Understanding Social Inequality in Individual Perceived Exposures to Air Pollution in Residential and Visited Neighborhoods: A Study Using Association Rule Mining

Abstract

Using individual-level data collected from two communities in Hong Kong, this study proposes a significant association rule mining method to identify the complex associations between individual socioeconomic characteristics and perceived air pollution in people’s daily life. It defines a measure, namely the rule inequality index, to assess the social inequality in perceived exposure to air pollution in both residential and visited neighborhoods. The results indicate that the associations between individual socioeconomic characteristics and perceived air pollution are not always consistent over communities, nor are the value ranges of perceived air pollution. Further, the tendency of different social groups to perceive high levels of air pollution can differ considerably depending on whether they are in their residential or visited neighborhoods. We also find that social groups based on different socioeconomic variables typically experience varying degrees of neighborhood effects on the associated social equalities. Our findings emphasize the importance of considering nonstationary associations and human mobility in research on social inequality related to mobility-dependent environmental exposure.

根据两个香港社区的个体数据, 本文提出显著关联规则的挖掘方法, 旨在识别日常生活中个体社会经济特征与感知的空气污染的复杂关联。本文提出“规则不平等指数”, 以评估住宅和到访社区的感知空气污染暴露的社会不平等。结果表明, 个体社会经济特征与感知空气污染的关联, 在不同社区有所不同, 感知的空气污染值范围也不同。不同社会群体感知高空气污染的趋势, 很大程度上取决于住宅区或者到访社区。拥有不同社会经济变量的社会群体, 常常具有不同程度的社会平等邻里效应。我们的研究结果, 强调了流动性环境暴露的社会不平等研究, 应当考虑非平稳关联和人类流动性的重要性。

Usando datos a nivel de individuo obtenidos en dos comunidades de Hong Kong, este estudio propone un método de minería de reglas de asociación significativa para identificar las complejas asociaciones entre las características socioeconómicas individuales y la polución atmosférica percibida en vida cotidiana de la gente. En el estudio se define una medida, es decir, el índice de desigualdad de reglas, para evaluar la desigualdad social en la exposición percibida en la polución atmosférica, tanto en barrios residenciales como en los visitados. Los resultados indican que la asociación entre las características socioeconómicas individuales y la contaminación atmosférica percibida no son siempre consistentes en todas las comunidades, ni lo son los intervalos de valores de la contaminación atmosférica percibida. Además, la tendencia de diferentes grupos sociales a percibir altos niveles de contaminación atmosférica puede diferir considerablemente, dependiendo de si ellos se encuentran en su barrio de residencia o en el de visita. Hallamos también que los grupos sociales basados en diferentes variables socioeconómicas típicamente experimentan distintos grados de efectos de vecindario sobre las desigualdades sociales asociadas. Nuestros descubrimientos enfatizan la importancia de considerar asociaciones no estacionarias, y la movilidad humana, en la investigación sobre la desigualdad social relacionada con la exposición ambiental dependiente de la movilidad.

Key Words:

• air pollution perception
• association rule mining
• neighborhood effect averaging problem
• nonstationarity
• social inequality

关键词:

• 空气污染感知
• 关联规则挖掘
• 邻里效应平均问题
• 非平稳性
• 社会不平等

Palabras clave:

• desigualdad social
• minería de reglas de asociación
• no estacionalidad
• percepción de la contaminación atmosférica
• problema de promediar el efecto de vecindario

Acknowledgments

We thank the editor and the reviewers for their helpful comments. In addition, we would also like to express our gratitude to all the participants who generously devoted their time to the study.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by grants from the Hong Kong Research Grants Council (General Research Fund Grant Nos. 14605920, 14606922; Collaborative Research Fund Grant No. C4023-20 GF; Research Matching Grants RMG 8601219, 8601242) and a grant from the Research Committee on Research Sustainability of Major Research Grants Council Funding Schemes (3133235) of the Chinese University of Hong Kong. Jiannan Cai was supported by an RGC Postdoctoral Fellowship awarded by the Research Grants Council of Hong Kong (PDFS2223-4H01).

Notes on contributors

Jiannan Cai

JIANNAN CAI is an RGC Postdoctoral Fellow in the Institute of Space and Earth Information Science at the Chinese University of Hong Kong, Hong Kong, China. E-mail: [email protected]. His research focuses on developing new geospatial data science approaches to uncover interesting spatiotemporal patterns hidden in geospatial big data and to facilitate the understanding of complex associations between human activities and urban environment.

Mei-Po Kwan

MEI-PO KWAN is Choh-Ming Li Professor of Geography and Resource Management and Director of the Institute of Space and Earth Information Science at the Chinese University of Hong Kong, Hong Kong, China. E-mail: [email protected]. Her research interests include environmental health, human mobility, sustainable cities, transport and health issues in cities, and GIScience. She is a leading researcher in deploying real-time Global Positioning System tracking and mobile sensing to collect individual-level data in environmental health research.