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4. The Morals in the Map: Stress and Distress

Published onMar 27, 2020
4. The Morals in the Map: Stress and Distress
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Over the years, I have used the Tobacco Problem as both a flexible lesson plan and a tool to investigate how cartographers, geographers, and statisticians (students and professionals) think about the work they do, or hope to do in the future. In a one-hour class or a two-hour seminar, the problem is presented and persons are asked whether they would make the map for ATT or turn down the commission. They are then asked if they would accept a similar assignment for far less money from a cancer-fighting agency seeking to develop a smoking cessation program targeting persons sixty-five years and older. If we have time, the question becomes what the difference would be between those two maps.

Other scenarios were created for special groups. In one, participants were asked to assume they were members of a religious group opposed to abortion. Would it be sinful, and thus inappropriate, to accept a contract to create a map of abortion centers whose purpose was to identify underserved areas where new facilities could be added? Would the map’s ethical posture be different if, still as members of that group, they were hired to create a map identifying sites where antiabortion protesters could stage effective demonstrations? What might be the differences, if any, between the maps they produced?

In these and other, similar scenarios, the goal was to introduce the idea of practical, applied ethics in a manner that would seem pertinent to the working lives of those in the classroom. The goal was to encourage participants to think about how mapped content reflects not simply an objective image of the world but the mapmaker’s assumptions about it. How might Harley’s observation, cited earlier, that all maps (and all studies) present eccentric worldviews, each with potential ethical consequences, be made concretely meaningful? What would be the consequences that participants might see for themselves as people who liked to think of themselves as ethical and upright?

Sometimes, but not always, the discussion ended on a consideration of the limits of “truth” as a realizable ideal and “fact” as an objective standard that required little consideration.

My agenda was as much anthropological (or ethnographic) as it was instructional. I wanted to understand the distinctions participants would make. Would they feel, as did the NACIS members, a tension between Dobson’s inner and outer drives, between social responsibilities and the immediate priorities of a person responsible for the success of a business that pays the rent and feeds the family? If so, would they see this as a personal conflict, something inherent in the nature of their profession, or a more general social problem?

In most classes, I would begin by loosely defining ethics as “the evaluation of human conduct, a practical set of ideas of right and wrong.” The rules that ethics imposes in evaluating any act, I would say, are typically derived from broadly general moral definitions. For example, if equality is defined as a human good, then it is grounded in the moral declaration that “all men [and all women] are created equal.” Ethically, we apply these declarations (“presuppositions” sounds so formal) in if-then propositions (practical syllogisms). If we believe all persons equal, then, all else being equal, it is ethically wrong to advance programs that promote inequality among persons. Maps and statistics that hide evidence of inappropriate inequalities can be criticized as ethically inappropriate, as “false truths.”

In undergraduate classes in both applied ethics and philosophy, definitions of ethics and morality would first be parsed and then carefully critiqued, their respective etymologic pedigrees discussed over several weeks of dense readings and lectures. But for students in other disciplines, “morality” is a big, big word, one that scares them. It’s personal and to be avoided because it refers to beliefs that brook no discussion. For most, the idea of ethics seemed a more manageable, and certainly more malleable, judgment category. That a discussion of propositional ethics (if and then) might necessarily involve moral suppositions was not something most participants had considered.

Undergraduates in geography typically found it unsettling that dilemmas might be inherent in applying the basic cartographic techniques they were being taught, that they might have to make difficult ethical choices. They had been taught to think of mapmaking as a technical craft or trade in which hard, objective data representing worldly realities are transposed to the two-dimensional, graphic plane. Professionalism, they assumed, was nothing more than making the most legible map possible using available data. Presenting those data in a visually pleasing graphic form was a technical, and at its higher levels aesthetic, exercise. Most assumed that the nature of the data and the manner of their manipulation were an incontestable given. That statistics are partial and “facts” are bounded by bias and authorial perspective was for most students a matter of some unease.

At first, most undergraduates did not know how to answer a question about their relation to, or responsibility for, their work. That they might be required to make a principled decision that would affect their income, and perhaps their careers, was deeply disconcerting for many. Even more unsettling was the realization that their work might adversely affect the lives of others in their community. The central question—“Would you take the contract?”—personalized ethics (and morality) into a concrete choice in which practical goals and professional aspirations seemed potentially to conflict with a sense of one’s place in society as a “good” person. And because these classes were interactive exchanges, not normal lectures, all participants had to announce (and sometimes defend) their choices. In most classes, the discussion followed a similar progression.

