ifs_dk: Test data sets for the MRG package

In MRG: Create Non-Confidential Multi-Resolution Grids

Test data sets for the MRG package

Description

Synthetic data set of Danish farming data, similar to the structure of the real Farm Structure Survey (FSS) data. It contains more than 37,000 synthetic records - generated in a way that should replicate the structure and the distribution of real data, but where the individual data are different from the real data.

Usage

data(ifs_dk)

Format

A data frame with 37,088 rows and 14 variables

• COUNTRY The name of the country

• YEAR The year of the survey data

• ID_SYNTH Unique ID of the record

• FARMTYPE Farm typology. Farms are classified into different types according to their dominant activity and standard output value (proxy for farm income). For further information see https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Farm_typology

• HLD_FEF Not used. Farm is included in frame extension (HLD_FEF=1) or main frame (HLD_FEF=0)

• REGIONS NUTS2 region

• GEO_LCT The geolocation in typical FSS-format, including both country, CRS and xy coordinates

• EXT_CORE The extrapolation weights for core data (1 in this data set)

• STRA_ID_CORE Which stratum the record belongs to - only used for the reliability checking

• UAA The utilized agricultural area of the farm

• UAAXK0000_ORG The organic utilized agricultural area, excluding kitchen gardens of the farm. UAAXK0000_ORG includes fully certified area and area under conversion

• Sample Whether the record should be included as a weighted subsample

• EXT_MODULE The extrapolation weights for the sample data

A data frame with 37088 rows and 14 variables

Details

The variables are as follows:

For practical purposes, we have derived a synthetic data set from the original 2020 agricultural census micro data. Although synthetic data sets are a feasible way to provide public access to the data by mitigating any confidentiality concerns, there have only been a few attempts made to create synthetic public files of micro data collected by official statistical institutes.

The attached data set has been produced by application of a hot-deck procedure - originally developed to impute missing information - to substitute a data entry from the original data (i.e., the recipient) by using a value from a similar record (i.e., the donor) within the same classification group (Andridge and Little, 2010; Ford, 1983; Joenssen and Bankhofer, 2012).

A single hot deck imputed data set is computed for each country individually. First, records are partitioned into homogeneous groups so that the donors follow the same distribution as the recipients. Data points from the recipients are substituted sequentially based on a value from a varying pool of donors. Furthermore, the nearest neighbour matching technique using distance metrics is applied to select the most appropriate donor from the pool of donors. For a few of the discrete variables, such as $FARMTYPE$, $SO_EUR$, $HLD_FEF$ and $NUTS2$, a donor was chosen randomly by preserving the original empirical distribution or they were simply randomly decoded (i.e., renamed). The variable containing information about the geographical location ($GEO_LCT$) of the agricultural holding was imputed by restricting the donor to the same country. To assess the quality rating system (i.e., the reliability), we created an artificial sample ($SAMPLE$) with the respective extrapolation factors ($EXT_MODULE$) based on stratification. The sample size consists of approximately one third of the synthetic 2020 census for Denmark.

The empirical distribution of the two main variables of interest of the synthetic data, $UAA$ and $UAAXK0000_ORG$ are widely preserved within the different economic size classes.

References

Andridge RR, Little RJ (2010). A review of hot deck imputation for survey non-response. International statistical review, 78(1), 40–64.

Ford BL (1983). An overview of hot-deck procedures.” Incomplete data in sample surveys, 2(Part IV), 185–207.

Joenssen DW, Bankhofer U (2012). Hot deck methods for imputing missing data. In P Perner (ed.), Machine Learning and Data Mining in Pattern Recognition, Lecture Notes in Computer Science, pp. 63–75. Springer, Berlin, Heidelberg. ISBN 978-3-642-31537-4. doi:10.1007/978-3-642-31537-4_6

MRG documentation built on Oct. 28, 2024, 5:07 p.m.