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"Survey data used in studies of human behavior and attitudes, however, often lack a small scale spatial dimension. As a result, researchers have to rely on the attributes of higher level spatial units such as local administrative units (e.g. municipalities or districts) or even whole countries. This dependability often reduces the reliability of the indicators for social contexts and increases the risk of ecological fallacies since the variability of the attributes across individuals within these higher levels units are ignored (Crowder & Downey, 2010). Therefore, to overcome these issues, a trend in the social sciences is to use Geographic Information System (GIS) techniques (Hillmert, Hartung, & Weßling, 2017). […] This document is a background reference as well as a hands-on to geocoding social science survey data and linking them to available geospatial data attributes. To appropriately hurdle challenges of spatial linking, it contains procedures that transparently and efficiently process three distinct data sources: social science survey data, georeferences and geospatial data. The focus of this paper is to explain these procedures by using example data." Der ALLBUS 2010 wird als Beispieldatensatz genutzt.
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ALLBUS-Bibliography
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Aufgenommen: 32. Fassung, März 2018
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Geocoding and Spatial Linking of Survey Data. An Introduction for Social Scientists
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(5), 2017
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Bibsonomy
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2017
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ALLBUS
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ALLBUS2010
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ALLBUS_input2017
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ALLBUS_pro
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ALLBUS_version32
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FDZ_ALLBUS
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OAproved
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english
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techreport
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