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Environmental hazards affect people from different income groups and migration backgrounds on different levels. The research on environmental inequalities and environmental justice has proposed several theories to explain such inequities; still, it remains unclear which of these theories applies to the German societal context. This research investigates whether individual-level income differences between Germans and migrants account for objectively measured exposure to the environmental goods and bads of land use, specifically soil sealing and green spaces. Marginal effects and predictions based on georeferenced survey data from the German General Social Survey reveal that Germans with higher incomes live in areas with better neighbourhood quality. Germans with lower incomes are exposed to fewer disadvantages stemming from land use, and there is no marginal difference between nonurban and urban municipalities. Spatial assimilation in high-income groups occurs; however, the difference in low-income groups can be explained by place stratification and discrimination in the housing market. While this study uses more indirect and non-hazardous measures of environmental quality - in contrast to air pollution or noise - it provides evidence that such indicators also create distributional injustices in Germany.

This book demonstrates the use of georeferenced data for social science survey research which builds upon survey data enriched with geo-coordinates. It reviews the prerequisites and challenges of applying these data to different social science research questions, highlighting the different branches of an interdisciplinary effort. At the center of this presentation is the method of spatial linking: the combination of georeferenced survey data with information from auxiliary geospatial data sources. A collection of spatial linking methods is applied in this book's empirical applications which underline these methods' flexibility in different social science sub-disciplines, such as health and family, political attitudes, and environmental inequalities. For this purpose, georeferenced survey data from the German General Social Survey (GGSS) 2014 and the GESIS Panel are used. These empirical applications are part of an emerging field of research for social scientists, requiring new analytic skills from diverse and foreign disciplines, like ecology and engineering. Navigating the organizational and technical requirements for the analysis of georeferenced survey data enables researchers to answer new and innovative research questions. ; Dieses Buch beschäftigt sich mit der Nutzung georeferenzierter Daten in der sozialwissenschaftlichen Umfrageforschung, deren Ausgangspunkt Umfragedaten sind, die mit Geokoordinaten angereichert wurden. Es widmet sich den Voraussetzungen und Herausforderungen, solche Daten für verschiedene sozialwissenschaftliche Fragestellungen nutzbar zu machen und betont dabei die verschiedenen interdisziplinären Verzweigungen dieses Unterfangens. Im Mittelpunkt der Präsentation steht die Methode der räumlichen Verknüpfung: die Kombination georeferenzierter Umfragedaten mit Informationen aus externen Geodatenquellen. Anhand mehrerer, aus unterschiedlichen Subdisziplinen der Sozialwissenschaften stammender empirischer Anwendungen im Bereich Familie und Gesundheit, politische Einstellungen sowie Umwelt und Ungleichheit wird die Flexibilität der Methode in Form verschiedener räumlicher Verknüpfungen betont. Dazu werden georeferenzierte Umfragedaten der Allgemeinen Bevölkerungsumfrage Sozialwissenschaften (ALLBUS) 2014 und dem GESIS Panel 2014 verwendet. Diese empirischen Anwendungen sind Teil eines aufstrebenden Forschungsfelds für Sozialforschende, welches neue analytische Fertigkeiten aus verschiedenen anderen Fachbereichen wie der Ökologie oder des Ingenieurswesen erfordert. Werden die organisatorischen und technischen Anforderungen zur Analyse georeferenzierter Umfragedaten gemeistert, eröffnet sich Forschenden die Möglichkeit, neue und innovative Fragestellungen zu beantworten.

AbstractDoes immigration erode the social integration of contemporary mass-scale societies? Continued immigration and corresponding growing ethnic, racial, and religious diversity have prompted this important and highly controversial question. This article gives a brief introduction to the scientific literature on this question and derives from it the following gap that it seeks to address: According to a small number of studies on related but different topics, immigration may be detrimental to social integration if it physically manifests as one of two specific types of ethnic residential segregation. The contested boundaries hypothesis has it that border regions sandwiched between ethnically defined neighborhoods are particularly conflict prone and characterized by increased rates of crime. The halo-effect hypothesis claims that majority members who live in homogenous mainstream neighborhoods that border on ethnically diverse ones (or are even encircled by them) are more likely to vote for right-wing populist parties. In this article, we expand both approaches to the study of social integration in theoretical and empirical terms. With respect to theory building, we discuss why social integration, as indicated by social trust and community attachment, should suffer from these two types of ethnic segregation. To test these claims empirically, we use data from the geo-coded German General Social Survey (ALLBUS/GGSS) 2016 and 2018 that we merge with 100-m × 100‑m spatial grid data from the German Census 2011. These data allow us to apply edge detection techniques to identify ethnic residential boundaries, and our recently developed donut-method to measure ethnic residential halos. To our knowledge our study is the first investigation into the arguably important question whether ethnic residential boundaries and halos erode social integration.

