The Earth
is flat

The Earth is not flat. That is the easy part.

Ten moves

From a claim about the shape of the Earth to the claims you make about cities. Jump to any section, or start with the difficult question.

The title of this text is obviously a provocation. The Earth is not flat. Or is it? How do we know either way? What entitles us to say, with some conviction, that the Earth is a roughly spherical object rotating on its axis, orbiting the Sun and travelling through space at a speed most of us can barely imagine?

Already, we must be careful. The Earth is not a perfect sphere. It is slightly wider around the equator than from pole to pole. Geodesy uses different models for different purposes, including a gravity-defined geoid and mathematically defined reference ellipsoids (Torge and Müller 2012). ‘The Earth is round’ is therefore not the most precise statement available. It is simply precise enough to distinguish a defensible account of the Earth from the claim that it is flat.

Most of us have never measured the curvature of the Earth. We have not inspected the calibration records of a satellite, repeated Eratosthenes’ calculation in several cities or checked every mathematical step used by a geodesist. Instead, we rely on photographs, maps, navigation systems, instruments, institutions and people whose work we have decided to trust.

This does not mean that scientific knowledge is merely a matter of trusting authority. Nor does agreement make something true. A large number of people can agree and still be wrong. Consensus is evidence about what a community accepts, not proof that its account is true. What matters is that different and partly independent lines of evidence converge. The changing position of the Sun, the appearance of different stars at different latitudes, the shape of the Earth’s shadow during a lunar eclipse, the disappearance of ships and buildings below the horizon, long-distance navigation, geodesic measurement and satellite positioning all support broadly the same account. These observations are produced by different people, using different instruments, for different purposes. They can be repeated, compared and challenged.

That convergence matters more than a photograph of the Earth taken from space. A photograph can be manipulated. An institution can lie. An instrument can be poorly calibrated. A scientist can make a mistake.

We therefore do not “know” that the Earth is roughly spherical simply because most people say so. We accept the claim because it offers the strongest available explanation of a large body of mutually reinforcing evidence. It also makes reliable predictions. Ships reach their destinations. Satellites remain in calculable orbits. Surveyors can locate points on the Earth’s surface. Navigation systems tell us where we are, usually with mildly disturbing precision.

Some of our knowledge begins in direct experience. We know that unsupported objects tend to fall because we have watched them fall. We know that fire can burn because we have felt its heat. This might be called experiential knowledge: knowledge acquired partly through perception, action and lived experience.

But experience does not interpret itself. Watching an apple fall does not tell us whether gravity is a force acting at a distance, a manifestation of curved spacetime or the work of an unusually punctual invisible spirit. The observation becomes evidence only in relation to a question and an explanation. Even apparently simple experience is shaped by concepts, language and previous knowledge.

Experience can also mislead us. From where we stand, the ground usually appears flat. The Sun appears to move across the sky while the Earth remains still. A railway track appears to narrow in the distance. We do not conclude that the rails eventually meet. We learn to distinguish between what appears to be the case and the explanation that best accounts for that appearance.

This is where testimony enters the picture. Much of what any individual knows depends on what other people tell them. We rely on teachers, researchers, technicians, witnesses, books, maps, databases and institutions. Even scientists depend on knowledge and instruments produced by other scientists. No individual can personally verify the entire chain of knowledge on which modern science depends (Hardwig 1985). Scientific knowledge is therefore unavoidably social.

Dependence on testimony is not an embarrassing exception to knowledge. It is one of its ordinary conditions. The important question is not whether we depend on others, because we do. The question is whether that dependence is justified. Who produced the evidence? How was it produced? Can the procedure be examined? Are the sources genuinely independent? What assumptions connect the evidence to the conclusion? Can errors in the methodology be identified? What happens when somebody presents contrary evidence?

Trust, in this sense, is not the opposite of criticism. Reasonable trust depends on the existence of organised criticism. Scientific communities establish procedures through which claims can be scrutinised, reproduced, challenged and revised. These procedures are imperfect. Scientific institutions are also shaped by money, prestige, competition, political power and sometimes exclusion. They can ignore inconvenient evidence and silence people whose knowledge does not fit established categories. Science deserves neither blind obedience nor indiscriminate suspicion.

