No abstract

We apply methods of exploratory spatial data analysis (ESDA) and spatial regression analysis to examine intercounty variation in child poverty rates in the US. Such spatial analyses are important because regression models that exclude explicit specification of spatial effects, when they exist, can lead to inaccurate inferences about predictor variables. Using county-level data for 1990, we re-examine earlier published results [Friedman and Lichter (Popul Res Policy Rev 17:91–109, 1998)]. We find that formal tests for spatial autocorrelation among county child poverty rates confirm and quantify what is obvious from simple maps of such rates: the risk of a child living in poverty is not (spatially) a randomly distributed risk at the county level. Explicit acknowledgment of spatial effects in an explanatory regression model improves considerably the earlier published regression results, which did not take account of spatial autocorrelation. These improvements include: (1) the shifting of “wrong sign” parameters in the direction originally hypothesized by the authors, (2) a reduction of residual squared error, and (3) the elimination of any substantive residual spatial autocorrelation. While not without its own problems and some remaining ambiguities, this reanalysis is a convincing demonstration of the need for demographers and other social scientists to examine spatial autocorrelation in their data and to explicitly correct for spatial externalities, if indicated, when performing multiple regression analyses on variables that are spatially referenced. Substantively, the analysis improves the estimates of the joint effects of place-influences and family-influences on child poverty. Copyright Springer Science+Business Media B.V. 2006Child poverty, Exploratory spatial data analysis, Spatial error models, Spatial lag models, Spatial regression,

This paper briefly reviews how to derive and interpret coefficients of spatial regression models, including topics of direct and indirect (spatial spillover) effects. These topics have been addressed in the spatial econometric literature over the past 5-6 years, but often at a level sometimes difficult for students new to the field. Our goal is to overcome this handicap by carefully presenting the mathematics behind these spatial effects and clearly illustrating how they work using two small fictive datasets and one large dataset with real data. The motivation for the paper is primarily pedagogical. Theoretical and conceptual impediments associated with the application of procedures are discussed.

Spatial demography, Spatial analysis, Ecological fallacy, Multilevel modeling,

As fertility differences in the United States diminish, population redistribution trends are increasingly dependent on migration. This research used newly developed county-level age-specific net migration estimates for the 1990s, supplemented with longitudinal age-specific migration data spanning the prior 40 years, to ascertain whether there are clear longitudinal trends in age-specific net migration and to determine if there is spatial clustering in the migration patterns. The analysis confirmed the continuation into the 1990s of distinct net migration "signature patterns" for most types of counties, although there was temporal variation in the overall volume of migration across the five decades. A spatial autocorrelation analysis revealed large, geographically contiguous regions of net in-migration (in particular, Florida and the Southwest) and geographically contiguous regions of net out-migration (the Great Plains, in particular) that persisted over time. Yet the patterns of spatial concentration and fragmentation over time in these migration data demonstrate the relevance of this "neighborhood" approach to understanding spatiotemporal change in migration.

Lakeshore development in Vilas County, northern Wisconsin (USA) is heterogeneous, ranging from lakes that are surrounded by homes and commercial establishments to lakes that have no buildings on their shorelines. Development in this recreational area has increased, and since the 1960s over half of new homes have been built on the lakeshore. We examined building density around lakes in relationship to 11 variables, including in-lake, shoreline, and social characteristics. Buildings in many parts of northern Wisconsin tend to be concentrated around shorelines; in Vilas County 61% of all medium-sized buildings (our proxy for residential development) on private land were < or =100 m of a lake. The probability of development on a lake was largely related to lake surface area, with larger, more accessible lakes showing a higher probability of development. Building density along shorelines varied with travel cost, lake surface area, presence of wetlands, and extent of public land ownership. Building density was greater on larger, more accessible lakes that were surrounded by forest (as opposed to wetlands) and public lands. Gaining a more precise understanding of human settlement patterns can help direct planning and resource protection efforts to lakes most likely to experience future development.

In this paper we return to an issue often discussed in the literature regarding the relationship between highway expansion and population change. Typically it simply is assumed that this relationship is well established and understood. We argue, following a thorough review of the relevant literature, that the notion that highway expansion leads to increased population growth in the vicinity of the improved infrastructure finds only weak and often conflicting support. Using data on all major highway expansions in Wisconsin covering the period from the late‐1960s through the 1990s from the Wisconsin Department of Transportation (WisDOT), and census data at the minor civil division (MCD) level covering the period 1970 to 2000, we deploy the analytical tools of geographic information system (GIS) software, and theory from the expanding literature in spatial analysis and modeling, to take a fresh look at this relationship. Our analysis reveals that there is a modest relationship between highway expansion and population growth among MCDs within 10–20 miles of the expanded major highway. The causal structure, however, is complex. Our starting hypothesis argues that population growth precedes highway expansion as frequently as population growth results from highway expansion, but the data show otherwise. The dominant causal influence appears to flow from highway expansion to population growth.

This study builds on research demonstrating that sub-regions within the United States have different processes that abet poverty and that child poverty is spatially differentiated. We focus on the social attributes of the local area to assess what the geographic place represents in terms of social characteristics, namely racial/ethnic composition and economic structure, and to resolve apparent inconsistencies in poverty research. Using spatial regime and spatial error regression techniques to analyze county census data, we examine spatial differentiation in the relationships that generate child poverty. Our approach addresses the conceptual and technical aspects of spatial inequality. Results show that local-area processes are at play with implications for more nuanced theoretical models and anti-poverty policies that consider systematic differences in factors contributing to child poverty according to the racial/ethnic and economic contexts.