Whence

Tracking data quality

Purpose: determine the level to which individual country scores are reliable based on whether and how the values were gap-filled (and year mismatches?).

The types of gapfilling we used:

• average o what spatial level: neighbors, sovereign country, region, world
• disaggregate model (i.e. value split from sovereign country using some form of weighting, e.g. relative proportion of coastal population)
• modeled (trash as proportional to population density?) Level of gap-filling:
• data set
• data layer
• score Proposed process:
• generate a label for the data layer that describes type and level of gapfilling
• generate an additional label if the gapfilling occurs during further calculations -> this needs to be embedded in the toolbox

(Year mismatches – matter when:

• the score is a ratio of two data layers from different years (e.g. employees in the tourism sector/workforce size)
• a score uses a spatial reference point such as a global average or max (e.g. wages - countries with less recent data are at a disadvantage)
• comparing scores across countries with data from very different years
• Any other ways that year mismatches may affect interpretation of results?

Is there an easy way to know what year is associated to a country’s goal score? How to keep track of this? Maybe just for certain goals, e.g. Livelihoods)

Provenance • Data o layername.csv • +field: value_whence_v01 column with basic data types. This can get tallied up by toolbox. 2013 documentation. And then how carried through. Shapefile limitation of 10 characters so could have little lookup table to go back and forth. • uncertainty_v01 having some free text format like: [measure]: [value]. [description]. Some datasets have only point estimate whereas others get gapfilled. o layername_whence-v01.csv • format for details & children • incorporate to gapfilling functions • Report o Per reporting region • • Examples • Document o See section 2013 documentation o Put in •