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.

Differences in the reporting units of data from diverse sources and changes in units over time are common obstacles to analysis of areal data. We compare common approaches to this problem in the context of changes over time in the boundaries of U.S. census tracts. In every decennial census many tracts are split, consolidated, or changed in other ways from the previous boundaries to reflect population growth or decline. We examine two interpolation methods to create a bridge between years, one that relies only on areal weighting and another that also introduces population weights. Results demonstrate that these approaches produce substantially different estimates for variables that involve population counts, but they have a high degree of convergence for variables defined as rates or averages. Finally the paper describes the Longitudinal Tract Data Base (LTDB), through which we are making available public-use tools to implement these methods to create estimates within 2010 tract boundaries for any tract-level data (from the census or other sources) that are available for prior years as early as 1970.

The growing ethnic and racial diversity of the United States is evident at all spatial scales. One of the striking features of this new mixture of peoples, however, is that this new diversity often occurs in tandem with racial concentration. This article surveys these new geographies from four points of view: the nation as a whole, states, large metropolitan areas, and neighborhoods. The analysis at each scale relies on a new taxonomy of racial composition that simultaneously appraises both diversity and the lack thereof (Holloway, Wright, and Ellis 2012). Urban analysis often posits neighborhood racial segregation and diversity as either endpoints on a continuum of racial dominance or mirror images of one another. We disturb that perspective and stress that segregation and diversity must be jointly understood-they are necessarily related, although not as inevitable binary opposites. Using census data from 1990, 2000, and 2010, the research points to how patterns of racial diversity and dominance interact across varying spatial scales. This investigation helps answer some basic questions about the changing geographies of racialized groups, setting the stage for the following articles that explore the relationship between geography and the participation of underrepresented groups in higher education.

Many geographic studies use distance as a simple measure of accessibility, risk, or disparity. Straight-line (Euclidean) distance is most often used because of the ease of its calculation. Actual travel distance over a road network is a superior alternative, although historically an expensive and labor-intensive undertaking. This is no longer true, as travel distance and travel time can be calculated directly from commercial Web sites, without the need to own or purchase specialized geographic information system software or street files. Taking advantage of this feature, we compare straight-line and travel distance and travel time to community hospitals from a representative sample of more than 66,000 locations in the fifty states of the United States, the District of Columbia, and Puerto Rico. The measures are very highly correlated ( > 0.9), but important local exceptions can be found near shorelines and other physical barriers. We conclude that for nonemergency travel to hospitals, the added precision offered by the substitution of travel distance, travel time, or both for straight-line distance is largely inconsequential.

The variations of breast cancer mortality rates from place to place reflect both underlying differences in breast cancer prevalence and differences in diagnosis and treatment that affect the risk of death. This article examines the role of access to health care in explaining the variation of late-stage diagnosis of breast cancer. We use cancer registry data for the state of Illinois by zip code to investigate spatial variation in late diagnosis. Geographic information systems and spatial analysis methods are used to create detailed measures of spatial access to health care such as convenience of visiting primary care physicians and travel time from the nearest mammography facility. The effects of spatial access, in combination with the influences of socioeconomic factors, on late-stage breast cancer diagnosis are assessed using statistical methods. The results suggest that for breast cancer, poor geographical access to primary health care significantly increases the risk of late diagnosis for persons living outside the city of Chicago. Disadvantaged population groups including those with low income and racial and ethnic minorities tend to experience high rates of late diagnosis. In Illinois, poor spatial access to primary health care is more strongly associated with late diagnosis than is spatial access to mammography. This suggests the importance of primary care physicians as gatekeepers in early breast cancer detection.

Regardless of destination, immigrants arrive with health profiles typical of people in their previous surroundings. Thus, immigrants change the epidemiological profile of destination communities, and immigrant neighborhoods may represent islands of infectious disease. Genotyping has emerged as a useful surveillance tool to track the spread of disease at the molecular level. Yet the spatial distribution of infectious disease at the molecular level associated with migration and immigrant neighborhoods has received little attention. Using molecular genotyping to characterize isolated from tuberculosis cases, this article analyzes spatial variations of unique molecular strains by zip code in Tarrant County, Texas. The results suggest that immigrant neighborhoods have higher rates of unique isolates of tuberculosis (suggestive of remote transmission) compared to neighborhoods occupied by the native-born. Neighborhoods dominated by the native-born have higher rates of clustered isolates (suggestive of recent transmission). Therefore, in addition to being culturally distinct, immigrant neighborhoods may also be pathogenically distinct from surrounding neighborhoods.

