Spatiotemporal analysis of retail: pre- and post-COVID dynamics in Gros, San Sebastian

The decrease of commercial activity that has been occurring in the last decade has recently accentuated by the COVID pandemic, affecting the livability of cities and public space. This paper analyzes the ground floor activities in Gros neighbourhood, in San Sebastian, Spain, observing both its temporal evolution and its spatial distribution. The study performs two in situ geolocation data collections in January and August 2020, immediately before and after the COVID lockdown in Spain. Through the collected data, it analyzes the distribution and evolution of ground floor space dedicated to public activities. The study concludes that the activities suffered a decrease of 1.8% in the analyzed period, and that activities located on pedestrianized streets or with fewer lanes have had fewer closures. The work also shows which efforts are needed for in situ data collection to guide urban policy. © 2022 Prensas de la Universidad de Zaragoza. All rights reserved.

Political beliefs affect compliance with government mandates.

We use the state-mandated stay-at-home orders during the coronavirus pandemic as a setting to study whether political beliefs inhibit compliance with government orders. Using geolocation data sourced from smartphones, we find residents in Republican counties are less likely to completely stay at home after a state order has been implemented relative to those in Democratic counties. Debit card transaction data shows that Democrats are more likely to switch to remote spending after state orders are implemented. Heterogeneity in factors such as Covid-19 risk exposure, geography, and county characteristics do not completely rule out our findings, suggesting political beliefs are an important determinant in the effectiveness of government mandates. Political alignment with officials giving orders may partially explain these partisan differences.

Political orientation and adherence to social distancing during the COVID-19 pandemic in Italy.

Many governments have implemented social distancing and lockdown measures to curb the spread of the novel coronavirus (COVID-19). Using province-level geolocation data from Italy, we document that political disbelief can limit government policy effectiveness. Residents in provinces leaning towards extreme right-wing parties show lower rates of compliance with social distancing order. We also find that, during the Italian lockdown, provinces with high protest votes virtually disregarded all social distancing orders. On the contrary, in provinces with higher political support for the current political legislation, we found a higher degree of social distancing compliance. These results are robust to controlling for other factors, including time, geography, local COVID-19 cases and deaths, healthcare hospital beds, and other sociodemographic and economic characteristics. Our research shows that bipartisan support and national responsibility are essential to implement and manage social distancing efficiently. From a broader perspective, our findings suggest that partisan politics and discontent with the political class (i.e., protest voting) might significantly affect human health and the economy.

Exposure density and neighborhood disparities in COVID-19 infection risk.

Although there is increasing awareness of disparities in COVID-19 infection risk among vulnerable communities, the effect of behavioral interventions at the scale of individual neighborhoods has not been fully studied. We develop a method to quantify neighborhood activity behaviors at high spatial and temporal resolutions and test whether, and to what extent, behavioral responses to social-distancing policies vary with socioeconomic and demographic characteristics. We define exposure density ([Formula: see text]) as a measure of both the localized volume of activity in a defined area and the proportion of activity occurring in distinct land-use types. Using detailed neighborhood data for New York City, we quantify neighborhood exposure density using anonymized smartphone geolocation data over a 3-mo period covering more than 12 million unique devices and rasterize granular land-use information to contextualize observed activity. Next, we analyze disparities in community social distancing by estimating variations in neighborhood activity by land-use type before and after a mandated stay-at-home order. Finally, we evaluate the effects of localized demographic, socioeconomic, and built-environment density characteristics on infection rates and deaths in order to identify disparities in health outcomes related to exposure risk. Our findings demonstrate distinct behavioral patterns across neighborhoods after the stay-at-home order and that these variations in exposure density had a direct and measurable impact on the risk of infection. Notably, we find that an additional 10% reduction in exposure density city-wide could have saved between 1,849 and 4,068 lives during the study period, predominantly in lower-income and minority communities.