Is PPGIS good enough? An empirical evaluation of the quality of PPGIS crowd-sourced spatial data for conservation planning
A significant barrier to the use of public participation GIS (PPGIS) and crowd-sourcing for conservation planning is uncertainty about the quality of the spatial data generated. This study examines the quality of PPGIS data for use in conservation planning. We evaluate two dimensions of spatial data quality, positional accuracy and data completeness using empirical PPGIS data from a statewide study of public lands in Victoria, Australia. Using an expert-derived spatial conservation model for Victoria as a benchmark, we quantify the performance of a crowd-sourced public in their capacity to accurately and comprehensively identify areas of high conservation importance in the PPGIS process. About 70% of PPGIS points that identified biological/conservation values were spatially coincident (position accurate) with modeled areas of high conservation importance, with greater accuracy associated with locations in existing protected areas. PPGIS data had less positional accuracy when participants identified biological values in urban areas and on non-public lands in general. The PPGIS process did not comprehensively identify all the largest, contiguous areas of high conservation importance in the state, missing about 20% of areas, primarily on small public land units in less densely populated regions of the state. Preferences for increased conservation/protection were over-represented in areas proximate to the Melbourne urban area and under-represented in more remote statewide locations. Our results indicate that if PPGIS data is to be integrated into spatial models for conservation planning, it is important to account for the spatial accuracy and completeness limitations identified in this study (i.e., urban areas, non-public lands, and smaller remote locations). The spatial accuracy and completeness of PPGIS data in this study suggests spatial data quality may be “good enough” to complement biological data in conservation planning but perhaps not good enough to overcome the mistrust associated with crowd-sourced knowledge. Recommendations to improve PPGIS data quality for prospective conservation planning applications are discussed.