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Erschienen in:
Changing Paradigm in Map Production and Geoinformation Management—An Introduction Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

18. Statistical Disclosure Control in Geospatial Data: The 2021 EU Census Example

verfasst von : Fabian Bach

Erschienen in: Service-Oriented Mapping

Verlag: Springer International Publishing

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Abstract

This chapter outlines challenges and modern approaches in statistical disclosure control of official high-resolution population data on the example of the EU census rounds 2011 and 2021, where a particular focus is on the European 1 km grid outputs derived from these censuses. After a general introduction to the topic and experiences from 2011, the recommended protection methods for geospatial data in the planned 2021 census 1 km grids are discussed in detail.

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Vorheriges Kapitel From Maps to Apps—Entering Another Technological Dimension Within the Geological Survey of Austria
Nächstes Kapitel Supply Chains and Decentralized Map Productions
Fußnoten
1
Regulation (EC) No 763/2008 of the European Parliament and of the Council of 9 July 2008 on population and housing censuses (OJ L 218, 13.8.2008, p. 14) and its implementing Acts.
 
2
“Local Administrative Units level 2”, typically representing municipalities.
 
3
EFGS/GEOSTAT 1 2012. GEOSTAT 1A—Representing Census data in a European population grid. Final report. http://​ec.​europa.​eu/​eurostat/​documents/​4311134/​4350174/​ESSnet-project-GEOSTAT1A-final-report_​0.​pdf.
 
4
The ESS is the joint body of Eurostat and the NSIs of all EU countries and Iceland, Liechtenstein, Norway and Switzerland. It is responsible for the development and quality assurance of official European statistics.
 
5
For a more detailed discussion on the appropriate/intended framework for the ESS, see Haldorson and Moström (2018), Chap. 9 in this volume.
 
6
Regulation (EC) No 223/2009 of the European Parliament and of the Council of 11 March 2009 on European statistics (OJ L 087 31.3.2009, p. 164), amended by Regulation (EU) 2015/759 of the European Parliament and of the Council of 29 April 2015 (OJ L 123, 19.5.2015, p. 90).
 
7
…or households etc.; for brevity we simply refer to “persons” in the following.
 
8
For a general discussion of disclosure risks in frequency tables, see e.g. Hundepool et al. (2010, 2012).
 
9
This classification of variables is not rigorous because it depends on the prior knowledge of an intruder, where reasonable assumptions need to be made for a risk assessment. Moreover, some variable categories might be considered identifying and sensitive (think about place of birth). Finally, different countries have different perceptions of what is sensitive information—the examples chosen here try to convey an intuitive, common-sense notion of the conceptual difference between identification and disclosure of sensitive information.
 
10
This risk can be reduced—but not completely eliminated—through random rounding, where in our example also ‘1’ and ‘5’ would be rounded to ‘3’ with some probability (and ‘4’ resp. ‘8’ to ‘6’, etc.); see e.g. Hundepool et al. (2010, 2012).
 
11
GEOSTAT is a joint cooperation of Eurostat with the European Forum for Geography and Statistics (EFGS); see http://​www.​efgs.​info/​geostat/​.
 
12
Directive 2007/2/EC of the European Parliament and of the Council establishing an Infrastructure for Spatial Information in the European Community (INSPIRE) (OJ L 108, 25.4.2007, p. 1) and its implementing Acts.
 
13
For a comprehensive quality assessment, including a detailed documentation of SDC treatments in the various participating countries, see: http://​www.​efgs.​info/​wp-content/​uploads/​geostat/​1b/​GEOSTAT1B-Appendix17-GEOSTAT-grid-POP-1K-ALL-2011-QA.​pdf.
 
14
Bringing together around 20 NSI representatives of census teams.
 
15
To be provided only “as far as possible”, i.e. the planned Regulation (see below) would oblige EU countries to justify the non-provision of these data (e.g. non-availability).
 
16
Common quality principles for European statistics—including accuracy, comparability and completeness mentioned in the text—are established in the revised “European Statistics Code of Practice” adopted by the ESS Committee on 28 September 2011; more details on http://​ec.​europa.​eu/​eurostat/​web/​ess/​about-us/​quality.
 
18
Only if equal person count is a necessary requirement for pairing similar households, but this is the default in the recommended method variant.
 
19
Cf. the ‘Confidentiality’ field of the GEOSTAT 1 quality assessment (Footnote 13).
 
20
Some particular method setups (not recommended by the project for various reasons partly connected to the hypercubes) would also include perturbations of some ‘0’ counts into non-‘0’, i.e. turning originally unpopulated grid squares into populated ones (cf. Sect. 18.5.1).
 
21
Note that record swapping (cf. Sect. 18.2.2) may further increase the SDC uncertainty on counts of person characteristics. And of course, all this refers only to the SDC uncertainty: naturally there are additional uncertainties (variance and/or bias) stemming from other sources such as data collection and processing.
 
Literatur
Hundepool A, Domingo-Ferrer J, Franconi L, Gießing S, Lenz R, Naylor J, Schulte Nordholt E, Seri G, de Wolf PP (2010) Handbook on statistical disclosure control. Version 1.2, ESSnet centre of excellence for statistical disclosure control
Hundepool A, Domingo-Ferrer J, Franconi L, Gießing S, Schulte Nordholt E, Spicer K, de Wolf PP (2012) Statistical disclosure control. Wiley, Chichester (Wiley Series in Survey Methodology)CrossRef
Shlomo N, Young C (2006) Statistical disclosure control methods through a risk-utility framework. Privacy in statistical databases. Springer, New York, pp 68–81CrossRef
Metadaten
Titel
Statistical Disclosure Control in Geospatial Data: The 2021 EU Census Example
verfasst von
Fabian Bach
Copyright-Jahr
2019
Buch
Service-Oriented Mapping

Print ISBN: 978-3-319-72433-1

Electronic ISBN: 978-3-319-72434-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-72434-8_18

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