Uncertainty in grid data: a theory and comprehensive robustness test

Akisato Suzuki ()
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Akisato Suzuki: University College Dublin

Quality & Quantity: International Journal of Methodology, 2023, vol. 57, issue 5, No 26, 4477-4491

Abstract: Abstract This methodological note makes two novel contributions to spatial political and conflict research using grid data. First, it develops a methodological theory of how uncertainty specific to grid data affects inference. Second, it introduces a comprehensive robustness test on sensitivity to this uncertainty, implemented in R. The uncertainty stems from (1) establishing the correct size of grid cells, (2) deciding the correct locations where the dividing lines of grid data are drawn, and (3) a greater effect of measurement errors due to finer grid cells. The proposed test diversifies grid cell sizes, by aggregating original grid cells into a multiple of these grid cells. The test also varies the locations of the diving lines, by using different starting points of grid cell aggregation (e.g., starting the aggregation from the corner of the entire map or one grid cell of the original size away from the corner). I apply the test to Theisen et al. (Int. Secur. 36(3):79–106, 2011), who utilize the PRIO-GRID data (Tollefsen et al., J. Peace Res. 49(2):363–374, 2012), to substantiate its use.

Keywords: Geocoding; Geo-referenced; Spatial data; Grid; Uncertainty; R package (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11135-022-01555-x

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