'My neighbourhood is fuzzy, not hard and fast': Individual and contextual associations with perceived residential neighbourhood boundaries among ageing Americans.

Neighborhoods are fluid social and spatial constructs that vary by person and place. How do residential neighborhoods shift as people age? This mixed-method study investigates how perceived neighborhood boundaries and size vary by individual and contextual characteristics. Semi-structured interviews with 125 adults aged 55-92 living in the Minneapolis (Minnesota) metropolitan area suggested that neighborhood boundaries are "fuzzy". Qualitative thematic analysis identified duration of residence and housing stability, race, life-space mobility, social capital, sense of safety, and the built and social environment as key neighborhood determinants. This informed quantitative analyses among 7,811 respondents (mean age 72) from the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study who self-reported how many blocks composed their neighborhoods. We tested individual and contextual factors identified in the qualitative results as related to perceived neighborhood size. Three-level gamma regression models showed that being older, white, less educated, lower income, less physically and cognitively healthy, less active, less socially supported, and feeling unsafe were significantly associated with smaller self-reported neighborhood sizes. Further, living in less racially diverse, less dense, and less affluent areas were significantly associated with smaller neighborhoods. The mixed-methods findings deepen understanding of scale in neighborhood-based research, inform urban planning interventions, and help understand what "neighborhood" means among diverse aging Americans.

Perceiving noise in daily life: How real-time sound characteristics affect personal momentary noise annoyance in various activity microenvironments and times of day.

Annoyance is a major health burden induced by environmental noise. However, our understanding of the health impacts of noise is seriously undermined by the fixed contextual unit and limited sound characteristics (e.g., the sound level only) used in noise exposure assessments as well as the stationarity assumption made for exposure-response relationships. To address these limitations, we analyze the complex and dynamic relationships between personal momentary noise annoyance and real-time noise exposure in various activity microenvironments and times of day, taking into account individual mobility, multiple sound characteristics and nonstationary relationships. Using real-time mobile sensing, we collected individual data of momentary noise annoyance, real-time noise exposure as well as daily activities and travels in Hong Kong. A new sound characteristic, namely sound increment, is defined to capture the sudden increase in sound level over time and is used along with the sound level to achieve a multi-faceted assessment of personal real-time noise exposure at the moment of annoyance responses. Further, the complex noise exposure-annoyance relationships are learned using logistic regression and random forest models while controlling the effects of daily activity microenvironments, individual sociodemographic attributes and temporal contexts. The results indicate that the effects of the real-time sound level and sound increment on personal momentary noise annoyance are nonlinear, despite the overall significant and positive impacts, and different sound characteristics can produce a joint effect on annoyance. We also find that the daily activity microenvironments and individual sociodemographic attributes can affect noise annoyance and its relationship with different sound characteristics to varying degrees. Due to the temporal changes in daily activities and travels, the noise exposure-annoyance relationships can also vary over different times of the day. These findings can inform both local governments and residents with scientific evidence to promote the creation of acoustically comfortable living environments.

Inconsistent Association between Perceived Air Quality and Self-Reported Respiratory Symptoms: A Pilot Study and Implications for Environmental Health Studies.

As public awareness of air quality issues becomes heightened, people's perception of air quality is drawing increasing academic interest. However, data about people's perceived environment need scrutiny before being used in environmental health studies. In this research, we examine the associations between people's perceptions of air quality and their self-reported respiratory health symptoms. Spearman rank correlation coefficients were estimated and the associations were tested at the 95% confidence level. Using data collected from participants in two representative communities in Hong Kong, the results indicate a weak but significant association between people's perceived air quality and their self-reported frequency of respiratory symptoms. However, there are disparities in such an association between different genders, age groups, household income levels, education levels, marital statuses, and geographic contexts. The most striking disparities are between genders and geographic contexts. Multiple significant associations were observed for male participants (correlation coefficients: 0.169~0.205, p-values: 0.021~0.049), while none was observed for female participants. Besides, multiple significant associations were observed in the old town (correlation coefficients: 0.164~0.270, p-values: 0.003~0.048), while none was observed in the new town. The results have significant implications for environmental health research using social media data, whose reliability depends on the association between people's perceived or actual environments and their health outcomes. Since inconsistent associations exist between different groups of people, researchers need to scrutinize social media data before using them in health studies.