The sensible position lies somewhere between those two temptations. We should not accept a claim merely because a powerful institution makes it. But rejecting every institution does not make us independent thinkers. It merely leaves us dependent on different authorities, often ones whose methods are much less visible. Or worse, it makes us into conspiracy theorists.

The question is therefore not simply: “Do I trust science?” Science is not one person, one institution or one method. A better set of questions would be:

These questions take us from belief to methodology. A belief is something we accept as true. A knowledge claim is a belief for which we can offer relevant justification, although philosophers have shown that justification alone does not solve every problem of knowledge (Gettier 1963). A research claim carries an additional obligation: the researcher must make the chain connecting question, evidence, method and conclusion available for examination.

Methodology makes that chain visible. It explains why certain evidence is relevant, why particular methods are suitable, which assumptions guide the inquiry and how far the conclusion can reasonably travel. It also identifies what the research cannot establish. A method produces or analyses material. A methodology explains why that material can bear the argumentative weight placed upon it.

This matters well beyond the shape of the Earth. Urbanists routinely make claims about sustainability, accessibility, identity, governance and justice. They use maps, interviews, statistics, observations, designs and historical documents. None of these automatically tells us the truth. A map can reveal a spatial pattern while concealing the political decisions that produced it. An interview can illuminate someone’s experience without representing an entire population. A site visit can reveal a maldistribution of resources but cannot by itself establish what caused it. A model can estimate future movement while reproducing the assumptions built into its design.

The difficult question is not whether we possess absolute certainty. Usually, we do not. The difficult question is whether we can justify what we claim, explain the limits of our evidence and remain capable of changing our minds.

The Earth is not flat. That is the easy part. The more interesting question is how we know, and whether we apply the same intellectual care to the claims we make about cities, societies and one another.

You probably believe that the Earth is round. You may also say that you know it. What changes when you move from belief to knowledge?

A belief is a proposition you accept as true. It may be well supported, poorly supported or false. Philosophers traditionally analysed knowledge as justified true belief. Gettier’s famous counterexamples showed that a belief can be true and apparently justified while still being true by luck (Gettier 1963).

Research does not require us to solve the whole philosophical problem before lunch. It requires something more practical and still demanding: a knowledge claim must be answerable to reasons and evidence. The researcher must show how the evidence was produced, how it bears on the claim, which inferential steps connect them and where the claim might fail.

Researchers seldom begin with certainty. They begin with a question, a puzzle, incomplete evidence and an obligation to make their reasoning transparent. Certainty is cheap, but justification is labour.

You cannot personally inspect every satellite, census form, air-quality sensor, legal record or laboratory. Even specialists depend on instruments they did not build and findings produced by colleagues. Testimony and epistemic dependence are therefore part of scientific knowledge, not embarrassing exceptions to it (Coady 1992; Hardwig 1985).

This does not mean that expertise should be accepted without criticism. It means that criticism must be aimed at the real chain of knowledge. Is the source competent in the relevant domain? Can the data and procedures be traced? Were alternative explanations considered? Did other researchers reach compatible conclusions through independent routes? Is uncertainty visible? Can mistakes be identified and corrected?

The distinction matters because a person can distrust one institution without having a better explanation. Distrust may be politically understandable and epistemically disastrous at the same time. Conversely, institutional authority can be socially powerful and still wrong. The sensible response is neither blind trust nor theatrical suspicion. It is warranted trust: confidence proportionate to the quality of the evidence, the reliability of the procedures and the openness of the community to criticism (Oreskes 2019).

Students often use method and methodology as if they mean the same thing. In fact, they do different work.

A method is a procedure used to produce or analyse material for inquiry: conducting an interview, mapping pedestrian movement, comparing plans, analysing a dataset or testing a design. Methodology is the reasoned account of how the inquiry is organised. It connects the research question to concepts, evidence, methods, forms of inference, ethics and conclusions (Crotty 1998).

Consider the claim that densification makes cities more sustainable. Before selecting a method, we need to slow down. What counts as densification? More residents, buildings or floor space? What counts as sustainability? Lower emissions, reduced land consumption, affordable housing, access to services or something else? At what scale should we look? Building, neighbourhood, city or region? Who benefits, who carries the burden and who decides which outcome matters?

