you had similar or other feelings? Am I missing something essential? Write me on Twitter (@lisacrost) or via email.
And here are all the feelings from the GIF up there:
And for everybody who doesn’t know how to code yet and is super afraid of it: There are great tutorials these days. They all start incredible easy. And you can always just stop right after the “Unjustified Pride” step.