  An understanding of pedestrian dynamics is indispensable for numerous urban
applications including the design of transportation networks and planing for
business development. Pedestrian counting often requires utilizing manual or
technical means to count individuals in each location of interest. However,
such methods do not scale to the size of a city and a new approach to fill this
gap is here proposed. In this project, we used a large dense dataset of images
of New York City along with computer vision techniques to construct a
spatio-temporal map of relative person density. Due to the limitations of state
of the art computer vision methods, such automatic detection of person is
inherently subject to errors. We model these errors as a probabilistic process,
for which we provide theoretical analysis and thorough numerical simulations.
We demonstrate that, within our assumptions, our methodology can supply a
reasonable estimate of person densities and provide theoretical bounds for the
resulting error.
