German Ageing Survey (DEAS): User Manual SUF DEAS2017_Regionaldaten_Infas360, Version 1.0

Abstract

Based on respondents’ addresses of residence the Institute for Applied Social Sciences (infas360) delivered a selection of regional context indicators mainly at the zip code level. The anonymity of the survey participants is guaranteed. Any address information has been removed. Only the resulting context indicators can be matched to the survey data. The indicator system of infas360 provides a nationwide collection of microgeographical information on the basis of official and non-official data. These are available for the different levels of the postal-official classification system, e.g. the level of the five digits zip codes down to the level of buildings. Details are the company’s secret. The variables of the regional context data SUF DEAS2017 relate mostly to the postal codes of the delivery areas, occasionally to single municipality, settlement blocs or buildings. Which level is used can be seen in the description of the variables. In Germany the zip codes (PLZ) cannot be adapted into the official classification scheme. Cities often have multiple zip codes, small municipalities in rural areas occasionally share one. Relevant is the zip code, alternative the municipality, if it is too small to have an own one. This means: for municipalities with multiple zip codes the smallest geographical unit is the one with the same zip code. Vice versa for small municipalities in rural areas which share one zip code, the smallest geographical unit is the municipality. In the interests of simplification the geographical unit is characterised as the variable "PLZ". Only for participants whose addresses of residence can specifically be referenced, infas360 has developed geographic features. The geographic structural features relate mostly to the end of the years 2016 or 2017, delivered by infas360 in 2019. For better understanding and use most original variables have been recoded and labelled to derived ones in a summarized version. To ensure the anonymity of the respondents, all relative values have been rounded, e.g. to integers. Most of the structural features as described below are part of SUF DEAS2014 as well (see Lejeune & Engstler 2018). Newly added features are primarily variables on geographic distances of the respondents’ residence to central places, malls and physicians. There are similar structural features for SUF DEAS2002, 2008, 2011 and 2014 (see Engstler 2012a, 2012b; 2018; Engstler & Lejeune 2018).