First, one or another student would say that his or her major was “commerce,” and “in commerce” there would be no question: you do the job you are hired for. Ethical responsibility for the content resides with the employer. That one might, on ethical or moral grounds, refuse a contract on grounds of conscience was simply not on these students’ radar. Those in prelaw would opine that if the offer from the tobacco interests was presented as a legitimate contract, there should be no problem. After all, Map-Off Ltd. is a business in the business of making maps for others. One student at the University of British Columbia simply shrugged when asked, replying rhetorically, “How could I not take the contract?”

Deontology ruled in classes whose students saw mapping as just another tool of commerce. To ask about the ethics of a map’s application made as much sense to those students as asking carpentry students about the ethical use of a hammer. The difference between a good map and a bad one, a map to be proud of and a map that was not an object of pride, rested not in its content but in the aesthetics of its presentation. Did the color ramp in a choropleth map adequately and clearly distinguish the categories of data? Was the title’s typeface appropriately chosen and correctly sized? At best, some students might ask if the state level of data was the best for “Still Smoking!” (fig. 3.1). Perhaps county-level data should have been employed. Which would yield a better image? How could the noncontinental US states (Alaska and Hawaii) best be included given the projection that was being used? Perhaps a different projection would be better. Maybe Alaska and Hawaii could just be ignored.

Sooner or later, someone would disagree. Sure, “The map is a tool,” as one student put it. “The question is how we feel about what’s being built. … Is it good or bad?” At this point, another student usually jumped in to argue that saying maps are just tools “is like saying a gun is just a tool and, hey, guns don’t kill people, people do.” Thus the map would be equated with a gun, and its use in a smoking campaign targeting seniors was seen for the first time as potentially deadly. The analogy typically led someone to extend the analogy, asking if, since guns do kill people, then why can’t maps kill, too? Even if maps are just tools, they are tools with a purpose. And in this case, the purpose is to promote tobacco use.

And if maps can kill (I would mention here Arthur K. Robinson’s military history, described in chapter 3), or at least contribute to killings, are mapmakers at least partially responsible for those deaths? If we blame bartenders who serve drinks to drunken patrons who then commit vehicular homicide, shouldn’t we blame mapmakers for maps whose effect is at least as deadly, albeit at a greater remove? Finally, someone would make the point that “everybody knows” prolonged tobacco use causes cancer. We blame tobacco manufacturers for the effects of the carcinogenic products they make and promote, so wouldn’t a map that promotes tobacco use be culpable, too?

At this point, someone else would nod and say, “My uncle [or aunt or grandfather], a smoker, died of lung cancer last year [or last month].” Thus the discussion then becomes personal. Taking the contract means supporting something that kills relatives, just as promoting guns, especially certain guns (the Uzi, the Kalashnikov, automatic weapons with overlarge magazines), promotes the killing of people. The equivalence, false or not, is established. “How can you live with that?” one student asked another who said the assignment would be “just business.”

Then the bell would ring to signal the end of class. In these classes, the best that could be offered was the beginning of a discussion. In a one-hour class, we rarely got to the question of the nature of the data, the choices the data reflected, or their presumed objectivity. When the classes ran longer, the discussion would get progressively more detailed and even more interesting.

University of Regina

Perhaps the most revealing of these sessions occurred at the University of Regina, where I was hired for a one-week seminar on mapping medicine. Previous annual summer sessions had focused on crime reportage and its mapped statistics, but declining enrollments required program supervisors find a more attractive and thus more remunerative subject. Health and medicine seemed a good choice, and subscriptions for the new seminar were strong. The almost forty paying attendees were professional cartographers, demographers, and statisticians. Some had university appointments. Others worked at one or another level of government, and, in one case, for an independent health agency.

