Understanding Neighbourhoods in the US through Big Data and Urban Analytics
2:00pm - 3:00pm /
Wednesday 12th October 2016
/ Venue: Lecture Theatre 2 Central Teaching Hub
Type: Lecture /
Category: Department
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The American Community Survey (ACS) is the principal source for neighborhood-scale information about the US population and economy. The ACS is used to allocate billions in federal spending and is a critical input to social scientific research in the US. However, estimates from the ACS can be highly unreliable. For example, in over 72% of census tracts the estimated number of children under 5 in poverty has a margin of error greater than the estimate. Uncertainty of this magnitude complicates the use of ACS data in policy making, research, and governance. This presentation argues that by reconceptualizing census geographies using novel sources of big data it may be possible to improve the usability and precision of the large national statistical surveys. I illustrate the idea by deriving neighborhoods egocentrically using an address-level map of the entire population of several US cities and through the use a spatial optimization algorithm.