Geographic Uncertainty

Though I do not have a lot of experience with geographic research beyond this class and GEOG 0120, I have encountered the uncertainty relating to choosing regions and the idea of ‘fuzzy logic’ (especially in social research). Especially in the Malcomb et al. (2014) study, the switch from 28 districts to over 250 traditional authorities showcased some uncertainty when we were making the workflow last week. Pertaining to reproducibility, the experience of even just recreating workflows for studies emphasizes how often studies do not have clear or certain methods when presenting data. Malcomb et al. (2014) also showcases some uncertainty/subjectivity within the ‘normalization’ process, as they used a vague combination of ‘observation, field work, interviews, and literature reviews.’ The vagueness and somewhat fuzzy logic, especially without much further details into this process, showcased some uncertainty when recreating the workflow.

As mentioned in the past week discussions, increasing transparency can improve how much uncertainty there is within a study as well as the reproducibility of that study. Researchers publishing studies, especially studies that tout their reproducibility, should make sure to at least address and acknowledge the uncertainty within their studies. Not only does this benefit future researchers that may learn from one’s study, but also makes one reflect on the data and representations they are using.

As mentioned in the Tullis and Kar article (2021), some ways geographers can improve provenance, location privacy, and data quality include methods such as including a range of stakeholders in the organization of data as well as addressing when bias is present. Acknowledgement of uncertainty should be a crucial step in the process of improving reproducibility as well as clarity of a study.

References

Longley, P. A., M. F. Goodchild, D. J. Maguire, and D. W. Rhind. 2008. Geographical Information Systems and Science 2nd ed. Chichester: Wiley.

Malcomb, D. W., E. A. Weaver, and A. R. Krakowka. 2014. Vulnerability modeling for sub-Saharan Africa: An operationalized approach in Malawi. Applied Geography 48:17–30. DOI:10.1016/j.apgeog.2014.01.004

Tullis, J. A., and B. Kar. 2021. Where Is the Provenance? Ethical Replicability and Reproducibility in GIScience and Its Critical Applications. Annals of the American Association of Geographers 111 (5):1318–1328. DOI:10.1080/24694452.2020.1806029