I was hired to deliver a plenary on the history of medical mapping and to teach a two-hour introductory class in computerized mapping using ESRI’s ArcGIS software. Across the program’s five days, attendees were introduced to at least eight different computer-based programs variously employed to promote data collection and organization, statistical analysis, and, finally, data mapping. On the penultimate day, a scheduled instructor was called away, and I was asked to fill in with another class. “They’ve had enough software for a year,” I said. “Let me do the ethics problem instead of another mapping session with more software most never have used before.”

In presenting the Tobacco Problem, I did the normal setup, distinguishing ethics from morality, before dividing attendees into working groups of three or four with at least one mapmaker and one statistician in each group. “Each of you is a member of a company, your group,” I said. “Each group is offered this highly lucrative contract from ATC to produce a map titled ‘Still Smoking: After All These Years!’” I then added that at the same time (in previous classes, this was a second problem), the Canadian Cancer Prevention Agency offered a far less lucrative contract ($10,000 versus, say, $100,000) for a map targeting long-lived smokers to be used in a smoking cessation campaign. “Take ten minutes or so and decide, as a group, if you take a contract and, if so, which one.”

Soon I was approached by one participant, who asked if group decisions had to be unanimous; and without much thought, I replied, “Ideally, and if at all possible.” Then I was asked if the majority in a company could fire a dissenting member. I said yes, but only if he or she could find another group to join. Then another asked if she could quit the company in which she found herself, one whose majority position she rejected as “ethically, morally … whatever” insupportable. Again, I said yes, but only if she could find another group in which to work. After ten minutes, most of the groups had fired one of their own, a cartographer or statistician who, with others who had been similarly discharged or quit, then created new groups of like-minded persons. Each group elected a speaker to present its decision, and the reasoning behind it, to the class.

Several of the groups said they would take the commission, no problem and no questions asked. It wasn’t illegal, and they needed the money. As the representative of one group, a statistician proudly said, “My job, well, our job, is data and statistics. I work the numbers, and all this ethics stuff is irrelevant.” Forget consequentialism; virtue lay in a well-performed contract. The woman behind him leaned forward and asked loudly, “So, if they asked you where to locate a concentration camp, you’d just ‘crunch the numbers’ and give them a prime location, you fascist?” The statistician turned to her and with equal force replied, “Who is going to decide which numbers are good and which are too hot to handle? Who is going to tell me what jobs to take? Is it going to be some muddy-headed liberal earth mama like you?” This was, he said, about freedom: you take the data you want and make the maps you want. If someone doesn’t like it, well, let him make his own damn map.

The statistician saw his work as deontological, based on the rules of commerce and contract, and thus could simply work the numbers he was given without any qualms. His critic was clearly consequential in her thinking at the societal level, concerned about the greater effect of the map and the data’s analysis. For her, his attitude was amoral if not actually despicable, ignoring the effect of the work he does. Had she been a classicist, he would have been Gorgias, and she would have been Socrates. For him, her judgments were an imposition on his rights and thus on his moral preserve (where freedom is the defined value). For her, his freedom imposed upon values she believed to be general, societal, and morally explicit. Both obviously had very different ideas about the nature of the virtue ethics they individually sought to promote.

After enjoining civility (too little and too late), I noted that both persons had raised fair points and fair questions. German concentration camps, I said, were precisely located, mapped to maximize the use of rail systems that brought in prisoners as forced workers and then carried out whatever war materials the inmates produced. The camps’ placement was a technical problem in locational analysis that drew on a range of spatial data in the formulation of an optimal solution. So Auschwitz, Dachau, and other camps were indeed examples of “just data” applied to a purpose that most students in the class, I assumed, would think repugnant. So it was fair to ask if data are always neutral, and if so, if the purposes to which data are put are therefore similarly free of ethical constraint. Did the statistician really mean any data … irrespective of purpose or source?

Had I been better prepared, I would have asked both students if they would have happily participated in the statistical analyses and subsequent mapping that, beginning in the United States in the 1930s, were used to identify poor areas where banks would not invest (fig. 4.1).1 This practice was called “redlining” because the cartographers drew red lines around poorer neighborhoods where data suggested that investment would be least likely to generate “safe” returns. In those areas, the price of services might be increased to discourage borrowers or denied altogether.

