Seminar at SMU Delhi

January 16, 2012 (Monday) , 3:30 PM at Webinar (PCM lecture)
Speaker: Alan Gelfand, Duke University
Title: Space is the Place: Why Spatial Thinking Matters for Environmental Problems
Abstract of Talk
Spatial methods have become an increasingly used approach for analyzing data in many fields. In particular, it is now routine to collect data layers where there is some geographic referencing. This information should be used in order to enhance inference. From a statistical perspective, we think in terms of formal inference, utilizing probabilistic or stochastic modeling; we think beyond purely descriptive summaries. In this sense, we exceed the capabilities of Geographic Information Systems (GIS) software to investigate complex processes over space and time. A particularly rich context for such investigation is environmental processes. Examples include analysis of weather/climate data, analysis of environmental exposure data, analysis of locations of disease occurrence, and analysis of distributions of species over a region. In this non-technical talk, I will describe the types of spatial (and, perhaps, spatio-temporal) data that we collect. I will discuss what we expect to see with regard to these types of data, i.e., what we mean by \lq\lq spatial pattern.\rq\rq \ I will raise a variety of issues that arise in modeling such data - explanation of local behavior through spatially referenced explanatory variables, explanation of uncertainty through structured dependence. I will illustrate, with a variety of datasets involving the foregoing processes, hopefully to illuminate that statistical thinking does matter when we have inferential objectives such as explanation, interpolation, and prediction.