  Moon Zoo is a citizen science project that utilises internet crowd-sourcing
techniques. Moon Zoo users are asked to review high spatial resolution images
from the Lunar Reconnaissance Orbiter Camera (LROC), onboard NASAs LRO
spacecraft, and perform characterisation such as measuring impact crater sizes
and identify morphological features of interest. The tasks are designed to
address issues in lunar science and to aid future exploration of the Moon. We
have tested various methodologies and parameters therein to interrogate and
reduce the Moon Zoo crater location and size dataset against a validated expert
survey. We chose the Apollo 17 region as a test area since it offers a broad
range of cratered terrains, including secondary-rich areas, older maria, and
uplands. The assessment involved parallel testing in three key areas: (1)
filtering of data to remove problematic mark-ups; (2) clustering methods of
multiple notations per crater; and (3) derivation of alternative crater
degradation indices, based on the statistical variability of multiple notations
and the smoothness of local image structures. We compared different
combinations of methods and parameters and assessed correlations between
resulting crater summaries and the expert census.
  We derived the optimal data reduction steps and settings of the existing Moon
Zoo crater data to agree with the expert census. Further, the regolith depth
and crater degradation states derived from the data are also found to be in
broad agreement with other estimates for the Apollo 17 region. Our study
supports the validity of this citizen science project but also recommends
improvements in key elements of the data acquisition planning and production.
