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"Kwan, Mei-Po"
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The Limits of the Neighborhood Effect: Contextual Uncertainties in Geographic, Environmental Health, and Social Science Research
This article draws on recent studies to argue that researchers need to be attentive to the limits of the neighborhood effect as conventionally understood. It highlights the complexities of contextual influences and the challenges in accurately representing and measuring individual exposures to those influences. Specifically, it discusses the idiosyncratic and multidimensional nature of contextual effects, the temporal complexities of contextual influences, the frame dependence of exposure measures, selective mobility bias, and publication bias in neighborhood effects research. It also discusses how contextual uncertainties could be mitigated in future research (e.g., through collecting and using high-resolution space-time data and moving toward frame-independent exposure measures with results that are not affected by how data are organized with respect to space and time).
Journal Article
The Neighborhood Effect Averaging Problem (NEAP): An Elusive Confounder of the Neighborhood Effect
Ignoring people’s daily mobility and exposures to nonresidential contexts may lead to erroneous results in epidemiological studies of people’s exposures to and the health impact of environmental factors. This paper identifies and describes a phenomenon called neighborhood effect averaging, which may significantly confound the neighborhood effect as a result of such neglect when examining the health impact of mobility-dependent exposures (e.g., air pollution). Several recent studies that provide strong evidence for the neighborhood effect averaging problem (NEAP) are discussed. The paper concludes that, due to the observed attenuation of the neighborhood effect associated with people’s daily mobility, increasing the mobility of those who live in disadvantaged neighborhoods may be helpful for improving their health outcomes.
Journal Article
Causal inference from cross-sectional earth system data with geographical convergent cross mapping
Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are not available or present insignificant variations, which causes a common challenge for earth system science. Meanwhile, there are few spatial causation models for fully exploring the rich spatial cross-sectional data in Earth systems. The generalized embedding theorem proves that observations can be combined together to construct the state space of the dynamic system, and if two variables are from the same dynamic system, they are causally linked. Inspired by this, here we show a Geographical Convergent Cross Mapping (GCCM) model for spatial causal inference with spatial cross-sectional data-based cross-mapping prediction in reconstructed state space. Three typical cases, where clearly existing causations cannot be measured through temporal models, demonstrate that GCCM could detect weak-moderate causations when the correlation is not significant. When the coupling between two variables is significant and strong, GCCM is advantageous in identifying the primary causation direction and better revealing the bidirectional asymmetric causation, overcoming the mirroring effect.
Temporal causation models perform poorly in causal inference for variables with limited temporal variations. This paper establishes a causal inference model, which can reveal the nonlinear complex casual associations based on cross-sectional Earth System data.
Journal Article
A Multilevel Analysis of Perceived Noise Pollution, Geographic Contexts and Mental Health in Beijing
With rapid urbanization and increase in car ownership, ambient noise pollution resulting from diversified sources (e.g., road traffic, railway, commercial services) has become a severe environmental problem in the populated areas in China. However, research on the spatial variation of noise pollution and its potential effects on urban residents’ mental health has to date been quite scarce in developing countries like China. Using a health survey conducted in Beijing in 2017, we for the first time investigated the spatial distributions of multiple noise pollution perceived by residents in Beijing, including road traffic noise, railway (or subway) noise, commercial noise, and housing renovation (or construction) noise. Our results indicate that there is geographic variability in noise pollution at the neighborhood scale, and road traffic and housing renovation/construction are the principal sources of noise pollution in Beijing. We then employed Bayesian multilevel logistic models to examine the associations between diversified noise pollution and urban residents’ mental health symptoms, including anxiety, stress, fatigue, headache, and sleep disturbance, while controlling for a wide range of confounding factors such as socio-demographics, objective built environment characteristics, social environment and geographic context. The results show that perceived higher noise-pollution exposure is significantly associated with worse mental health, while physical environment variables seem to contribute little to variations in self-reported mental disorders, except for proximity to the main road. Social factors or socio-demographic attributes, such as age and income, are significant covariates of urban residents’ mental health, while the social environment (i.e., community attachment) and housing satisfaction are significantly correlated with anxiety and stress. This study provides empirical evidence on the noise-health relationships in the Chinese context and sheds light on the policy implications for environmental pollution mitigation and healthy city development in China.
