From GPS to semantic data: how and why\u2014a framework for enriching smartphone trajectories

Ahmed Ibrahim,Sarah Clinch,Simon Harper,Heng Zhang

doi:10.1007/s00607-021-00993-z

Abstract

Deriving human behaviour from smartphone location data is a multitask enrichment process that can be of value in behavioural studies. Optimising the algorithmic details of the enrichment tasks has shaped the current advances in the literature. However, the lack of a processing framework built around those advances complicates the planning for implementing the enrichment. This work fulfils the need for a holistic and integrative view that comprehends smartphone-specific requirements and challenges to help researchers plan the implementation. We propose a structural framework from a systematic literature review conducted to pinpoint the main challenges and requirements of research on enriching location data. We classify findings based on the enrichment task and integrate them accordingly into workflows that facilitate the task’s implementation. These workflows help researchers better streamline their implementations of the enrichment process and analyse errors within and across tasks. Moreover, researchers can integrate the presented findings with the proposed opportunities to better predict the impact of their research.

Highlights

Semantic enrichment of location data is the process of transforming raw data collected from mobility tracking devices into behaviours [1]
This paper focuses on semantic enrichment of Geographical Positioning System (GPS) data collected using smartphones since the majority of the population near-continuously carries a smartphone featuring a GPS sensor [7]
When we rely on the corrected data, the algorithm’s performance can better reflect its ability to recognise behaviours motivated by interests. This is a result of avoiding errors that propagate from segmentation and annotation layers. 6.5 SWOT analysis To better benefit from this review findings in helping future research on semantic enrichment of GPS trajectories; we summarise and organise limitations and opportunities found in the selected papers into a SWOT analysis framework

Summary

Introduction

Semantic enrichment of location data is the process of transforming raw data collected from mobility tracking devices into behaviours [1] These behaviours may express human activities, or they may be descriptions of non-human actions (such as animal behaviours or ship traffic and air navigation) [2]. If a person moves from one place to the other, the captured raw data is enriched to answer human-centred questions such as: how long does the person stay, does the stay duration significant enough to be considered, what defines significance and how to decode that from data These types of analysis go beyond the mere labelling of GPS data to build a semantic enrichment process that is human-centric

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Discussion

Conclusion