ESRA 2027 sessions by theme

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How can big geodata and emerging methodologies improve the way survey research captures the environments in which respondents live, move, and form their perceptions, attitudes and behaviors?

Survey research has traditionally relied on self-reports and aggregate statistics to characterize respondents' spatial contexts. The proliferation of big geodata creates new possibilities for augmenting surveys with detailed information about respondents' social, built and natural environments. Concurrently, advances in geospatial AI, machine learning and spatial data science are expanding what can be extracted from these data and how individual contexts can be measured. These developments raise new questions about which spatial indicators can be captured, how they should be constructed, and which representations of context are most valid.

First, we focus on novel measures, proxies and data sources. Surveys are increasingly augmented with streetview imagery, crowdsourced and commercial geodata, government request data, footfall and mobility data, social media and AI-generated georeferenced data. We welcome contributions that introduce, validate, compare or apply such data, assess their potential and develop experimental measures of spatial context.

Second, we focus on emerging methodologies for operationalizing spatial context. Computational approaches such as (social) network analysis, geospatial AI, machine and deep learning, virtual reality methods and synthetic urban data enable researchers to derive new contextual indicators from complex big geodata. We welcome work that develops or evaluates such methods and examines how they can produce alternative definitions, scales and measures of context for survey research.

We especially welcome experimental, unconventional and exploratory contributions: new computational and statistical methods, proof-of-concept applications, emerging data sources, and provocative or high-risk ideas still under development. We encourage work that challenges existing practices, tests unusual combinations of data and methods, or simply asks: could we measure spatial context differently, and how? The session aims to provide a safe space