As we translate real world phenomena into data structures that we can store in a computer, we must determine the most appropriate spatial representation and how it relates to the characteristics of such a phenomenon. All spatial representations are derivatives of graph theory and should therefore be described in such terms. This then helps to understand the principles of low-level GIS operations. A constraint-driven approach allows the reader to evaluate implementations of the geo-relational principle in terms of the hierarchical level of mathematical space adopted.

As we translate real world phenomena into data structures that we can store in a computer, we must determine the most appropriate spatial representation and how it relates to the characteristics of such a phenomenon. All spatial representations are derivatives of graph theory and should therefore be described in such terms. This then helps to understand the principles of low-level GIS operations. A constraint-driven approach allows the reader to evaluate implementations of the geo-relational principle in terms of the hierarchical level of mathematical space adopted.

As we translate real world phenomena into data structures that we can store in a computer, we must determine the most appropriate spatial representation and how it relates to the characteristics of such a phenomenon. All spatial representations are derivatives of graph theory and should therefore be described in such terms. This then helps to understand the principles of low-level GIS operations. A constraint-driven approach allows the reader to evaluate implementations of the geo-relational principle in terms of the hierarchical level of mathematical space adopted.