All maps have biases. A new online exhibit explores the history of map distortions, from intentional propaganda to basic data literacy.
This interview is adapted from the latest edition of MapLab, CityLab’s biweekly newsletter about maps that reveal and shape our urban spaces. Sign up for the newsletter here.
Every map is infused with its maker’s decisions, which ultimately present a pattern, story or argument. Sometimes those choices of design, labeling, data selection, and data slicing show up as obvious biases, as in the case of Donald Trump’s infamously augmented 2019 map of Hurricane Dorian. More often, though, this inherent “truthiness” flies under the radar of a map’s tidy, matter-of-fact visual presentation, as in the many maps and models being made now of semi-reliable Covid-19 case data.
So while it’s relatively easy to make a map in an age of abundant data and digital tools, it isn’t always easy to read them. How can you tell what’s real, and what’s a distortion?
An online exhibit launched today by the Boston Public Library’s Leventhal Map Center aims to help visitors up their data literacy skills. Bending Lines explores the long history of how maps and other visual data reduce and manipulate the realities they present, whether to push commercial advertising, land speculation, wartime propaganda, or a perspective on the news. It also offers helpful guidance that a responsible map reader can use to suss out the trustworthiness of whatever they’re examining.
Spearheaded by Garrett Dash Nelson, the BPL’s newest curator of maps and occasional contributor to CityLab, the show was planned in pre-coronavirus times as an in-person experience. Instead, it opens as an immersive online experience that students of all ages and interest levels, anywhere, can wander through on their devices at home. I spoke with Nelson about the exhibit for MapLab.
How has this exhibit evolved since it was first conceived?
The original topic was to do an exhibition of a classic category of maps called persuasive cartography, which tends to refer to propaganda maps, ads, political campaign maps, maps that obviously you can tell have an agenda. We have those materials in our collections of about a quarter million flat maps, atlases, globes and other cartographic materials. But we decided in recognition of what’s going on now to expand into a bigger theme about how maps produce truth, and how trust in maps and other visual data is produced in media and civil society. So rather than thinking about just about maps which are obviously treacherous, distorting, and deceptive, we wanted to think about how every map goes about presenting the world and how they can all reflect biases and absences or incorrect classifications of data. We also wanted to think about this as a way to promote data literacy, which is a critical attitude towards media and data visualizations, to bring together this long history of how maps produce our sense of reality.
What do you mean when you say ‘what’s going on now’? What about this exhibit speaks to our moment?
Well, there are certain persuasive maps we can clearly see to be pernicious: On a map where an octopus is shown in place of Russia, you know the mapmaker is making an argument. But so much of cartography we see today has a visual language of scientific objectivity, and it’s hard to remember someone produced this and made choices about how it was framed. Those choices and data sources are hidden and buried very often. So thinking about when we see an electoral map in the news, or visualizations of coronavirus cases, we have to be careful about what’s being portrayed.
Can you give an example of how the show goes about deconstructing all of this?
We commissioned a special set of maps where we compiled geographic data about the state of Massachusetts across a few different categories, like demographics, infrastructure, and the environment. We gave the data to a handful of cartographers and asked them to make a pair of maps that show different conclusions that disagree with each other. One person made two maps from environmental data from toxic waste sites: One map argues that cities are most impacted by pollution, and the other says it’s more rural towns that have a bigger impact. So this project was really meant to say, we’d like to think that numbers speak for themselves, but whenever we’re using data there's a crucial role for the interpreter, and the way people make those maps can really reflect the assumptions they've brought into the assignment.
What are some questions a good interpreter can ask as she’s interrogating a map?
In one section of the show called “How the Lines Get Bent,” we talk about some of the most common cartographic techniques that deserve our scrutiny: whether the data is or isn’t normalized to population size, for example, will produce really different outcomes. We also look at how data is produced by people in the world by looking at how census classifications change over time, not because people themselves change but because of racist attitudes about demographic categorizations that were encoded into census data tables. So you have to ask: What assumptions can data itself hold on to? Throughout the show we look at historic examples as well as more modern pieces to give people questions about how to look at a map, whether it’s simple media criticism, like: Who made this and when? Do they show sources? What are their methods, and what kinds of rhetorical framing like titles and captions do they use? We also hit on geographic analysis, like data normalization and the modifiable area unit problem.
You mean the different boundaries a mapmaker can choose to draw?
Exactly. There’s one section that asks, how many people live in Boston? It depends on what you mean - do you mean city or metropolitan area? How we draw those borders and how we aggregate all kinds of demographic statistics makes a huge impact on our conclusions.
What skills does a reader need to interpret the countless coronavirus maps out there?
That is a great example of one of the powerful things about maps, which is their ability to pull out and visualize the geographic patterns in information. If I gave you hundreds of thousands of reports or medical records of people who’d been infected, you’d never be able to sift through it and tell me anything about the geography of the virus. On the flip side, the simple maps in the media make the point immediately of the places in the world affected the most. But they also mask the details of those cases, as well as the many unreported cases, or cases where people already had symptoms of something else and got tested. So maps have both a power to pull out patterns that we wouldn’t otherwise see, but they do so by simplifying. The only truly accurate map of the world is the world itself, and as soon as you start making maps you are always making choices about what to leave out and what to generalize.
Another thing about coronavirus maps is the incredible institutional complexity about how their datasets are produced. In this digital world we think, ‘oh the data just drops down from sky,’ but no, all the information is being compiled by doctors and public health boards and state agencies with all kinds of different practices for how to collect and organize it. Sorting through those practices really pushes you into the social life of that data.
I love that expression, the “social life of data.” Speaking of which, this exhibit also makes an argument that a person’s ability to sniff out reliable information is a bedrock of democratic society. How is that so?
So rather than think about maps as simply being true or false, we want to think about them as trustworthy or untrustworthy and to think about social and political context in which they circulate. A lot of our evidence of parts of the world we’ve never seen is based on maps: For example, most of us accept that New Zealand is off the Australian coast because we see maps and assume they're trustworthy. So how do societies and institutions produce that trust, what can be trusted and what happens when that trust frays? The conclusion shouldn’t be that we can’t trust anything but that we have to read things in an informed skeptical manner and decide where to place our trust.
About the Author
"how" - Google News
May 28, 2020 at 08:19PM
https://ift.tt/2yG0SoZ
How to Deconstruct and Interpret Maps - CityLab
"how" - Google News
https://ift.tt/2MfXd3I
https://ift.tt/3d8uZUG
Bagikan Berita Ini
0 Response to "How to Deconstruct and Interpret Maps - CityLab"
Post a Comment