  Various applications leverage location data to increase transparency,
efficiency, and safety in intralogistics. There are several properties of
location data, such as the data's degrees of freedom, system latency, update
rate, or accuracy. To select a suitable indoor localization system,
corresponding data requirements must be derived by analyzing the considered
application. To date, the dependencies of the system performance and location
data requirements have not been satisfactorily described in the literature.
Thus, no method exists to adequately derive location data requirements. For
intralogistics, such a method is of particular relevance due to the high-cost
sensitivity and heterogeneity of partially safety-relevant indoor localization
applications. To fill this gap, a method for selecting and quantifying location
data requirements for the application in intralogistics is presented in this
work, creating substantial added value for warehouse managers and system
integrators. The method is based on a spatial model that is built on the
premise that location data is used to determine the presence or absence of an
entity in a multidimensional interest space. The usage of the method is
demonstrated in an exemplary case study for the application of 'Automated
Pallet Booking'.
