Individuals living in areas with high background radon: a GIS method to identify populations at risk

to identify and link populations and individuals that live within high risk areas.

census registers and disease registers which contain data on individuals can only give aggregate statistics relating to postal code districts, town, county or state boundaries. However environmental risk factors rarely, if ever, respect these man-made boundaries. What is needed is a method to rapidly identify individuals who may live within a described area or region and to further identify the disease(s) occurring among these individuals and/or in these areas.

this paper describes a method for linking the standard registers available in Sweden, notably the residence-property addresses they contain and the geographical coordinate setting of these, to map the population as a point coverage. Using standard GIS methods this coverage could be linked, merged or intersected with any other map to create new subsets of population. Representation of populations down to the individual level by automatised spatialisation of available census data is in its simplicity a new informatics method which in the designated GIS medium adds a new power of resolution.

We demonstrate this using the radon maps provided by the local communes. The Swedish annual population registration records of 1991 for the county of Ostergötland and the property register available at the Central Statistical Bureau of Sweden formed the main data sources. By coupling the address in the population register to the property register each individual was mapped to the centroid of a property. By intersecting the population coverage with the radon maps, the population living in high, normal or low risk areas was identified and then analysed and stratified by commune, sex and age. The resulting tables can be linked to other database registers, to visualise and analyse geographical and related patterns. The methodology can be adapted for use with any other environmental map or small area. It can also be expanded to the fourth dimension by linking likewise available migration information to generate immediately coordinate-set, accumulated exposition and similar data.