Publisher Summary This chapter presents an alternative approach to the logical modeling of spatial objects based on a database model called a constraint data model. The constraint data model is based on the belief that the main limitation of relational databases with respect to spatial data is their inability to represent and manipulate infinite relations. Constraint databases mainly aim at extending the relational paradigm to handle these infinite relations. This results in a data model that encompasses classical and spatial data and that allows the programmer to express spatial queries with pure relational languages. Another strong feature of this model is its ability to represent and manipulate data in an arbitrary dimension within a uniform framework. In particular, there is a fundamental distinction between the logical level, which proposes simple structures and query languages to the end user, and the physical level, where the actual storage and query processing take place. A linear constraint data model, on the other hand, is based on a constraint, which is a polynomial on variables x and y. The keyword “linear” suggests that the polynomial is linear in x and y. More precisely, this approach focuses on the geometric objects in the d-dimensional space Rd, which can be represented with constraints using only the + operation and the ≤ predicate. The chapter discusses these forms of data representation as well as the associated query languages with which the process of modeling of geographic entities with these constraint-based approaches is developed. The chapter also provides examples pertaining to spatio-temporal data and field-based data, giving simple structured query language (SQL) queries on these objects.