A tale of three cities: uncovering human-urban interactions with geographic-context aware social media data.

Seeking spatiotemporal patterns about how citizens interact with the urban space is critical for understanding how cities function. Such interactions were studied in various forms focusing on patterns of people's presence, action, and transition in the urban environment, which are defined as human-urban interactions in this paper. Using human activity datasets that utilize mobile positioning technology for tracking the locations and movements of individuals, researchers developed stochastic models to uncover preferential return behaviors and recurrent transitional activity structures in human-urban interactions. Ad-hoc heuristics and spatial clustering methods were applied to derive meaningful activity places in those studies. However, the lack of semantic meaning in the recorded locations makes it difficult to examine the details about how people interact with different activity places. In this study, we utilized geographic context-aware Twitter data to investigate the spatiotemporal patterns of people's interactions with their activity places in different urban settings. To test consistency of our findings, we used geo-located tweets to derive the activity places in Twitter users' location histories over three major U.S. metropolitan areas: Greater Boston Area, Chicago, and San Diego, where the geographic context of each location was inferred from its closest land use parcel. The results showed striking spatial and temporal similarities in Twitter users' interactions with their activity places among the three cities. By using entropy-based predictability measures, this study not only confirmed the preferential return behaviors as people tend to revisit a few highly frequented places but also revealed detailed characteristics of those activity places.

Impact of Absolute and Relative Commute Time on Work-Family Conflict: Work Schedule Control, Child Care Hours, and Life Satisfaction.

Flexibility is crucial when employees manage their work and family demands and their commute between home and work. The current study examined the direct and moderation effects of variables from multiple domains including work schedule control (work domain), childcare hours (family domain), and life satisfaction (overall life domain). The impact of the geographic context on work-family conflict was tested with two contextual variables that were generated with Geographic Information System (GIS) technology, where 'absolute' and 'relative commute time' were investigated in relation to work-family conflict. The participants participated in the National Study of Changing Workforce and completed an online survey on many work and family related variables. Results support the fact that commute time has an impact on work-family dynamics, that life satisfaction can influence this relationship, and that it is important to consider neighborhood in future research to better comprehend work-family interface issues. The study also highlights the importance of urbanization, relative and absolute commute time, etc. in impacting work-family conflict. Additionally, the study discusses the impact of COVID-19 on commute and one's work-family dynamics. Future research directions are put forward to better understand work and family experiences in the post COVID-19 world.

Associations of co-exposures to air pollution and noise with psychological stress in space and time: A case study in Beijing, China.

Air pollution and noise are both ubiquitous environmental stressors that pose great threats to public health. Emerging evidence has noticed the combined health risks caused by the coexistence of traffic-related air pollutants and noise in the residential context. However, less is known about how mobile individuals are simultaneously exposed to multiple sources of air pollution and noise, and thus respond with more acute psychological responses beyond the residence. This study examines the co-exposures to fine particles (PM2.5) and noise across spatiotemporal contexts where the concurrent exposures are jointly associated with momentary psychological stress. An innovative research protocol, including GPS-equipped activity-travel diaries, air pollutant and noise sensors, and ecological momentary assessment, was adopted to collect real-time data from a sample of residents in Beijing, China. The results showed a minor correlation between PM2.5 and noise exposures after accounting for individual mobility and the spatiotemporal dynamics of these two environmental pollutants. Further, exposure to PM2.5 was more associated with momentary psychological stress given the insignificant independent effect and the weak moderating effect of noise exposure. Three specific spatiotemporal contexts involving the health risks of co-exposures were delineated, including morning rush hours and traveling by public transits with intensified stress risks caused by combined exposures to air pollution and noise, workplaces with counteracting stress effect of both exposures, and evening time at home with stress-induced air pollution and stress-relieving social noise. In conclusion, the mobility-based and context-aware analysis provides a more nuanced understanding of the associations of co-exposures to environmental pollution and synchronous psychological stress in space and time.

Time to address the spatiotemporal uncertainties in COVID-19 research: Concerns and challenges.

