Hi there! My name is Brooke and I am a senior Geography and Environmental Studies major at Middlebury College. I am passionate about using GIS, remote sensing techniques, and cartographic design to expand on studies of environmental justice, recreational access, climate change adaptation, and landcover change. On this page you will find my work from various courses, independent study, and research positions.
As big data sources become increasingly available in the public domain for analysis and visualization, it is necessary to consider the ethics in expanding this type of work. In a recent webinar provided by the Geospatial Fellows Webinar Series , researcher and professor Xun Shi presented his strategy for transitioning to a bottom up approach for epidemic modeling, in comparison to a traditional top down approach. For a summary of the webinar and more general information, you can visit the Github pages of my peers: Alitzel and Arielle.
I believe that the bottom up approach that Xun Shi introduces should be further implemented and expanded in the world of geospatial research, and the study of epidemics and other human-environmental interactions. When data at the individual level is available, and follows privacy and ethnic guidelines, a bottom up approach is a strong way to
(1)Prevent simplification of results and errors of ecological >fallacy
(2) Depict the reality that communities to have variation and >complexity in the ways people interact with space
(3) Allow for the development of stronger epidemic modeling and >concrete and detailed analysis, which can be implemented in >new, community centered, local policy.
(Xun Shi, 2021)
These points, however, are not to say that the top down approach should be phased out or overridden by the bottom up approach, as Xun Shi argues in favor of research that integrates these two methods. When used together, they can be mutually supportive, providing stronger end results in the areas of analysis.
Those who are against the expansion of bottom up, personal scale mobility research are likely to fear breaches in privacy, and the use of data that could encroach on personal security. While those concerns are valid, research like the studies being conducted by Xun Shi use transparency and provide methodology that helps to limit areas of uncertainty in the study. Clear documentation on Github, metadata sections, and webinars that clearly documented research intentions (such as this one) help to build public trust and illustrate the ways that big data can be used to improve communities. With the expansion of personal level data comes the need to continuously consider and acknowledge the ways that data are people, data can have an extreme impact on people’s lives, and there are various steps in the research process that must be taken to ensure that GIS work is both ethical and reproducible. Important steps to consider are outlines in Zook et al “Ten Simple Rules for Responsible Big Data Research” and Professional and Practical Ethics of GIS&T. With the continuation of studying ethics and responsibility in big data research, the use of big data can grow in positive ways, such as this study.
Works Cited:
DiBiase, David, Francis Harvey, Christopher Goranson and Dawn Wright (2012). The GIS Professional Ethics Project: Practical Ethics for GIS Professionals. In Unwin, David, Ken Foote, Nick Tate and David DiBiase, Eds. Teaching Geographic Information Science and Technology in Higher Education. London: Wiley and Sons.
Zook M, Barocas S, boyd d, Crawford K, Keller E, Gangadharan SP, et al. (2017) Ten simple rules for responsible big data research. PLoS Comput Biol 13(3): e1005399. https://doi.org/10.1371/journal.pcbi.1005399