ESRA 2027 sessions by theme
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Conceptualizing, Measuring, and Triangulating Space in Survey Research |
| Coordinator 1 | Dr Manuela Schmidt (University of Kaiserslautern-Landau) |
| Coordinator 2 | Dr Alice Barth (University of Bonn) |
| Coordinator 3 | Dr Claudia Schmiedeberg (University of Heidelberg) |
Session Details
Spatial context plays an important role in social science research, e.g. in studies on neighbourhood effects, urban inequality, or (unequal) exposure to environmental hazards. The conceptualization of space, though, varies widely. Surveys routinely measure respondents’ ‘lived space’, asking about neighborhood quality, infrastructure, and environmental exposure. In recent years, the growing supply of geodata, remote sensing, mobility traces, and machine learning-derived indicators has made linking survey data and external spatial data increasingly attractive. This raises methodological questions about the relation and comparability of spatial indicators from different sources.
Often, implicit assumptions behind linking surveys and external data sources are that the latter provide an objective counterpart to the subjective views captured in surveys, and that finer spatial resolution brings us closer to the truth. This session aims at questioning and testing these kinds of assumptions. We proceed from the premise that a grid cell, an administrative unit, and a respondent's sense of ‘here’ each rest on distinct, largely unstated notions of space, and the resulting divergence may well be systematic rather than incidental.
We invite conceptual and methodological contributions on how spatial constructs are defined, measured and compared. Topics include: conceptualizations of space underlying survey instruments; frames of reference and question wording; the choice of spatial units and its consequences (edge effects, egohoods, activity spaces); potential mismatch between fuzzy constructs and sharply resolved indicators, including deliberately imprecise or fuzzy operationalization (buffers, respondent-drawn neighborhoods); validation of AI- and ML-derived spatial indicators against survey reports; harmonization and construct equivalence of spatial measures across studies, countries and levels of aggregation; and the measurement error introduced when linked indicators substitute for survey items.
We particularly welcome work that treats divergence between ‘subjective’ perception and ‘objective’ geodata as substantively informative and/or bridges urban sociology/geography and survey methodology.