In this correspondence, we emphasize methodological caveats of ecological studies assessing associations between COVID-19 and its physical and social environmental determinants. First, we stress that inference is error-prone due to the modifiable areal unit problem and the modifiable temporal unit problem. The possibility of confounding from using aggregated data is substantial due to the neglect of person-level factors. Second, studying the viral transmission of COVID-19 solely on people's residential neighborhoods is problematic because people are also exposed to nonhome locations and environments en-route along their daily mobility path. We caution against an uncritical application of aggregated data and reiterate the importance of stronger research designs (e.g., case-control studies) on an individual level. To address environmental contextual uncertainties due to people's day-to-day mobility, we call for people-centered studies with mobile phone data.

Understanding the relationships among individual-based momentary measured noise, perceived noise, and psychological stress: A geographic ecological momentary assessment (GEMA) approach.

This study aims to understand how the relationship between individual-based noise exposure and psychological stress is influenced by perceived noise and context. Using geographic ecological momentary assessment, along with activity-travel diaries, GPS tracking, and portable noise sensors, this study collected real-time data of individuals' daily movement, noise exposure, and self-reported noise perception and psychological stress. Structural equation modeling was used to examine the direct and indirect pathways among context, momentary measured noise, perceived noise, and psychological stress. The study finds that momentary measured noise influences psychological stress through the mediating effect of perceived noise. Further, different activity and travel, social, and temporal contexts significantly influence people's momentary measured noise, perceived noise, and psychological stress. These findings advance our understanding of specific contexts, individual-based objectively measured and subjectively perceived environmental exposures, and their effects on psychological health at a high spatiotemporal resolution.

Double Jeopardy in Contemporary China: Intersecting the Socioeconomic Gradient and Geographic Context on Early Childhood Development.

Family socioeconomic status (SES) differences in early childhood development (ECD) are well documented, as are the neighborhood effects in early development outcomes. However, little is known about whether the SES gradient in ECD outcomes varies across geographic contexts by county-level variables in contemporary China. This study examines the effects of county-level socioeconomic background on inequalities in the developmental outcomes of young Chinese children. Individual-level child development data based on four early development milestones-taking a first step, first sentences, counting 10 objects, fully independent toileting-were combined with family- and county-level socioeconomic data from the China Family Panel Studies (CFPS). Using a hierarchical linear model (HLM) to examine how the broader socioeconomic context plays a role in the attainment of developmental milestones at expected times as young children grow and develop, we have found significant cross-level interaction effects between family SES and county-level variables in relation to developmental milestone attainment. The family SES gradient in the achievement of children's developmental milestones is steeper for those in the under-developed regions than their counterparts in the more developed regions. Our findings suggest that low-SES children who are living in socioeconomically deprived regions suffer from a double disadvantage in terms of early development outcomes. Further research would be needed to contextualize the observed interactions and better explain the underlying mechanisms.

Examining Ethnic Exposure through the Perspective of the Neighborhood Effect Averaging Problem: A Case Study of Xining, China.

An increasing number of studies have observed that ignoring individual exposures to non-residential environments in people's daily life may result in misleading findings in research on environmental exposure. This issue was recognized as the neighborhood effect averaging problem (NEAP). This study examines ethnic segregation and exposure through the perspective of NEAP. Focusing on Xining, China, it compares the Hui ethnic minorities and the Han majorities. Using 2010 census data and activity diary data collected in 2013, the study found that NEAP exists when examining ethnic exposure. Respondents 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). By contrast, respondents 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). Therefore, taking into account individuals' daily activities in non-residential contexts in the assessment of environmental exposure will likely lead to an overall tendency towards the mean exposure. Using Tobit models, we further found that specific types of activity places, especially workplaces and parks, contribute to NEAP. Ignoring individual exposures in people's activity places will most likely result in misleading findings in the measurement of environmental exposure, including ethnic exposure.

Hybridization patterns between two marine snails, Littorina fabalis and L. obtusata.

