Uncertainty in Grid Data: A Theory and Comprehensive Robustness Test

TL;DR

This paper develops a theory on how grid data uncertainty impacts spatial inference and introduces a comprehensive robustness test in R to assess sensitivity to grid size, location, and measurement errors in spatial political research.

Contribution

It presents a novel theory of grid data uncertainty effects and a new robustness test for sensitivity analysis, implemented in R, for spatial political and conflict research.

Findings

Robustness test reveals sensitivity of results to grid size and location.

Aggregation shifts can significantly affect inference outcomes.

The method is applied to a case study to demonstrate its utility.

Abstract

This article makes two novel contributions to spatial political and conflict research using grid data. First, it develops a 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) what is the correct size of grid cells, (2) what is the correct locations on which to draw dividing lines between these grid cells, and (3) a greater effect of measurement errors due to finer grid cells. My test aggregates grid cells into a larger size of choice as the multiple of the original grid cells. It also enables different starting points of grid cell aggregation (e.g., whether to start the aggregation from the corner of the entire map or one grid cell of the original size away from the corner) to shift the diving lines. I apply my test to Tollefsen, Strand, and Buhaug (2012) to substantiate its use.