ESRA 2025 Preliminary Program
All time references are in CEST
Advances in the integration of geospatial data for social research |
| Session Organisers |
Dr Dennis Abel (GESIS - Leibniz Institute for the Social Sciences)
Dr Stefan Jünger (GESIS - Leibniz Institute for the Social Sciences)
Mr Jonas Lieth (GESIS - Leibniz Institute for the Social Sciences)
Ms Anne-Kathrin Stroppe (GESIS - Leibniz Institute for the Social Sciences)
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| Time | Tuesday 15 July, 13:45 - 15:00 |
| Room |
Ruppert C - 0.23 |
Data integration of social indicators from surveys and social media content with geospatial context variables has rapidly progressed in the last years. Applications in the social sciences are manifold and cover issues such as conflict and migration, political participation, environmental attitudes, and inequality. Geospatial approaches allow researchers to introduce new perspectives in explaining societal processes, stress the local in questions of global relevance, and extend the potential of inter- and transdisciplinary research. To further progress the data integration based on spatial identifiers, advances are required in several key aspects: 1. High-quality georeferenced survey datasets are needed to expand the pool of linkable social indicators; 2. Social scientists require easier access to georeferenced context variables; 3. Similarly, linking processes need to be simplified and standardized. These steps are crucial for this line of research but bring their own challenges in terms of data quality and fit-for-purpose of the respective datasets to be linked, as well as pitfalls in the linking process itself. Therefore, this session will provide a platform to address these potentials as well as barriers in detail. It collects contributions that aim to advance geospatial social research and is open to empirical, methodological, data-focused, and conceptual papers. Possible topics include (but are not limited to):
- New methods and applications of linking survey and spatial data
- Utilization of novel sources of spatial data to integrate into survey research and answer social science questions
- Quality assurance of the data integration process and resulting data
- Applications and real-world examples integrating survey data with big spatial data, earth observation data, or other small-scale spatial data
Keywords: geospatial data, data integration, data linkage
Papers
Measuring Distance to University in Germany: How Accurate is the Straight-Line Approach?
Mr Daniel Hein (German Centre for Higher Education Research and Science Studies) - Presenting Author
Many studies in the field of higher education use distance as a simple measure of accessibility, commuting, or moving. They often define distance as the straight-line distance, while only a few studies measure distance using the actual travel distance. This approach is supposedly more sophisticated, accurate, and realistic. Our aim is to assess whether the straight-line is an adequate proxy for travel distances by car and multimodal public transportation in Germany.
We compare the straight-line and the travel distances between the former school and the current university. We also distinguish between the shortest and the best route. The linear relation between the straight-line and travel distance was analyzed using ordinary least-squares regression. To examine outliers, the difference between the actual travel distance and the predicted travel distance, which is the straight-line distance multiplied by the regression slope, was used. The straight-line distance is a good proxy when the absolute difference between the actual travel distance and the predicted travel distance is less than 5 km, or the relative difference is less than 10 %. The results are based on a representative sample of 2,903 different routes taken by German students.
In 96 % of the cases, the straight-line distance is an adequate proxy for the shortest travel distance by car. However, the straight-line is a good approximation of the best car route 80 % of the time. For the shortest and best public transportation routes, the straight-line is a reliable proxy 66 % and 60 % of the time, respectively. The largest discrepancies occur in areas with physical obstacles such as lakes, rivers, mountains or wilderness and nature conservation areas. These findings suggest that future studies should use travel distances for more realistic results, as they provide significantly greater accuracy than straight-line distances.
Reproductive decision-making in armed conflict settings
Ms Ruth Overmann (University of the Bundeswehr Munich) - Presenting Author
Professor Tim Williams (University of the Bundeswehr Munich)
Reproductive decisions are sensitive to objective and subjective stressors that reduce the stability and predictability of life circumstances, as previous research has shown. The perception of and coping with uncertainty depends not only on personal characteristics, but also on the political, cultural and social contexts in which individuals are embedded. In the midst of increasing global uncertainty, cross-national research on the impact of crises on population dynamics is therefore of high relevance.
