Operationalization



Operationalization is the process of defining variables, expectations, or criteria that are vague into operational factors. An operational variable is one that can be measured, computed, and analyzed, while vague concepts describe an entity, phenomenon or process in ways that could have alternative meetings and thus cannot be reliably identified or applied by different readers or scholars. Operationalization is thus the process of precisely specifying the extension of a concept; describing what is and what is not part of the concept.

Operationalizing GIS
Geographic problems and tasks are often defined in general terms that make sense to humans, but are not operational for tools such as GIS and Statistics. We operationalize these terms by strictly defining variables into measurable factors.

For example, in the task "find the best location for a new store," the expectation of "best" is not defined, and is therefore insufficient to develop a GIS model to solve the task. One would need to operationalize this expectation by developing a precise way to measure whether one candidate location is "better" than another (e.g., it has more customers who are likely to travel to that location rather than competitor stores). Also, the various input variables and criteria would need to be operationalized. For example, the criterion "near a highway" would require a precise definition for what constitutes both "near" and "highway." This is usually done by consulting with the client, performing empirical research, or studying published research.

In another example, one might want to measure the general health of a certain region, however there is no single measure of "general health." General health could be operationalized through indicators such a hours of exercise per week, body mass index, tobacco smoking, immunization rates, etc. The variables which are being defined could be quantitative or qualitative. Exercise per week could be defined quantitatively by hours while immunization rates could be defined qualitatively as complete, partially complete and incomplete.