Studying Neighborhoods Using Uncertain Data from the American Community Survey: A Contextual Approach

摘要

In 2010 the American Community Survey (ACS) replaced the long form of the decennial census as the sole national source of demographic and economic data for small geographic areas such as census tracts. These small area estimates suffer from large margins of error, however, which makes the data difficult to use for many purposes. The value of a large and comprehensive survey like the ACS is that it provides a richly detailed, multivariate, composite picture of small areas. This article argues that one solution to the problem of large margins of error in the ACS is to shift from a variable-based mode of inquiry to one that emphasizes a composite multivariate picture of census tracts. Because the margin of error in a single ACS estimate, like household income, is assumed to be a symmetrically distributed random variable, positive and negative errors are equally likely. Because the variable-specific estimates are largely independent from each other, when looking at a large collection of variables these random errors average to zero. This means that although single variables can be methodologically problematic at the census tract scale, a large collection of such variables provides utility as a contextual descriptor of the place(s) under investigation. This idea is demonstrated by developing a geodemographic typology of all U.S. census tracts. The typology is firmly rooted in the social scientific literature and is organized around a framework of concepts, domains, and measures. The typology is validated using public domain data from the City of Chicago and the U.S. Federal Election Commission. The typology, as well as the data and methods used to create it, is open source and published freely online. 2010 ?,??????(ACS)???????????????????,?????????????????????????????????????,???????????,?????????????????? ACS ????????????,?????????????????????????,ACS ?????????????,????????????,??????????????????????ACS ???????,??????,?????????????,????????????????????????,???????,?????????????,????????????????,???????,??????????????????,??????????,?????????????????????????,???????????????????????????????????????????,??????????????????????????????????????????????????????????????????,??????,????????? En 2010 la Encuesta sobre la Comunidad Estadounidense (ACS, por su sigla en ingles) sustituyo el extenso formulario del censo decenal, como unica fuente nacional de datos demograficos y economicos para areas geograficas pequenas, tales como las secciones censales. Sin embargo, estos estimativos de area pequena sufren de grandes margenes de error, lo cual determina que los datos sean dificiles de usar para muchos propositos. El valor de una encuesta vasta y comprensiva como el ACS es que suministra un cuadro compuesto de las areas pequenas, abundante en detalles y multivariado. En este articulo se sostiene que una de las soluciones al problema de los amplios margenes de error de la ACS consiste en cambiar de un modo de investigacion basado en variables a otro que enfatice un cuadro compuesto multivariado de secciones censales. Debido a que el margen de error en un estimativo individual de la ACS, como el ingreso familiar, se asume como si se tratase de una variable aleatoria distribuida simetricamente, los errores positivos y negativos son igualmente probables. Por cuanto los calculos por variable especifica son en gran medida dependientes entre si, cuando nos enfrentamos a una coleccion grande de variables estos errores aleatorios se promedian en cero.