How does a city know when to re-pave a road, re-build a waterway, or fix a train track? Monitoring infrastructure systems is difficult due to a lack of high-quality data.
Can groups of citizens fill in the gaps by collecting high-quality, local infrastructure data?
A recent paper in Risk Analysis explores how that might work; and asks some interesting questions about ensuring quality and accuracy in data collected by non-scientists.
From the paper:
Generally, there is worry among professional scientists, engineers, and decision makers about using citizen-science data. While this worry is understandable because professionals must defend the quality of the data they use, the professionals themselves can be inconsistent and require reliability assessment. Regardless of who collects the data, this issue leads to the same question: What constitutes high-quality infrastructure data? The answer to this question is often reduced to accuracy or, in other words, the closeness of the data value to the true value. The emphasis on accuracy when evaluating data quality is common in both the citizen-science literature and the infrastructure management and engineering literature. However, the quality of these data should not be reduced to accuracy alone. Instead, the data should be evaluated considering multiple characteristics that a data set should possess to be trusted to serve the intended purpose (Batini & Scannapieca,2006; Lee, Strong, Kahn, & Wang, 2002; Wang, Ziad,& Lee, 2001). In the case of infrastructure monitoring, the purpose is to inform the development of infrastructure improvement programs. In this context,we argue that the data should be (1) accurate, (2) complete, (3) current and timely, and (4) equitable.The stringency of these characteristics can be used to measure the overall quality of the data (Dasu &Johnson, 2003).