"The Spectacle of the Scaffold"

Michel Foucault

I was recently on a call discussing how to approach intractable problems. Where do you begin? Well, I had an answer. For the past 20 months I have been writing a book about geospatial analytics. Understanding the broad applicability of location intelligence across a wide-spectrum of industries and gaps in knowledge is my secret sauce.

For example, the concept of networks continues to be applicable to my consultancy. I dedicated an entire chapter to a powerful Python package, OSMnx. You may have heard of OpenStreetMap (OSM), open source global mapping . The geocoding aspect is addressed by Nominatim layered on top of the OSM data.

You can learn a lot about road and transportation networks. The image below is the street and road network of Paris, France. The wee little red dot on the image on the left indicates betweenness centrality. What is that? It shows how central a location (node) is within a larger network. If you have been to Paris it might not surprise you to learn that the highest rate of centrality is 11%. That is not that high and I will explain why in a minute but more importantly, it speaks to how functional the overall network actually is. Only 11% of the information can’t go anywhere else unless it passes through this node. The image on the right is a comparative network showing the interconnected nodes of a functional network.

Now consider your organization or your goal: a systems analysis to sort through the data and bottlenecks in articulating a data question around your intractable problem. If your manager needs to approve all process improvements—that node is now 100%. If you can’t move past that node—its game over.

Try arranging your current method of addressing problems. You can do this in a mind map or simply by focused attention. The systems approach will hopefully yield a way forward.

Perhaps you are a clinician managing patients with substance use disorders. You need to define the pain points. The nodes where solutions seem to go to shrivel up and never see the light of day. Is it the treatment protocols? Where to find them? How to see if they are appropriate for my patient(s)? What about discovery? Where is the data underlying the research?

Imagine this happens over a dozen times each day.

Each patient operates within a network. There are lengths to the closest family member or friend and along the way, edges that align and nodes for connection.

Medicine often distorts pathways forward by creating metrics that confine solutions. For example, I find that the “we do it this way because we have always done it this way” proof in this way of thinking. We are collecting these measures, defining what “normal” looks like and now medicalizing the goal of getting populations to these ranges.

These values now define “normal” for a small representative of the population that adheres to these norms. A certain weight, gender, race, sex, height, level of metabolic function, psychometry…you get the idea.

In modern medicine we are witnessing the spectacle of the scaffold. The story is told by French philosopher Michel Foucault. In his book, Discipline and Punish, he describes an execution in the 17th century that was so horrific and incompetent that the crowd rioted and attacked the executioner. The crowds that gather metaphorically at our institutions are witnessing perceived levels of ineptitude and are becoming outraged and challenging the status quo.

We need networks to be open and accessible to everyone. Everyone. As more of us experience the dysfunction of current treatment for our loved ones with substance abuse disorders—we need to rush the gallows, destroy them, and hold the patients with grace, humility, and compassion. Open the pathways to success by spending time understanding the systems and persistent barriers.

Meanwhile, familiarize yourself with networks. Think about the different models and measures. What is network density? How cohesive is the network?

Here is a network analysis of my Substack. There are about 1000 + members of the network and the betweenness centrality is visible in the large red node.

Applying the network perspective to systems, functions, and even our own literal networks can provide a deeper dive into what drives our processes and where effective interventions are possible.

Python for Geospatial Data Analysis: Theory, Tools, and Practice for Location Intelligence 1st Edition— pre-order on Amazon