Location data quality in context: directions and challenges

TL;DR

This paper discusses the importance and challenges of assessing indoor location data quality, emphasizing the dependency on technology and environment in real-time positioning applications.

Contribution

It provides an analysis of indoor positioning data quality issues and highlights the complexities in defining and measuring quality metrics.

Findings

Indoor positioning data quality varies with technology and environment

Standard quality metrics are difficult to define due to contextual dependencies

Assessing data quality is crucial for reliable location-based services

Abstract

In the last decade, following the emergence of the mobile applications domain, the significance of location information has changed radically. Nowadays, location data not only is a key component of geospatial databases, but also a critical resource for a broad spectrum of applications, including Location-based Services and IOT solutions. This change of perspective has been made possible by the availability of localization technologies providing accurate and reliable location information on moving entities in real time. We refer to real-time location as positioning data. Positioning data has unique characteristics, such as the strong dependency from both the localization technology and the environmental context, which make the specification of standard quality metrics and quality assessment procedures a complex task. In this paper, we elaborate on such an aspect, focusing in particular on indoor positioning data.