In this article, we present documentation of the georeferenced social science survey data that are spatially linked to geospatial data attributes. We introduce the challenges of documentation, as different metadata standards are used for both data sources: social science survey data and geospatial data. In particular, we analyze the extent to which the social sciences metadata standard DDI Lifecycle is capable of incorporating the geosciences metadata standard ISO 19115. We find that the most challenging attributes to describe are those concerning the geographic structure of the geospatial data, especially if they stem from different sources. To navigate these issues, we developed and evaluated four workaround approaches which we demonstrate in a case study on the georeferenced German General Social Survey. Because not all of the approaches apply equally to every research project and institution, we provide a scheme to assist in making informed and weighted decisions.

This book demonstrates the use of georeferenced data for social science survey research which builds upon survey data enriched with geo-coordinates. It reviews the prerequisites and challenges of applying these data to different social science research questions, highlighting the different branches of an interdisciplinary effort. At the center of this presentation is the method of spatial linking: the combination of georeferenced survey data with information from auxiliary geospatial data sources. A collection of spatial linking methods is applied in this book's empirical applications which underline these methods' flexibility in different social science sub-disciplines, such as health and family, political attitudes, and environmental inequalities. For this purpose, georeferenced survey data from the German General Social Survey (GGSS) 2014 and the GESIS Panel are used. These empirical applications are part of an emerging field of research for social scientists, requiring new analytic skills from diverse and foreign disciplines, like ecology and engineering. Navigating the organizational and technical requirements for the analysis of georeferenced survey data enables researchers to answer new and innovative research questions.

Zwischen dem 15. Oktober 2018 und dem 13. Januar 2019 wurden innerhalb des von der Deutschen Forschungsgemeinschaft (DFG) geförderten Projekts "Sozial‐Raumwissenschaftliche Forschungsdateninfrastruktur" (SoRa) 39 Personen zum Thema
georeferenzierte Daten befragt. Ziel der Befragung war zum einen zu erfassen, inwiefern Vorkenntnisse zu Daten und Methoden unter Forschenden vorhanden sind und zum anderen,
welche Bedarfe an etwaigen Services und Datenangeboten bestehen. Dazu wurden unterschiedliche Fragen zu den Erfahrungen und Interessen der Befragten aus jenen Bereichen entwickelt und in einem Online‐Survey eingesetzt. Das vorliegende Papier beschreibt kurz die Motivation und den Hintergrund der Befragung, ihre Durchführung und in größeren Umfang die Ergebnisse. Wir starten mit einer kurzen Einführung zur Relevanz des Themas und seine Einbindung im Projektkontext sowie einer Stichprobenbeschreibung. Danach stellen wir die Ergebnisse aufgeschlüsselt nach Themenblöcken vor. In einem letzten Schritt ordnen wir die Ergebnisse angesichts ihrer Implikationen für die Ziele des Surveys ein. Im Anhang befindet sich zudem der Fragebogen der Erhebung.

Survey data are still the most commonly used type of data in the quantitative social sciences. However, as not everything that is of interest to social scientists can be measured via surveys, and the self-report data they provide have certain limitations, such as recollection or social desirability bias, researchers have increasingly used other types of data that are not specifically created for research. These data are often called "found data" or "non-designed data" and encompass a variety of different data types. Naturally, these data have their own sets of limitations. One way of combining the unique strengths of survey data and these other data types and dealing with some of their respective limitations is to link them. This guideline first describes why linking survey data with other types of data can be useful for researchers. After that, it focuses on the linking of survey data with three types of data that are becoming increasingly popular in the social sciences: geospatial data, social media data, and sensor data. Following a discussion of the advantages and challenges associated with linking survey data with these types of data, the guideline concludes by comparing their similarities, presenting some general recommendations regarding linking surveys with other types of (found/non-designed) data, and providing an outlook on current developments in survey research with regard to data linking.

Research on environmental justice comprises health and well-being aspects, as well as topics related to general social participation. In this research field, among others, there is a need for an integrated use of social science survey data and spatial science data, e.g. for combining demographic information from survey data with data on pollution from spatial data. However, for researchers it is challenging to link both data sources, because (1) the interdisciplinary nature of both data sources is different, (2) both underlie different legal restrictions, in particular regarding data privacy, and (3) methodological challenges arise regarding the use of geo-information systems (GIS) for the processing and analysis of spatial data.

In this article, we present an infrastructure of distributed web services which supports researchers in the process of spatial linking. The infrastructure addresses the challenges researchers have to face during that process. We present an example case study on the investigation of environmental inequalities with regards to income and land use hazards in Germany by using georeferenced survey data of the GESIS Panel and the German Socio-economic Panel (SOEP), and by using spatial data from the Monitor of Settlement and Open Space Development (IOER Monitor). The results show that increasing income of survey respondents is associated with less exposure to land-use-related environmental hazards in Germany.