Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US

The United States spends more than $1B each year on initiatives such as the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed half a decade. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may provide a cheaper and faster alternative. Here, we present a method that determines socioeconomic trends from 50 million images of street scenes, gathered in 200 American cities by Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22M automobiles in total (8% of all automobiles in the US), was used to accurately estimate income, race, education, and voting patterns, with single-precinct resolution. (The average US precinct contains approximately 1000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a 15-minute drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next Presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographic trends may effectively complement labor-intensive approaches, with the potential to detect trends with fine spatial resolution, in close to real time.Comment: 41 pages including supplementary material. Under review at PNA

Plot-based urbanism : towards time-consciousness in place-making

Some of us have recently argued that what we still miss is the serious consideration of the factor of time in urbanism, or, in other words, a deeper "time conscious" approach (Thwaites, Porta, Romice, & Greaves, 2008). Inevitably, that means focusing on change as the essential dynamic of evolution in the built environment, which in turn leads to re-addressing concepts like control, self-organization and community participation. After time and change have been finally firmly placed at the centre stage, the whole discipline of urban planning and design, its conceptual equipment as well as its operational toolbox, reveals its weaknesses under a new light and calls for the construction of a different scenario. This paper poses the problem of this scenario in disciplinary terms, it argues about its premises and outlines its essential features. The scope of this paper is not to deliver a comprehensive model for a new approach to urban planning and design, but to set the right framework and rise the right questions so that we can start thinking of issues such as urban regeneration, informal settlements and massive urbanization, community participation and representation, beauty and humanity in space, in a different way

Global Trade Impacts: Addressing the Health, Social and Environmental Consequences of Moving International Freight Through Our Communities

Examines freight transportation industry trends; the impact of global trade on workers, the environment, and health in both exporting and importing countries; and organizing strategies and policy innovations for minimizing the damage and ensuring health