Theoretical Statistics and Mathematics Unit, ISI 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.