Seth Spielman, Geography, UC-Boulder: How Well Do We Understand America’s Neighborhoods? Reducing Uncertainty in the American Community Survey (ACS) By Reconceptualizing Neighborhoods

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Seth Spielman
September 30, 2014
12:30PM - 1:30PM
Location
038 Townshend Hall

Date Range
Add to Calendar 2014-09-30 12:30:00 2014-09-30 13:30:00 Seth Spielman, Geography, UC-Boulder: How Well Do We Understand America’s Neighborhoods? Reducing Uncertainty in the American Community Survey (ACS) By Reconceptualizing Neighborhoods

How Well Do We Understand America’s Neighborhoods?  Reducing Uncertainty in the American Community Survey (ACS) By Reconceptualizing Neighborhoods

Abstract:  In 2010 the American Community Survey (ACS) replaced the Decennial Census as 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.  For example, the ACS indicates that Census Tract 196 in Brooklyn, New York has 169 children under 5 in poverty ± 174 children, suggesting that somewhere between 0 and 343 young children in the area live in poverty.  This presentation argues that by reconceptualizing census geographies it may be possible to improve the usability and precision of the ACS.   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.  

038 Townshend Hall Institute for Population Research popcenter@osu.edu America/New_York public
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How Well Do We Understand America’s Neighborhoods?  Reducing Uncertainty in the American Community Survey (ACS) By Reconceptualizing Neighborhoods

Abstract:  In 2010 the American Community Survey (ACS) replaced the Decennial Census as 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.  For example, the ACS indicates that Census Tract 196 in Brooklyn, New York has 169 children under 5 in poverty ± 174 children, suggesting that somewhere between 0 and 343 young children in the area live in poverty.  This presentation argues that by reconceptualizing census geographies it may be possible to improve the usability and precision of the ACS.   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.