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Place connectivity is explored between geographic locations extracted from comments on Reddit. Unlike formally structured geographic data, this corpus of unstructured text provides connections derived from co-occurring locations, capturing subconscious links between them, alongside inherent biases. Our work demonstrates the ability to link locations mentioned by unique users, building ‘mental’ place connections for over 50,000 unique locations in the United Kingdom. Sentiment regarding locations is compared against their levels of connectivity, demonstrating that user opinions regarding locations are likely drivers in mental place connectivity.

This paper explores cognitive place associations; conceptualised as a place‐based mental model that derives subconscious links between geographic locations. Utilising a large corpus of online discussion data from the social media website Reddit, we experiment on the extraction of such geographic knowledge from unstructured text. First we construct a system to identify place names found in Reddit comments, disambiguating each to a set of coordinates where possible. Following this, we build a collective picture of cognitive place associations in the United Kingdom, linking locations that co‐occur in user comments and evaluating the effect of distance on the strength of these associations. Exploring these geographies nationally, associations were shown to be typically weaker over greater distances. This distance decay is also highly regional, rural areas typically have greater levels of distance decay, particularly in Wales and Scotland. When comparing major cities across the UK, we observe distinct distance decay patterns, influenced primarily by proximity to other cities. [ABSTRACT FROM AUTHOR]

This paper provides an analysis of working from home patterns in England using data from the 2021 Census to understand (1) how patterns of working from home (WFH) in England have shifted since the COVID‐19 pandemic and (2) whether human mobility indicators, specifically Google Community Mobility Reports, provide a reliable proxy for WFH patterns recorded by the 2021 Census, providing a formal evaluation of the reliability of such datasets, whose applications have grown exponentially over the COVID‐19 pandemic. We find that WFH patterns recorded by the 2021 Census were unique compared with previous UK censuses, reflecting an unprecedented increase likely caused by persistent changes to employment during the COVID‐19 pandemic, with a clear social gradient emerging across the country. We also find that Google mobility in 'Residential' and 'Workplace' settings provides a reliable measurement of the distribution of WFH populations across Local Authorities, with varying uncertainties for mobility indicators collected in different settings. These findings provide insights into the utility of such datasets to support population research in intercensal periods, where shifts may be occurring, but can be difficult to quantify empirically. [ABSTRACT FROM AUTHOR]

UK‐wide multivariate neighbourhood classifications have been built using small area population data following every census since 1971, and have been built using Output Area geographies since 2001. Policy makers in both the public and private sectors find such taxonomies, typically arranged into hierarchies of Supergroups, Groups and Subgroups, useful across a wide range of applications in business and service planning. Recent and forthcoming releases of small area census statistics pose new methodological challenges. For example, the 2022 Scottish Census was carried out a year after those in other UK nations, and some of the variables now collected across different jurisdictions do not bear direct comparison with one another. Here we develop a methodology to accommodate these issues alongside the more established procedures of variable selection, standardisation, transformation, class definition and labelling. [ABSTRACT FROM AUTHOR]