Building CapaCITY/É for sustainable transportation: protocol for an implementation science research program in healthy cities.

INTRODUCTION: Improving sustainable transportation options will help cities tackle growing challenges related to population health, congestion, climate change and inequity. Interventions supporting active transportation face many practical and political hurdles. Implementation science aims to understand how interventions or policies arise, how they can be translated to new contexts or scales and who benefits. Sustainable transportation interventions are complex, and existing implementation science frameworks may not be suitable. To apply and adapt implementation science for healthy cities, we have launched our mixed-methods research programme, CapaCITY/É. We aim to understand how, why and for whom sustainable transportation interventions are successful and when they are not. METHODS AND ANALYSIS: Across nine Canadian municipalities and the State of Victoria (Australia), our research will focus on two types of sustainable transportation interventions: all ages and abilities bicycle networks and motor vehicle speed management interventions. We will (1) document the implementation process and outcomes of both types of sustainable transportation interventions; (2) examine equity, health and mobility impacts of these interventions; (3) advance implementation science by developing a novel sustainable transportation implementation science framework and (4) develop tools for scaling up and scaling out sustainable transportation interventions. Training activities will develop interdisciplinary scholars and practitioners able to work at the nexus of academia and sustainable cities. ETHICS AND DISSEMINATION: This study received approval from the Simon Fraser University Office of Ethics Research (H22-03469). A Knowledge Mobilization Hub will coordinate dissemination of findings via a website; presentations to academic, community organisations and practitioner audiences; and through peer-reviewed articles.

Tilted platforms: rental housing technology and the rise of urban big data oligopolies.

ABSTRACT: This article interprets emerging scholarship on rental housing platforms-particularly the most well-known and used short- and long-term rental housing platforms-and considers how the technological processes connecting both short-term and long-term rentals to the platform economy are transforming cities. It discusses potential policy approaches to more equitably distribute benefits and mitigate harms. We argue that information technology is not value-neutral. While rental housing platforms may empower data analysts and certain market participants, the same cannot be said for all users or society at large. First, user-generated online data frequently reproduce the systematic biases found in traditional sources of housing information. Evidence is growing that the information broadcasting potential of rental housing platforms may increase rather than mitigate sociospatial inequality. Second, technology platforms curate and shape information according to their creators' own financial and political interests. The question of which data-and people-are hidden or marginalized on these platforms is just as important as the question of which data are available. Finally, important differences in benefits and drawbacks exist between short-term and long-term rental housing platforms, but are underexplored in the literature: this article unpacks these differences and proposes policy recommendations. POLICY AND PRACTICE RECOMMENDATIONS: As rental housing technologies upend traditional market processes in favor of platform oligopolies, policymakers must reorient these processes toward the public good.Long-term and short-term rental platforms offer different market benefits and drawbacks, but the latter in particular requires proactive regulation to mitigate harm.At a minimum, policymakers must require that short-term rental platforms provide the information necessary for cities to enforce current, let alone new, housing regulations.Practitioners should be cautious inferring market conditions from rental housing platform data, due to difficult-to-measure sampling biases.