ABSTRACT
This chapter demonstrates how an area classification can be built from real data with continuous data types. It uses Nigeria’s 774 local government areas as the case study and explains the series of decisions made during the creation of a classification system. The chapter provides a general description of the administrative and political geography of the country. The rationale for developing an area classification is also examined. This is followed by a discussion on data preprocessing. A partitional unsupervised machine learning algorithm is deployed here generating six distinct groups. The geographic patterns of these groups are explored and their characteristics are also described.
