Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility

ABSTRACT

Identifying meaningful locations, such as home or work, from human mobility data has become an increasingly common prerequisite for geographic research. Although location-based services (LBS) and other mobile technology have rapidly grown in recent years, it can be challenging to infer meaningful places from such data, which – compared to conventional datasets – can be devoid of context. Existing approaches are often developed ad-hoc and can lack transparency and reproducibility. To address this, we introduce an R package for inferring home locations from LBS data. The package implements pre-existing algorithms and provides building blocks to make writing algorithmic ‘recipes’ more convenient. We evaluate this approach by analyzing a de-identified LBS dataset from Singapore that aims to balance ethics and privacy with the research goal of identifying meaningful locations. We show that ensemble approaches, combining multiple algorithms, can be especially valuable in this regard as the resulting patterns of inferred home locations closely correlate with the distribution of residential population. We hope this package, and others like it, will contribute to an increase in use and sharing of comparable algorithms, research code and data. This will increase transparency and reproducibility in mobility analyses and further the ongoing discourse around ethical big data research.

KEYWORDS:

• Identifying home locations
• location-based services (LBS)
• Twitter
• R package
• comparison and reproducibility

Acknowledgments

This research, led together with the Housing and Development Board, is supported by the Singapore Ministry of National Development and the National Research Foundation, Prime Minister’s Office under the Land and Liveability National Innovation Challenge (L2 NIC) Research Programme (L2 NIC Award No. L2NICTDF1-2017-4). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the Housing and Development Board, Singapore Ministry of National Development and National Research Foundation, Prime Minister’s Office, Singapore.

Data and code availability statement

Along with this paper, we publish three specific data & code artifacts:

1. The homelocator R package itself, with code hosted on: https://github.com/spatialnetworkslab/homelocator. The package contains a small test sample dataset so readers can quickly try out the package. A documentation site, including a tutorial and installation instructions, is available at: https://homelocator-website.netlify.app/.

2. The deidentified LBS dataset, which can be downloaded from Figshare: https://doi.org/10.6084/m9.figshare.13394102.

3. All code & data to reproduce the analysis and figures within this paper. This is available as a series of RMarkdown files hosted on: https://github.com/spatialnetworkslab/identifying-meaningful-locations. It includes a Binder link so readers can explore the analysis directly from their browser.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. The Chicago Area Transportation Study, which is one of the first examples of a large-scale travel survey, cost $650,000 in 1956 for just the survey fieldwork (Black 1990).

2. Open datasets, such as WorldPop, do exist on a finer spatial scale than planning zones, but the estimation process underlying these datasets is not reliable in the case of Singapore, especially in industrial areas where such datasets (erroneously) show a high number of estimated residents.

3. Singapore Residents by Planning Area and Type of Dwelling, June 2015, a dataset published publicly on Data.gov.sg

Additional information

Funding

This work was supported by the Singapore Ministry of National Development and the National Research Foudation, Prime Minister's Officer under the Land and Liveability National Innovation Challenge (L2 NIC) Research Programme (L2 NIC Award NO. L2NICTDF1-2017-4).

Notes on contributors

Qingqing Chen

Qingqing Chen is a Ph.D. student in the Department of Geography at the University at Buffalo - SUNY, USA. Her research focuses on critically understanding urban space by leveraging big data, combined with data science and machine techniques. She is interested in urban data science, geocomputation, non-visual sensory measuring and monitoring, social media and big data. 

Ate Poorthuis

Ate Poorthuis is an Assistant Professor of Big Data and Human-Environment Systems in the Department of Earth and Environmental Sciences at KU Leuven, Belgium. His research focuses on exploring the possibilities and limitations of big data, through quantitative analysis and visualization, to better understand how our cities work, where he is particularly interested in applying these academic insights within urban planning and policy.