Rideshare Trips and Alcohol-Involved Motor Vehicle Crashes in Chicago.

Objective: Rideshare companies such as Uber and Lyft have substantially changed transportation markets in the United States and globally. The aim of this study was to examine whether ridesharing is associated with reductions in alcohol-involved crashes.

Method: This case-series study used highly spatially and temporally resolved trip-level rideshare data and motor vehicle crash data from the Chicago Data Portal from November 2018 to December 2019.

Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets.

Multi-temporal, globally consistent, high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health, wealth, and resource access, and monitoring change in these over time. The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multi-temporal scales. This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas.

Palm Oil in Myanmar: A Spatiotemporal Analysis of the Effects of Industrial Farming on Biodiversity Loss.

Background: Palm oil consumption is potentially deleterious to human health, and its production has resulted in 11 million hectares of deforestation globally. Importing roughly 394,000 metric tons of palm oil in 2012 alone, the Burmese government has recently pushed for intensive oil palm development to sate domestic demand for consumption and become international market players. Given well-studied linkages between biodiversity loss and ecosystem instability, this study aims to characterize the nature of deforestation for oil palm production in Myanmar, its relationship to increased biodiversity loss, and contextualize the potential impacts of this loss on diets and human health in rural Myanmar.