Inference and analysis across spatial supports in the big data era: Uncertain point observations and geographic contexts

Robertson, Colin; Feick, Rob

doi:10.1111/tgis.12321

Cited by 26 publications

(13 citation statements)

References 119 publications

(139 reference statements)

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“…Geographic uncertainty is an inescapable phenomenon [9], and there has been much work done to identify and deal with sources and causes of such uncertainty [10][11][12][13]. These problems have largely involved conceptual issues of spatial mismatch between the phenomenon under study and the available data.…”

Section: Geographic Uncertaintymentioning

confidence: 99%

Day time, night time, over time: geographic and temporal uncertainty when linking event and contextual data

2021

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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 uncertainty.

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Section: Geographic Uncertaintymentioning

confidence: 99%

Day time, night time, over time: geographic and temporal uncertainty when linking event and contextual data

2021

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“…Of course, the issue of what is “context” remains largely unanswered in geographical analysis (Kwan 2012; Robertson and Feick 2018).…”

Section: Reproducibility Replicability and Uncertainties In Geograpmentioning

confidence: 99%

Reproducibility and Replicability in Geographical Analysis

Kedron

Frazier

Trgovac

et al. 2019

Geographical Analysis

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The scientific method is predicated on the assumption that research designs and results can be reproduced and replicated. However, recent findings in some disciplines suggest that many studies fail to reach this standard, moving issues surrounding reproducibility and replicability forward into the research agenda of those fields. While the topic has yet to become a point of controversy in geography, the intricacies of geographic phenomena and spatial data analysis make the field vulnerable to criticism. This commentary discusses how uncertainties related to the conception, measurement, analysis, and communication of geographic analyses contribute to difficulties in the reproduction and replication of geographic research. Investigating how these uncertainties collectively impact the reproducibility and replicability of spatial data analyses should be a critical focus of future Geographical Analysis research. A call to action for geographers to improve the reproducibility and replicability of their work and specific recommendations on how Geographical Analysis might facilitate this process conclude the commentary.

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“…Some scholars have raised concerns that the large amount of data analyzed by governments is often non-representative of the entire population (Ash et al, 2018;Dalton et al, 2016;Shearmur, 2015). This is because data are often generated through a non-random sampling or based on a relatively small number of people within the population (Robertson and Feick, 2018). More generally, policymaking based on such data may incorporate only limited kinds of observations, while ignoring the wider consequences of politics, culture, and governance that shape the lives of the citizenry (Kitchin, 2014a).…”

Section: Introductionmentioning

confidence: 99%

Big Data and government: Evidence of the role of Big Data for smart cities

Hong

Kim

et al. 2019

Big Data & Society

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Scholars are becoming increasingly interested in whether and how government use of Big Data will affect public policy outcomes. Despite such growing scholarly interests, however, little evidence exists on the role Big Data can play in improving government service. We undertake one of the first quantitative studies revealing the potential utility and limitations of ''Big Data-based policymaking'' by exploring its recent use by the Seoul Metropolitan Government. In 2013, the government introduced the ''Owl Bus''-a late-night bus system-the routes of which were selected based on government analyses of Big Data relating to citizens' late-night taxi and mobile phone use. The findings suggest that the average number of passengers utilizing the Owl Bus routes was significantly greater than that on other daytime bus routes with comparable characteristics. That said, we also present the potential limitations of evidence-based policymaking in general, especially when politics and equity considerations are factored in.

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Inference and analysis across spatial supports in the big data era: Uncertain point observations and geographic contexts

Cited by 26 publications

References 119 publications

Day time, night time, over time: geographic and temporal uncertainty when linking event and contextual data

Day time, night time, over time: geographic and temporal uncertainty when linking event and contextual data

Reproducibility and Replicability in Geographical Analysis

Big Data and government: Evidence of the role of Big Data for smart cities

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