LOCAL POPULATION CHANGE AND VARIATIONS IN RACIAL INTEGRATION IN THE UNITED STATES, 2000-2010.

While population growth has been consistently tied to decreasing racial segregation at the metropolitan level in the United States, little work has been done to relate small-scale changes in population size to integration. We address this question through a novel technique that tracks population changes by race and ethnicity for comparable geographies in both 2000 and 2010. Using the Theil Index, we analyze the fifty most populous Metropolitan Statistical Areas in 2010 for changes in multigroup segregation. We classify local areas by their net population change between 2000 and 2010 using a novel unit of analysis based on aggregating census blocks. We find strong evidence that growing parts of rapidly growing metropolitan areas of the United States are crucial to understanding regional differences in segregation that have emerged in past decades. Multigroup segregation declined the most in growing parts of growing metropolitan areas. Comparatively, growing parts of shrinking or stagnant metropolitan areas were less diverse and had smaller declines in segregation. We also find that local areas with shrinking populations had disproportionately high minority representation in 2000 before population loss took place. We conclude that the regional context of population growth or decline has important consequences for the residential mixing of racial groups.

Spatial Variation in the Quality of American Community Survey Estimates.

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.

Migration in the 1930s: Beyond the Dust Bowl.

This paper analyzes in detail the role of environmental and economic shocks in the migration of the 1930s. The 1940 U.S. Census of Population asked every inhabitant where they lived five years earlier, a unique source for understanding migration flows and networks. Earlier research documented migrant origins and destinations, but we will show how short term and annual weather conditions at sending locations in the 1930s explain those flows, and how they operated through agricultural success. Beyond demographic data, we use data about temperature and precipitation, plus data about agricultural production from the agricultural census. The widely known migration literature for the 1930s describes an era of relatively low migration, with much of the migration that did occur outward from the Dust Bowl region and the cotton South. Our work about the complete U.S. will provide a fuller examination of migration in this socially and economically important era.

Community clusters of tsunami vulnerability in the US Pacific Northwest.

Many coastal communities throughout the world are threatened by local (or near-field) tsunamis that could inundate low-lying areas in a matter of minutes after generation. Although the hazard and sustainability literature often frames vulnerability conceptually as a multidimensional issue involving exposure, sensitivity, and resilience to a hazard, assessments often focus on one element or do not recognize the hazard context. We introduce an analytical framework for describing variations in population vulnerability to tsunami hazards that integrates (i) geospatial approaches to identify the number and characteristics of people in hazard zones, (ii) anisotropic path distance models to estimate evacuation travel times to safety, and (iii) cluster analysis to classify communities with similar vulnerability. We demonstrate this approach by classifying 49 incorporated cities, 7 tribal reservations, and 17 counties from northern California to northern Washington that are directly threatened by tsunami waves associated with a Cascadia subduction zone earthquake. Results suggest three primary community groups: (i) relatively low numbers of exposed populations with varied demographic sensitivities, (ii) high numbers of exposed populations but sufficient time to evacuate before wave arrival, and (iii) moderate numbers of exposed populations but insufficient time to evacuate. Results can be used to enhance general hazard-awareness efforts with targeted interventions, such as education and outreach tailored to local demographics, evacuation training, and/or vertical evacuation refuges.

Reducing uncertainty in the american community survey through data-driven regionalization.

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.

The Past, Present and Future of Geodemographic Research in the United States and United Kingdom.

This article presents an extensive comparative review of the emergence and application of geodemographics in both the United States and United Kingdom, situating them as an extension of earlier empirically driven models of urban socio-spatial structure. The empirical and theoretical basis for this generalization technique is also considered. Findings demonstrate critical differences in both the application and development of geodemographics between the United States and United Kingdom resulting from their diverging histories, variable data economies, and availability of academic or free classifications. Finally, current methodological research is reviewed, linking this discussion prospectively to the changing spatial data economy in both the United States and United Kingdom.

The co-evolution of residential segregation and the built environment at the turn of the 20th century: a Schelling model.

To what degree does the built environment of cities shape the social environment? In this paper we use a Schelling-like agent based model to consider how changes to the built environment of cities relate to changes in residential segregation by income and ethnicity. To develop this model we exploit insights from a high resolution historical GIS which maps 100% of the population of Newark, NJ in 1880. Newark in 1880 had a complex social landscape characterized by areas of significant social and economic segregation and areas of relative integration. We develop a Schelling model capable of reproducing these residential patterns. We use this model to explore the decentralization of housing, a specific phenomenon associated with the demise of the walking city in the late 19th century. Holding agent preferences constant, but allowing the landscape of the Schelling model to evolve in ways that reflect historical changes to the built environment produces changes to the social landscape that are also consistent with history. Our work suggests that changes in residential segregation do not necessarily imply changes to individual attitudes and preferences. Changes in residential segregation can be generated by changes to the built environment, specifically the geographic distribution of housing.

Spatial Collective Intelligence? credibility, accuracy, and Volunteered Geographic Information.

