Daniel Rhoads

TL;DR: This paper combines public data sources, large-scale street imagery and computer vision techniques to approach pedestrian and vehicle safety with an automated, relatively simple, and universally-applied data-processing scheme.

TL;DR: An extensible, statistically-solid, and reliable framework to measure the differences between the communication patterns of two groups and assess how house mixing strategies, in combination with public and private investment, may help to overcome segregation is developed.

TL;DR: This work uses sidewalk data from ten cities in three continents, to first analyse the distribution of sidewalk and roadbed geometries, and finds that cities present an unbalanced distribution of public space, favouring automobiles at the expense of pedestrians.

TL;DR: This work applies percolation theory to examine whether the sidewalk infrastructure in cities can withstand the tight pandemic social distancing imposed on the authors' streets, and proposes a shared-effort heuristic that delays the sidewalk connectivity breakdown, while preserving the road network's functionality.

TL;DR: An extensible, statistically solid and reliable framework to measure the differences between the communication patterns of two groups, and identifies the districts of the Istanbul province where the variation between the ways the two populations communicate is largest.