Search

Accurately predicting the next track point of vehicle travel is crucial for various Intelligent Transportation System (ITS) applications, such as travel behavior studies, traffic control, and traffic congestion monitoring. Recent works on trajectory prediction follow a paradigm that first represents the raw trajectory and subsequently makes predictions based on that representation. Currently, trajectory representation methods tend to project trajectory points to road networks by map matching and represent trajectories based on the representation of matched roads. However, precisely matching trajectories to roads is a challenge in ITS, as the matching precision is greatly affected by the quality of the trajectory. Meanwhile, since it is difficult to discern whether trajectory matching results are accurate or confounded, how to effectively utilize this type of uncertain geographic context information is also a challenge, which is defined as the Uncertain Geographic Context Problem (UGCoP) in geographic information science. Therefore, we propose a flexible strategy of subgraph learning, referred to as SLM, for predicting the next track point of vehicles. Specifically, a subgraph generation module is first proposed to extract topology contextual information of the roads around historical trajectory points. Secondly, a subgraph learning module is designed to learn rich spatial and temporal features from generated subgraphs. Finally, the extracted spatiotemporal features will be fed into a prediction module to predict the next track points of vehicles on road networks. Our model enables the effective utilization of uncertain geographic context information of trajectories on road networks while avoiding the error brought by map matching. Extensive experiments based on trajectory datasets in two different cities confirm the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Neighbourhoods are fluid social and spatial constructs that vary by person and place. How do residential neighbourhoods shift as people age? This mixed-methods study investigates how perceived neighbourhood boundaries and size vary by individual and contextual characteristics. Semi-structured interviews with 125 adults aged 55–92 years living in the Minneapolis (Minnesota) metropolitan area suggested that neighbourhood 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 neighbourhood determinants. This informed quantitative analyses among 7811 respondents (mean age 72) from the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study who self-reported how many blocks composed their neighbourhoods. We tested individual and contextual factors identified in the qualitative results as related to perceived neighbourhood 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 neighbourhood sizes. Further, living in less racially diverse, less dense, and less affluent areas were significantly associated with smaller neighbourhoods. The mixed-methods findings deepen understanding of scale in neighbourhood-based research, inform urban planning interventions, and help understand what 'neighbourhood' means among diverse ageing Americans. [ABSTRACT FROM AUTHOR]

Understanding the relationship between the retail food environment and individual and community health is important for addressing questions about diet-related disease and food security. A wide range of methodological approaches have been used to understand this complex relationship, but the studies have resulted in a lack of consensus on how and whether the food retail environment influences health outcomes and food choice. Inconsistency in measuring the food environment could explain inconsistent results. We calculate five different boundaries of the food retail environment to investigate the robustness of boundary measures. These five boundaries include an administrative boundary (census tract of residence), two Euclidean boundaries, and two network area boundaries. We compare four different aspects of the food retail environment—availability, accessibility, affordability, and realized purchase behavior—across these five fundamentally different geographic boundary definitions. Using the National Food Acquisition and Purchase Survey, Nielsen TDLinx, and Information Resources, Inc. retail and consumer data, we find that food environment measures using administrative boundary definitions are statistically different from all other boundary definitions. We find general similarities within the two Euclidean distance and two network area boundary definitions. Only a small portion of food-at-home purchase events and expenditures by households occur within their own census tract boundary, suggesting the boundary definition has limitations and households are mobile in their food acquisitions. [ABSTRACT FROM AUTHOR]

Based on different conceptualizations and measures of individual-level environmental exposure, this study examines how the uncertain geographic context problem (UGCoP) and the neighborhood effect averaging problem (NEAP) might affect the assessment of COVID-19 risk. Using the COVID-19 data on an open-access government Web site and the individual-level activity data of sixty confirmed COVID-19 cases (infected persons) in Hong Kong, we first represent COVID-19 risk environments using case-based and venues-based high-risk locations. The COVID-19 risk of each of the sixty selected cases is then evaluated by three approaches based on their exposures to the case-based or venues-based risk environments: the mobility-based approach, the residence-based approach, and the activity space–based approach. The results indicate that the UGCoP and the NEAP exist in the assessment of COVID-19 risk, which has significant implications: Ecological COVID-19 studies need to address the uncertainties due to the UGCoP and the NEAP by considering people's daily mobility. Otherwise, ignoring peoples' daily mobility and its interactions with complex and dynamic COVID-19 risk environments could lead to misleading results and misinform government nonpharmaceutical intervention measures. [ABSTRACT FROM AUTHOR]

