The point that I was making a wee 1123 days ago is similar to what he starts with: machine sensing is going to accelerate the race to map the entire world, and we need to think about the consequences that has on how we use that data.

On a more abstract level, I tried to make people think about the relation between data and the capital-T Truth in geography. How people measure the world shapes their view of the world and shapes the decisions they make.

In one of my classes that I took in Calgary we discussed the role of data in environmental science. How do you measure and understand urban pollution? You know better than I that dBA noise contours are used all the freakin' time. However, it's used so much because it is easy to measure, not necessarily because it is the best measure. With noise pollution, the data can get pretty close to the Truth (the perceived noise pollution). So our data-driven understanding of the world is rather close to the actual understanding of the world. Because the data is easy to measure, many cities are able to curb noise pollution with policy measures. One of my classmates did a side gig where they measured ecology health by driving a stick into the ground and sending a sample to a chemical lab to test for nutrients.

More complex (pollution) issues are by definition harder to measure—it is much harder to estimate the decrease in lifespan that an increase in vehicles would bring. And because it is harder to measure, the full complexity just doesn't get measured. Or it doesn't make it to the political agenda at all, even though it may be very important. One of the big points I make with my thesis is a critique of transportation planning practices. To put it bluntly, for as long as transport planning has existed decisions have been made on numbers that are easy to model, not on numbers that residents actually care about. Instead of getting people to where they want to go, planners have fetishized travel-time-savings and use it to justify spending billions. Instead of making an attempt to understand the plight of those with the worst accessibility, their cost-benefit analysis told them to build a new highway to a rich suburb.

It's "garbage-in, garbage-out" but in the urban domain. My point was that we need to be really critical of the data we collect, why we collect those things (and not other things), because they will end up shaping our spatial understanding, our decisions and thus our world. What I like about the article is that I think he understands that (even though it is hidden in his meandering way of writing):

With the stakes so high, we need to keep asking critical questions about how machines conceptualize and operationalize space. How do they render our world measurable, navigable, usable, conservable?

and builds on top of that idea:

What I really want to discuss is this: How can we use all these new and old technologies to improve the physical world that we humans (and our non-human companions) read and inhabit?

And thus follows his exploration of lots of non-machine ways of understanding the world to explore how machines can become better at approximating that. I don't see it as a relational vs absolute cartographic battle. Instead I think it's more about capturing our relational, contextual meanings. A small example: because dBA doesn't really capture how often planes go by, European airports often use a cumulative daily measure instead. It's an improvement that uses more data but in a way that is closer to the capital-T Truth.

A bigger example is that Borneo article: "whose woods are these?" How do we think of ownership when you have two entirely different approaches to ownership? We can now do more with data than ever before, and that's a good thing, but we also make more stupid assumptions than ever before. Instead of taking data at face value and just applying it, it is so much more important to start with the problem and figure out the best way to solve that problem.