Violent conflict represents an unparalleled source of uncertainty, and its detrimental effects on populations have been well studied. Yet, studies focusing specifically on fertility intentions are limited. This study aims to further investigate the vulnerability of childbearing desires in armed conflict settings using harmonized data on women’s short-term fertility intentions in over 60 countries from 1988 to 2023.
We combine nationally representative micro-level data from the Demographic and Health Surveys with high-quality georeferenced event data from the Uppsala Conflict Data Program including information on the type of government from the Variety of Democracy dataset to perform geospatial analysis. The analysis is limited to married women aged 15-49 years. Multilevel modelling is applied for an in-depth consideration of country characteristics, allowing for an adequate analysis of the temporal scope and decisiveness of fertility intentions. Conflict events are differentiated according to the duration, intensity, type and geographical proximity of the conflict. We account for a range of control variables.
We expand previous research on the relationship between the exposure to armed conflict and fertility preferences by concentrating on (a) short-term fertility intentions rather than long-term fertility ideals and (b) general effect mechanisms that transcend specific conflict contexts. This focus helps to shed light on the impact of structural constraints on the formation and adaption of childbearing plans.
Health Disparities from Living Near Industry: Linking Dutch Health Survey Data to Environmental Data
Dr Annette Scherpenzeel (Nivel) - Presenting Author
Professor Michel Dückers (Nivel)
Concerns about the health impacts of living near industrial areas in the Netherlands have been rising, with studies highlighting elevated risks of chronic and acute health conditions in affected populations. This research builds on previous findings, such as the increased prevalence of lung cancer, COPD, and diabetes in industrial zones, particularly around the IJmond region and Tata Steel facilities, where emissions of heavy metals and carcinogens exceed expected levels.
Using the Dutch Health Monitor (GEMON) survey data, this study comprehensively assesses health disparities among residents living near industrial sites compared to the general population. GEMON provides harmonized and longitudinal health data from thousands of adults across the Netherlands, covering demographics, lifestyle factors, and self-reported health outcomes, including non-specific complaints, mental health issues, and chronic conditions. By linking GEMON data with income information available at Statistics Netherlands and environmental exposure data (e.g., air pollution and proximity to industrial sites), the study investigates the following aspects:
1. The differences in acute and chronic health outcomes between residents near industrial zones and the broader population, accounting for factors like age, gender, lifestyle, and household income.
2. Deviations in health risks in the IJmond region from national trends, given its previously documented elevated health risks.
3. The consistency of these health disparities over time using data from 2016, 2020, and 2022.
This quantitative study involves the integration of GEMON responses with geocoded exposure metrics and socioeconomic data. The findings address gaps in understanding the full spectrum of health impacts, particularly in vulnerable populations living near industrial areas.
EarthLinks - Advancing flexible linking of Earth observation data with social indicators
Dr Dennis Abel (GESIS - Leibniz Institute for the Social Sciences) - Presenting Author
Dr Stefan Jünger (GESIS - Leibniz Institute for the Social Sciences)
A growing interest in economics and the social sciences in Earth observation (EO) data has led to a broad spectrum of publications in recent years. They range from studying environmental attitudes and behavior, economic development, conflicts and causes of flight, and electoral behavior. However, social science researchers also face many obstacles in applying and using these data, resulting from 1) a lack of technical expertise, 2) a lack of knowledge of data sources and how to access them, 3) unfamiliarity with complex data formats, such as high-resolution, longitudinal raster datacubes, and 4) lack of expertise in integrating the data into existing social science datasets. Despite the increased interest in the data, for the majority of researchers in the social sciences, EO data represents a black box after all. In this session, we present our new project “EarthLinks” which aims to close the gap and create an automated interface to EO data and complementary resources for social science research. The project's goal is creating an open-source tool to link time- and space-sensitive social science datasets with data from Earth observation programs based on a Shiny App in R. The project advances the automatization of these data integration processes between social science data and EO data based on an open-source, user-friendly tool that does not require users' programming skills. The EarthLinks workflow will be exemplified on the basis of a research project which we currently conduct on the effects of flooding exposure on climate change opinion.
