The cityseer Python package for pedestrian-scale network-based urban analysis

cityseer-api is a Python package consisting of computational tools for fine-grained street-network and land-use analysis, helpful in assessing the morphological precursors to vibrant neighbourhoods. It is underpinned by network-based methods developed specifically for urban analysis at the pedestrian scale. cityseer-api computes a variety of node and segment-based network centrality methods, land-use accessibility and mixed-use measures, and statistical aggregations. Accessibilities and aggregations are computed dynamically over the street-network while taking walking distance thresholds and the direction of approach into account, and can optionally incorporate spatial impedances and network decomposition to increase spatial precision. The use of Python facilitates compatibility with popular computational tools for network manipulation (NetworkX), geospatial topology (shapely), geospatial data state management (GeoPandas), and the NumPy stack of scientific packages. The provision of robust network cleaning tools aids the use of OpenStreetMap data for network analysis. Underlying loop-intensive algorithms are implemented in Numba JIT compiled code so that the methods scale efficiently to larger cities and regions. Online documentation is available from cityseer.benchmarkurbanism.com, and the Github repository is available at github.com/benchmark-urbanism/cityseer. Example notebooks are available at cityseer.benchmarkurbanism.com/examples

Detection and prediction of urban archetypes at the pedestrian scale: computational toolsets, morphological metrics, and machine learning methods

Granular, dense, and mixed-use urban morphologies are hallmarks of walkable and vibrant streets. However, urban systems are notoriously complex and planned urban development, which grapples with varied interdependent and oft conflicting criteria, may — despite best intentions — yield aberrant morphologies fundamentally at odds with the needs of pedestrians and the resiliency of neighbourhoods. This work addresses the measurement, detection, and prediction of pedestrian-friendly urban archetypes by developing techniques for high-resolution urban analytics at the pedestrian scale. A spatial-analytic computational toolset, the cityseer-api Python package, is created to assess localised centrality, land-use, and statistical metrics using contextually sensitive workflows applied directly over the street network. cityseer-api subsequently facilitates a review of mixed-use and street network centrality methods to improve their utility concerning granular urban analysis. Unsupervised machine learning methods are applied to recover ‘signatures’ — urban archetypes — using Principal Component Analysis, Variational Autoencoders, and clustering methods from a high-resolution multi-variable and multi-scalar dataset consisting of centralities, land-uses, and population densities for Greater London. Supervised deep-learning methods applied to a similar dataset developed for 931 towns and cities in Great Britain demonstrate how, with the aid of domain knowledge, machine-learning classifiers can learn to discriminate between ‘artificial’ and ‘historical’ urban archetypes. These methods use complex systems thinking as a departure point and illustrate how high-resolution spatial-analytic quantitative methods can be combined with machine learning to extrapolate benchmarks in keeping with more qualitatively framed urban morphological conceptions. Such tools may aid urban design professionals in better anticipating the outcomes of varied design scenarios as part of iterative and scalable workflows. These techniques may likewise provide robust and demonstrable feedback as part of planning review and approvals processes

The application of mixed-use measures at the pedestrian-scale

Mixed-use urbanism affords access to diverse assortments of land-uses within a pedestrian-accessible context. It confers advantages such as reductions to driving, air pollution, and Body Mass Index with associated increases in active transportation and improvements to health. However, whereas mixed-use urbanism is clearly beneficial, methods for measuring and assessing the presence of mixed-uses at a granular level of analysis remain murkier. This work demonstrates techniques for gauging mixed-uses in more spatially precise terms concurring more readily with an urbanist's conception of pedestrian-accessible mixed-uses. It does so through the use of the cityseer-api Python package, which facilitates the use of spatially granular land-use classification data assigned to adjacent street edges and then aggregated dynamically, with distances measured from each point of analysis to each accessible land-use while taking the direction of approach into account. It is argued that Hill Numbers is a suitable measure of diversity because it can mirror the intent of traditional indices while behaving more intuitively. Further, distance-weighted formulations of Hill diversity can be applied with spatial impedances, thus conferring a particularly spatially nuanced gauge of local access to mixed-uses. These methods and indices are demonstrated for Greater London with observations correlated to Principal Component Analysis derived from a range of land-use accessibilities measured from the same locations and for the same point-of-interest dataset. The Hill diversity measures, particularly the distance-weighted formulations, offer the most robust correlations for both expansive mixed-use districts and more local 'high-street' mixes of uses while yielding the most intuitive and spatially precise behaviour in the accompanying plots.Comment: Adds arXiv identifiers / references for associated paper

Author Correction: A consensus-based transparency checklist.