<p><a href="#c11247_004.xhtml#fig_001a">Figure 4.1</a> In this 1938 residential security map of the Bronx, neighborhoods were constructed and then ranked on the basis of an economic index. “Good” neighborhoods could get bank loans denied to others.</p>

Figure 4.1 In this 1938 residential security map of the Bronx, neighborhoods were constructed and then ranked on the basis of an economic index. “Good” neighborhoods could get bank loans denied to others.

The resulting US “residential security maps” were made at the behest of the 1935 Federal Home Loan Bank Board in a directive to the New Deal’s Home Owners’ Loan Corporation (HOLC).2 They were thus federally inspired and carefully constructed, applying the best available numbers to the solely economic definition of neighborhoods (best to worst) in cities like Baltimore, Buffalo, and the Bronx, New York.

Here is a wonderful example of the general problem. In times of economic distress, wouldn’t it be insane to refuse a government contract? The numbers in the map just reflected the way things were. No big deal.

But as a commentator pointed out in 2016, the maps were as much about racial segregation as they were about economic neighborhood divisions.3 They created a no-go, segregated area whose mostly African American residents were denied the money they would need to build a business or sell a house and move. The result contributed to the construction of underserved, poorer African American neighborhoods that since the 1930s have been continually disenfranchised economically. The effects of this redlining can be read in the landscape of many American cities today.

Here we have at least three ethical imperatives in conflict. The first assumes racial equality is the issue and that redlining violated its promise. The second is purely economic or at least business based. Banks are obliged to do whatever is needed to maximize their returns for shareholders. If African Americans or any other group are a poor financial risk, well, numbers don’t lie: too bad for them. Finally, there is the question of the role of the government and the manner in which it enacts its moral suppositions.

Finally, this whole question challenged the very ideas of neighborhood and of local communities: how should they be defined, and by what standard should they be disavowed or supported? If neighborhoods are seen as aggregations of people who deserve equal treatment within the city, state, and nation, well, then redlining was a bad thing. Redlining was neutral at worst, sensible at best, if neighborhoods are merely economic districts whose potential is defined by the possibility of safe loans providing adequate returns to the lenders. Either way, an ethical stance based on practical definitions with moral underpinnings is advanced.

At this point, the interplay of broad economic patterns, general morality, personal ethics, and social policy comes together. For the statistician, numbers are abstracts whose analysis and manipulation rarely carry ethical weight. It is not his or her job to challenge the assignment, not even for a Nobel Prize. Officialdom sets the policies; the practical programs that result are theirs to judge. Simple ethics requires only that numbers not be falsified and analyzed using acceptable statistical techniques. Simple economics insists that money comes first and banks need to focus on the bottom line. Society may decide some outcomes are undesirable, or unacceptable (concentration camps, racial ghettos), but that’s for officialdom to decide. It is not for the cartographer, demographer, geographer, or statistician to question.

For others, however, the redlined neighborhood maps are ethically bad maps, albeit well drawn. In other classes, I would introduce, here, the idea of a “public goods problem.” Public goods are things held in common, shared resources like streetlights in cities and good air or water for urban residents. Redlining was a public goods problem if, but only if, one thought of neighborhoods as public, shared goods—communities to be nurtured in common—rather than mere economic zones to be mined for financial gain. Since the idea was federal, one could say the US government was advancing a set of moral definitions that was economically biased rather than socially responsive. The result barred the poor from the assistance they might need and otherwise legitimately seek.

Here an interesting question was raised. Was the mapped division of cities into economic zones good business? It was the insistence that the poor are worthy of microcredit financial support (even without collateral) that earned Muhammad Yunus and Grameen Bank the Nobel Peace Prize in 2006.4 Lending money in grade 4 neighborhoods can be, we now know, good banking, good business, and a benefit to society. Certainly the disenfranchisement of redlined African American communities has had long-term, costly effects. Might the “security zones” have simply been renamed “opportunity zones” for small-loan programs?5 And more deeply—but still practically—are neighborhoods no more or less than the average or mean income level of their residents? Is that how we wish to measure the places where we live, the urban areas we seek to preserve and promote?

Introducing redlining in other sessions was merely another way to introduce the idea that the Tobacco Problem was designed to present: how we analyze the data we choose to collect reflects not simply objective facts but social perspectives that result in concrete actions with human consequences. If people believe economic exclusion to be ethically wrong (violating common moral definitions), demographers and cartographers should, as ethical citizens, argue against the production of maps that promote such a practice. If we believe in freedom and equal opportunity, then anything that inhibits their fulfillment is, as one participant impatient with my academic language put it, “just plain, you know, like, duh, bad.”

