Replicable and extensible spatial data acquisition.

Having the ability to reproduce empirical results is foundational to the scientific process. Increasingly, there is an emphasis within the research community to create workflows which are not only reproducible but also replicable in that analytical approaches can be applied to new data. However, a disproportionate number of technical solutions designed to foster reproducibility and replicability have focused on data analysis, particularly within geography. This paper argues for greater attention on a phase of the research process which precedes analysis and is often taken for granted – that of data acquisition. Through code examples using the R Project for Statistical Computing, this paper demonstrates a path toward replicable spatial data acquisition for both secondary and primary data acquisition, with a focus on crowdsourced geographic information. The modular approach demonstrated is flexible in that it allows for straightforward replication but also enables extension to new spatial questions. Such work is valuable because it conserves labor, promotes research provenance, and allows for more robust analyses. [ABSTRACT FROM AUTHOR]