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Comparative transmission of SARS-CoV-2 Omicron (B.1.1.529) and Delta (B.1.617.2) variants and the impact of vaccination: national cohort study, England

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant (B.1.1.529) rapidly replaced Delta (B.1.617.2) to become dominant in England. Our study assessed differences in transmission between Omicron and Delta using two independent data sources and methods. Omicron and Delta cases were identified through genomic sequencing, genotyping and S-gene target failure in England from 5-11 December 2021. Secondary attack rates for named contacts were calculated in household and non-household settings using contact tracing data, while household clustering was identified using national surveillance data. Logistic regression models were applied to control for factors associated with transmission for both methods. For contact tracing data, higher secondary attack rates for Omicron vs. Delta were identified in households (15.0% vs. 10.8%) and non-households (8.2% vs. 3.7%). For both variants, in household settings, onward transmission was reduced from cases and named contacts who had three doses of vaccine compared to two, but this effect was less pronounced for Omicron (adjusted risk ratio, aRR 0.78 and 0.88) than Delta (aRR 0.62 and 0.68). In non-household settings, a similar reduction was observed only in contacts who had three doses vs. two doses for both Delta (aRR 0.51) and Omicron (aRR 0.76). For national surveillance data, the risk of household clustering, was increased 3.5-fold for Omicron compared to Delta (aRR 3.54 (3.29-3.81)). Our study identified increased risk of onward transmission of Omicron, consistent with its successful global displacement of Delta. We identified a reduced effectiveness of vaccination in lowering risk of transmission, a likely contributor for the rapid propagation of Omicron

The History and Prehistory of Natural-Language Semantics

Contemporary natural-language semantics began with the assumption that the meaning of a sentence could be modeled by a single truth condition, or by an entity with a truth-condition. But with the recent explosion of dynamic semantics and pragmatics and of work on non- truth-conditional dimensions of linguistic meaning, we are now in the midst of a shift away from a truth-condition-centric view and toward the idea that a sentence’s meaning must be spelled out in terms of its various roles in conversation. This communicative turn in semantics raises historical questions: Why was truth-conditional semantics dominant in the first place, and why were the phenomena now driving the communicative turn initially ignored or misunderstood by truth-conditional semanticists? I offer a historical answer to both questions. The history of natural-language semantics—springing from the work of Donald Davidson and Richard Montague—began with a methodological toolkit that Frege, Tarski, Carnap, and others had created to better understand artificial languages. For them, the study of linguistic meaning was subservient to other explanatory goals in logic, philosophy, and the foundations of mathematics, and this subservience was reflected in the fact that they idealized away from all aspects of meaning that get in the way of a one-to-one correspondence between sentences and truth-conditions. The truth-conditional beginnings of natural- language semantics are best explained by the fact that, upon turning their attention to the empirical study of natural language, Davidson and Montague adopted the methodological toolkit assembled by Frege, Tarski, and Carnap and, along with it, their idealization away from non-truth-conditional semantic phenomena. But this pivot in explana- tory priorities toward natural language itself rendered the adoption of the truth-conditional idealization inappropriate. Lifting the truth-conditional idealization has forced semanticists to upend the conception of linguistic meaning that was originally embodied in their methodology

A consensus-based transparency checklist

We present a consensus-based checklist to improve and document the transparency of research reports in social and behavioural research. An accompanying online application allows users to complete the form and generate a report that they can submit with their manuscript or post to a public repository