Later iterations of this kind of lecture would include maps of world poverty and global income inequality (figs. 5.7 and 5.8). In undergraduate classes, I once added reading materials about the collapse of the illegally constructed Rana Plaza in Bangladesh, where more than 1,100 persons died in 2013.6 Those killed and injured were low-paid garment workers producing clothes for, among others, Canadian department stores. In maps of global inequality and world poverty, one sees that Bangladesh is one of the poorest countries in the world. Foreign clothing manufacturers (including Canadians) see the country as a business opportunity, a fine source of cheap, nonunionized labor.

As Canadian purchasers of Bangladeshi-made, Canadian-labeled clothing, should we be proud of supporting Bangladesh’s garment industry, and thus providing work to its impoverished citizens? Or should we be ashamed to wear clothes (inexpensive and good looking though they might be) produced by Bangladeshi people living lives of abysmal poverty while working ill-paid jobs in dangerous factories? Aren’t we in some part responsible?

This was a way to ask if there was a scale of diminishing responsibility as we moved from neighborhood to city to state to nation to world. Do our moral definitions extend to humanity at large, or is equality limited to those with whom we share a legal compact (a constitution, for example) and a shared daily social environment? I tried this scenario only once, and the general result was mixed. Sure, most said. We feel bad about the Bangladeshi folk. But hey, the world is a big place, and we can’t worry about everyone in it.

A Practical Solution?

Returning to the Tobacco Problem and its Regina presentation, one group thought it had the answer. “Our group has a solution,” said a participant, “at least to the Tobacco Problem: We take the ATC money and produce the map of long-lived tobacco users. Then we take that map and rework it for the cancer prevention folk at a discount. We make money. We don’t have to make uncomfortable ethical choices, and everybody is happy.” Perhaps, she continued, there is harm in the tobacco map, but the benefits of using the ATC monies to subsidize the Cancer Prevention Agency work would outweigh that harm.

There was a lot to be said for this thoroughly pragmatic solution. Not the least of its benefits was that it removed the mapmaker and statistician alike from the need to make a principled choice that might be financially ruinous. They could in good conscience collect the money that ATC offered and which their struggling company certainly needed. Working for the antismoking campaign, they then could transform the first map to create a socially responsive second map. Public morality might condemn tobacco use, but public morality also called for impartiality of service and treatment. Voilà! Problem solved.

I had not anticipated this answer and was tempted to prevent its inclusion by introducing an exclusivity clause in the ATC contract. Then I realized the proposed solution was perfect because it introduced a fundamental misunderstanding. It assumed that it would be easy to turn the ATC map “Still Smoking: After All These Years!” into a new map with a new title, perhaps “Still Smoking? After All These Years?” Changing the title would not change the fundamental message, however, because the map would still propose a correlation between smoking and longevity. Any map using this dataset would always present a positive correlation between longevity and tobacco use.

Data are collected for a purpose. A map designed to urge long-lived addicts to quit would need a different data set, perhaps one tabulating tobacco-related deaths as a percentage of an addicted cohort population in relation to the population of active smokers who have yet to die. That map might be titled “Bad Odds: Two-thirds Die, One-third Survives.”

Wittgenstein: “A Picture Is a Fact”

It was the idea of supposedly objective facts serving as realities that Ludwig Wittgenstein was criticizing with his famous aphorism “A picture is a fact.”7 He did not mean to suggest that the inverse is true, however: that “a fact is a picture” and thus a firm reflection of the objective world. For Wittgenstein, a fact was made real, brought into being through the lens of our perceptions, our picturing, our speech.

The idea came to him during courtroom reconstructions by witnesses using toy cars to model an automobile collision. It was in the physical imaging of the accident that it became comprehensible and thus real. Often, drivers of different cars reconstructed the accident both were involved in very differently. The recitation of those distinct, after-the-fact versions of a shared event remembered differently was each witness’s “objective” truthful statement. Each was a memory made distinctly real through the act of modeling.

