Citizen surveillance for environmental monitoring: combining the efforts of citizen science and crowdsourcing in a quantitative data framework

Abstract

Citizen science and crowdsourcing have been emerging as methods to collect data for surveillance and/or monitoring activities. They could be gathered under the overarching term citizen surveillance. The discipline, however, still struggles to be widely accepted in the scientific community, mainly because these activities are not embedded in a quantitative framework. This results in an ongoing discussion on how to analyze and make useful inference from these data. When considering the data collection process, we illustrate how citizen surveillance can be classified according to the nature of the underlying observation process measured in two dimensions—the degree of observer reporting intention and the control in observer detection effort. By classifying the observation process in these dimensions we distinguish between crowdsourcing, unstructured citizen science and structured citizen science. This classification helps the determine data processing and statistical treatment of these data for making inference. Using our framework, it is apparent that published studies are overwhelmingly associated with structured citizen science, and there are well developed statistical methods for the resulting data. In contrast, methods for making useful inference from purely crowd-sourced data remain under development, with the challenges of accounting for the unknown observation process considerable. Our quantitative framework for citizen surveillance calls for an integration of citizen science and crowdsourcing and provides a way forward to solve the statistical challenges inherent to citizen-sourced data.

Crowd surveillance: estimating citizen science reporting probabilities for insects of biosecurity concern

“…Twitter), to deliberate through unstructured (spatially and temporally opportunistic) reporting of species via dedicated online portals (e.g. iNaturalist, https ://www.inatu ralis t.org/), to deliberate structured (designed) surveys (Welvaert and Caley 2016). The potential surveillance power of the general public is evident from a New Zealand study, where nearly half of all new exotic species detections over a 3-year period were from members of the general public (Froud et al 2008).…”

“…Detecting environmental biosecurity events from human social media communications in a timely manner faces some particular challenges, some technical (Daume 2016) and others largely arising from uncertainty and bias relating to the observation process (Welvaert and Caley 2016). In comparison with self-reported syndromic human health surveillance, the spatial scale and number of events to be detected is small initially (at the time when detection is most critical), and the direct impact on individuals typically minimal.…”

Making Messy Data Work for Conservation

“…Overall, judgments about the utility of messy data should be made with reference to a specific objective and should consider the full costs of both collection and analysis given that different questions place different requirements on data quality. Users with restricted budgets should be wary of assuming that large volumes of cheap-to-collect, unstructured data will be better than nothing; 50 the signal-to-noise ratio in unstructured data can be low, 51 and not accounting for biases could lead to misleading conclusions. 52 Therefore, the decision about which datasets to use to answer a question should be taken carefully and deliberately ( Figure 2).…”

Improving big citizen science data: Moving beyond haphazard sampling

“…Each citizen science project aimed at collecting broadscale biodiversity data falls along a continuum, from unstructured to structured, based on the objectives, survey design, flexibility, rigorousness, and detail collected about the observation process [10,11]. Projects with clear objectives, clearly planned data analysis, and rigorous protocols, for instance, are classified as structured projects.…”