Those questions concern different but connected layers of the inquiry. They are not interchangeable, and they are not simply consecutive steps.

Ontology concerns what exists and what kind of thing is being studied. It asks not only, “What is real?” but also, “What is the nature of this reality?” A researcher studying neighbourhood change might treat a neighbourhood as a bounded geographical area, a collection of buildings, a network of social relations, a lived place or some combination of these. Each choice produces a different object of inquiry.

Epistemology concerns what can be known about that object and how knowledge claims can be justified. If a neighbourhood is partly a lived place, measurements of population and land value will not be sufficient. Residents’ accounts, observations and historical records may also be necessary. Epistemology therefore asks what counts as knowledge, whose knowledge is considered credible, and what limits affect what the researcher can claim.

Methodology provides the reasoned logic of the inquiry. It explains how the research question, theoretical assumptions, evidence and forms of analysis fit together. A methodology is not simply a list of methods. It explains why particular kinds of evidence and analysis are appropriate for answering the question.

Methods are the concrete procedures used to produce and analyse evidence. Interviews, surveys, mapping, observation, archival research, statistical analysis and design experiments are methods. A method tells us what the researcher did. The methodology explains why doing it could produce a defensible answer.

Axiology concerns the values, ethical commitments and obligations that shape the inquiry. It asks what the research considers important, desirable, harmful or just. It also asks who may benefit from the research, who may bear its risks, whose interests guide the question and what responsibilities the researcher has towards participants and affected communities.

Research design arranges these decisions into a feasible project. It specifies what will be studied, which cases or participants will be selected, what evidence will be collected, in what sequence, over what period and how the different forms of evidence will be combined. Research design is therefore the practical architecture of the project.

Consider a study asking whether urban densification improves sustainability. Ontology asks what densification and sustainability are assumed to be. Epistemology asks what could count as credible knowledge of their relationship. Methodology explains the logic through which the relationship will be investigated. Methods produce and analyse the relevant evidence. Axiology makes visible the values involved, including whose version of sustainability matters and who bears the costs of densification. Research design turns all these decisions into a project that can actually be carried out.

These layers influence one another, but none mechanically determines the others. The same method can serve different methodologies, and researchers who share a methodology may still make different ethical or design choices. The purpose of distinguishing the layers is not to place research into sealed compartments. It is to make its reasoning visible and open to scrutiny (Crotty 1998; Guba and Lincoln 1994).

Evidence is material that bears on whether a claim should be accepted, rejected or qualified. A photograph, interview, planning decision, drawing, model, measurement or statistical pattern can become evidence. None carries a fixed evidential meaning on its own. Its relevance depends on the claim, its provenance and the assumptions used to interpret it.

A photograph may support a bounded claim that an object existed at a recorded time. It cannot by itself establish a social cause, institutional motive or universal pattern. An interview can establish what a participant said and may reveal an interpretation or experience. It cannot automatically establish that every resident shares that experience.

Looking beyond one photograph from space

A photograph of Earth from space is powerful evidence, but a person who believes every space agency belongs to a conspiracy can reject it. We therefore need more than one route to the conclusion.

The case is strong because partly independent evidence converges. In research methodology, deliberate comparison across different evidential routes is often called triangulation: astronomy, navigation, geometry and geodesy support the same broad account through different observations, instruments and inferences (Denzin 1978). Triangulation is useful because different routes do not share all the same vulnerabilities. If every result relies on the same dataset, instrument or assumption, apparent convergence may simply reproduce one error. Geodesy itself distinguishes the Earth’s irregular physical surface, a gravity-defined geoid and mathematically defined reference ellipsoids (Torge and Müller 2012).

Research also compares rival explanations. Does the evidence fit my explanation? Does another explanation fit it better? Fit alone is not enough: several explanations may fit the same observation. We therefore need evidence that can distinguish between them. If public-transport use rises after new housing is built beside a station, density may have contributed. So might a new fare policy, fewer parking spaces, higher fuel prices or the arrival of residents who already use public transport. One before-and-after comparison cannot separate those explanations. Doing so requires checking timing, comparing similar stations without new housing, and examining fares, parking policies and the characteristics of new residents. The findings need not agree perfectly. Convergence strengthens an explanation; divergence may reveal that it is incomplete.

