What is the NEAP?
The neighborhood effect averaging problem (NEAP) refers to the problem that individual mobility-based exposures to environmental factors tend towards the mean level of the participants or population of a study area when compared to their residence-based exposures. As a result of the NEAP, ignoring people's daily mobility and exposures to nonresidential contexts in geographic or epidemiological studies may lead to erroneous results in the study of mobility-dependent exposures (e.g., noise and air pollution) and their health impact because people's daily mobility may amplify or attenuate the exposures they experienced in their residential neighborhoods. This means that using residence-based neighborhoods to estimate individual exposures to and the health impact of environmental factors may overestimate the statistical significance and effect size of the neighborhood effect.
The NEAP was first identified and described by Kwan (2018), who suggested that "for health
outcomes that are also affected by exposures to environmental factors in people's nonresidential
neighborhoods as they move around in their daily life (mobility-dependent exposures), using
residence-based neighborhoods to estimate individual exposures to and the health impact of
environmental factors will tend to overestimate the statistical significance and effect size
of the neighborhood effect because it ignores the confounding effect of neighborhood effect
averaging that arises from human daily mobility."
Evidence on the NEAP
The NEAP was first identified in Kwan (2018) based on recent studies on individual exposures to air pollution (e.g., Dewulf et al. 2016; Yu et al. 2018). To investigate the validity of the notion, Kwan and her associates undertook further studies on individual exposures to traffic congestion, air pollution and other ethnic groups in the U.S. and China. Based on these studies, five subsequent papers provide further evidence for the existence of the NEAP and how its extent is related to the daily mobility of different social groups. For instance, Kim and Kwan (2019) examined whether individual exposures to traffic congestion are significantly different between assessments obtained with and without considering individuals' activity-travel patterns. The study used crowdsourced real-time traffic congestion data and the activity-travel data of 250 individuals in Los Angeles to compare these two assessments of individual exposures to traffic congestion. The results indicated the existence of the NEAP: the distribution of exposures converges towards the average value when individuals' activity-travel patterns are considered (when compared to one obtained when those patterns are not considered). This study is important in that the NEAP was observed even when only part of a person's daily mobility is ignored (by considering only the commute trip).Kim and Kwan (2021a) provide another in-depth examination of the NEAP. The study assessed individual exposures to ground-level ozone using the activity-travel diary data of 2,737 individuals collected in Los Angeles. The study found that the NEAP exists and that high-income, employed, younger, and male participants (when compared to low-income, non-working, older, and female participants) are associated with higher levels of neighborhood effect averaging because of their higher levels of daily mobility. Another study using the same data found that non-workers (e.g., the unemployed, homemakers, the retired, and students) do not experience downward averaging (Kim and Kwan 2020b). This means that non-workers are far less likely to experience downward averaging that could have attenuated their high exposures experienced in their residential neighborhoods while traveling to other neighborhoods (thus, being doubly disadvantaged).
In another study, Ma et al. (2020) used GPS and mobile sensor data to compare the mobility-based and residential-based exposures of 106 participants to air pollution in a high-pollution community in Beijing, China. The study found that most participants experienced the NEAP and could lower their exposure by their daily mobility. However, three social groups with low daily mobility could not avoid the high pollution in their residential neighborhoods: (1) low-income people with low mobility and limited travel outside their residential neighborhoods, (2) blue-collar workers with long work hours at low-end workplaces, and (3) elderly people who face mobility and household constraints.
These four studies on the NEAP are on air pollution exposure. Tan et al (2020), however, examined whether the NEAP exists in a study of ethnic segregation using the notion of segregation as limited exposure to other ethnic groups. The paper conceives a person's exposure to people of other ethnic groups as the person's ethnic exposure, which is used to capture the extent to which he/she experiences ethnic segregation. The study compared the Hui ethnic minorities and the Han majorities in Xining, China using census and activity diary data. It found that the NEAP exists when examining ethnic exposure. Participants who live in highly mixed neighborhoods (with high exposures to the other ethnic group) experience lower activity-space exposures because they tend to conduct their daily activities in ethnically less mixed areas outside their home neighborhoods (which are more segregated). In contrast, participants who live in highly segregated neighborhoods (with low exposures to the other ethnic group) tend to have higher exposures in their activity locations outside their home neighborhoods (which are less segregated). The study further found that specific types of activity places, especially workplaces and parks, are associated with high levels of the NEAP.
