  Distributed sensors have been proposed to detect nuclear materials if they
enter urban areas. Past work on small detector systems has shown that data
fusion can improve detection. Here, we show how this could be done for a large
detector network using Pearsons Method. We evaluate how a sensor network would
perform in New York City using a combination of radiation transport and
geographic information systems. We use OpenStreetMap data to construct a grid
over the streets and analyze vehicle paths using pickup and drop off data from
the New York State Department of Transportation. The results show that data
synthesis in a large network not only improves the time to the first detection
but reduces the number of missed sources.
