Correction: Reasoning COVID-19: the use of spatial metaphor in times of a crisis.

[This corrects the article DOI: 10.1057/s41599-022-01264-8.].

Reasoning COVID-19: the use of spatial metaphor in times of a crisis.

As other crises before, the COVID-19 pandemic put established discursive routines at stake. By framing the pandemic as a crisis, an immediate search for adequate counter-measures started to define proper means of mitigation and protection for the population. In the early stages of COVID-19, when little reliable information on the virus and its transmission behaviour was available, an intense use of metaphor to explain and govern the crisis had to be expected. Beside its well-known impact on (geo-)politics, a thorough analysis especially of the use of spatial metaphors to reason about the crises is still missing. In our approach, we rely on the foundational work of Lakoff and Johnson (1980) on image schemata, and prior work on spatial metaphors as part of argumentation patterns from cultural geography (Schlottmann, 2008). After a thorough analysis of prominent examples according to the argumentation scheme of Toulmin (1976 [1958]), we explored examples from the pre-existing corpus on COVID-19, deliberately compiled by DWDS for analysis of language patterns used throughout the pandemic. In a subsequent filter-refinement approach building on methods from cognitive linguistics and utilising a chunk of the same corpus, we were able to obtain and discuss results on the variety of spatial metaphors used at that time.

Neighborhood Greenspace and Socioeconomic Risk are Associated with Diabetes Risk at the Sub-neighborhood Scale: Results from the Prospective Urban and Rural Epidemiology (PURE) Study.

Greenspace and socioeconomic status are known correlates of diabetes prevalence, but their combined effects at the sub-neighborhood scale are not yet known. This study derives, maps, and validates a combined socioeconomic/greenspace index of individual-level diabetes risk at the sub-neighborhood scale, without the need for clinical measurements. In two Canadian cities (Vancouver and Hamilton), we computed 4 greenspace variables from satellite imagery and extracted 11 socioeconomic variables from the Canadian census. We mapped 5125 participants from the Prospective Urban and Rural Epidemiology Study by their residential address and used age- and sex-dependent walking speeds to estimate individual exposure zones to local greenspace and socioeconomic characteristics, which were then entered into a principal component analysis to derive a novel diabetes risk index (DRI-GLUCoSE). We mapped index scores in both study areas and validated the index using fully adjusted logistic regression models to predict individual diabetes status. Model performance was then compared to other non-clinical diabetes risk indices from the literature. Diabetes prevalence among participants was 9.9%. The DRI-GLUCoSE index was a significant predictor of diabetes status, exhibiting a small non-significant attenuation with the inclusion of dietary and physical activity variables. The final models achieved a predictive accuracy of 75%, the highest among environmental risk models to date. Our combined index of local greenspace and socioeconomic factors demonstrates that the environmental component of diabetes risk is not sufficiently explained by diet and physical activity, and that increasing urban greenspace may be a suitable means of reducing the burden of diabetes at the community scale.