In 2010 the American Community Survey (ACS) replaced the long form of the United States decennial census. The ACS is now the principal source of high-resolution geographic information about the U.S. population. The margins of error on ACS census tract-level data are on average 75 percent larger than those of the corresponding 2000 long-form estimate. The practical implications of this increase is that data are sometimes so imprecise that they are difficult to use. This paper explains why the ACS tract and block group estimates have large margins of error. Statistical concepts are explained in plain English. ACS margins of error are attributed to specific methodological decisions made by the Census Bureau. These decisions are best seen as compromises that attempt to balance financial constraints against concerns about data quality, timeliness, and geographic precision. In addition, demographic and geographic patterns in ACS data quality are identified. These patterns are associated with demographic composition of census tracts. Understanding the fundamental causes of uncertainty in the survey suggests a number of geographic strategies for improving the usability and quality ACS.

As a concept, social vulnerability describes combinations of social, cultural, economic, political, and institutional processes that shape socioeconomic differentials in the experience of and recovery from hazards. Quantitative measures of social vulnerability are widely used in research and practice. In this paper, we establish criteria for the evaluation of social vulnerability indicators and apply those criteria to the most widely used measure of social vulnerability, the Social Vulnerability Index (SoVI). SoVI is a single quantitative indicator that purports to measure a place's social vulnerability. We show that SoVI has some critical shortcomings regarding theoretical and internal consistency. Specifically, multiple SoVI-based measurements of the vulnerability of the same place, using the same data, can yield strikingly different results. We also show that the SoVI is often misaligned with theory; increases in variables that contribute to vulnerability, like the unemployment rate, often decrease vulnerability as measured by the SoVI. We caution against the use of the index in policy making or other risk-reduction efforts, and we suggest ways to more reliably assess social vulnerability in practice.

Social science research, public and private sector decisions, and allocations of federal resources often rely on data from the American Community Survey (ACS). However, this critical data source has high uncertainty in some of its most frequently used estimates. Using 2006-2010 ACS median household income estimates at the census tract scale as a test case, we explore spatial and nonspatial patterns in ACS estimate quality. We find that spatial patterns of uncertainty in the northern United States differ from those in the southern United States, and they are also different in suburbs than in urban cores. In both cases, uncertainty is lower in the former than the latter. In addition, uncertainty is higher in areas with lower incomes. We use a series of multivariate spatial regression models to describe the patterns of association between uncertainty in estimates and economic, demographic, and geographic factors, controlling for the number of responses. We find that these demographic and geographic patterns in estimate quality persist even after we account for the number of responses. Our results indicate that data quality varies across places, making cross-sectional analysis both within and across regions less reliable. Finally, we present advice for data users and potential solutions to the challenges identified.

Copyright 2015 AIP Publishing. This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing.Direct simulations of the incompressible Navier-Stokes equations are limited to relatively low-Reynolds numbers. Hence, dynamically less complex mathematical formulations are necessary for coarse-grain simulations. Eddy-viscosity models for large-eddy simulation is probably the most popular example thereof: they rely on differential operators that should properly detect different flow configurations (laminar and 2D flows, near-wall behavior, transitional regime, etc.). Most of them are based on the combination of invariants of a symmetric tensor that depends on the gradient of the resolved velocity field, . In this work, models are presented within a framework consisting of a 5D phase space of invariants. In this way, new models can be constructed by imposing appropriate restrictions in this space. For instance, considering the three invariants P GG T , Q GG T , and R GG T of the tensorGG T , and imposing the proper cubic near-wall behavior, i.e., , we deduce that the eddy-viscosity is given by . Moreover, only R GG T -dependent models, i.e., p > - 5/2, switch off for 2D flows. Finally, the model constant may be related with the Vreman’s model constant via ; this guarantees both numerical stability and that the models have less or equal dissipation than Vreman’s model, i.e., . The performance of the proposed models is successfully tested for decaying isotropic turbulence and a turbulent channel flow. The former test-case has revealed that the model constant, C s3pqr , should be higher than 0.458 to obtain the right amount of subgrid-scale dissipation, i.e., C s3pq = 0.572 (p = - 5/2), C s3pr = 0.709 (p = - 1), and C s3qr = 0.762 (p = 0).Peer ReviewedPostprint (published version

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.

Segregated areas may occur around an attractive park or a waste incinerator, but the magnitude and group membership of the people in closest proximity will likely be difierent. We therefore introduce a local segregation measure that can be applied to any location within a metropolitan area, and that can identify the group that is relatively more concentrated around that reference location. We further introduce an inference approach to identify the statistical significance of a particular segregation value. In an exploratory setting the index can be used to generate a map of hot spots, and seed the question: “why is this group significantly concentrated around that location?”

This article introduces a new approach to measuring neighborhood change. Instead of the traditional method of identifying “neighborhoods” a priori and then studying how resident attributes change over time, this approach looks at the neighborhood more intrinsically as a unit that has both a geographic footprint and a socioeconomic composition. Therefore, change is identified when both aspects of a neighborhood transform from one period to the next. The approach is based on a spatial clustering algorithm that identifies neighborhoods at two points in time for one city. The authors also develop indicators of spatial change at both the macro (city) level and the local (neighborhood) scale. The authors illustrate these methods in an application to an extensive database of time-consistent census tracts for 359 of the largest metropolitan areas in the United States for the period 1990-2000.

The identification of regions is both a computational and conceptual challenge. Even with growing computational power, regionalization algorithms must rely on heuristic approaches in order to find solutions. Therefore, the constraints and evaluation criteria that define a region must be translated into an algorithm that can efficiently and effectively navigate the solution space to find the best solution. One limitation of many existing regionalization algorithms is a requirement that the number of regions be selected a priori. The max-p algorithm, introduced in Duque et al. (2012), does not have this requirement, and thus the number of regions is an output of, not an input to, the algorithm. In this paper we extend the max-p algorithm to allow for greater flexibility in the constraints available to define a feasible region, placing the focus squarely on the multidimensional characteristics of region. We also modify technical aspects of the algorithm to provide greater flexibility in its ability to search the solution space. Using synthetic spatial and attribute data we are able to show the algorithm's broad ability to identify regions in maps of varying complexity. We also conduct a large scale computational experiment to identify parameter settings that result in the greatest solution accuracy under various scenarios. The rules of thumb identified from the experiment produce maps that correctly assign areas to their “true” region with 94% average accuracy, with nearly 50 percent of the simulations reaching 100 percent accuracy.