After my first, rather disastrous, year of graduate school in Boulder, I almost transferred to Geography. Or at least, I thought a lot about it. While the math in Economics was kind of kicking my butt, everyone working with graphs and maps seemed so blissfully happy. Ultimately, I stuck it out in Economics, and am extremely glad that I did, but I haven’t lost my love of maps and have always been curious about spatial research.
Next week, I’ll be doing a three-day workshop at the University of Colorado‘s Institute of Behavioral Science. Many of my economics professors were associated with IBS, but none really did spatial analysis, so I was left to find out some of it on my own. A few years ago, I helped design a survey on handwashing and other hygiene behaviors for a group building latrines and protecting water sources in Nepal. The data are fascinating and though we started analyzing it, everyone had limited use of one of the two tools necessary to do spatial regression. I had the Stata skills and my coauthors had limited GIS skills, but combining them wasn’t going to happen. This short course is hopefully the next step in getting those papers off the ground and into journals, but also more importantly, back to the community where we did the research. Though we’ve presented some findings to them, I’m sure there are many more insights to be had with these data.
With that, I’ll be reading a lot of spatial analysis papers over the next week. The syllabus has hundreds of pages of reading, much of which I’ve printed out and am planning for my long trip back to Colorado next week, but I’m willing to share the “lite” version with you all.
For definitional purposes, spatial analysis is “the formal quantitative study of phenomena that manifest themselves in space,” according to Luc Anselin. More informatively, I think, spatial analysis allows us to “interpret what ‘near’ and ‘distant’ mean in a particular context” and showcase whether and how proximity or location have an effect on an outcome we’re interested in.
Anselin divides spatial analysis into two categories–data-driven analysis and model-driven analysis, and highlights the challenges of each, which I imagine will get plenty of air time next week and are a little bit daunting to a student and devotee of econometrics:
Indeed, the characteristics of spatial data (dependence and heterogeneity) often void the attractive properties of standard statistical techniques. Since most EDA techniques are based on an assumption of independence, they cannot be implemented uncritically for spatial data…As a result, many results from the analysis of time series data will not apply to spatial data.
Model-driven analysis seems much more up my alley and suited to regression, but the main problem, which I encountered in my own research, “is how to formalize the role of ‘space.'”
Just like this basic the ideas and tools used in spatial regression seem fairly consistent with my view of econometrics in general. There are tradeoffs to employing different models and assumptions, and measurement error is alive and well. Notably, although this could be out of date by now: “Spatial effects in models with limited dependent variables, censored and truncated distributions, or in models that have count data have been largely ignored…multivariate dependent distributions other than the normal are highly complex.” More to come, I’m sure. My colleague has already told me I have to teach him in the Fall, and I’m hoping to be able to incorporate some of this into my Methods class, so get ready for some spatial econometrics here.
As an aside, if you happen to be in Colorado, check out these cool solar events that are happening, including a world-record-braeking attempt at the most people in one place to watch a solar eclipse together at CU’s Folsom Stadium. Or, well, you could just go look at it where you are, too.
Referenced: Anselin, Luc. 1989. “What Is Special about Spatial Data? Alternative Perspectives on Spatial Data Analysis.” Conference Proceedings, Spatial Statistics: Past, Present, and Future. Institute of Mathematical Geography, Syracuse University.