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| aboutThis blog post is inspired by a webinar given by Xun Shi of Dartmouth College that advocated for more integration of bottom up modelling into spatial modeling. Xun Shi supported bottom up modeling as a way of more accurately representing the complexities of situations locally. By using intricate personal data, bottom-up the accuracy and detail of local -level modelling can be used to extrapolate greater spatial patterns at a more generalizable level. However, this incredible accuracy requires the use of deeply personal data, which raises ethical questions about the use of bottom-up modelling.
Xun Shi’s research relied heavily on the use of personal location information to map spatial interactions and patterns of epidemics. The research incorporated human mobility data sourced from the government that is heavily restricted, however, a lot of other personal identifying information can be still gleaned from location data, even after it is de-identified. Conversations surrounding the ethics of using location-based personal information and considerations of is becoming increasingly important to consider issues of personal location privacy when working with this kind of information. Kerski 2016 defines location privacy as “concerns the claim of individuals to determine when, how, and to what extent location information about them is communicated to others… or the right to be left alone.” It can be hard to work with large datasets in which each individual participant has consented to and has the information to determine how they want their information to be used.
The landscape of big data is also becoming increasingly complex as research agendas have broadened their scope beyond traditional computational and natural sciences to try to understand human behavior and interaction, and public health (Zook et al 2017). Social scientists are now using big data and advanced computing in their research while computer scientists are incorporating human subjects into their work more. As Zook et al points out, “While the connection between individual datum and actual human beings can appear quite abstract, the scope, scale, and complexity of many forms of big data creates a rich ecosystem in which human participants and their communities are deeply embedded and susceptible to harm.” Therefore, while bottom- up modelling admittedly has incredible value to add to modelling, its use must be curtailed until the academic community has figured out a set of ethical research standards for working with location-based personal data. The harm that can be done by misuse of such deeply personal information is too big to deal with in the aftermath of research and must be addressed before we continue to implement research practices like bottom-up modelling.
References:
Kerski, J. (2016). Location Privacy. The Geographic Information Science & Technology Body of Knowledge (3rd Quarter 2016 Edition), John P. Wilson (ed.). DOI: 10.22224/gistbok/2016.3.2
Zook, M., Barocas, S., boyd, danah, Crawford, K., Keller, E., Gangadharan, S. P., … Pasquale, F. (2017). Ten simple rules for responsible big data research. PLOS Computational Biology, 13(3). https://doi.org/10.1371/journal.pcbi.1005399