The familiar sequence of question, hypothesis, experiment, analysis and conclusion represents one simplified form of hypothesis testing. It can be useful. It does not describe every serious inquiry, including quite a lot of science (Godfrey-Smith 2021).

Astronomers observe processes they cannot control. Historians reconstruct events that cannot be repeated. Urban researchers interpret plans, interview residents, map change, compare cases and examine institutions. Designers may learn by making and testing a spatial proposition. Different questions demand different methods because they demand different evidence and different forms of inference.

Methodological pluralism follows from this diversity. It does not mean that any method will do, or that every claim is as good as every other claim. The question still constrains the design; evidence must be relevant and sufficient; methods must be competently executed; inference must be open to scrutiny; and ethics cannot be added as decorative paperwork at the end.

Research also refuses to march in a straight line simply because the proposal contains a Gantt chart. A field observation may change the question. An interview may expose a category that makes little sense to residents. A failed model may reveal an assumption that had remained invisible. Revision is part of inquiry. The obligation is to record why the project changed, what was learned and how the change affects the claim.

Credibility differs from truth. A true claim may be poorly supported, while a false claim may appear persuasive. In this text, epistemic credibility means that a claim is warranted by the quality of its evidence and reasoning, the competence with which the inquiry was conducted and the conditions under which it can be criticised.

Merton described four institutional imperatives of modern science: communalism, universalism, disinterestedness and organised scepticism (Merton 1973). Communalism places scientific knowledge in a shared domain. Universalism asks that claims be judged by impersonal criteria rather than the status of the claimant. Disinterestedness concerns institutional restraints on conflicts between inquiry and self-interest. Organised scepticism requires claims to remain open to critical scrutiny.

These norms are ideals, not a flattering group photograph of actual science. Paywalls restrict access. Prestige affects whose work receives attention. Funding arrangements create conflicts. Racism, sexism and continuing colonial hierarchies shape whose knowledge is recognised (Fricker 2007; Quijano 2000). Originality also circulates as a scientific value, but it was not one of Merton’s four norms.

Longino argues that critical interaction can contribute to objectivity when a community has recognised avenues for criticism, shared standards, responsiveness to criticism and tempered equality of intellectual authority (Longino 1990). Fricker’s account of epistemic injustice adds a sharper political question by asking who is treated as credible and whose capacity to contribute knowledge is diminished by prejudice or exclusion (Fricker 2007).

The social character of knowledge therefore creates both strength and vulnerability. A community can correct errors that no individual would see. It can also reproduce shared blind spots. Reliability depends on the quality of criticism, the plurality of relevant perspectives and the institutional capacity to respond when criticism is justified.

Serious research is often expected to be indifferent to the race, gender or country of origin of the person making a claim. There is an important principle here: evidence should not become stronger because the researcher has the ‘right’ identity, nor weaker because she does not. But this principle is often stretched into a much less defensible assumption: that race, gender, nationality and colonial history can be ignored without affecting the inquiry. They cannot.

Research does not begin in a social vacuum. Someone decides which problems deserve attention, which categories will be used, what counts as evidence and who is qualified to interpret it. These decisions are made within institutions shaped by history. Colonialism may formally end while coloniality persists: durable hierarchies that organise people, labour, knowledge and authority according to categories produced through colonial rule (Quijano 2000). María Lugones shows that this history also reorganised gender relations, tying racial classification, patriarchy and colonial domination together rather than producing three separate problems (Lugones 2007).

These structures do not merely introduce personal bias at the end of an otherwise “neutral” investigation. They can shape the entire research process: the language of the question, access to education and funding, the construction of archives, the classification of populations, the credibility assigned to witnesses and the journals in which findings become legitimate knowledge.

Consider a map of an informal settlement. The map may accurately record legal parcels, registered roads and officially recognised buildings. It may also present the settlement as disordered, deficient or almost empty. Residents might possess detailed knowledge of footpaths, drainage, customary tenure, care networks and places of danger. Their knowledge is not automatically correct, but neither is it automatically anecdotal. If the categories used by the map cannot register those realities, greater cartographic precision will not solve the problem. A perfectly executed method can still ask the wrong question or make the relevant evidence disappear.

