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.

从智能手机位置数据中获取人类行为是一个多任务丰富过程，在行为研究中很有价值。优化浓缩任务的算法细节塑造了当前的文献进展。然而，缺乏围绕这些进步建立的处理框架使实施浓缩的计划复杂化。这项工作满足了对整体和综合观点的需求，该观点包含智能手机特定的要求和挑战，以帮助研究人员规划实施。我们从系统的文献综述中提出了一个结构框架，以查明丰富位置数据研究的主要挑战和要求。我们根据丰富任务对结果进行分类，并将它们相应地集成到促进任务实施的工作流程中。这些工作流程帮助研究人员更好地简化富集过程的实施，并分析任务内和任务间的错误。此外，研究人员可以将所呈现的发现与提议的机会相结合，以更好地预测其研究的影响。