![]() ![]() Input data for the anonymisation of cluster geocodes: cluster geocodes (green dots) and boundary shapefiles (yellow polygons).īefore the displacement of clusters, cluster centroids are determined. The input data for the anonymisation of cluster geocodes are (1) geocodes of clusters (cluster boundaries as polygon layer, cluster centroids as point layer, multi-points per cluster as multi-point layer, or sampled household coordinates as point layer) and (2) boundary shapefiles representing the sampling frame of MICS at one level lower than the sample stratification/reporting domains (e.g., national admin levels 2). The process of displacement and anonymisation of cluster geocodes is achieved through a custom-built software plugin working on the open-source Geographic Information System (GIS) software QGIS. It should be noted that the DHS methodology is under constant refinement, as described here. The overall procedure for geographical displacement of MICS clusters geocodes is based on the present global gold standard method of anonymisation of cluster locations, as developed by the Demographic and Health Surveys ( DHS) program with small modifications. How do we then ensure that the location is close enough so that it’s useful but not close enough to compromise the anonymity of those who are surveyed? But to maintain the confidentiality of households, the precise location of clusters must not be revealed, and each cluster must be displaced within a set of parameters. The location of clusters is an essential element of geospatial analysis. One of the main challenges to using geospatial data for household survey data like MICS lies in the ethical use data. ![]() Our specialist data protection lawyers can help you to use anonymisation and pseudonymisation to maximise the benefits of data to your business and document their use correctly.Geospatial data can enable higher resolution analysis and yield more targeted and effective programming. In our experience, organisations sometimes find it difficult to differentiate anonymous data from pseudonymous data, which can make it harder to draft and negotiate appropriate contractual obligations. providing an 'appropriate technical and organisational measure' which can improve security.achieving the principle of data protection by design and. ![]()
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