Miranda Fricker calls this epistemic injustice: an injustice suffered by someone specifically in their capacity as a knower. Testimonial injustice occurs when prejudice causes a person’s account to receive less credibility than it deserves. Hermeneutical injustice occurs when unequal participation in producing shared concepts leaves people without adequate resources to explain experiences that affect them (Fricker 2007). Silencing can also occur when an audience lacks the willingness or interpretive competence to hear what is being said (Dotson 2011).

The answer is not to declare every marginalised account true, or to distribute credibility according to identity. Experience can illuminate, distort and contradict. Local knowledge is partial, but so is expert knowledge. The methodological question is whether some people have far greater power to define the problem, impose the categories and deliver the final interpretation while others appear only as sources of data. Decolonising research therefore also concerns control, authorship, ownership and the purposes for which knowledge is produced (Smith 2021).

Epistemic justice asks practical questions. Who formulated the research question? Whose concepts organise the evidence? Who can correct the record, refuse participation or challenge the interpretation? Who owns the results? Who benefits from them? What does the chosen method make difficult or impossible to see?

None of this lowers standards of evidence. It makes those standards more demanding. Good research must test claims rigorously, but it must also test whether its own categories and institutions allow relevant knowledge to appear.

Whose knowledge counts in urbanism?

These problems become concrete in urbanism, where cities are known through different epistemic practices. A mobility model can estimate movement across a network. A bus driver’s repeated observations may identify where delays recur. A neighbourhood organisation may reveal why residents avoid a public space that appears successful in official data. These accounts concern different features, scales and positions. They cannot simply be stacked as interchangeable evidence.

Transdisciplinary research brings academic, professional and situated knowledge into a shared inquiry. Its promise lies in joint problem framing, collaborative knowledge production and the integration or application of results (Pohl and Hirsch Hadorn 2007; Lang et al. 2012). Its danger is consultation theatre: inviting people into a process after the decisive categories, budget and spatial options have already been fixed.

Power shapes the epistemic conditions of research. Who frames the question? Which categories appear in a survey? Which languages are accepted? When do meetings take place? Who owns the resulting data? Who is thanked for participating while someone else receives authorship, funding and professional recognition?

Plural knowledge does not make every assertion equally convincing. Claims remain answerable to evidence and to standards appropriate to the inquiry. The methodological task is to determine which claims each source can warrant, how findings can be related and where disagreement should remain visible. When participation affects problem framing and interpretation, it broadens the community able to identify errors and question the terms of the research.

Conspiracies happen. The mere allegation of coordinated secrecy cannot be dismissed in advance. A conspiracy claim should be examined as an explanation: Does it identify competent actors? Does it specify a plausible mechanism? Is the alleged coordination possible at the proposed scale? What independent evidence supports it? Does it explain more than competing accounts?

A self-sealing conspiracy claim installs a trapdoor beneath every objection. Contrary evidence becomes proof of the cover-up. Missing evidence proves how efficient the conspirators are. Failed predictions generate new excuses. No possible observation is allowed to count against the central belief. The problem is methodological before it is psychological: the claim has been protected from correction.

Flat-Earth arguments often follow this pattern. The conclusion is false, but simply announcing that fact teaches very little. More useful questions concern the reasoning: What sources are admitted? Which institutions are rejected, and why? Can the explanation make risky predictions? What would have to happen for the claim to be revised?

Research on conspiracy belief identifies several motives, including efforts to understand uncertain events, restore control and sustain a positive social identity (Douglas et al. 2017). These findings describe tendencies, not a diagnosis of every believer. Ridicule may entertain the already convinced. It is a poor substitute for analysing evidence and argument.

Corrections usually improve accuracy, but their effects vary. False information can continue to influence reasoning after a correction, and identity can shape whether a person accepts it (Ecker et al. 2022). This complicates the comforting assumption that better facts automatically produce better public judgement. Facts matter. So do trust, institutions, political identity and the design of the spaces in which people meet those facts.