The facts we picture, Wittgenstein argued, are themselves meaningless in the sense that there is no absolute static, Platonic plane in which “truth” resides, assured and independent and therefore beyond our construction. We imagine and then image (in words or pictures, in charts or graphs or maps) the thing-in-itself in a context establishing its being and thus its seeming truthfulness in the world. It was this thinking that would inform later authors, cited in chapter 1, including Austin, Collingwood, and Roland Barthes.

Denis Wood and John Fels hammered home the same idea in The Natures of Maps. Everything changes, they argued, once the map is seen as a field of concepts rather than a barnyard of facts; everything changes once statistics are seen as concept fields rather than firm and complete number sets describing an objective reality. In their telling, concepts are first and foremost ideas about the world whose result “is the social manifestation of what the map [any map] presents as its ‘intrinsic’ and ‘incontrovertible’ factuality.”8 Similarly, charts, graphs, and tables appear to be factual and thus ethically neutral. But that factuality is based on prior choices (what dataset for what purpose? How is it organized?).

Wittgenstein did not write about maps, statistics, or even “pictures” as things-in-frames. Like Austin and Collingwood later, he was interested in language and logic as tools of understanding. But the idea serves here if the map is a Wittgenstein picture, a construction or reconstruction of an idea about things we put together to create a world we wish to present. We saw this in the news map (fig. 3.2) and its proposition that US military personnel are always native, everywhere local, and universally righteous. The redlined residential security maps (fig. 4.1) created an economic city in which residential areas are nothing but economic zones with a racial bias. The effect on affected populations and the long-term effects on the greater city are simply … off the map.

So the redlined maps are true and accurate presentations of data supporting the notion of the neighborhood as nothing more than a potential opportunity for banks and mortgage lenders. But by other measures of community and community cooperation, the maps falsely divided neighborhoods on the basis of a definition of neighborhood worth and economic opportunity.

The Nature of Things, Together

All of this cuts to the very heart of our concern. Because we can’t think of a photograph, a map (a dataset, or anything else), as if it existed outside its context, its point of view, Wittgenstein insisted we couldn’t judge accuracy or truthfulness as if it were a thing that just is.9 If Wittgenstein’s point was that knowledge is secondary to thinking and to its mode of presentation (speech), then every analysis—cartographic, graphic, journalistic, statistical—is secondary to the inaction of grounded propositions determining the elements that are collected and considered together. Taken from this perspective, mapping is, again, a kind of speech.10 Various cartographic techniques (color ramps, projections, symbology, text size, etc.) are the rhetorical tools that make real—effectively or not—the mapped proposition and its ethical predicates; algorithms make real the statistics we generate about how we think of things in place. Implicit in the proposition’s syllogistic form is an ethical view of us in the world.

“If everyone, or even a substantial number, assent to a map’s vision of the world … then that is the world.”11 Realities, as Wittgenstein might say (or Austin or Barthes or Collingwood), are dependent on us and not independent of our efforts. Our prejudices direct our work. Think again about the residential security map of the Bronx (fig. 4.2), in which divisions ran down, between, and across individual streets, separating the best areas (green) from their blue (grade 2) inferiors and both from the even-less-desirable yellow (grade 3) as well as the undesirable red (grade 4) neighborhoods. The boundaries were not physical realities but cartographic imaginings carving up the city into better and worse economic ghettos. Mapping made them real.

<p><a href="#c11247_004.xhtml#fig_002a">Figure 4.2</a> Detail of the 1938 Bronx security map in which green (grade 1) neighborhoods are distinguished from poorer blue (grade 2), yellow (grade 3), and red (grade 4) neighborhoods.</p>

Figure 4.2 Detail of the 1938 Bronx security map in which green (grade 1) neighborhoods are distinguished from poorer blue (grade 2), yellow (grade 3), and red (grade 4) neighborhoods.

The results were certainly fantastic to those who lived in the yellow zone at East Fordham Road near Tiebout Avenue in the Bronx, one short block to the west of a “blue” neighborhood and one block to the east of the “red.” Aqueduct Avenue was not magically transformed into a new street at West 184th Street and University. Webster Avenue was not divided by barriers to the east and west. Driving down Webster, one would see just one more street. But in the map, it is yellow on one side and red on the other. The facticity of the map, the picture of the city it created as if it presented an objective reality, was the statistical result of propositions imposed on urban (a city of streets) and political (a city of political jurisdictions) geographies.

