ABSTRACT
This chapter is dedicated to the analysis of areal patterns using both global and local spatial statistics. Unlike Chapter 6, which focused on point patterns, in this chapter, we will explore spatial datasets that are reported or received at aggregated spatial levels < specifically areal, polygon, or group-level data. Such datasets are becoming increasingly common due to the growing need for confidentiality and privacy of data records. Many public and private agencies as well as data centers are now obliged to present data at aggregated unit levels. Typically, the individual-level spatial data information is aggregated at spatial scales such as census tracts, zip codes, health service areas, community districts, counties, or higher levels. To evaluate the areal patterns, it is incumbent on a data scientist to recognize the unique attributes and challenges that are inherent in the use of such data, and to choose the appropriate techniques for spatial analysis. The methods presented in this chapter will be helpful in the exploration and analysis of these spatial datasets. Each technique will be discussed
alongside a case study to illustrate the computational steps, following which the interpretation of the test results and the methodological limitations, if any, will be presented.