Collective intelligence is the idea that under the right circumstances collections of individuals are smarter than even the smartest individuals in the group (Suroweiki 2004), that is a group has an "intelligence" that is independent of the intelligence of its members. The ideology of collective intelligence undergirds much of the enthusiasm about the use of "volunteered" or crowdsourced geographic information. Literature from a variety of fields makes clear that not all groups possess collective intelligence, this paper identifies four pre-conditions for the emergence of collective intelligence and then examine the extent to which collectively generated mapping systems satisfy these conditions. However, the "intelligence" collectively generated maps is hard to assess because there are two difficult to reconcile perspectives on map quality- the credibility perspective and the accuracy perspective. Much of the current literature on user generated maps focuses on assessing the quality of individual contributions. However, because user generated maps are complex social systems and because the quality of a contribution is difficult to assess this strategy may not yield an "intelligent" end product. The existing literature on collective intelligence suggests that the structure of groups more important that the intelligence of group members. Applying this idea to user generated suggests that systems should be designed to foster conditions known to produce collective intelligence rather than privileging particular contributions/contributors. The paper concludes with some design recommendations and by considering the implications of collectively generated maps for both expert knowledge and traditional state sponsored mapping programs.

Patterns and causes of uncertainty in the American Community Survey.

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.

Identifying Regions Based on Flexible User Defined Constraints.

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.

Using High-Resolution Population Data to Identify Neighborhoods and Establish Their Boundaries.

Neighborhoods are about local territory, but what territory? This paper offers one approach to this question through a novel application of "local" spatial statistics. We conceptualize a neighborhood in terms of both space and social composition; it is a contiguous territory defined by a bundle of social attributes that distinguish it from surrounding areas. Our method does not impose either a specific social characteristic or a predetermined spatial scale to define a neighborhood. Rather we infer neighborhoods from detailed information about individual residents and their locations. The analysis is based on geocoded complete-count census data from the late 19(th) Century in four cities: Albany, NY, Buffalo, NY, Cincinnati, OH, and Newark, NJ. We find striking regularities (and some anomalies) in the spatial structure of the cities studied. Our approach illustrates the "spatialization" of an important social scientific concept.

Identifying and Bounding Ethnic Neighborhoods.

This study presents three novel approaches to the question of how best to identify ethnic neighborhoods (or more generally, neighborhoods defined any aspect of their population composition) and to define their boundaries. It takes advantage of unusual data on the residential locations of all residents of Newark, NJ, in 1880 to avoid having to accept arbitrary administrative units (like census tracts) as the building blocks of neighborhoods. For theoretical reasons the street segment is chosen as the basic unit of analysis. All three methods use information on the ethnic composition of buildings or street segments and the ethnicity of their neighbors. One approach is a variation of k-functions calculated for each adult resident, which are then subjected to a cluster analysis to detect discrete patterns. The second is an application of an energy minimization algorithm commonly used to enhance digital images. The third is a Bayesian approach previously used to study county-level disability data. Results of all three methods depend on decisions about technical procedures and criteria that are made by the investigator. Resulting maps are roughly similar, but there is no one best solution. We conclude that researchers should continue to seek alternative methods, and that the preferred method depends on how one's conceptualization of neighborhoods matches the empirical approach.

The spatial dimensions of neighborhood effects.

In the decade or so of renewed interest in neighborhood contexts and health, significant progress has been made conceptualizing the relationships between the urban environment and public health. Applied research on the link between the environment and health remains limited by the way spatial concepts, such as "the neighborhood" or "the built environment" are operationalized. In this paper we argue that representations of these spatial concepts in statistical models should be based upon the individuals, the place, and the problem under study. Through a series of simulation experiments we describe the sensitivity of estimates of the association between neighborhoods and health to the operationalization of spatial concepts. We explore the practice of conducting the same analysis at multiple scales and find that using model fit to "discover" the spatial dimension is problematic. In sum, there is a gap between our understanding of how the environment influences health and spatial statistical modeling techniques. For quantitative spatial inquiry into the relationship between the neighborhood environment and health to be effective this gap must be closed.

Optimization of aeromedical base locations in New Mexico using a model that considers crash nodes and paths.

In a recent paper, Tokar Erdemir et al. (2008) introduce models for service systems with service requests originating from both nodes and paths. We demonstrate how to apply and extend their approach to an aeromedical base location application, with specific focus on the state of New Mexico (NM). The current aeromedical base locations of NM are selected without considering motor vehicle crash paths. Crash paths are the roads on which crashes occur, where each road segment has a weight signifying relative crash occurrence. We analyze the loss in accident coverage and location error for current aeromedical base locations. We also provide insights on the relevance of considering crash paths when selecting aeromedical base locations. Additionally, we look briefly at some of the tradeoff issues in locating additional trauma centers vs. additional aeromedical bases in the current aeromedical system of NM. Not surprisingly, tradeoff analysis shows that by locating additional aeromedical bases, we always attain the required coverage level with a lower cost than with locating additional trauma centers.

Oral health and health care for older adults: a spatial approach for addressing disparities and planning services.

The purpose of this paper was to examine the geographic distribution of New York City adults aged 65 and older by race/ethnicity and poverty status. Also analyzed was seniors' access to dental care as defined by the location of dental providers and their proximity to the subway system lines in Manhattan and the Bronx. ArcGIS software was used to create a geographic information system (GIS) incorporating relevant data from a variety of sources. Individual and overlay maps were then produced to examine the aims of this analysis. Data showed that Black race, Hispanic ethnicity, and poverty status tend to co-occur spatially among seniors in Northern Manhattan and the South Bronx. Further, a spatial/transportation barrier may inhibit access to dental care for seniors who reside in these areas. By presenting multiple layers of local information juxtaposed, GIS can help provide directions for planning oral health service delivery for seniors.