You may consider yourself an urban planner, but are you an urban scientist?
Many of us planners in our daily jobs are busy helping the public in creating, understanding, and implementing plans and ordinances, or otherwise, helping clients with development proposals. Fewer of us are actually “in the lab,” conducting experiments and testing hypotheses. For planning, “science” as practiced will normally mean social science, where researchers compile and analyze a wide variety of socio-economic, demographic and geographic data (quantitative and qualitative) and then attempt to draw out explanations and/or conclusions based on those sets of data.
But when I pose the question, “are you an urban scientist?” I am not referring to your job description but rather how you think about planning. Let’s take an example. Consider the following sentence:
“More compact forms of settlement help preserve the natural environment.”
This is not just a statement. Nor is it merely a goal, objective or policy. It is a hypothesis. Meaning, it is an observation about the real world that can be tested. Further, it is not a self-evident claim, such as, “cities are where people live.” This latter statement is self-evident for the very reason that the definition of “city” includes “people.” This is known as an analytical statement, where the definition of city itself entails the notion of “population,” or people. In other words, it is a tautology; it is necessarily true.
However, we need to understand a general distinction between different claims in knowledge. Our original statement above is not analytical but synthetic. That is, it is not necessarily true but contingently true. It is reliant upon an external set of affairs or factors. Its validity depends upon a “synthesis” of seemingly disparate facts that can be observed.
Now, I fully realize that our example statement may seem to be self-evident to urban planners, especially if you are a land use planner and/or you accept a wider set of related principles like Smart Growth. However, if this be you, realize that you either a) accept the hypothesis as given (true) based on what you believe to be credible authorities; or b) you understand the hypothesis intuitively but may not have thoroughly examined it empirically.
Scientists ask many questions. Often, they have more questions than answers. This is what keeps science going. Yet, each new answer (i.e., a hypothesis that has been tested and verified) builds upon previous answers. This is what we call scientific progress.
Returning to our statement, “More compact forms of settlement help preserve the natural environment,” for hypothesis framing purposes, it would help to re-word it as an “if-then” statement:
“If more compact forms of settlement are built, then it will help preserve the natural environment.”
Now that we have a clearer hypothesis statement, we first ask basic questions about the terms used in the hypothesis. For example, what is meant by “compact forms of settlement,” “preserve” and “natural environment”? Before we test the hypothesis, we must define the terms. If the terms are too fuzzy, the hypothesis will not be testable. Once we have defined the terms, we can create a model to test the hypothesis. Social scientists use statistical models most of the time, but these are no longer the only kind of models at our disposal. Thanks to the advancement of computational science allowing for rapid development of GIS theory and technology, we now have spatial models as well.
But don’t get too intimated by these sophisticated means. A model begins as a simple explanation for how something works. It could be as modest as a flow chart or diagram hastily sketched onto a cocktail napkin. And yet, once fully developed and tested, it could transform into something more meaningful that topples a current (and widely-accepted) understanding for some set of observable phenomena.
After modeling and testing, you must re-examine the hypothesis, viewing it in light of the data and the model helping to explain the data. Does the hypothesis stand or fall? In other words, is it more or less true?
Realize that science (as opposed to philosophy) is an inductive process, whereby individual observations are pieced together and may eventually (if successfully cohering) form a larger theory. Nothing is ever “absolutely proven” in science in the same way that something can be absolutely proven to be true in philosophy or mathematics via a deductive method of reasoning (here think of Euclid’s proofs of geometry). Nevertheless, if a hypothesis leads to the development of an authentic theory, this means it has garnered a heavy weight of observational evidence such that to deny it would be irrational or even downright absurd.
Hypotheses, laws and theories, teamed with observable facts (or data), round-out the four basic concepts of scientific discovery. Tested hypotheses can lead to the discovery scientific laws, which, distinguished from theories that explain a wide array of connected phenomena, explain only a single phenomenon. Additionally, scientific laws merely describe a phenomenon and not how or why they work. Yet, scientific theories often depend on such laws for their overall validity and usefulness.
Given the above, scientists must always remain objective and therefore yield to theories that are widely substantiated and accepted by the consensus of other scientists in the chosen field. This “yielding” is not a defeat of science– on the contrary, a well-attested theory screams out to be proven false (“falsified”), and thus encourages scientists to be ever-busy creating new hypotheses and testing them.
Science is always restless, never satisfied with “victories.” It likes an underdog that can come in from nowhere, topple previous understandings, and build-up a new structure from a foundation of past victories and failures alike. This new structure is nothing less than the advancement of truth and of knowledge themselves.
We are now getting a better picture of what it means to think like an urban scientist. Below is a rough outline of the “markers” we’ve been discussing. Ask yourself these questions:
- (Hypothesis Framing) Do I see planning-related statements as hypotheses, and if necessary can I restate them as such?
- (Scrutinizing Statements) Can I comprehend the difference between a statement that is self-evident and one that is not? (Hint: a scientist uses the latter)
- (Introspection) Have I thoroughly examined my own planning beliefs, in order to understand more precisely why I accept them as true?
- (Progressivism) Can I see one small “truth” of planning science as building upon previous others and contributing to a wider set of coherent observations that scientists call a “theory”?
- (Fountain of Rational Inquiry) Does my supply of questions always or nearly always overflow my supply of answers?
- (Model-Attuned Discovery) Am I seeking to interpret and understand the data through the framework of what scientists call a “model”?
- (Hypothesis Testing) After examining and interpreting the data, do I bring it back to the original hypothesis and challenge its validity?
- (The “Open Door” Policy) Do I understand and accept that there cannot be “absolute proofs” in science?
- (Professional Integrity) If multitudinous coherent and consistent observations affirm a theory that I disagree with, do I have the self-honesty to abandon my past beliefs and accept the new understanding?
- (The Underdog Mindset) Do “great big scary theories” drive me to create my own opposing hypotheses?
- (Failure “as” Victory) Can I see past failures (i.e. overturned hypotheses) as victories, since scientific progress, ultimately, is built upon a foundation of both failures and victories?
If you answered “yes” to any (or all) of the above, then you may just be an urban scientist.
Image Credit: Posters of Andres Duany and Nikola Tesla created by author from the following sources:
– Andres Duany image: Knight Foundation, urban planners look at Biloxi plans, under creative commons license.
– Nikola Tesla image: Wikipedia under public domain.