Recent studies indicate a relationship between measures of urban form as applied to urban and suburban areas, and obesity, a risk factor for heart disease. Measures of urban form for exurban and rural areas are considerably scarce; such measures could prove useful in measuring relationships between urban form and both mortality and morbidity in such areas. In modeling area-level mortality, geographic relationships between counties warrant consideration because geographically adjacent areas tend to have more in common than areas farther from each other. We modify county-level indices of urban form found in the literature so that they can be applied to exurban and rural counties. We then use these indices in a Bayesian spatial model that accounts for spatial autocorrelation to determine if there is a relationship between such measures and cardiovascular disease mortality for white males age 35 and older for the time period 1999-2001. Issues related to the formation and usefulness of the indices, and issues related to the spatial model, are discussed. Maps of observed and expected relative risk of mortality are presented.

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As a pattern of growth, sprawl is often criticized for its extensive negative impacts. These impacts range from economic costs to health and environmental problems. Critics of sprawl have also emphasized the negative consequences of this type of growth for social neighborhood ties. The physical environment of sprawling areas, characterized by low population density, segregation of land-uses, and lack of public spaces does not provide spaces for social interaction. On the contrary, transit-oriented and mixed-use neighborhoods might encourage interaction among residents because individuals are more likely to walk from place to place which might increase opportunities for informal contact and gather. Although there is a large body of research that study the impacts of sprawl, there is little empirical research of the impacts of sprawl on social interactions among neighbors. The purpose of this study is to contribute to the understanding of sprawl impacts and to fill this gap in the current literature by exploring the relationship between urban sprawl and neighborhood social interactions at the metropolitan level. According to my results, while neither an overall index of sprawl, nor individual indicators are observed to have a statistical significant association with different dimensions of neighbor interaction; a statistical significant association was found between the use of public spaces and the type and frequency of neighbor interaction among participants in this research. As such, the use of public parks and plazas, public libraries, and in some cases community centers is positively associated with neighborhood social interaction. These results, obtained while statistically controlling for demographic characteristics, highlight the importance of public spaces on the behavior of participants.

