Individual and environmental bicycling determinants during a pandemic.

Introduction: Research has identified many factors associated with bicycling, but little is known on their relative influence for an individual's decision to bicycle or what led to the surge in bicycling during the COVID-19 pandemic in the U.S. Methods: Our research leverages a sample of 6735 U.S. adults to identify key predictors and their relative influence on both increased bicycling during the pandemic and on whether an individual commutes by bicycle. LASSO regression models identified a reduced set of predictors for the outcomes of interest from 55 determinants included in the modeling. Results: We find individual and environmental factors have a role in explaining the shift towards bicycling-with key differences in predictors for increased overall cycling during the pandemic compared to bicycle commuting. Conclusions: Our findings add to the evidence base that policies can impact bicycling behavior. Specifically, increasing e-bike accessibility and limiting residential streets to local traffic are two policies that show promise for encouraging bicycling.

Modeling state preferences for Covid-19 policies: Insights from the first pandemic summer.

INTRODUCTION: During the COVID-19 pandemic, governments have experimented with a wide array of policies to further public health goals. This research offers an application of multilevel regression with post-stratification (MRP) analysis to assess state-level support for commonly implemented policies during the pandemic. METHODS: We conducted a national survey of U.S. adults using The Harris Poll panel from June 17-29, 2020. Respondents reported their support for a set of measures that were being considered in jurisdictions in the U.S. at the time the survey was fielded. MRP analysis was then used to generate estimates of state-level support. RESULTS: The research presented here suggests generally high levels of support for mask mandates and social distancing measures in June 2020-support that was consistent throughout the United States. In comparison, support for other policies, such as changes to the road environment to create safer spaces for walking and bicycling, had generally low levels of support throughout the country. This research also provides some evidence that higher support for coronavirus-related policies could be found in more populous states with large urban centers, recognizing that there was low variability across states. CONCLUSION: This paper provides a unique application of MRP analysis in the public health field, uncovering noteworthy state-level patterns, and offering several avenues for future research. Future research could examine policy support at a small geographic level, such as by counties, to understand the distribution of support for public policies within states.

Estimating stimuli from contrasting categories: truncation due to boundaries.

In this article, the authors present and test a formal model that holds that people use information about category boundaries in estimating inexactly represented stimuli. Boundaries restrict stimuli that are category members to fall within a particular range. This model posits that people increase the average accuracy of stimulus estimates by integrating fine-grain values with boundary information, eliminating extreme responses. The authors present 4 experiments in which people estimated sizes of squares from 2 adjacent or partially overlapping stimulus sets. When stimuli from the 2 sets were paired in presentation, people formed relative size categories, truncating their estimates at the boundaries of these categories. Truncation at the boundary of separation between the categories led to exaggeration of differences between stimuli that cross categories. Yet truncated values are shown to be more accurate on average than unadjusted values.

Spatial categories and the estimation of location.

Four experiments are reported in which people organize a space hierarchically when they estimate particular locations in that space. Earlier work showed that people subdivide circles into quadrants bounded at the vertical and horizontal axes, biasing their estimates towards prototypical diagonal locations within those spatial categories (Psychological Review 98 (1991) 352). In this work Huttenlocher, Hedges, and Duncan showed that the use of such spatial categories can increase the accuracy of estimation of inexactly represented locations. The stimulus locations we examined were uniformly distributed across the circle. In the present study we explore whether variation in the distribution of locations affects how the circle is categorized. Other things being equal, categories that capture high density regions in a stimulus space should contribute most to accuracy of estimation. However, precision of boundaries is also important to accuracy; with imprecise boundaries stimuli may be misclassified, leading to large errors in estimation. We found that people use the same spatial categories regardless of the distribution of the locations. We argue that this spatial organization nevertheless can maximize the accuracy of estimates because vertical and horizontal category boundaries are the most exact, minimizing misclassification of stimuli.