Journal Article
Contextual Uncertainties, Human Mobility, and Perceived Food Environment: The Uncertain Geographic Context Problem in Food Access Research
We examined the uncertainty of the contextual influences on food access through an analytic framework of the uncertain geographic context problem (UGCoP). We first examined the compounding effects of two kinds of spatiotemporal uncertainties on people’s everyday efforts to procure food and then outlined three key dimensions (food access in real time, temporality of the food environment, and perceived nutrition environment) in which research on food access must improve to better represent the contributing environmental influences that operate at the individual level. Guidelines to address the UGCoP in future food access research are provided to account for the multidimensional influences of the food environment on dietary behaviors.
Journal Article
Mobility-oriented measurements of people’s exposure to outdoor artificial light at night (ALAN) and the uncertain geographic context problem (UGCoP)
Advanced nighttime light (NTL) remote sensing techniques enable the large-scope epidemiological investigations of people’s exposure to outdoor artificial light at night (ALAN) and its health effects. However, multiple uncertainties remain in the measurements of people’s exposure to outdoor ALAN, including the representations of outdoor ALAN, the contextual settings of exposure measurements, and measurement approaches. Non-exposed but included outdoor ALAN and causally irrelevant outdoor ALAN may manifest as contextual errors, and these uncertain contextual errors may lead to biased measurements and erroneous interpretations when modeling people’s health outcomes. In this study, we systematically investigated outdoor ALAN exposure measurements in different geographic contexts using either residence-based or mobility-oriented measurements, different spatial scales, and multiple NTL data sources. Based on the GPS data collected from 208 participants in Hong Kong, outdoor ALAN exposures were measured from NTL imagery at 10 m, 130 m, and 500 m spatial resolutions using in-situ methods or 100 m, 300 m, and 500 m buffer zone averaging. Descriptive analysis, multiple t-tests, and logistic regression were employed to examine the differences between outdoor ALAN exposure measurements using various contextual settings and their effects on modeling people’s overall health. Our results confirmed that different contextual settings may lead to significantly different outdoor ALAN exposure measurements. Our results also confirmed that contextual errors may lead to erroneous conclusions when using improper contextual settings to model people’s overall health. Consequentially, we suggest measuring people’s exposure to outdoor ALAN using the mobility-oriented approach, NTL representation with the high spatial resolution, and a very small buffer zone as a contextual unit to derive outdoor ALAN exposure. This study articulates essential methodological issues induced by uncertainties in outdoor ALAN exposure measurements and can provide essential implications and suggestions for a broad scope of studies that need accurate outdoor ALAN exposure measurements.
Journal Article
The impact of immediate urban environments on people’s momentary happiness
The research interest of urban researchers and geographers in the relationship between urban environments and happiness has been increasing. Previous studies have mostly focused on people’s long-term overall wellbeing. However, there is limited evidence that momentary happiness is associated with immediate urban environments. This study provides new evidence on this issue. 144 participants living in Guangzhou, China, were asked to repeatedly self-report their momentary happiness through ecological momentary assessment (EMA) and the day reconstruction method (DRM). The microenvironment variables were captured by portable sensors, while the built environment variables were captured by associating the GPS response locations with objective spatial data. The results indicate that momentary happiness is influenced by immediate microenvironment variables and built environment characteristics including temperature, noise, PM2.5, population, POI density, POI types and street intersections. On the other hand, the use of different sizes of contextual units affects the results. The built environment in 100 m buffers and the microenvironment has higher explanatory power for momentary happiness recorded by EMA than the built environment in 500 m buffers. Similarly, the temporality of the contextual influences also affects the results. Urban environment features have higher explanatory power for real-time momentary happiness recorded by EMA than recalled momentary happiness recorded by DRM. These results also strongly corroborate the results of recent studies on the uncertain geographic context problem (UGCoP) and partly explain the inconsistency in the results of past research.
