Why Invest in Digital Anthropology?
By: One Concern
Tags: Careers, Startup, Data, Career Advice, Data Science

Why Invest in Digital Anthropology?

A Q&A with Trigg Hutchinson, Data Acquisition Lead at One Concern

One Concern’s best-in-class hazard models incorporate static and dynamic data from an array of different sources: public data sets from the USGS, World Bank, and U.S. Census Bureau; live updates on social media; remote sensing data; and private repositories. Together, these paint a near-complete picture of the built, natural, and human environment.

Occasionally, we can’t easily get the data required for high-resolution hazard maps. Some governments may not have collected enough historical building information. Other times, there isn’t enough time to locate the best international partner before an urgent deployment. This is where digital anthropology fills in the gaps.

Trigg Hutchinson leads our Data Acquisition efforts. Over the next few weeks, we’ll be highlighting his work by publishing a series of posts he’s written from the field. Today, Trigg is sharing the ins and outs of his unique line of work.

What do you do for digital anthropology at One Concern?

I manage all data acquisition for One Concern. Before and after natural disasters, it’s critical for us to ingest a ton of different data. That’s for two purposes. First, we have to make sure that we have the most up-to-date, accurate, and comprehensive data to model a city. Second, we also need robust training data to ensure our AI-powered models are accurate.

Prior to a natural disaster, we might be deploying our product to a new city. I’ll lead the effort to acquire or create new data sets that help us better understand the built, natural, and population environment of a city. Then after a disaster happens, I’ll lead the effort to organize building tagging, which is the identification of damage data.

Our product incorporates a ton of data sources to feed the machine learning model. How does your work on the ground fit into the bigger picture there?

One Concern has categorized an enormous amount of different data types. When we start to look at modeling a new city, we’ll first inventory what’s actually out there. So we’ll go through these dozens and dozens of categories of data explaining a single building, or a single piece of critical infrastructure. Then, we’ll identify and inventory what we have and don’t have. The gaps in those catalogs are where we start exploring for new data creation.

Can you tell me about an especially challenging data acquisition project?

The most complicated data acquisition process that we’ve worked on so far was Dhaka. Bangladesh is a really challenging country: in terms of density, traffic, pollution, and the city being just enormous. We were charged with collecting building damage data for a representative sample of the city, but there were small things there wouldn’t be issues anywhere else. For instance, taking a photo of the building was much harder. The roads are narrow, and traffic is crazy. It was weirdly challenging to find a camera angle where our computer vision models can see and identify features of the building.

For our data acquisition team in Dhaka, we got everybody smartphones. But the app we use for tagging had issues with GPS access because coverage is really bad. So we had this smartphone-enabled methodology, but paired it with pen and paper. It’s funny because you think this is an AI company, right? We’re applying these really high-tech solutions to complex physical problems. And as amazing as it is to have all the power of artificial intelligence at our fingertips, sometimes these really simple solutions can also help answer these questions.

What experiences and background did you have before this role?

I was military before, and I’d worked in a data collection capacity for a couple of years. Then, I got a business degree at UCLA where I was focused on streamlining operations and supply chain for natural disaster response. Understanding the impact of hazards and optimizing relief has always been something that I’ve been really passionate about.

What I really like about working at One Concern is that it allows me to put those interests front and center. I can take a lot of my previous skills and experiences and apply them to solving a problem that I’m really passionate about.

How did your experience in the military inform your approach to your current work?

I get really weird about meetings starting late. That would be the first thing — I’m insanely punctual to a fault [laughs].

But a lot of the military — especially the expeditionary side — is about working in complex, ambiguous environments. You learn very quickly that everything is about preparation and planning. The more you know about a place and the better you plan, the better off you’ll be when you actually get on the ground.

Concurrently, I think the military infuses a sense of flexibility. As great as your plan may be, things may be totally different in practice. A lot of our data acquisition involves techniques that have never been tried before, and we’re rolling out methodologies or equipment that we’re building ourselves. So sometimes things go really, really wrong — it’s my job to just figure it out.

What does ‘resilience’ mean to you?

That’s a really good question. That’s a really unfair question to ask. The easy answer is robustness and rapidity.

Okay, hard answer?

The hard answer is keeping a family from having to sleep in a tent for six months at a time.

When we use words like robustness and rapidity, we’re looking at it from a very academic perspective. It’s detached. But when you go somewhere like Lombok, you see people who own a countable number of items, they’ve lost all of it, and they’re sleeping on a blue tarp. That gives you perspective on the importance of growing resilience in a way that looking at things on a balance sheet or making a plan in a government office never will.

So, resilience to me is real-world application of the theory and concepts that we discuss in this office. I think there’s a face to it.