The neighborhoods created in the map’s areas were not neighborhoods in the sense of communities defined solely by specific populations (racial or ethnic), unique geographies (“the Heights,” “the Bluffs,” “Riverside”) or historical permanence (“Old Town”). They were not even official political designations. They were fictions created by functionaries who defined “neighborhoods” as simple economic zones. Those definitions in turn were based on a presupposition that made economics the central moral focus and ethics no more than efficient financial stewardship guaranteeing adequate returns on investment. Redlining cartographers and statisticians enacted the proposition that the city is a collection of investment opportunities to be promoted (with loans) or investment traps to be avoided. Consequential results focused on the lenders, not the people.

To say the city pictured in this way was imaginary does not mean it was not real. The result—call it “bankers’ city”—took on an enormous reality for people who lived in red zones and thus could not get loans, and those in yellow zones for whom bank loans might be difficult even if, just across the street, blue-zone neighbors had an easy time of it. To live in a red zone meant that house prices would forever after be depressed because people would rather live where home loans were easier to get and social prestige certainly greater. In the relined zones, money for improvements would be unavailable.

The Cancer Example

A final map, or in this case an atlas, may drive home the point that maps and statistics are about ideas (and prejudices), and underlying those ideas are ethical propositions. In 1975 the US Department of Health, Education, and Welfare’s Public Health Service published the Atlas of Cancer Mortality for U.S. Counties: 1950–1969.12 For the first time, US health officials presented the geography of almost two decades of cancer deaths occurring within the forty-eight contiguous states. Basing their statistics on death certificates, researchers calculated the mortality rates of various cancers at 95 percent confidence levels across the nation’s 3,056 counties. These data were then aggregated to the level of “economic area populations,” what today we would call major metropolitan areas. The maps were based on tables, included in the atlas, for different cancers, each with separate mortality rates for males and females.

The goal was to identify areas with higher incidence of specific cancers in hopes of discovering causal factors influencing geographical variations in disease incidence: “The maps should serve to identify counties, or clusters of counties, with elevated cancer rates which in turn may provide etiologic clues.”13 Other countries had engaged in similar studies of variable cancer rates, beginning with Alfred Haviland’s pioneering nineteenth-century work in Great Britain.14 By the first decades of the twentieth century, statistics of cancer incidence were being calculated and then mapped in England and across Europe.15 Life insurance companies were particularly active participants in developing these materials.

The ethics and morality of this work were seemingly straightforward. Physicians and public officials are charged with combating disease because the health of the population is their responsibility in societies where life is declared a moral good. If environmental or social causes of cancer can be identified, they must be addressed. Because Americans believe the lives of citizens are precious, cancer is to be combated. From this perspective, the Atlas of Cancer Mortality was a virtuous achievement. More practically, insurers needed to know something about the lifespan of people seeking their policies. The atlas and its data on cancer deaths were, for insurers, good business.

Consider figure 4.3 and note that it mapped only the biliary and liver cancer mortality of white American females. African American, Asian, Latin American, and Native American females with these conditions were … off the map. All the other maps and their supporting tables were similarly exclusive. In this manner, the atlas presented cancer in the United States as a mortal, whites-only threat to well-being. The conclusion was that a meaningful portrait of national cancer incidence could occur within a context of racial division. In the introduction to the atlas, the authors stated that there were practical reasons for the racial division but did not elaborate on what those might be. Instead they promised a future study (and presumably mapping) of cancers in nonwhite populations.

<p><a href="#c11247_004.xhtml#fig_003a">Figure 4.3</a> This map of biliary and liver cancer mortality in white females was one of a series of maps that attempted to distill county-level mortality data to create a national portrait of cancer incidence. US National Institutes of Health.</p>

Figure 4.3 This map of biliary and liver cancer mortality in white females was one of a series of maps that attempted to distill county-level mortality data to create a national portrait of cancer incidence. US National Institutes of Health.