Policy implications of the NEAP
The results of these studies on the NEAP have important implications for all studies on mobility-dependent exposures such as air and noise pollution or traffic congestion. First, due to the existence of the NEAP, accurate assessments of individual mobility-dependent exposures and their health impact require taking people's daily mobility into account; otherwise, assessments of individual exposures may be erroneous. Second, policy-makers should be aware of the effects of neighborhood effect averaging on individual exposures when formulating policy interventions to address the specific needs of disadvantaged social groups: high daily mobility may attenuate people's high exposures in their residential neighborhoods, but low daily mobility would prevent certain socially disadvantaged groups to avoid the high exposures in their residential neighborhoods (Kim and Kwan 2020a,b; Ma et al. 2020). Specifically, it is important to recognize that some social groups may be doubly disadvantaged (e.g., low-income people and women who experience more mobility constraints in their daily lives, which in turn limit their access to jobs and urban opportunities). When these groups live in neighborhoods with high pollution levels, there is little neighborhood effect averaging for them: their limited daily mobility makes it difficult for them to lower their exposures to high pollution levels, as found in Kim and Kwan (2020a,b) and Ma et al. (2020). In this light, these socially disadvantaged groups call for particular attention in public policies.Further, there are some important implications of the NEAP for public health policies. For example, increasing the mobility of those who live in disadvantaged neighborhoods through better, safer, and more reliable public transit, in addition to improving neighborhood quality in situ, may help improve their health outcomes. Further, to mitigate the environmental injustice revealed by the exceptions of the NEAP (i.e., people with low daily mobility or low socioeconomic status), special attention should be paid to the low-mobility vulnerable groups to reduce their hidden environmental risks (Kim and Kwan 2020a,b; Ma et al. 2020).
References
-- Please see the following article for a discussion of the NEAP:Kwan, M.-P. (2018) The neighborhood effect averaging problem (NEAP): An elusive confounder of the neighborhood effect. International Journal of Environmental Research and Public Health, 15: 1841.
-- The following recent studies provide strong evidence for the NEAP:
Dewulf, B., T. Neutens, W. Lefebvre, G. Seynaeve, C. Vanpoucke, C. Beckx, and N. van de Weghe (2016) Dynamic assessment of exposure to air pollution using mobile phone data. International Journal of Health Geogrraphics, 15: 14.
Huang, J., and M.-P. Kwan. (2022) Uncertainties in the assessment of COVID-19 risk: A Study of people's exposure to high-risk environments using individual-level activity data. Annals of the American Association of Geographers, 112(4): 968-987.
Kim, J., and M.-P. Kwan (2019) Beyond commuting: Ignoring individuals' activity-travel patterns may lead to inaccurate assessments of their exposure to traffic congestion. International Journal of Environmental Research and Public Health, 16(1): 89.
Kim, J., and M.-P. Kwan (2021a) How neighborhood effect averaging may affect assessment of individual exposures to air pollution: A study of ozone exposures in Los Angeles. Annals of the American Association of Geographers, 111(1): 121-140.
Kim, J., and M.-P. Kwan (2021b) Assessment of sociodemographic disparities in environmental exposure might be erroneous due to neighborhood effect averaging: Implications for environmental inequality research. Environmental Research, 195: 110519.
Ma, X., X. Li, M.-P. Kwan, and Y. Chai (2020) Who could not avoid exposure to high levels of residence-based pollution by daily mobility? Evidence of air pollution exposure from the perspective of the neighborhood effect averaging problem (NEAP). International Journal of Environmental Research and Public Health, 17(4): 1223.
Tan, Y., M.-P. Kwan, and Z. Chen (2020) Examining ethnic exposure through the perspective of the neighborhood effect averaging problem: A case study of Xining, China. International Journal of Environmental Research and Public Health, 17: 2872.
Wang, J., M.-P Kwan, G. Xiu, X Peng, and Y. Liu (2023) Investigating the neighborhood effect averaging problem (NEAP) in greenspace exposure: A study in Beijing. Landscape and Urban Planning, 243: 104970.
Yu, H., A. Russell, J. Mulholland, and Z. Huang (2018) Using cell phone location to assess misclassification errors in air pollution exposure estimation. Environmental Pollution, 233: 261-266.