The widespread loss and degradation of habitat constitutes the largest threat to biodiversity in North America. While regulatory programs such as the Endangered Species Act of 1973 and wetland permitting under the Clean Water Act have addressed acute assaults on critical habitat, large areas of unprotected uplands have been lost. Urban development, particularly the advent of lower density suburban and rural sprawl, has greatly diminished the extent of contiguous patches of forest habitat and introduced a host of other undesirable effects on ecosystem function. This study sought to evaluate the extent of urban sprawl and its effects on ecological integrity in Florida using Landsat-derived land cover data collected by the Florida Fish and Wildlife Conservation Commission (FWC) circa 1987 and 2003. Chapter 1 described a novel GIS technique for correcting the systematic errors in the FWC 1987 and 2003 land cover data and converting those data to a common classification system so that they could be used in any ad hoc land cover change analysis. Comparison to ground-truth observations demonstrated a significant improvement in the accuracy of the land cover data following the Land Cover Correction Process (LCCP). Change detection between 1987 and 2003 using the correct land cover revealed trends in land cover conversion that were very different from previously published results derived from the original FWC land cover data. Conversion to urban uses in the corrected data was 47,293 ha lower, and conversion to agricultural uses was reduced by 196,773 ha, resulting in 244,067 ha less anthropogenic land conversion than had been previously estimated. Although the corrected land cover data showed that overall land conversion of natural areas was lower compared to the earlier estimate, the corrected data showed proportionally greater habitat losses for four important habitat types: Pinelands (-10.08% in the corrected land cover as compared to -5.90% in the original FWC data); upland forest (-9.46% versus 6.37%); sandhill (-13.90% versus 11.18%); and scrub (-15.52% versus -9.83%). Given the relatively small areal extent of some of these habitats, the larger percent loss estimates over the study period revealed by the corrected land cover data are cause for even greater concern by conservation planners and policymakers. Now that its utility has been demonstrated, the LCCP technique can be applied to any pair of roughly similar land cover mapping datasets provided that their original classification systems can be composed by a cross-walk into a single scheme, and that one or more ancillary data sets are available to serve in the tie-breaker role performed here by the land use data from Florida's Water Management Districts. The Soil Survey Geographic (SSURGO) and State Soil Geographic (STATSGO) soils data of the National Resource Conservation Service, the National Land Cover Dataset (NLCD) or the statewide habitat mapping of the USGS GAP Analysis Program could be adapted to provide the ancillary tie-breaker data required by the LCCP to conduct change detection between disparate land cover data sources heretofore considered too incompatible for that purpose. In Chapter 2, measures of urban sprawl, habitat loss and fragmentation in Florida were estimated using the corrected land cover data for 1987 and 2003. The Northwest and North regions of the state exhibited significantly higher indices of urban sprawl, habitat loss and habitat fragmentation via application of the Moran's I statistic. Reducing urban sprawl and habitat fragmentation spatial metrics to simple ordination variables through the use of non-metric multidimensional scaling produced new measures of urban sprawl and habitat fragmentation that correlated strongly with the original FRAGSTATS metrics, but could be more easily mapped and interpreted. Urban and Habitat ordination metrics were each spatially autocorrelated (Local Moran's I and K-means grouping analyses) but not correlated to each other using the Procrustes analysis PROTEST statistic (m2 = 0.952, p = 0.061). In contrast, individual urban sprawl metrics (CA, NP, LPI, ED, SHAPE_AM and DCAD) correlated with habitat fragmentation. NP and DCAD appeared to be particularly useful in predicting fragmentation, and county governments should take measures to reduce establishment of new urban patches to minimize NP and DCAD. Chapter 3 explored the relationship between environmental outcomes in habitat loss and fragmentation and the quality of county local government comprehensive plans. The use of NMS analysis provided a powerful technique for capturing the intrinsic variability of the Local Government Comprehensive Plan (LGCP) plan scoring systems of Brody (2003) and Pannozzo (2013) into a pair of variables each that could be used to explore associations with metrics of urban sprawl, habitat fragmentation and other county characteristics that influence urban growth and development. The geographic distribution of LGCP plan quality favored coastal counties with higher quality plans over inland counties, and there was some evidence that plans in Central and South regions of Peninsular Florida were superior to those in the North and Northwest Panhandle regions. Key factors in plan quality, specifically Coordination and Management, were strongly associated with urban sprawl or habitat fragmentation outcomes. The resources available to counties in the form of tax revenues, whether the county possessed a rural or urban economy, and the county's political makeup also appeared related to LGCP plan quality, urban sprawl or habitat fragmentation outcomes. More research will be needed to elucidate the specific causal mechanisms behind the implementation of local government planning that resulted in the observed environmental outcomes.

This dissertation investigates the economic consequences of urban sprawl for US minorities. Each essay focuses on a key empirical debate related to that relationship. The first essay establishes a set of attributes and empirical measures of sprawl based upon a comprehensive review of the literature. I define sprawl as a multi-faceted pattern of three land-use attributes: low density, deconcentration, and decentralization. I then resolve several methodological inconsistencies in the measurement of sprawl. Extensive analysis of spatial and economic data finds that metropolitan areas do not commonly exhibit high-sprawl (or low-sprawl) features across multiple measures. Instead, they often exhibit unique combinations of low-sprawl and high-sprawl attributes. The second essay examines the effect of sprawl on minority housing consumption gaps since the housing bust. I make two contributions to the literature. First, I reveal a facet of the relationship between sprawl and the Black-White housing gap not examined by previous econometric studies: Sprawl only contributes to reducing that gap once a metropolitan area reaches a critical threshold level of sprawl, typically at high levels of sprawl. Below a threshold, sprawl facilitates an expansion of the Black-White housing gap. Second, I compare results for Blacks, Asians, and Hispanics using recent data. For Blacks, the benefits from sprawl occur above an even higher threshold, as compared to preceding studies using 1990's data. For Asians, sprawl yields significant gains in housing consumption relative to Whites. As such, arguments that anti-sprawl policies reduce minority gains in housing should be treated with considerable skepticism in the post-Great Recession economy. The third essay explores the relationship between sprawl and racial and ethnic segregation. This econometric study advances the understanding of that relationship in two ways. First, I examine the effect of countervailing patterns of multiple land-use attributes, i.e. unique combinations of low-sprawl and high-sprawl attributes, on all five of the dimensions of segregation. Second, I compare outcomes for Blacks, Hispanics, and Asians. The study analyzes the contribution and transmission of countervailing spatial patterns of land use to increasing (or decreasing) segregation. These complex effects bring new precision and insights to the analysis of racial and ethnic inequality in an age of rapid demographic change.

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