城市研究人员和地理学家对城市环境和幸福感之间关系的研究兴趣一直在增加。以前的研究大多集中在人们的长期整体幸福感上。然而，有限的证据表明短暂的幸福感与直接的城市环境有关。这项研究提供了这方面的新证据。144名生活在中国广州的参与者被要求通过生态瞬间评估 (EMA) 和日间重建法 (DRM) 反复自我报告他们的瞬间幸福感。微环境变量由便携式传感器捕捉，而建筑环境变量则通过将全球定位系统响应位置与客观空间数据相关联来捕获。结果表明，瞬间幸福感受即时微环境变量和建筑环境特征的影响，包括温度、噪声、PM2.5、人口、兴趣点密度、兴趣点类型和街道交叉口。另一方面，使用不同的环境单位尺度会影响结果。100米缓冲区的建筑环境和微环境对EMA记录的瞬间幸福感的解释力高于500米缓冲区的建筑环境。同样，环境影响的暂时性也会影响结果。城市环境特征对EMA记录的实时瞬间幸福感的解释力高于DRM记录的回忆瞬间幸福感。这些结果也有力地证实了最近关于不确定地理环境问题 (UGCoP) 的研究结果，并部分解释了过去研究结果的不一致性。
Journal Article
Assessment of air pollution and air quality perception mismatch using mobility-based real-time exposure
Air pollution poses a threat to human health. Public perceptions of air pollution are important for individual self-protection and policy-making. Given the uncertainty faced by residence-based exposure (RB) measurements, this study measures individuals’ real-time mobility-based (MB) exposures and perceptions of air pollution by considering people’s daily movement. It explores how contextual uncertainties may influence the disparities in perceived air quality by taking into account RB and MB environmental factors. In addition, we explore factors that are related to the mismatch between people’s perceived air quality and actual air pollution exposure. Using K-means clustering to divide the PM 2.5 values into two groups, a mismatch happens when the perceived air quality is poor but the air pollution level is lower than 15.536μg/m 3 and when the perceived air quality is good but the air pollution level is higher than 15.608μg/m 3 . The results show that there is a mismatch between air pollution exposure and perception of air pollution. People with low income are exposed to higher air pollution. Unemployed people and people with more serious mental health symptoms (e.g., depression) have a higher chance of accurately assessing air pollution (e.g., perceiving air quality as poor when air pollution levels are high). Older people and those with a higher MB open space density tend to underestimate air pollution. Students tend to perceive air quality as good. People who are surrounded by higher MB transportation land-use density and green space density tend to perceive air quality as poor. The results can help policymakers to increase public awareness of high air pollution areas, and consider the health effects of landscapes during planning.
Journal Article
Impacts of Individual Daily Greenspace Exposure on Health Based on Individual Activity Space and Structural Equation Modeling
Previous studies on the effects of greenspace exposure on health are largely based on static contextual units, such as residential neighborhoods, and other administrative units. They tend to ignore the spatiotemporal dynamics of individual daily greenspace exposure and the mediating effects of specific activity type (such as physical activity). Therefore, this study examines individual daily greenspace exposure while taking into account people’s daily mobility and the mediating role of physical activity between greenspace exposure and health. Specifically, using survey data collected in Guangzhou, China, and high-resolution remote sensing images, individual activity space for a weekday is delineated and used to measure participants’ daily greenspace exposure. Structural equation modeling is then applied to analyze the direct effects of individual daily greenspace exposure on health and its indirect effects through the mediating variable of physical activity. The results show that daily greenspace exposure directly influences individual health and also indirectly affects participants’ health status through physical activity. With respect to the total effects, daily greenspace exposure helps improve participants’ mental health and contributes to promoting their social health. It also helps improve participants’ physical health, although to a lesser extent. In general, the higher the daily greenspace exposure, the higher the physical activity level and the better the overall health (including physical, mental, and social health).
Journal Article