Characterizing the patterns of hybridization between closely related species is crucial to understand the role of gene flow in speciation. In particular, systems comprising multiple contacts between sister species offer an outstanding opportunity to investigate how reproductive isolation varies with environmental conditions, demography and geographic contexts of divergence. The flat periwinkles, Littorina obtusata and L. fabalis (Gastropoda), are two intertidal sister species with marked ecological differences compatible with late stages of speciation. Although hybridization between the two was previously suggested, its extent across the Atlantic shores of Europe remained largely unknown. Here, we combined genetic (microsatellites and mtDNA) and morphological data (shell and male genital morphology) from multiple populations of flat periwinkles in north-western Iberia to assess the extent of current and past hybridization between L. obtusata and L. fabalis under two contrasting geographic settings of divergence (sympatry and allopatry). Hybridization signatures based on both mtDNA and microsatellites were stronger in sympatric sites, although evidence for recent extensive admixture was found in a single location. Misidentification of individuals into species based on shell morphology was higher in sympatric than in allopatric sites. However, despite hybridization, species distinctiveness based on this phenotypic trait together with male genital morphology remained relatively high. The observed variation in the extent of hybridization among locations provides a rare opportunity for future studies on the consequences of different levels of gene flow for reinforcement, thus informing about the mechanisms underlying the completion of speciation.

Understanding Racial Disparities in Exposure to Traffic-Related Air Pollution: Considering the Spatiotemporal Dynamics of Population Distribution.

This study investigates the effect of spatiotemporal distributions of racial groups on disparities in exposure to traffic-related air pollution by considering people's daily movement patterns. Due to human mobility, a residential neighborhood does not fully represent the true geographic context in which people experience racial segregation and unequal exposure to air pollution. Using travel-activity survey data containing individuals' activity locations and time spent at each location, this study measures segregation levels that an individual might experience during the daytime and nighttime, estimates personal exposure by integrating hourly pollution maps and the survey data, and examines the association between daytime/nighttime segregation and exposure levels. The proximity of each activity location to major roads is also evaluated to further examine the unequal exposure. The results reveal that people are more integrated for work in high-traffic areas, which contributes to similarly high levels of exposure for all racial groups during the daytime. However, white people benefit from living in suburbs/exurbs away from busy roads. The finding suggests that policies for building an extensive and equitable public transit system should be implemented together with the policies for residential mixes among racial groups to reduce everyone's exposure to traffic-related air pollution and achieve environmental justice.

Geographic context affects the landscape change and fragmentation caused by wind energy facilities.

Wind energy generation affects landscapes as new roads, pads, and transmission lines are constructed. Limiting the landscape change from these facilities likely minimizes impacts to biodiversity and sensitive wildlife species. We examined the effects of wind energy facilities' geographic context on changes in landscape patterns using three metrics: portion of undeveloped land, core area index, and connectance index. We digitized 39 wind facilities and the surrounding land cover and measured landscape pattern before and after facility construction using the amount, core area, and connectivity of undeveloped land within one km around newly constructed turbines and roads. New facilities decreased the amount of undeveloped land by 1.8% while changes in metrics of landscape pattern ranged from 50 to 140%. Statistical models indicated pre-construction development was a key factor explaining the impact of new wind facilities on landscape metrics, with pre-construction road networks, turbine spacing, and topography having smaller influences. As the proportion of developed land around facilities increased, a higher proportion of the facility utilized pre-construction developed land and a lower density of new roads were built, resulting in smaller impacts to undeveloped landscapes. Building of new road networks was also a predictor of landscape fragmentation. Utilizing existing development and carefully placing turbines may provide opportunities to minimize the impacts of new wind energy facilities.

An Analytical Framework for Integrating the Spatiotemporal Dynamics of Environmental Context and Individual Mobility in Exposure Assessment: A Study on the Relationship between Food Environment Exposures and Body Weight.

In past studies, individual environmental exposures were largely measured in a static manner. In this study, we develop and implement an analytical framework that dynamically represents environmental context (the environmental context cube) and effectively integrates individual daily movement (individual space-time tunnel) for accurately deriving individual environmental exposures (the environmental context exposure index). The framework is applied to examine the relationship between food environment exposures and the overweight status of 46 participants using data collected with global positioning systems (GPS) in Columbus, Ohio, and binary logistic regression models. The results indicate that the proposed framework generates more reliable measurements of individual food environment exposures when compared to other widely used methods. Taking into account the complex spatial and temporal dynamics of individual environmental exposures, the proposed framework also helps to mitigate the uncertain geographic context problem (UGCoP). It can be used in other environmental health studies concerning environmental influences on a wide range of health behaviors and outcomes.

