Seth E. Spielman

Social area analysis, data mining, and GIS

There is a long cartographic tradition of describing cities through a focus on the characteristics of their residents. A review of the history of this type of urban social analysis highlights some persistent challenges. In this paper existing geodemographic approaches are extended through coupling the Kohonen Self-Organizing Map algorithm (SOM), a data-mining technique, with geographic information systems (GIS). This approach allows the construction of linked maps of social (attribute) and geographic space. This novel type of geodemographic classification allows ad hoc hierarchical groupings and exploration of the relationship between social similarity and geographic proximity. It allows one to filter complex demographic datasets and is capable of highlighting general social patterns while retaining the fundamental social fingerprints of a city. A dataset describing 79 attributes of the 2217 census tracts in New York City is analyzed to illustrate the technique. Pairs of social and geographic maps are formally compared using simple pattern metrics. Our analysis of New York City calls into question some assumptions about the functional form of spatial relationships that underlie many modeling and statistical techniques.

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

Neighborhoods are about local territory, but what territory? This article 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 nineteenth century in four cities: Albany, New York; Buffalo, New York; Cincinnati, Ohio; and Newark, New Jersey. 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.

Dasymetric Modeling and Uncertainty.

Dasymetric models increase the spatial resolution of population data by incorporating related ancillary data layers. The role of uncertainty in dasymetric modeling has not been fully addressed as of yet. Uncertainty is usually present because most population data are themselves uncertain, or the geographic processes that connect population and the ancillary data layers are not precisely known. A new dasymetric methodology—the penalized maximum entropy dasymetric model (P–MEDM)—is presented that enables these sources of uncertainty to be represented and modeled. The P–MEDM propagates uncertainty through the model and yields fine-resolution population estimates with associated measures of uncertainty. This methodology contains a number of other benefits of theoretical and practical interest. In dasymetric modeling, researchers often struggle with identifying a relationship between population and ancillary data layers. The P–MEDM model simplifies this step by unifying how ancillary data are included. The P–MEDM also allows a rich array of data to be included, with disparate spatial resolutions, attribute resolutions, and uncertainties. Although the P–MEDM does not necessarily produce more precise estimates than do existing approaches, it does help to unify how data enter the dasymetric model, it increases the types of data that can be used, and it allows geographers to characterize the quality of their dasymetric estimates. We present an application of the P–MEDM that includes household-level survey data combined with higher spatial resolution data such as from census tracts, block groups, and land cover classifications.

Location coverage models with demand originating from nodes and paths: Application to cellular network design

Location covering problems, though well studied in the literature, typically consider only nodal (i.e. point) demand coverage. In contrast, we assume that demand occurs from both nodes and paths. We develop two separate models - one that handles the situation explicitly and one which handles it implicitly. The explicit model is formulated as a Quadratic Maximal Covering Location Problem - a greedy heuristic supported by simulated annealing (SA) that locates facilities in a paired fashion at each stage is developed for its solution. The implicit model focuses on systems with network structure - a heuristic algorithm based on geometrical concepts is developed. A set of computational experiments analyzes the performance of the algorithms, for both models. We show, through a case study for locating cellular base stations in Erie County, New York State, USA, how the model can be used for capturing demand from both stationary cell phone users as well as cell phone users who are in moving vehicles.

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, 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 crowd-sourced geographic information. Literature from a variety of fields makes clear that not all groups possess collective intelligence, this article identifies four pre-conditions for the emergence of collective intelligence and then examines 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 is more important than the intelligence of group members. Applying this idea to user-generated maps suggests that systems should be designed to foster conditions known to produce collective intelligence rather than privileging particular contributions/contributors. The article concludes with some design recommendations and by considering the implications of collectively generated maps for both expert knowledge and traditional state sponsored mapping programs.

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 by any aspect of their population composition) and to define their boundaries. The authors use 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 ones conceptualization of neighborhoods matches the empirical approach.

Community clusters of tsunami vulnerability in the US Pacific Northwest

Significance We present an analytical framework for understanding community-level vulnerability to tsunamis that integrates population exposure, demographic sensitivity, and evacuation potential.We identify three types of communities along the US Pacific Northwest coast that are directly threatened by tsunamis associated with a Cascadia subduction zone earthquake: (i) demographically diverse with low numbers of exposed people, (ii) high numbers of exposed populations but sufficient time to evacuate, and (iii) moderate numbers of exposed populations but insufficient time to evacuate. This approach is a significant advance over current practice because traditional measures of social vulnerability do not relate population structure to specific hazard characteristics. Results help managers to develop risk reduction strategies that are tailored to local conditions and needs. 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.

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

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.

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.