The last chapter presented several ideas of how to perform relatively simple forms of spatial analysis. Many of these approaches, though insightful, have been superceded by more advanced analytical techniques. This chapter will present a few of these approaches, namely methods of clustering, interpolation, and spatial association. Other concepts will also be addressed, such as spatial autocorrelation and the measures that can be used to find spatial clusters of significantly high (hot spots) or low (cold spots) values. Two additional cluster methods will also be discussed, these being nearest neighbor hierarchical clustering and the spatial filter. Kernel density interpolation will be introduced as the interpolation method for discrete incident locations. A discussion about spatial regression analysis will conclude this chapter. The analyses and examples shown in this chapter will again be based upon linked birth and death certificate data for East Baton Rouge Parish.