Implementing GEOSS DMP6 Data QualityControl Describing the Quality

Implementing GEOSS DMP-6, Data Quality-Control: Describing the Quality of Geospatial Data Integrated with Socioeconomic Data Robert R. Downs rdowns@ciesin. columbia. edu NASA Socioeconomic Data and Applications Center (SEDAC) Center for International Earth Science Information Network (CIESIN) The Earth Institute, Columbia University GEO Symposium 2018 11 -12 June 2018, Geneva, Switzerland Session: Challenges and Opportunities for Data Sharing and Data Management in GEOSS Monday, 11 June 2018 Copyright 2018. The Trustees of Columbia University in the City of New York.

GEOSS DMP 6: Data Quality-Control • Data Management Principles Category: Usability • Other DMP in Usability category: – DMP-3: Data Encoding; – DMP-4: Data Documentation; – DMP-5: Data Traceability • GEOSS DMP 6: Data Quality Control – Data will be quality-controlled and the results of quality control shall be indicated in metadata; data made available in advance of quality control will be flagged in metadata as unchecked. 2

SEDAC Collection Development Focuses on Human Interactions in the Environment Current Themes • • • • Agriculture Climate Conservation Governance Hazards Health Infrastructure Land Use Marine and Coastal Population Poverty Remote Sensing Sustainability Urban Water Selected Data Collections • • • • • Climate Effects on Food Supply Compendium of Environmental Sustainability Indicators Energy Infrastructure Global Agricultural Lands Global Fertilizer and Manure Global Roads Global Rural-Urban Mapping Project (GRUMP) Gridded Population of the World (GPW), v 4 Historical Anthropogenic Sulfur Dioxide Emissions India Data Collection Indicators of Coastal Water Quality Intergovernmental Panel on Climate Change (IPCC) Land Use and Land Cover (LULC) Millennium Ecosystem Assessment (MA) Population Dynamics Population Exposure to Natural Disasters Satellite-Derived Environmental Indicators Spatial Economic Data U. S. Census Grids Urban Spatial Data 3

Comprehensive Scientific Data Product Review Data Selection and Development Planning Data Product and Service Review 4

Pilot Study of Quality Metrics of SEDAC Data • Review of data quality information – Quality information for 21 recent (2016 - 2017) data product releases – Limited to data quality information published with the data • Identified and categorized sources of data quality information – 8 categories of sources of data quality information published with data • Identified terminology used to describe data quality – 67 unique terms used for socioeconomic data quality • Categorized the identified data quality terminology – 8 categories of terms used for socioeconomic data quality *Based on Downs, 2018 5

Data Quality Sources for Recent SEDAC Releases • Data files – Published files containing data • Documentation – Documentation document published with data • Methodological Documentation – Documentation published with data that addresses specific issues • Metadata – Published with data and referencing documentation and article • Article – Sometimes published with data or linked, depending on rights acquired • Article Supplemental Information – Sometimes published with data or linked, depending on rights acquired • Report – Published with data • FAQ – Based on questions answered and published with data 6

67 Unique Socioeconomic Data Quality Terms accuracy adjustments aggregation alternative sources applicable use appropriate use assumptions backcast bias caveats coarsening comission errors comparison confounding factors disaggregation errors estimation evaluation exceptions exclusions filters gaps implications for use improvements inappropriate use incompleteness inconsistencies inflation known issues problems projections proportions quality assurance quality checking quality control quality issues quality problems rationale recommended use references on methods small errors sources quality substitutions suitability for use constraints on use corrections currency data challenges limitations log of changes by version matching missing values thresholds unavailable data uncertainty undercounts data quality indicators data sources deviations difficulties no data omissions other factors possible errors usage issues validation 7

Aggregated Socioeconomic Data Quality Terms - 1 • Caveats – Accuracy, assumptions, appropriate, bias, caveats, comparison, confounding factors, data challenges, deviations, difficulties, evaluation, exceptions, exclusions, inconsistencies, known issues, inflation, limitations, other factors, problems, quality issues, quality problems, rationale, uncertainty, undercounts • Corrections – Adjustments, corrections, estimation, improvements, substitutions • Errors – Comission errors, possible errors, small errors • Missing data – Gaps, incompleteness, missing values, no data, omissions, unavailable data 8

Aggregated Socioeconomic Data Quality Terms - 2 • Modification – Aggregation, backcast, coarsening, currency, disaggregation, filters, matching, projections, proportions, thresholds • Use – Applicable use, appropriate use, constraints on use, implications for use, inappropriate use, recommended use, suitability for use, usage issues • Quality Control – Data quality indicators, log of changes by version, quality assurance, quality checking, quality control, references on methods, validation • Sources – Alternative sources, data sources, sources’ quality 9