Nicholas N. Nagle

Geographical variability and network structure

Abstract In this paper, we explore the potential implications of geographical variability for the structure of social networks. Beginning with some basic simplifying assumptions, we derive a number of ways in which local network structure should be expected to vary across a region whose population is unevenly distributed. To examine the manner in which these effects would be expected to manifest given realistic population distributions, we then perform an exploratory simulation study that examines the features of large-scale interpersonal networks generated using block-level data from the 2000 U.S. Census. Using a stratified sample of micropolitan and metropolitan areas with populations ranging from approximately 1000 to 1,000,000 persons, we extrapolatively simulate network structure using spatial network models calibrated to two fairly proximate social relations. From this sample of simulated networks, we examine the effect of both within-location and between-location heterogeneity on a variety of structural properties. As we demonstrate, geographical variability produces large and distinctive features in the “social fabric” that overlies it; at the same time, however, many aggregate network properties can be fairly well-predicted from relatively simple spatial demographic variables. The impact of geographical variability is thus predicted to depend substantially on the type of network property being assessed, and on the spatial scale involved.

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.

Extrapolative Simulation of Neighborhood Networks based on Population Spatial Distribution: Do they Predict Crime?

Abstract Objectives Previous criminological scholarship has posited that network ties among neighborhood residents may impact crime rates, but has done little to consider the specific ways in which network structure may enhance or inhibit criminal activity. A lack of data on social ties has arguably led to this state of affairs. We propose to avoid this limitation by demonstrating a novel approach of extrapolatively simulating network ties and constructing structural network measures to assess their effect on neighborhood crime rates. Methods We first spatially locate the households of a city into their constituent blocks. Then, we employ spatial interaction functions based on prior empirical work and simulate a network of social ties among these residents. From this simulated network, we compute network statistics that more appropriately capture the notions of cohesion and information diffusion that underlie theories of networks and crime. Results We show that these network statistics are robust predictors of the levels of crime in five separate cities (above standard controls) at the very micro geographic level of blocks and block groups. Conclusions We conclude by considering extensions of the approach that account for homophily in the formation of network ties.

The Relationship of Age to Personal Network Size, Relational Multiplexity, and Proximity to Alters in the Western United States

OBJECTIVES This study examines the association of age and other sociodemographic variables with properties of personal networks; using samples of individuals residing in the rural western United States and the City of Los Angeles, we evaluate the degree to which these associations vary with geographical context. For both samples, we test the hypothesis that age is negatively associated with network size (i.e., degree) and positively associated with network multiplexity (the extent of overlap) on 6 different relations: core discussion members, social activity participants, emergency contacts, neighborhood safety contacts, job informants, and kin. We also examine the relationship between age and spatial proximity to alters. METHOD Our data consist of a large-scale, spatially stratified egocentric network survey containing information about respondents and those to whom they are tied. We use Poisson regression to test our hypothesis regarding degree while adjusting for covariates, including education, gender, race, and self-reported sense of neighborhood belonging. We use multiple linear regression to test our hypotheses on multiplexity and distance to alters. RESULTS For both rural and urban populations, we find a nonmonotone association between age and numbers of core discussants and emergency contacts, with rural populations also showing nonmonotone associations for social activity partners and kin. These nonmonotone relationships show a peak in expected degree at midlife, followed by an eventual decline. We find a decline in degree among the elderly for all relations in both populations. Age is positively associated with distance to nonhousehold alters for the rural population, although residential tenure is associated with shorter ego-alter distances in both rural and urban settings. Additionally, age is negatively associated with network multiplexity for both populations. DISCUSSION Although personal network size ultimately declines with age, we find that increases for some relations extend well into late-midlife and most elders still maintain numerous contacts across diverse relations. The evidence we present suggests that older people tap into an wider variety of different network members for different types of relations than do younger people. This is true even for populations in rural settings, for whom immediate access to potential alters is more limited.

Establishing relationships between parcel data and land cover for demographic small area estimation

Dasymetric and other small area estimation methods often use land-cover data in order to refine the spatial resolution of population data. The attribute tables of these land-cover data, however, are often related only weakly to population distribution. Recent studies have examined the use of parcel data, but parcel data are not available in all places. Thus, it becomes useful to identify the links between parcel data and land-cover data so that land-cover data can be used where parcel data are not available. This article identifies and validates the relationships between land-cover and parcel data to improve small area estimation. Establishing this link between parcel data and land cover makes it possible to estimate the distribution of building types within each land-cover type. This article develops a method to do this and illustrates its general use with a case study for Boulder County, CO. A ground truth layer combines census block group data, individual parcel records, and land cover. Target zones constructed by homogeneous patches of land cover found in census block group units permit identification of the distribution of building types within small areas. Land cover is enriched with a simple pattern metric called the inner dimension metric in order to indicate how far inside of a developed region each developed land pixel is located. Poisson generalized linear models establish the relationship between parcel building type and land-cover type. The results suggest strong and significant relationships between residential building counts and land-cover data. This research will improve selection of related variables for dasymetric models to create small area population estimates of census housing attributes.

