As a geographer I’m interested in how we map the world. I know “mapping the world” seems old school, really old school, like an 18th or 19th century problem so some explanation is necessary.  As a researcher I develop better ways to digitally represent social space and statistically reason with those representations. That sounds super academic (and it is), so let me provide some practical examples.

The federal government allocates $400 Billion to communities in the United States each year using a map from the US Census Bureau, this map describes demographic and economic conditions in every state, county, city, and neighborhood in the US.  In theory, this data allows the federal government to target aid to communities in need.  These Census maps show things like the percent of the population in poverty or the number of school age children.  These are not maps of rivers, roads, and mountains – but they’re maps.

What if that poverty map is wrong? Communities in need might not get aid and communities who don’t need help might receive it.  Maps that represent things that are hard to see, like social space, are difficult to create and evaluate.  In my academic work I study these maps, develop software tools to make these maps better and create statistical and Machine Learning techniques to analyze such maps.

Maps describing social space are used widely in the sciences, business, and governance.  I am especially interested in the concept of “neighborhoods”- I’ve developed methods for measuring neighborhoods using individual-level data and have studied the relationship between neighborhoods and well-being.  I served on the Department of Interior Hurricane Sandy Strategic Science Group and in a recent issue of PNAS I developed a new way to measure neighborhood-level social vulnerability to natural hazards.

Prior to joining the faculty at Colorado I was the Associate Director of the S4 Initiative at Brown University.  Before Brown I was at the Columbia University Graduate School of Architecture, Planning and Preservation, first as a masters student and later as an Adj. Assistant Professor and Associate Research Scientist. Once upon a time I was the owner of an antiquarian bookshop in the Flatiron Building in New York City. I’m also a fisherman, a bird-watcher, a really good snowboarder, and a bike geek.  I program in R, Python, and Scala (badly) and whenever possible I work to make my research reproducible by publishing data and code.