This article uses accessibility as an analytical tool to examine health care access among immigrants in a multicultural urban setting. It applies and improves on two widely used accessibility models—the gravity model and the two-step floating catchment area model—in measuring spatial accessibility by Mainland Chinese immigrants in the Toronto Census Metropolitan Area. Empirical data on physician-seeking behaviors are collected through two rounds of questionnaire surveys. Attention is focused on journey to physician location and utilization of linguistically matched family physicians. Based on the survey data, a two-zone accessibility model is developed by relaxing the travel threshold and distance impedance parameters that are traditionally treated as a constant in the accessibility models. General linear models are used to identify relationships among spatial accessibility, geography, and socioeconomic characteristics of Mainland Chinese immigrants. The results suggest a spatial mismatch in the supply of and demand for culturally sensitive care, and residential location is the primary factor that determines spatial accessibility to family physicians. The article yields important policy implications.

There is an increasing need for a quick, simple method to represent diurnal population change in metropolitan areas for effective emergency management and risk analysis. Many geographic studies rely on decennial U.S. Census data that assume that urban populations are static in space and time. This has obvious limitations in the context of dynamic geographic problems. The U.S. Department of Transportation publishes population data at the transportation analysis zone level in fifteen-minute increments. This level of spatial and temporal detail allows for improved dynamic population modeling. This article presents a methodology for visualizing and analyzing diurnal population change for metropolitan areas based on this readily available data. Areal interpolation within a geographic information system is used to create twenty-four (one per hour) population surfaces for the larger metropolitan area of Salt Lake County, Utah. The resulting surfaces represent diurnal population change for an average workday and are easily combined to produce an animation that illustrates population dynamics throughout the day. A case study of using the method to visualize population distributions in an emergency management context is provided using two scenarios: a chemical release and a dirty bomb in Salt Lake County. This methodology can be used to address a wide variety of problems in emergency management.

Obesity is a growing epidemic in the United States. Walkable neighborhoods, characterized as having the 3Ds of walkability (population Density, land use Diversity, and pedestrian-friendly Design), have been identified as a potentially promising factor to prevent obesity for their residents. Past studies examining the relationship between obesity and walkability vary in geographic scales of neighborhood definitions and methods of measuring the 3Ds. To better understand potential influences of these sometimes arbitrary choices, we test how four types of alternative measures of land use diversity measured at three geographic scales relate to body mass index for 4960 Salt Lake County adults. Generalized estimation equation models demonstrate that optimal diversity measures differed by gender and geographic scale and that integrating walkability measures at different scales improved the overall performance of models.

The objectives are to (1) quantify, map, and analyze vegetation cover distributions and changes across Accra, Ghana, for 2002 and 2010; and (2) examine the statistical relationship between vegetation cover and a housing quality index (HQI) for 2000 at the neighborhood level. Pixel-level vegetation cover maps derived using threshold classification of 2002 and 2010 QuickBird normalized difference vegetation index images have very high overall accuracies and yield an estimate of 5.9 percent vegetation cover reduction over the study area between 2002 and 2010. A high degree of variance in vegetation cover for individual dates is explained by HQI at the neighborhood level, although minimal covariability between absolute or relative vegetation cover change and HQI for 2000 was observed.

Since the 2010s local government debt has boomed in China because the government relies on debt financing for infrastructure investment. The debt mainly consists of the issuance of bonds and later local government bonds. Using data from more than 300 cities from 2009 to 2020, this article maps its spatial dynamics to further the understanding of intergovernmental relations in the studies on local government debt. We find that, from 2009 to 2014, most cities had large bond-issuing amounts. The dynamics were affected by the economic stimulus target set by the central government and the interjurisdictional competition in borrowing among local governments. After 2015 the cities with better economies issued more bonds because the central government tried to match local government debt with local fiscal capacity to maintain financial security. The spatial dynamics show the increasing intervention by the central government in local fiscal income and expenditure, reflecting fiscal centralization. Fiscal centralization did not effectively contain the financial risk in the less-developed cities. Motivated by the competition, the less-developed cities did not use bonds efficiently and had higher ratios of bond issuance to fiscal income, experiencing higher financial risk.

