Optimized spatial information for 1990, 2000, and 2010 U.S. census microdata.

We report on the successful completion of a project to upgrade the positional accuracy of every response to the 1990, 2000, and 2010 U.S. decennial censuses. The resulting data set, called Optimized Spatial Census Information Linked Across Time (OSCILAT), resides within the restricted-access data warehouse of the Federal Statistical Research Data Center (FSRDC) system where it is available for use with approval from the U.S. Census Bureau. OSCILAT greatly improves the accuracy and completeness of spatial information for older censuses conducted prior to major quality improvements undertaken by the Bureau. Our work enables more precise spatial and longitudinal analysis of census data and supports exact tabulations of census responses for arbitrary spatial units, including tabulating responses from 1990, 2000, and 2010 within 2020 block boundaries for precise measures of change over time for small geographic areas.

Day time, night time, over time: geographic and temporal uncertainty when linking event and contextual data.

BACKGROUND: The growth of geolocated data has opened the door to a wealth of new research opportunities in the health fields. One avenue of particular interest is the relationship between the spaces where people spend time and their health outcomes. This research model typically intersects individual data collected on a specific cohort with publicly available socioeconomic or environmental aggregate data. In spatial terms: individuals are represented as points on map at a particular time, and context is represented as polygons containing aggregated or modeled data from sampled observations. Uncertainty abounds in these kinds of complex representations. METHODS: We present four sensitivity analysis approaches that interrogate the stability of spatial and temporal relationships between point and polygon data. Positional accuracy assesses the significance of assigning the point to the correct polygon. Neighborhood size investigates how the size of the context assumed to be relevant impacts observed results. Life course considers the impact of variation in contextual effects over time. Time of day recognizes that most people occupy different spaces throughout the day, and that exposure is not simply a function residential location. We use eight years of point data from a longitudinal study of children living in rural Pennsylvania and North Carolina and eight years of air pollution and population data presented at 0.5 mile (0.805 km) grid cells. We first identify the challenges faced for research attempting to match individual outcomes to contextual effects, then present methods for estimating the effect this uncertainty could introduce into an analysis and finally contextualize these measures as part of a larger framework on uncertainty analysis. RESULTS: Spatial and temporal uncertainty is highly variable across the children within our cohort and the population in general. For our test datasets, we find greater uncertainty over the life course than in positional accuracy and neighborhood size. Time of day uncertainty is relatively low for these children. CONCLUSIONS: Spatial and temporal uncertainty should be considered for each individual in a study since the magnitude can vary considerably across observations. The underlying assumptions driving the source data play an important role in the level of measured uncertainty.

The Contributions of Places to Metropolitan Ethnoracial Diversity and Segregation: Decomposing Change Across Space and Time.

Although the trend toward greater ethnoracial diversity in the United States has been documented at a variety of geographic scales, most research tracks diversity one scale at a time. Our study bridges scales, asking how the diversity and segregation patterns of metropolitan areas are influenced by shifts in the racial/ethnic composition of their constituent places. Drawing on 1980-2010 decennial census data, we use a new visual tool to compare the distributions of place diversity for 50 U.S. metro areas over three decades. We also undertake a decomposition analysis of segregation within these areas to evaluate hypotheses about the roles of different types of places in ethnoracial change. The decomposition indicates that although principal cities continue to shape the overall diversity of metro areas, their relative impact has declined since 1980. Inner suburbs have experienced substantial increases in diversity during the same period. Places with large white majorities now contribute more to overall metropolitan diversity than in the past. In contrast, majority black and majority Hispanic places contribute less to metropolitan diversity than in the past. The complexity of the patterns we observe is underscored through an inspection of two featured metropolises: Chicago and Dallas.

Segregation as a multi-scalar phenomenon and its implications for neighborhood-scale research: the case of South Seattle 1990-2010.

Neighborhoods and neighborhood change are often at least implicitly understood in relation to processes taking place at scales both smaller than and larger than the neighborhood itself. Until recently our capacity to represent these multi-scalar processes with quantitative measures has been limited. Recent work on "segregation profiles" by Reardon and collaborators (Reardon et al., 2008, 2009) expands our capacity to explore the relationship between population measures and scale. With the methodological tools now available, we need a conceptual shift in how we view population measures in order to bring our theories and measures of neighborhoods into alignment. I argue that segregation can be beneficially viewed as multi-scalar; not a value calculable at some 'correct' scale, but a continuous function with respect to scale. This shift requires new ways of thinking about and analyzing segregation with respect to scale that engage with the complexity of the multi-scalar measure. Using block level data for eight neighborhoods in Seattle, Washington I explore the implications of a multi-scalar segregation measure for understanding neighborhoods and neighborhood change from 1990 to 2010.

Spatial variations in US poverty: beyond metropolitan and non-metropolitan.

Because poverty in rural and urban areas of the US often has different causes, correlates and solutions, effective anti-poverty policies depend on a thorough understanding of the ruralness or urbanness of specific places. This paper compares several widely used classification schemes and the varying magnitudes of poverty that they reveal in the US. The commonly used 'metropolitan/non-metropolitan' distinction obscures important socioeconomic differences among metropolitan areas, making our understanding of the geography of poverty imprecise. Given the number and concentration of poor people living in mixed-rural and rural counties in metropolitan regions, researchers and policy-makers need to pay more nuanced attention to the opportunities and constraints such individuals face. A cross-classification of the Office of Management and Budget's metro system with a nuanced RUDC scheme is the most effective for revealing the geographical complexities of poverty within metropolitan areas.

Agents of Change: Mixed-Race Households and the Dynamics of Neighborhood Segregation in the United States.

This article explores the effects of mixed-race household formation on trends in neighborhood-scale racial segregation. Census data show that these effects are nontrivial in relation to the magnitude of decadal changes in residential segregation. An agent-based model illustrates the potential long-run impacts of rising numbers of mixed-race households on measures of neighborhood-scale segregation. It reveals that high rates of mixed-race household formation will reduce residential segregation considerably. This occurs even when preferences for own-group neighbors are high enough to maintain racial separation in residential space in a Schelling-type model. We uncover a disturbing trend, however; levels of neighborhood-scale segregation of single-race households can remain persistently high even while a growing number of mixed-race households drives down the overall rate of residential segregation. Thus, the article's main conclusion is that parsing neighborhood segregation levels by household type-single versus mixed race-is essential to interpret correctly trends in the spatial separation of racial groups, especially when the fraction of households that are mixed race is dynamic. More broadly, the article illustrates the importance of household-scale processes for urban outcomes and joins debates in geography about interscalar relationships.