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.

An Innovative Context-Based Crystal-Growth Activity Space Method for Environmental Exposure Assessment: A Study Using GIS and GPS Trajectory Data Collected in Chicago.

Scholars in the fields of health geography, urban planning, and transportation studies have long attempted to understand the relationships among human movement, environmental context, and accessibility. One fundamental question for this research area is how to measure individual activity space, which is an indicator of where and how people have contact with their social and physical environments. Conventionally, standard deviational ellipses, road network buffers, minimum convex polygons, and kernel density surfaces have been used to represent people's activity space, but they all have shortcomings. Inconsistent findings of the effects of environmental exposures on health behaviors/outcomes suggest that the reliability of existing studies may be affected by the uncertain geographic context problem (UGCoP). This paper proposes the context-based crystal-growth activity space as an innovative method for generating individual activity space based on both GPS trajectories and the environmental context. This method not only considers people's actual daily activity patterns based on GPS tracks but also takes into account the environmental context which either constrains or encourages people's daily activity. Using GPS trajectory data collected in Chicago, the results indicate that the proposed new method generates more reasonable activity space when compared to other existing methods. This can help mitigate the UGCoP in environmental health studies.

Beyond Commuting: Ignoring Individuals' Activity-Travel Patterns May Lead to Inaccurate Assessments of Their Exposure to Traffic Congestion.

This research examines whether individual exposures to traffic congestion are significantly different between assessments obtained with and without considering individuals' activity-travel patterns in addition to commuting trips. We 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 revealed that individual exposures to traffic congestion are significantly underestimated when their activity-travel patterns are ignored, which has been postulated as a manifestation of the uncertain geographic context problem (UGCoP). The results also highlighted that the probability distribution function of exposures is heavily skewed but tends to converge to its average when individuals' activity-travel patterns are considered when compared to one obtained when those patterns are not considered, which indicates the existence of the neighborhood effect averaging problem (NEAP). Lastly, space-time visualizations of individual exposures illustrated that people's exposures to traffic congestion vary significantly even if they live at the same residential location due to their idiosyncratic activity-travel patterns. The results corroborate the claims in previous studies that using data aggregated over areas (e.g., census tracts) or focusing only on commuting trips (and thus ignoring individuals' activity-travel patterns) may lead to erroneous assessments of individual exposures to traffic congestion or other environmental influences.

Multi-Contextual Segregation and Environmental Justice Research: Toward Fine-Scale Spatiotemporal Approaches.

Many environmental justice studies have sought to examine the effect of residential segregation on unequal exposure to environmental factors among different social groups, but little is known about how segregation in non-residential contexts affects such disparity. Based on a review of the relevant literature, this paper discusses the limitations of traditional residence-based approaches in examining the association between socioeconomic or racial/ethnic segregation and unequal environmental exposure in environmental justice research. It emphasizes that future research needs to go beyond residential segregation by considering the full spectrum of segregation experienced by people in various geographic and temporal contexts of everyday life. Along with this comprehensive understanding of segregation, the paper also highlights the importance of assessing environmental exposure at a high spatiotemporal resolution in environmental justice research. The successful integration of a comprehensive concept of segregation, high-resolution data and fine-grained spatiotemporal approaches to assessing segregation and environmental exposure would provide more nuanced and robust findings on the associations between segregation and disparities in environmental exposure and their health impacts. Moreover, it would also contribute to significantly expanding the scope of environmental justice research.

Individual exposure estimates may be erroneous when spatiotemporal variability of air pollution and human mobility are ignored.

This study aims to empirically demonstrate the necessity to consider both the spatiotemporal variability of air pollution and individual daily movement patterns in exposure and health risk assessment. It compares four different types of exposure estimates generated by using (1) individual movement data and hourly air pollution concentrations; (2) individual movement data and daily average air pollution data; (3) residential location and hourly pollution levels; and (4) residential location and daily average pollution data. These four estimates are significantly different, which supports the argument that ignoring the spatiotemporal variability of environmental risk factors and human mobility may lead to misleading results in exposure assessment. Additionally, three-dimensional (3D) geovisualization presented in the paper shows how person-specific space-time context is generated by the interactions between air pollution and an individual, and how the different individualized contexts place individuals at different levels of health risk.