one night in New York, it was really quite late.
a strange creature climbed up the Empire State.
but instead of going all the way to the top,
it climbed 20 storeys then decided to stop.
everyone wondered but no one could say,
why it stopped where it did and didn’t go all the way.
some thought it was scared some thought it was tired.
some thought that maybe it saw someone it admired.
But forget what they tell you.
forget what you read.
if it goes any higher, its nose starts to bleed.
-Tim Burton
What would you do if you are tasked to help the beast? Help him get down the tower? Help him rise to the top? Arrange for him to meet the person he admired? What you observe is the same for everyone, but what you make of it shapes your intervention, the resources you spend, and what actually happens to the beast. The real reason had nothing to do with what anyone could see from where they stood. The real reason was simpler, more personal, and completely invisible to anyone watching from the outside.
We've Become Really Good at Observing
As much as this might sound obvious, much of our knowledge creation for serious decisions falls into the same trap. We have perfected the art of observation, but confused the accuracy of measurement with the accuracy of interpretation.
Take transport planning for example. Modern transport planning has remarkable tools for observation. We know where people go, what mode they take, how long their journeys are, how that changes by time of day, season, or income bracket. We collect data from phones, portable devices, cars, buses, payment cards, and cameras.
But movement is behaviour. And behaviour is just the beast stopping at the 20th floor.
We do ask why as well. We conduct interviews, run focus groups, send people out with cameras to photograph their routes. There is a serious qualitative tradition in planning research that genuinely tries to get beneath the surface.
A researcher designing a deep qualitative study faces months of work, small sample sizes, and findings that are difficult to integrate with the quantitative base that the rest of the decision-making apparatus runs on. The person being studied is asked to give hours of their time, to articulate in words and sketches something that may not naturally live in words and sketches. The result is rich, valuable, and chronically underfunded. So we do it occasionally, at the margins, and then return to the data we can afford to collect at scale.
A Philosophical Problem, Not Just a Practical One
There is a deeper structure to this failure that philosophers have been circling for a long time. In a 1941 paper on the analysis of experience, Heinemann drew a distinction that cuts directly to the heart of what planning keeps getting wrong. He separated what he called ‘experiencing experience’, the raw, immediate, lived process of encountering the world, from ‘experienced experience’: the processed, remembered, communicable result that becomes the input to knowledge and science.
From Aristotle through Locke, Hume and Kant, Heinemann observed, philosophers almost universally focused on the second kind, on experience that had already been tamed, categorised, turned into data. And there is a reason for that. Heinemann insisted that experience which is not expressed is of no value. Expression, the act of giving form to what has been lived, is not optional decoration. It is the mechanism by which inner experience becomes shareable knowledge.
This is what Susan Sontag calls interpretation; the conscious act of the mind in plucking a set of elements from what is observed, and translating them into something manageable. The interpreter looks at something and says, look, don't you see that X is really A? That Y is really B?
But critically, the form of expression shapes what can be known. If the only tools available for expression are structured surveys and GPS traces, or comments on a map or a couple of photos, then only certain kinds of experience can enter the record. Everything else remains invisible, not because it does not exist, but because we built no channel for it.
In fields like urban planning and transport planning, our data systems capture behaviours (the objective truth), and our qualitative studies capture experienced experience. We analyse both, find patterns, and treat the result as objective knowledge. What both consistently ignore is the experiencing itself: the felt quality of a journey, the mood that makes someone choose to walk rather than take the bus, the shifting feeling that makes one route feel right today and wrong tomorrow.
The Assumption That Limited Us
For too long, in transport planning, we treated the integration of qualitative understanding and quantitative data as a cost and validation problem, too expensive, too slow, too hard to scale, and too difficult to objectify into a form that could sit alongside hard data in a decision-making process.
What we didn’t do was treat it as a design problem. We didn’t seriously invest in building technologies that could bridge the two, that could let people express the texture of their urban experience in ways that are natural to them, at scale, in multimedia forms that can actually talk to the objective data we’ve spent decades accumulating.
Our digital tools inherited this assumption and amplified it. Structure came first. We built data collection tools around what was easy to process, and then called what came out of them objective truth.
The irony is that what we called objective was never really a clean read of reality. It was a record of expressed experience, but expressed through a very narrow channel: the trip diary, the sensor, the count. The richer experience, the one that explains why people actually do what they do, was always there. We just never gave it a proper language.
Why Now is Different
I have been trying to build things to change this since 2012. For most of that time, the technological gap was real. Capturing rich, multimedia, narrative experience at the scale and in the forms that could connect to quantitative datasets was genuinely hard. The tools did not exist. So the argument that it was too expensive and hard to analyze had some merit.
That is no longer true. We now have the computational capacity, the multimodal interfaces, and the AI-assisted analysis tools to do what was previously impractical. We can capture experience in the forms it naturally takes, voice, image, sketch, story, and begin to build the bridges to the objective record that planning has always lacked.
The remaining barrier is not technological. It is conceptual. We are still treating the problem as a cost problem, still defaulting to the data we know how to process, still assuming that the beast stopped because it was scared or tired, because those are the explanations our models can hold.
Plotpoint is our first serious attempt to bridge the gap between qualitative and quantitative analysis, in a way that makes collecting and analyzing real experiences, expressed in multi-media formats possible.
