Biased Assumptions and Oversimplifications in Evaluations of Citizen Science Data Quality

Specht, Hannah; Lewandowski, Eva

doi:10.1002/bes2.1388

Cited by 28 publications

(23 citation statements)

References 19 publications

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“…Despite data quality concerns (Kosmala et al 2016;Aceves-Bueno et al 2017;Specht and Lewandowski 2018), citizen science has great potential to address pressing matters in biodiversity monitoring, conservation and research (Theobald et al 2015;Chandler et al 2017;Pocock et al 2018). Open access to citizen science data would maximise this potential through increased reuse and the application of new 'big data' techniques and crossdisciplinary studies (Culina et al 2018b;Farley et al 2018;Ma et al 2018;Tulloch et al 2018), as well as yielding benefits of increased transparency and public trust in science (Soranno et al 2015).…”

Section: Citizen Scientist Support For Open Accessmentioning

confidence: 99%

Opinions of citizen scientists on open access to UK butterfly and moth occurrence data

et al. 2019

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Citizen science plays an increasingly important role in biodiversity research and conservation, enabling large volumes of data to be gathered across extensive spatial scales in a cost-effective manner. Open access increases the utility of such data, informing land-use decisions that may affect species persistence, enhancing transparency and encouraging proliferation of research applications. However, open access provision of recent, fine-scale spatial information on the locations of species may also prompt legitimate concerns among contributors regarding possible unintended negative conservation impacts, violations of privacy and commercial exploitation of volunteer-gathered data. Here we canvas the attitudes towards open access of contributors (104 regional coordinators and 510 recorders) of species occurrence records to two of the largest citizen science biodiversity recording schemes, the UK's Butterflies for the New Millennium project and National Moth Recording Scheme. We find that while the majority of participants expressed support for open access in principle, most were more cautious in practice, preferring to limit the spatial resolution of records, particularly of threatened species, and restrict commercial reuse of data. In addition, citizen scientists' opinions differed between UK countries, taxonomic groups and the level of involvement volunteers had in the schemes. In order to maintain successful and democratic citizen science schemes, organisers, funders and data users must understand and respect participants' expectations and aspirations regarding open data while seeking to optimise data use for scientific and societal benefits.

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Section: Citizen Scientist Support For Open Accessmentioning

confidence: 99%

Opinions of citizen scientists on open access to UK butterfly and moth occurrence data

et al. 2019

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“…Through this canvassing, they can also update eligibility information within the tool to provide better program estimates and validate the modeling. Issues with residents sharing personal information and validating any responses are still expected, but are not different than those encountered with professional data (Specht and Lewandowski, 2018).…”

Section: Community Eligibility and Methods Validation Toolmentioning

confidence: 99%

Coupling data science with community crowdsourcing for urban renewal policy analysis: An evaluation of Atlanta’s Anti-Displacement Tax Fund

Auerbach

Blackburn

Barton

et al. 2018

Environment and Planning B: Urban Analytics and City Science

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Cities across the United States are undergoing great transformation and urban growth. Data and data analysis has become an essential element of urban planning as cities use data to plan land use and development. One great challenge is to use the tools of data science to promote equity along with growth. The city of Atlanta is an example site of large-scale urban renewal that aims to engage in development without displacement. On the Westside of downtown Atlanta, the construction of the new Mercedes-Benz Stadium and the conversion of an underutilized rail-line into a multi-use trail may result in increased property values. In response to community residents' concerns and a commitment to development without displacement, the city and philanthropic partners announced an Anti-Displacement Tax Fund to subsidize future property tax increases of owner occupants for the next twenty years. To achieve greater transparency, accountability, and impact, residents expressed a desire for a tool that would help them determine eligibility and quantify this commitment. In support of this goal, we use machine learning techniques to analyze historical tax assessment and predict future tax assessments. We then apply eligibility estimates to our predictions to estimate the total cost for the first seven years of the program. These forecasts are also incorporated into an interactive tool for community residents to determine their eligibility for the fund and the expected increase in their home value over the next seven years.

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“…The consequences of this trade-off are a topic of active research (Aceves-Bueno et al 2017;Bayraktarov et al 2018;Kelling et al 2018;Specht & Lewandowski 2018;Boersch-Supan, Trask & Baillie 2019;Johnston et al 2020;Robinson et al 2020), and there is a growing set of modelling approaches to address the challenges of unstructured datasets using auxiliary structured biodiversity data and/or observation models that account for preferential sampling, usually at the cost of increased model complexity and computational demands (van Strien, van Swaay & Termaat 2013;Fithian et al 2015;Robinson, Ruiz-Gutierrez & Fink 2018;Isaac et al 2019;Johnston et al 2019Johnston et al , 2020.…”

Section: Boersch-supan and Robinson 2021mentioning

confidence: 99%

Integrating structured and unstructured citizen science data to improve wildlife population monitoring

Boersch‐Supan

Robinson

2021

Preprint

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Accurate and robust population trend assessments are key to successful biodiversity conservation. Citizen science surveys have provided good evidence of biodiversity declines whilst engaging people with them. Citizen scientists are also collecting opportunistic biodiversity records at unprecedented scales, vastly outnumbering records gathered through structured surveys. Opportunistic records exhibit spatio-temporal biases and heterogeneity in observer effort and skill, but their quantity offers a rich source of information. Data integration, the combination of multiple information sources in a common analytical framework, can potentially improve inferences about populations compared to analysing either in isolation. We combine count data from a structured citizen science survey and detection-nondetection data from an opportunistic citizen science programme. Population trends were modelled using dynamic N-mixture models to integrate both data sources. We applied this approach to two different inferential challenges arising from sparse data: (i) the estimation of population trends for an area smaller than a structured survey stratum, and (ii) the estimation of national population trends for a rare but widespread species. In both cases, data integration yielded population trajectories similar to those estimated from structured survey data alone but had higher precision when the density of opportunistic records was high. In some cases this allowed inferences about population trends where indices derived from single data sources were too uncertain to assess change. However, there were differences in the trend magnitude between the integrated and the standard survey model. We show that data integration of large-scale structured and unstructured data is feasible and offers potential to improve national and regional wildlife trend estimates, although a need to independently validate trends remains. Smaller gains are achieved in areas where uptake of opportunistic recording is low. The integration of opportunistic records from volunteer-selected locations alone may therefore not adequately address monitoring gaps for management and policy applications. To achieve the latter, scheme organisers should consider providing incentives for achieving representative coverage of target areas in both structured and unstructured recording schemes.

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Biased Assumptions and Oversimplifications in Evaluations of Citizen Science Data Quality

Cited by 28 publications

References 19 publications

Opinions of citizen scientists on open access to UK butterfly and moth occurrence data

Opinions of citizen scientists on open access to UK butterfly and moth occurrence data

Coupling data science with community crowdsourcing for urban renewal policy analysis: An evaluation of Atlanta’s Anti-Displacement Tax Fund

Integrating structured and unstructured citizen science data to improve wildlife population monitoring

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