Networks, Space, and Residents' Perception of Cohesion

Community scholars increasingly focus on the linkage between residents’ sense of cohesion with the neighborhood and their own social networks in the neighborhood. A challenge is that whereas some research only focuses on residents’ social ties with fellow neighbors, such an approach misses out on the larger constellation of individuals’ relationships and the spatial distribution of those relationships. Using data from the Twin Communities Network Study, the current project is one of the first studies to examine the actual spatial distribution of respondents’ networks for a variety of relationships and the consequences of these for neighborhood and city cohesion. We also examine how a perceived structural measure of cohesion—triangle degree—impacts their perceptions of neighborhood and city cohesion. Our findings suggest that perceptions of cohesion within the neighborhood and the city depend on the number of neighborhood safety contacts as well as on the types of people with which they discuss important matters. On the other hand, kin and social friendship ties do not impact cohesion. A key finding is that residents who report more spatially dispersed networks for certain types of ties report lower levels of neighborhood and city cohesion. Residents with higher triangle degree within their neighborhood safety networks perceived more neighborhood cohesion.

Modeling Ambiguity in Census Microdata Allocations to Improve Demographic Small Area Estimates

This article describes a methodology for allocating demographic microdata to small enumeration areas such as census tracts, in the presence of underlying ambiguities. Maximum Entropy methods impute population weights that are constrained to match a set of census tract-level summary statistics. Once allocated, the household characteristics are summarized to revise estimates of tract-level demographic summary statistics, and to derive measures of ambiguity. The revised summary statistics are compared with original tract summaries within a context of expected variation. Allocation ambiguity is quantified for each household as a function of the distribution of imputed sample weights over all census tracts, and by computed metrics of confusion and variety of allocation to any census tract. The process reported here allows differentiation of households with regard to inherent ambiguity in the allocation decision. Ambiguity assessment represents an important component that has been neglected in spatial allocation work to date but can be seen as important additional knowledge for demographers and users of small area estimates. For the majority of tested variables, the revised tract level summaries correlate highly with original tract summary statistics. In addition to assessments for individual households, it is also possible to compute average allocation ambiguity for individual tracts, and to associate this with demographic characteristics not utilized in the allocation process.

Using High-Resolution Remotely Sensed Data to Examine the Relationship Between Agriculture and Fertility in Mali

Mali reports one of the highest fertility levels in the world. Most Malians grow their own food or rely on locally grown food to feed their families. Because Mali is potentially facing a loss of existing arable land due to climate change, however, concern over the ability of the country to meet the nutritional needs of its growing population is high. Building on historical studies of fertility and agriculture, in this research we examine the impact of local food production on fertility outcomes, taking advantage of geo-referenced health data and recently developed analytic strategies from the remote sensing literature. To examine this relationship we rely on the Demographic and Health Survey data from 2006 as well as on a collection of very high-resolution remotely sensed imagery. Results suggest that fertility, and in some cases fertility aspirations, is positively related to food production and broader scale food production strategies. These results hold even after accounting for individual variation in socioeconomic status.

Validation of spatiodemographic estimates produced through data fusion of small area census records and household microdata

Abstract Despite the increasing availability of current national censuses, these datasets are limited by their lack of small area demographic depth. At the same time, spatial microdata that include detailed demographic information are only available for limited geographies, thus limiting the complex analysis of population subgroups within and between small areas. Techniques such as Iterative Proportional Fitting have been previously suggested as a means to generate new data with the demographic granularity of individual surveys and the spatial granularity of small area tabulations of censuses and surveys. This article explores internal and external validation approaches for synthetic, small area, household- and individual-level microdata using a case study for Bangladesh. Using data from the Bangladesh Census 2011 and the Demographic and Health Survey, we produce estimates of infant mortality rate and other household attributes for small areas using a variation of an iterative proportional fitting method called P-MEDM. We conduct an internal validation to determine: whether the model accurately recreates the spatial variation of the input data, how each of the variables performed overall, and how the estimates compare to the published population totals. We conduct an external validation by comparing the estimates with indicators from the 2009 Multiple Indicator Cluster Survey (MICS) for Bangladesh to benchmark how well the estimates compared to a known dataset which was not used in the original model. The results indicate that the estimation process is viable for regions that are better represented in the microdata sample, but also revealed the possibility of strong overfitting in sparsely sampled sub-populations.

Can Small-Scale Agricultural Production Improve Children's Health? Examining Stunting Vulnerability among Very Young Children in Mali, West Africa

Stunting affects an individuals educational and wage-earning potential and can even affect the next generation of children. Most research of childhood stunting focuses on the determinants and correlates that lead to stunting—through nutritional or early infant experiences, with one potential solution to stunting being an increased supply of locally produced food. This research examines the interplay of community-level cropped area as a factor relating to childhood stunting. We use the most recently collected Demographic and Health Survey (DHS) data for Mali, very high resolution remotely sensed imagery, and other remotely sensed data relating to geophysical characteristics to examine the impact of local cultivation on childrens health. We focus on evaluating the environmental, community, household, and individual characteristics of the children who report healthy anthropometrics despite the presence of specific stunting risk factors. In adopting this approach to studies of childrens health we can shed light on how small-scale agricultural production impacts childhood stunting among at-risk children.