Spatial flow co-location patterns (FCLPs) are important for understanding the spatial dynamics and associations of movements. However, conventional point-based co-location pattern discovery methods ignore spatial movements between locations and thus may generate erroneous findings when applied to spatial flows. Despite recent advances, there is still a lack of methods for analyzing multivariate flows. To bridge the gap, this paper formulates a novel problem of FCLP discovery and presents an effective detection method based on frequent-pattern mining and spatial statistics. We first define a flow co-location index to quantify the co-location frequency of different features in flow neighborhoods, and then employ a bottom-up method to discover all frequent FCLPs. To further establish the statistical significance of the results, we develop a flow pattern reconstruction method to model the benchmark null hypothesis of independence conditioning on univariate flow characteristics (e.g. flow autocorrelation). Synthetic experiments with predefined FCLPs verify the advantages of our method in terms of correctness over available alternatives. A case study using individual home-work commuting flow data in the Chicago Metropolitan Area demonstrates that residence- or workplace-based co-location patterns tend to overestimate the co-location frequency of people with different occupations and could lead to inconsistent results. [ABSTRACT FROM AUTHOR]

Background: Residential histories linked to cancer registry data provide new opportunities to examine cancer outcomes by neighborhood socioeconomic status (SES). We examined differences in regional stage colon cancer survival estimates comparing models using a single neighborhood SES at diagnosis to models using neighborhood SES from residential histories.Methods: We linked regional stage colon cancers from the New Jersey State Cancer Registry diagnosed from 2006 to 2011 to LexisNexis administrative data to obtain residential histories. We defined neighborhood SES as census tract poverty based on location at diagnosis and across the follow-up period through 31 December 2016 based on residential histories (average, time-weighted average, time-varying). Using Cox proportional hazards regression, we estimated associations between colon cancer and census tract poverty measurements (continuous and categorical), adjusted for age, sex, race/ethnicity, regional substage, and mover status.Results: Sixty-five percent of the sample was nonmovers (one census tract); 35% (movers) changed tract at least once. Cases from tracts with >20% poverty changed residential tracts more often (42%) than cases from tracts with <5% poverty (32%). Hazard ratios (HRs) were generally similar in strength and direction across census tract poverty measurements. In time-varying models, cases in the highest poverty category (>20%) had a 30% higher risk of regional stage colon cancer death than cases in the lowest category (<5%) (95% confidence interval [CI] = 1.04, 1.63).Conclusion: Residential changes after regional stage colon cancer diagnosis may be associated with a higher risk of colon cancer death among cases in high-poverty areas. This has important implications for postdiagnostic access to care for treatment and follow-up surveillance. See video abstract: http://links.lww.com/EDE/B705. [ABSTRACT FROM AUTHOR]

It is now generally accepted that spatially-based neighborhood or contextual attributes influence individual behaviors. However, studies of contextual effects often operationalize “neighborhoods” as static, single-level administrative units that are chosen for data availability rather than theoretical reasons. This practice has led to new calls for sound conceptual models that guide data collection efforts and statistical analyses related to these phenomena. While many such models are in use or being proposed in the social sciences, this article argues that research in the field of evolutionary studies offers alternative and interesting ways of investigating neighborhood effects. Accordingly, the article pursues two objectives. First, it makes connections between neighborhood effects research in the social sciences and relevant literature in evolutionary game theory and evolutionary urban geography. Second, these interdisciplinary interactions guide the development of a cross-level conceptual model of neighborhood effects on urban social behavior. The conceptual model is then translated into an empirical model that tests whether and how property maintenance behavior in a selected U.S. study area changes as a function of neighborhood context. The findings reveal that neighborhood effects operate at multiple, interacting spatial levels in the study area, which suggests that conventional single-level administrative boundaries are not equipped to capture these effects. While they are proffered as exploratory, the results nonetheless imply that insights from evolutionary research can add depth and theoretical grounding to contextual effects studies in the social sciences.

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). [ABSTRACT FROM AUTHOR]

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. • The effects of the sound level and increment on momentary annoyance are nonlinear. • Different sound characteristics can produce a joint effect on momentary annoyance. • Activity microenvironments can affect momentary annoyance to varying degrees. • Momentary noise exposure-annoyance relationships can vary throughout the day. [ABSTRACT FROM AUTHOR]

Overweight is a serious threat to public health. Although many studies have examined the association of the built environment with overweight, it remains unclear whether the relationship varies by urban and rural contexts. This study applied binary logit regression models to explore how the built environment was associated with overweight, using a sample of 1350 adults from urban (the built-up area) and rural (Chongming Island) areas in Shanghai. The results show that mixed land use and fewer supermarkets are positively related to overweight in both urban and rural areas. Higher road density has a negative association with overweight in urban areas, but is not significantly related to overweight in rural areas. We conclude that associations of the built environment with overweight differ in urban and rural contexts. [ABSTRACT FROM AUTHOR]

