Day Time, Night Time, Over Time: Geographic and Temporal Uncertainty When Linking Event and Contextual Data

Abstract

Background: The growth of geolocated data has opened the door to a wealth of new research opportunities in the health fields. One avenue of particular interest is the relationship between the spaces where people spend time and their health outcomes. This research model typically intersects individual data collected on a specific cohort with publicly available socioeconomic or environmental aggregate data. In spatial terms: individuals are represented as points on map at a particular time, and context is represented as polygons containing aggregated or modeled data from sampled observations. Uncertainty abounds in these kinds of complex representations.Methods: We present four sensitivity analysis approaches that interrogate the stability of spatial and temporal relationships between point and polygon data. Positional accuracy assesses the significance of assigning the point to the correct polygon. Neighborhood size investigates how the size of the context assumed to be relevant impacts observed results. Life course considers the impact of variation in contextual effects over time. Time of day recognizes that most people occupy different spaces throughout the day, and that exposure is not simply a function residential location. We use eight years of point data from a longitudinal study of children living in rural Pennsylvania and North Carolina and eight years of air pollution and population data presented at 0.5 mile (0.805 km) grid cells. We first identify the challenges faced for research attempting to match individual outcomes to contextual effects, then present methods for estimating the effect this uncertainty could introduce into an analysis and finally contextualize these measures as part of a larger framework on uncertainty analysis.Results: Spatial and temporal uncertainty is highly variable across the children within our cohort and the population in general. For our test datasets, we find greater uncertainty over the life course than in positional accuracy and neighborhood size. Time of day uncertainty is relatively low for these children.Conclusions: Spatial and temporal uncertainty should be considered for each individual in a study since the magnitude can vary considerably across observations. The underlying assumptions driving the source data play an important role in the level of measured

Quantitative methods I: Reckoning with uncertainty

“…This is not primarily a methodological challenge, but a data challenge. Folch, Fowler and Mikaelian (2021) also consider how uncertainty in context and exposure can be measured, estimating air and water toxicity and child mobility, both over time and from day to day (home versus daycare location). Their analysis explicitly addresses questions around uncertainty: positional accuracy of children, relative to toxicity measures, neighbourhood context and child mobility.…”

Aging, (un)certainty and HIV management in South Africa

“…Although work in this domain is still emerging, renewed interest within the discipline reveals how limits of knowledge become spatially generative, and/or operate as a direct force shaping social life (Senanayake & King, 2021). Drawing on frameworks in the fields of science and technology studies, anthropology, sociology and development, scholars demonstrate how uncertainty mediates health-environment interactions (Cavallo, 2021) shapes subjectivities and social relations (Senanayake, 2020), and becomes transcribed onto changing landscapes (Folch et al, 2021). By attending to the materialization of health-environment interactions, this literature moves beyond causal disease relations of the atomized body, shifting interventions upstream to highlight the emergent effects and processes that shape health-environment dynamics (Brown et al, 2011;Senanayake & King, 2019).…”