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Geographic Citizen Science Mapping

Public participatory mapping is a method of crowdsourcing where the public contribute spatial information for a range of applications including nature conservation planning. When used to collect wildlife observation data, participatory mapping is a type of “geographic citizen science” that involves members of the public. In 2018, researchers in Australia implemented a participtory mapping project in northern New South Wales (Australia) where the public was asked to identify and map the location and frequency of koala (Phascolarctos cinereus) sightings using an internet mapping application.

Researchers evaluated the validity of citizen-mapped observation data from the two perspectives of validity-as-accuracy (positional accuracy and data completeness of observations) and validity-as-credibility (characteristics of citizen contributors). Validity-as-accuracy was assessed by comparing the distribution of citizen koala observations to an expert-derived probability distribution of koalas in the study region. Validity-as-credibility was assessed by analyzing non-spatial survey questions to determine which participant characteristics increased the credibility/accuracy of observational data.

The study found significant spatial association between crowd-sourced koala observations and the "expert-derived" koala likelihood model (see Figure 1 below). However, the citizen data under-reported in more rural, remote areas. Participant charactertistics contributing to accuracy in koala observations included participant knowledge of koalas, age, length of residence, and formal education. The study concluded that crowdsourced koala observations can effectively augment field-based koala research by covering large geographic areas while engaging a broader public in koala conservation efforts.

The article describing these methods is published here. For more information about the study, contact Greg Brown.

Figure 1. Maps showing (a) Koala likelihood map with cell probabilities, (b) spatial distribution of koala observations from spatial survey (weekly, monthly, yearly, once, dead/injured) in the study area, (c) bivariate local indicators of spatial association (BiLISA), and (d) significance levels for BiLISA clusters. Cell probabilities and koala observations are categorized into quintiles for visual comparison.