Assessing spatial autocorrelation (SA) of statistical estimates such as means is a common practice in spatial analysis and statistics. Popular spatial autocorrelation statistics implicitly assume that the reliability of the estimates is irrelevant. Users of these SA statistics also ignore the reliability of the estimates. Using empirical and simulated data, we demonstrate that current SA statistics tend to overestimate SA when errors of the estimates are not considered. We argue that when assessing SA of estimates with error, it is essentially comparing distributions in terms of their means and standard errors. Using the concept of the Bhattacharyya coefficient, we proposed the Spatial Bhattacharyya coefficient (SBC) and suggested that it should be used to evaluate the SA of estimates together with their errors. A permutation test is proposed to evaluate its significance. We concluded that the SBC more accurately and robustly reflects the magnitude of SA than traditional SA measures by incorporating errors of estimates in the evaluation.

Underrepresentation among U.S. citizen racial and ethnic minorities in geography has a long history, one perpetuated through-and readily measurable by-its doctoral degree-granting record. This article examines the history of efforts to redress underrepresentation since the 1960s, explores modern underrepresentation, measures the degree of its persistence in the discipline and within individual departments, and identifies drivers that exacerbate the racial and ethnic representation disparity among U.S. citizens in geography doctoral programs. To quantify the degree to which the discipline is underperforming demographically, we contrasted the rate of domestic underrepresented minority doctoral degree conferrals with those of White doctoral recipients in geography, the social sciences, and the entire academy over a twenty-three-year period from 1997 through 2019. During that span, geography consistently trailed the social sciences and the academy: This underrepresentation gap has widened in the past decade. Four drivers were identified: (1) lack of dedicated funding for underrepresented minority doctoral students, (2) minimal prior exposure to academic and professional geography, (3) passive recruitment strategies, and (4) pervasive Whiteness of departments. We conclude with a call to action for geographers to meet the moral imperative of racial and ethnic representational equity by becoming agents of measurable change.

Domestic racial and ethnic minorities have been persistently underrepresented in U.S. geography doctoral programs. Efforts to improve diversity have taken many forms over the years, but most have been short-lived with limited success. In this article, we introduce the Advancing Geography Through Diversity Program (AGTDP), a four-pronged cohort-based model that systematically and sustainably increases the presence of African Americans, Hispanic Americans, and Native Americans in geography doctoral programs. The program is currently in its fourth year in the Department of Geography, Environment, and Spatial Sciences at Michigan State University. Context for the development of the framework is provided, followed by a detailed discussion of each pillar of the program: recruitment, support, engagement, and retention. We evaluate the current state of the program along with lessons learned for successful implementation. To date, the program has effectively increased the representation across all three underrepresented groups within the department's doctoral program. We believe the AGTDP can serve as a model for more widespread deployment to other geography departments.

California experienced a severe multi-year drought stretching from 2011-2017, significantly reducing surface water supply for ecosystems, agriculture, and humans, and prompting coordinated conservation efforts. Given that agriculture is the largest consumptive use of water in the state, one anticipated response to a severe drought would be to decrease production of low-value, high-water-use crops such as alfalfa. In this paper we use a multi-methods approach to examine both spatial distribution and public perceptions of alfalfa production in California over the course of the 2011-2017 drought. We find that while California alfalfa production did decline at the state level, it persisted and even increased in specific areas of the state. We also find that alfalfa persisted even though discourses and understandings that were critical of alfalfa production emerged in public forums during this time. We situate these findings within a broader context of California's water management system, which meant that in practice, infrastructure and water rights allocation practices left many growers with little incentive to change growing practices even in the face of serious meteorological drought.

Studies of neighborhood change rely on interpolated data to cope with inconsistent boundaries of geographic units over time. The standard approach introduces error by assuming, counterfactually, that all kinds of people are distributed in the same manner within tracts as the whole population. This study evaluates estimates of 2000 neighborhood characteristics using 2010 boundaries in the Longitudinal Tract Data Base (LTDB) that uses the standard approach, and an alternative trait-based (TB) method that uses additional small area data to account for spatial heterogeneity. Both are compared to the true (but confidential) original census data. For variables that are available from full-count census data at the block level (including race, age, and some housing characteristics), the TB estimates are much better than the LTDB estimates. However, the same general approach is ineffective when the small area data are subject to sampling variability and published with less spatial granularity.