The same obligation to make claims examinable applies when inquiry proceeds through design. Urbanists make plans, maps, scenarios, models and spatial propositions. Design activity can contribute to knowledge when it is organised and documented as inquiry. A design may expose relationships that remained invisible, bring several constraints into productive conflict or allow a proposition to be tested through its consequences.

Design can therefore do more than illustrate an answer reached by other means. Drawing may reveal a spatial relationship that was difficult to formulate in words. A model may expose an incompatibility between density, access, sunlight and public space. A scenario may show that solving one problem merely moves it somewhere else. In such cases, making is part of the reasoning. If the drawing only presents a conclusion already reached, however, design is functioning as communication rather than as a research method.

A good design is not automatically research. Professional practice may solve a brief for a particular client and place without producing a contribution that others can examine and use. Biggs and Büchler identify four connected requirements of academic research: a question and an answer, knowledge, an appropriate method and an audience. Because research is cumulative, the finding must be new or consequential for a relevant community, not merely new to the designer. They also ask what work is performed by images and artefacts, how form relates to content, how experience can be communicated and what textual explanation contributes (Biggs and Büchler 2008).

These criteria do not require design to disguise itself as conventional science. Different research traditions may work with different forms of evidence and different ideas about what constitutes an adequate answer. They must nevertheless establish a coherent relationship between the problem, the method, the outcome and the audiences for whom the result matters (Rocco, Biggs and Büchler 2009).

Practice-based research therefore needs a research question, an organised inquiry, explicit criteria for assessment, documented decisions, a reasoned account of alternatives and an argument about what has been learned. Documentation is not simply a diary produced after the interesting work is finished. It allows others to reconstruct how decisions were made, why alternatives were rejected, where the proposal failed and how those failures changed the inquiry.

The design and the accompanying text consequently perform different but related tasks. A drawing, model or spatial proposition may embody a discovery that prose could not have produced in the same way. The text provides the necessary critical distance: it identifies the question, explains the method, situates the work in relation to existing knowledge and states what can reasonably be claimed from it. It should not translate every visual quality into words. It should explain why the design matters as knowledge.

The UK Council for Graduate Education made a related point in its 1997 report on practice-based doctorates. Its survey found broad agreement that creative work should be permanently documented, placed in its theoretical, historical, critical or visual context and accompanied by evidence of appropriate research methods and an original contribution to knowledge. The report concluded that practical work alone, however original or critically acclaimed, was insufficient for doctoral assessment because the context and criteria supporting its claim to originality might not be self-evident from the work itself (UK Council for Graduate Education 1997).

Research through design should not imitate a laboratory experiment. It must still make its reasoning, evidence and limitations available for examination. A polished masterplan can conceal a weak inquiry just as easily as an impressive methodology diagram can conceal an ordinary one. The difficult question is what the design does as knowledge, not whether the drawing looks impressively research-like.

Research reports often become weakest at the moment they reach the conclusion. A case study becomes a universal lesson. An association becomes a cause. A promising design becomes a proven solution. A workshop becomes evidence that a community agrees. Methodological honesty requires the scope and strength of the conclusion to remain proportionate to the evidence.

Suppose your study finds that residents of two dense, well-connected neighbourhoods make fewer car journeys than the city average. You may conclude that those cases show an association between density, accessibility and travel behaviour. You cannot yet conclude that densification will reduce car use everywhere. Residents may differ in income, age, household structure or prior preferences. Public-transport and parking policy may explain part of the result.

This does not make the study useless. It tells you what the next question should be.

The final question matters in urbanism because research can shape plans, investment, regulation and displacement. A methodological choice may decide whose experience becomes visible, which project receives funding and whose loss remains classified as an unfortunate externality.

Good methodology does not promise the removal of uncertainty. It identifies the sources of uncertainty, states the assumptions and explains how those limits enter the conclusion. This allows a reader to see how the problem became a conclusion and where that conclusion may need revision.

Earthrise is evidence within a wider chain: spacecraft trajectory, mission records, optics, film, independent observation and the continuing accuracy of space-based navigation. The image also does something else. It asks us to see Earth as a shared and finite place. That ethical and political meaning does not follow from the pixels alone.

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