Reducing Uncertainty in the American Community Survey through Data-Driven Regionalization
Reducing Uncertainty in the American Community Survey through Data-Driven Regionalization
复制标题
通过数据驱动的区域化减少美国社区调查的不确定性
作者：
S. Spielman;D. Folch
美国社区调查（ACS）是美国最大的家庭调查，也是有关美国人口和经济的邻里规模信息的主要来源。ACS用于分配数十亿美元的联邦支出，是美国社会科学研究的关键投入。然而，ACS的估计可能非常不可靠。例如，在72%以上的人口普查区域，5岁以下贫困儿童的估计数的误差幅度大于估计数。如此巨大的不确定性使得社会数据在政策制定、研究和治理中的使用变得复杂。本文提出了一种启发式空间优化算法，该算法能够通过创建新的复合地理（称为区域化的过程）来减少测量数据中的误差幅度。区域化是一个复杂的组合问题。在这里，而不是专注于区域化的技术方面，我们演示了如何使用一个专门构建的开源区域化算法来处理调查数据，以减少误差幅度到用户指定的阈值。
The American Community Survey (ACS) is the largest survey of US households and 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 social data in policy making, research, and governance. This article presents a heuristic spatial optimization algorithm that is capable of reducing the margins of error in survey data via the creation of new composite geographies, a process called regionalization. Regionalization is a complex combinatorial problem. Here rather than focusing on the technical aspects of regionalization we demonstrate how to use a purpose built open source regionalization algorithm to process survey data in order to reduce the margins of error to a user-specified threshold.
影响因子：
4.9
作者：
Spielman, Seth E.;Folch, David;Nagle, Nicholas
通讯作者：
Nagle, Nicholas