A flexible approach for statistical disclosure control in geospatial data
Jon Olav Skøien et al.
What the paper says
Due to confidentiality restrictions in releasing census and survey data, such as agricultural data from the European farm structure survey (9 million records), the data are aggregated to a coarse resolution (NUTS2 administrative regions) before public release. Even when other types of census data are released as grids, grid cells may be suppressed in locations where confidentiality rules have not been respected. Here, we present a method, implemented in the R package MRG , for creating multi-resolution grids that respect restrictions while maximizing the spatial resolution at which the data are disseminated. The method can be adjusted for different restrictions, it can create the same grid structure for a set of variables, and it allows for a contextual suppression of some grid cells (i.e., suppress if all neighbors are non-confidential, merge if several others are also confidential) if this results in a generally higher information content, a combination of features that has not previously been available. The method is exemplified with a synthetic data set.
1 citation
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.16 × 0.4 = 0.06 |
| M · momentum | 0.53 × 0.15 = 0.08 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.