TY - JOUR
T1 - Citizen surveillance for environmental monitoring
T2 - combining the efforts of citizen science and crowdsourcing in a quantitative data framework
AU - Welvaert, Marijke
AU - Caley, Peter
PY - 2016/10/28
Y1 - 2016/10/28
N2 - 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.
AB - 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.
KW - Citizen science
KW - Crowdsourcing
KW - Data generation process
KW - Environmental statistics
KW - General surveillance
UR - http://www.scopus.com/inward/record.url?scp=84994056177&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/citizen-surveillance-environmental-monitoring-combining-efforts-citizen-science-crowdsourcing-quanti
U2 - 10.1186/s40064-016-3583-5
DO - 10.1186/s40064-016-3583-5
M3 - Article
SN - 2193-1801
VL - 5
SP - 1
EP - 14
JO - Springer Plus
JF - Springer Plus
IS - 1
M1 - 1890
ER -