Environmental pollution is a major problem in China, subjecting people to significant health risk. Surprisingly little is known, though, about how these risks are distributed spatially or socially. Drawing on a large-scale survey conducted in Beijing in 2013, we examine how environmental hazards and health, as perceived by residents, are distributed at a fine (subdistrict) scale in urban Beijing and investigate the association between hazards, health, and geographical context. A Bayesian spatial multilevel logistic model is developed to account for spatial dependence in unobserved contextual influences (neighborhood effects) on health. The results reveal robust associations between exposure to environmental hazards and health. A unit decrease on a five-point Likert scale in exposure is associated with increases of 15.2 percent (air pollution), 17.5 percent (noise), and 9.3 percent (landfills) in the odds of reporting good health, with marginal groups including migrant workers reporting greater exposure. Health inequality is also evident and is associated with age, income, educational attainment, and housing characteristics. Geographical context (neighborhood features like local amenities) also plays a role in shaping the social distribution of health inequality. The results are discussed in the context of developing environmental justice policy within a Chinese social market system that experiences tension between its egalitarian roots and its pragmatic approach to tackling grand public policy challenges.

‘Social frontiers’ – places of sharp difference in social/ethnic characteristics between neighbouring communities – have largely been overlooked in quantitative research. Advancing this nascent field first requires a way of identifying social frontiers in a robust way. Such frontiers may be ‘open’ – an area may contrast sharply with a neighbourhood in one direction, but blend smoothly into adjacent neighbourhoods in other directions. This poses some formidable methodological challenges, particularly when computing inference for the existence of a social frontier, an important goal if one is to distinguish true frontiers from random variation. We develop a new approach using Bayesian spatial statistical methods that permit asymmetries in spatial effects and allow for spatial autocorrelation in the data. We illustrate our method using data on Sheffield and find clear evidence of ‘open’ frontiers. Permutations tests and Poisson regressions with fixed effects reveal compelling evidence that social frontiers are associated with higher rates of crime.

Whilst air pollution is a major problem in China, little is known about how it is distributed socially and how such distributions are changing over time. We use a fine-grained population census and air quality data for 2000 and 2010 to explore socio-spatial and temporal inequalities in air pollution for Beijing, using distributional analyses and spatial regression models. We find that environmental inequalities exist with respect to measures of social disadvantage, such as hukou migrant status, very young children (0–4 years), and the elderly (≥65 years). Our temporal analysis reveals that environmental inequality increases for migrants and the elderly, who bear a disproportionate and increasing share of declining air quality from 2000 to 2010. Regression results emphasise the spatial and temporal variations in environmental inequality, as the associations between air pollution and social demographics differ between different urban zones of Beijing; and their geographic patterns change significantly over time.

This paper critically examines the relationship between air pollution and deprivation. We argue that focusing on a particular economic or social model of urban development might lead one to erroneously expect all cities to converge towards a particular universal norm. A naive market sorting model, for example, would predict that poor households will eventually be sorted into high pollution areas, leading to a positive relationship between air pollution and deprivation. If, however, one considers a wider set of theoretical perspectives, the anticipated relationship between air pollution and deprivation becomes more complex and idiosyncratic. Specifically, we argue the relationship between pollution and deprivation can only be made sense of by considering processes of risk perception, path dependency, gentrification and urbanization. Rather than expecting all areas to eventually converge to some universal norm, we should expect the differences in the relationship between air pollution and deprivation across localities to persist. Mindful of these insights, we propose an approach to modeling which does not impose a geographically fixed relationship. Results for Scotland reveal substantial variations in the observed relationships over space and time, supporting our argument.

Existing literature has examined the determinants of subjective well-being in China from the social, economic and psychological perspectives. Very few studies explore the impacts of residential environment on subjective well-being. Drawing on a large scale questionnaire survey in Beijing, this paper investigates the role of residential environment by decomposing the variations of subjective well-being at fine-grained spatial scales, i.e. district and neighbourhood levels. A bivariate response binomial multilevel model is employed to assess the relative importance of geographical contexts and individual characteristics, in particular, the household registration (hukou) status, in influencing subjective well-being. The results show significant heterogeneities in subjective well-being among districts and neighbourhoods. Neighbourhood types are significantly correlated with subjective well-being, with residents in commercial housing neighbourhoods reporting higher levels of subjective well-being than those in work-unit and affordable housing neighbourhoods. However, the impacts of neighbourhood types are not uniformly experienced by people with different hukou status. Migrants tend to express lower levels of subjective well-being than local residents. Such disparities are more pronounced in urban villages compared with other neighbourhoods.

Geographical variable distributions often exhibit both macroscale geographic smoothness and microscale discontinuities or local step changes. Nonetheless, accounting for both effects in a unified statistical model is challenging, especially when the data under study involve a multiscale structure and non-Gaussian response variables. This study develops a locally adaptive spatial multilevel logistic model to examine binomial response variables that integrates an innovative locally adaptive spatial econometric model with a multilevel model. It takes into account global spatial autocorrelation, local step changes, and vertical dependence effects arising from the multiscale data structure. Another appealing feature is that the spatial correlation structure, implied by a spatial weights matrix, is learned along with other model parameters via an iterative estimation algorithm, rather than being presumed to be invariant. Bayesian Markov chain Monte Carlo (MCMC) samplers are derived to implement this new spatial multilevel logistic model. A data augmentation approach, drawing on recently devised Pólya-gamma distributions, is adopted to reduce computational burdens of calculating binomial likelihoods with a logit link function. The validity of the developed model is evaluated by a set of simulation experiments, before being applied to analyze self-rated health for the elderly in Shijiazhuang, the capital city of Hebei Province, China. Model estimation results highlight a nuanced geography of self-rated health and identify a range of individual- and area-level correlates of health for the elderly. Key Words: geography of health, local spatial modeling, multilevel models, spatial autocorrelation, spatial econometrics.