  This paper offers an expository overview of the field of spatial
econometrics. It first justifies the necessity of special statistical
procedures for the analysis of spatial data and then proceeds to describe the
fundamentals of these procedures. In particular, this paper covers three
crucial techniques for building models with spatial data. First, we discuss how
to create a spatial weights matrix based on the distances between each data
point in a dataset. Next, we describe the conventional methods to formally
detect spatial autocorrelation, both global and local. Finally, we outline the
chief components of a spatial autoregressive model, noting the circumstances
under which it would be appropriate to incorporate each component into a model.
This paper seeks to offer a concise introduction to spatial econometrics that
will be accessible to interested individuals with a background in statistics or
econometrics.