Measurements of neighborhood exposures likely vary depending on the definition of “neighborhood” selected. This study examined the extent to which neighborhood definition influences findings regarding spatial accessibility to tobacco retailers among youth. We defined spatial accessibility to tobacco retailers (i.e., tobacco retail density, closest tobacco retailer, and average distance to the closest 5 tobacco retailers) on the basis of circular and network buffers of 400 m and 800 m, census block groups, and census tracts by using residential addresses from the 2008 Boston Youth Survey Geospatial Dataset (n = 1,292). Friedman tests (to compare overall differences in neighborhood definitions) were applied. There were differences in measurements of youths' access to tobacco retailers according to the selected neighborhood definitions, and these were marked for the 2 spatial proximity measures (both P < 0.01 for all differences). For example, the median average distance to the closest 5 tobacco retailers was 381.50 m when using specific home addresses, 414.00 m when using census block groups, and 482.50 m when using census tracts, illustrating how neighborhood definition influences the measurement of spatial accessibility to tobacco retailers. These analyses suggest that, whenever possible, egocentric neighborhood definitions should be used. The use of larger administrative neighborhood definitions can bias exposure estimates for proximity measures. [ABSTRACT FROM PUBLISHER]

The uncertain geographic context problem (UGCoP), first articulated by Kwan (2012; The uncertain geographic context problem. Annals of the Association of American Geographers, 102 (5), 958–968), refers to the problem that findings about the effects of area-based contextual variables on individual behaviors or outcomes may be affected by how contextual units (e.g., neighborhoods) are geographically delineated and the extent to which these areal units deviate from the true geographic context. It is a significant methodological problem because it means that analytical results can differ for different delineations of contextual units even if everything else is the same. Drawing upon Kwan (2012) and recent social science studies (especially environmental health and neighborhood effects research), this article further elaborates on the nature of the UGCoP and explores how recent advances in geographical information system (GIS) and geospatial technologies can help address the problem. It discusses possible means for mitigating the UGCoP, especially with regard to the collection of detailed individual space–time data with global positioning systems, construction of individual activity spaces, and the use of qualitative and web-based GIS to capture people's activity locations and everyday experiences. It also discusses the challenges for future research that seeks to address the UGCoP. [ABSTRACT FROM AUTHOR]

Any study that examines the effects of area-based attributes on individual behaviors or outcomes faces another fundamental methodological problem besides the modifiable areal unit problem (MAUP). It is the problem that results about these effects can be affected by how contextual units or neighborhoods are geographically delineated and the extent to which these areal units deviate from the true geographic context. The problem arises because of the spatial uncertainty in the actual areas that exert the contextual influences under study and the temporal uncertainty in the timing and duration in which individuals experienced these contextual influences. Using neighborhood effects and environmental health research as a point of departure, this article clarifies the nature and sources of this problem, which is referred to as the uncertain geographic context problem (UGCoP). It highlights some of the inferential errors that the UGCoP might cause and discusses some means for mitigating the problem. It reviews recent studies to show that both contextual variables and research findings are sensitive to different delineations of contextual units. The article argues that the UGCoP is a problem as fundamental as the MAUP but is a different kind of problem. Future research needs to pay explicit attention to its potential confounding effects on research results and to methods for mitigating the problem. [ABSTRACT FROM AUTHOR]

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 (PM 2.5) 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 PM 2.5 and noise exposures after accounting for individual mobility and the spatiotemporal dynamics of these two environmental pollutants. Further, exposure to PM 2.5 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. • Co-exposures to air pollution and noise are measured by portable sensors. • Psychological stress is measured by ecological momentary assessment. • Exposures to air pollution and noise co-vary weakly in space and time. • Air pollution is more acute as an environmental stressor than noise. • Three contexts are discerned with divergent stress risks of co-exposures. [ABSTRACT FROM AUTHOR]

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. • Number of ecological studies of environmental determinants of COVID-19 is increasing. • Their results may be biased and confounded by spatiotemporal aggregation of data. • People's day-to-day mobility away from the home can further bias their results. • Stronger research designs on a person-level with day-to-day mobility are needed. [ABSTRACT FROM AUTHOR]

Noise is a pervasive environmental stressor that has adverse impacts on people's mental health. However, past studies on the relationship between individuals' chronic noise exposure and psychological stress were inconclusive, partly due to their ignorance of the role of individual mobility and the temporality of the exposure-effect relationship. This study examines how individual mobility and temporality influence the relationship between noise exposure, noise annoyance, and psychological stress. An approach combining geographic ecological momentary assessment (GEMA) and portable noise sensors is used to collect real-time data from a sample of residents in Beijing, China. Using structural equation modeling, the study found that noise annoyance mediates the association between noise exposure and psychological stress in real-time. Further, participants reported divergent psychological stress levels in locations other than their residences and workplaces. Compared with the home locations, recreational and commercial places are noisier but less stressful, while participants tend to experience higher levels of noise and annoyance during travel. In addition, the cumulative effect of noise exposure on stress decreases in magnitude over time but stays significant within 3 h of the exposure. The results suggest that health impact assessments should take into account people's real-time noise exposure and psychological responses to noise. • Noise exposure and psychological stress are measured by portable sensors and Geographic Ecological Momentary Assessment. • Noise annoyance fully mediates the association between noise exposure and psychological stress in real-time. • Individuals are exposed to varying noise levels and report divergent stress levels in locations other than residences. • The psychological effects of noise exposure are immediate, cumulative , and dispositional from the long-to short-term. [ABSTRACT FROM AUTHOR]

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.

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., Competing Interests: The authors declare no conflict of interest.