The stated goal of the atlas was to promote studies based on local and regional data that might identify cancer “hot spots” and, hopefully, then discover their causes. “Once the data was mapped, people saw clusters and said, ‘Aha! I know what’s there.’”16 And it worked. Researchers found, for example, higher rates of pulmonary cancers among shipyard workers exposed to asbestos in World War II. In another example, the maps revealed unexpectedly higher rates of oral cancers in white women using snuff and chew tobacco in regions where, later investigation revealed, cigarette smoking was assumed to be unladylike. But those “hot spots” were whites-only cancer areas, and so the segregated picture was, at best, incomplete.

Had cartographers or statisticians complained that the racial division was ethically unacceptable—if all persons are equal, then segregation like this is simply morally wrong—they might have proposed cancer as a general, rather than racially explicit, phenomenon. They could have argued that mapping the total cancer population would be a more effective way of identifying cancer hot spots. What about African Americans exposed to asbestos in navy shipyards during the war? What about women of color who used chew or snuff? Add them to the national picture, and the result at the least would have been more complete.

Eventually, a “separate but equal” cancer atlas was produced in 1999: The Atlas of Cancer Mortality in the United States, 1950–94.17 In it cancer mortality rates were standardized for forty forms of cancers and then calculated by race (whites, blacks) and sex. The segregation can be justified on various grounds, both epidemiological and social. Some ethnicities may have a greater tendency to one or another cancer. Records for some may be better than for others. But still there is the stated goal of the atlas: to find areas of high incidence. Where those are environmental (and increasingly we know most are), racial divisions diminish rather than enlarge the relation between citizens, cancers, and their environmental (biogeographic and social) causes.

Truth and Lies

Anybody can lie with maps,18 in news stories,19 or with statistics.20 It is easy to quote Mark Twain, who famously railed against “lies, damn lies, and statistics” (he credited Benjamin Disraeli with saying it first). Perhaps the most popular book about statistics in the second half of the twentieth century was Darrell Huff’s 1954 How to Lie with Statistics.21 More recently, Joel Best’s Damned Lies and Statistics promised “to untangle numbers from the media, politicians, and activists.”22

<p><a href="#c11247_004.xhtml#fig_004a">Figure 4.4</a> Most books on how to lie with maps or statistics (or rhetoric) are really about how to tell the best truth possible. After all, self-conscious mendacity is easy.</p>

Figure 4.4 Most books on how to lie with maps or statistics (or rhetoric) are really about how to tell the best truth possible. After all, self-conscious mendacity is easy.

None of these works were fashioned as how-to manuals for the mendacious. Articles and books about lying with maps or statistics are really about how to tell the best truth possible (fig. 4.4, for example). It is no great trick to self-consciously choose a scale or perspective that promotes what we may wish to argue but know, upon reflection, to be uncertain or untrue. It is easy to find a dataset that will argue almost any point of view. Change the confidence level of a dataset, or select one that is exclusive rather than inclusive (whites only, for example), and voilà! The result is what one desired. The challenge is not how to lie effectively but how to identify and assess the rather bounded truths we choose to tell.

The Tobacco Problem was a fiction created to serve as a teaching example. But if the case was fictional, the lessons were as real as the “security maps” of the 1930s and the US cancer atlas based on exclusory incidence by segregated race.

Readers and Users

In presenting the Tobacco Problem to various groups, I was surprised at how quickly students and seminar participants engaged the issues and how their sometimes disparate ideas of practical ethics and their underlying morality were engaged. I had thought it would be harder to raise these issues than it was. As I thought about the issues and people’s responses, I began to wonder why, and how, as readers and producers we so often ignore the conflicts inherent in a map or a table; why and how we can read uncritically the news stories whose flagrant statements are so easily questioned—if, of course, we choose to consider them critically. Are we simply lazy in our ethics and sloppy in attending to our moral definitions? Are we just uncritical readers and thus easy dupes? I did not want to believe that, and I do not believe it today.

In the next part of the book, the focus shifts from the mapmaker to the map reader and the means by which we all are so often blind to the moral failures embedded in the public artifacts we share on a daily basis as readers and reviewers. Daily we accept reflexively the limited truths in maps and stories whose arguments are, on a moment’s consideration, clearly spurious and often ethically problematic. How does that happen, and what is the effect?

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Copyright © 2017 Massachusetts Institute of Technology. (All rights reserved.)
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