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Special Issue "Human Mobility, Spatiotemporal Context, and Environmental Health: Recent Advances in Approaches and Methods"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Health".

Deadline for manuscript submissions: 1 October 2018

Special Issue Editor

Guest Editor
Prof. Dr. Mei-Po Kwan

Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Website | E-Mail
Interests: environmental health; human mobility; sustainable cities; GIScience; ICT

Special Issue Information

Dear Colleagues,

Environmental health researchers have long recognized the importance of geographic context for understanding the effects of different environmental factors on human health. While geographic context and neighborhood effects are fundamental constructs for assessing people’s exposure to contextual or environmental influences, they still tend to be conceptualized largely in static spatial terms, which ignores that people move around in their daily lives and come under the influence of many different neighborhood contexts outside their residential neighborhoods. Past studies also tend to ignore the role of human mobility at various spatial and temporal scales (e.g., daily travel, migratory movements, and movements over the life course) in various health issues. They tend to ignore the temporality of exposures that shapes people’s exposure to environmental influences and subjective wellbeing such as the duration, frequency, and recency of exposure, as well as residential history and cumulative exposure over the life-course.

Recent studies, however, have started to incorporate human mobility, non-residential neighborhoods, and the temporality of exposures through collecting and using data from GPS, accelerometers, mobile phones, various types of sensors, and social media. Innovative approaches and methods have also emerged. This Special Issue aims to showcase studies that use new approaches, methods, and data to examine the role of various forms of human mobility, non-residential contexts, and the temporality of exposures on human health behaviors and outcomes. Studies that illustrate the use of these new methods and data to address questions concerning a wide range of health behaviors and outcomes are welcome. These include but are not limited to individual exposure to air pollution, access to green/blue spaces and subjective wellbeing, environmental influences on physical activity, food environmental and diet behavior, psychosocial stress and drug use behavior, socio-environmental factors that affect sleep patterns and sleep hygiene, risk factors that influence mental health and suicide mortality, exposure to noise and stress, and broader social issues such as environmental justice, health disparities, and racial/ethnic segregation.

Prof. Dr. Mei-Po Kwan
Guest Editor

Manuscript Submission Information

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Keywords

  • human mobility
  • environmental exposures
  • geographic context
  • temporality of context
  • neighborhood effects
  • the uncertain geographic context problem
  • the modifiable areal and/or temporal unit problem
  • life-course perspectives
  • GPS data
  • accelerometer data

Published Papers (6 papers)

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Research

Open AccessArticle An Innovative Context-Based Crystal-Growth Activity Space Method for Environmental Exposure Assessment: A Study Using GIS and GPS Trajectory Data Collected in Chicago
Int. J. Environ. Res. Public Health 2018, 15(4), 703; doi:10.3390/ijerph15040703
Received: 9 March 2018 / Revised: 1 April 2018 / Accepted: 3 April 2018 / Published: 9 April 2018
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Abstract
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
[...] Read more.
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. Full article
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Open AccessArticle Real-Time Estimation of Population Exposure to PM2.5 Using Mobile- and Station-Based Big Data
Int. J. Environ. Res. Public Health 2018, 15(4), 573; doi:10.3390/ijerph15040573
Received: 5 March 2018 / Revised: 16 March 2018 / Accepted: 16 March 2018 / Published: 23 March 2018
Cited by 1 | PDF Full-text (2144 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Extremely high fine particulate matter (PM2.5) concentration has been a topic of special concern in recent years because of its important and sensitive relation with health risks. However, many previous PM2.5 exposure assessments have practical limitations, due to the assumption
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Extremely high fine particulate matter (PM2.5) concentration has been a topic of special concern in recent years because of its important and sensitive relation with health risks. However, many previous PM2.5 exposure assessments have practical limitations, due to the assumption that population distribution or air pollution levels are spatially stationary and temporally constant and people move within regions of generally the same air quality throughout a day or other time periods. To deal with this challenge, we propose a novel method to achieve the real-time estimation of population exposure to PM2.5 in China by integrating mobile-phone locating-request (MPL) big data and station-based PM2.5 observations. Nationwide experiments show that the proposed method can yield the estimation of population exposure to PM2.5 concentrations and cumulative inhaled PM2.5 masses with a 3-h updating frequency. Compared with the census-based method, it introduced the dynamics of population distribution into the exposure estimation, thereby providing an improved way to better assess the population exposure to PM2.5 at different temporal scales. Additionally, the proposed method and dataset can be easily extended to estimate other ambient pollutant exposures such as PM10, O3, SO2, and NO2, and may hold potential utilities in supporting the environmental exposure assessment and related policy-driven environmental actions. Full article
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Open AccessArticle Estimating Vehicle Fuel Consumption and Emissions Using GPS Big Data
Int. J. Environ. Res. Public Health 2018, 15(4), 566; doi:10.3390/ijerph15040566
Received: 14 February 2018 / Revised: 12 March 2018 / Accepted: 20 March 2018 / Published: 21 March 2018
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Abstract
The energy consumption and emissions from vehicles adversely affect human health and urban sustainability. Analysis of GPS big data collected from vehicles can provide useful insights about the quantity and distribution of such energy consumption and emissions. Previous studies, which estimated fuel consumption/emissions
[...] Read more.
The energy consumption and emissions from vehicles adversely affect human health and urban sustainability. Analysis of GPS big data collected from vehicles can provide useful insights about the quantity and distribution of such energy consumption and emissions. Previous studies, which estimated fuel consumption/emissions from traffic based on GPS sampled data, have not sufficiently considered vehicle activities and may have led to erroneous estimations. By adopting the analytical construct of the space-time path in time geography, this study proposes methods that more accurately estimate and visualize vehicle energy consumption/emissions based on analysis of vehicles’ mobile activities (MA) and stationary activities (SA). First, we build space-time paths of individual vehicles, extract moving parameters, and identify MA and SA from each space-time path segment (STPS). Then we present an N-Dimensional framework for estimating and visualizing fuel consumption/emissions. For each STPS, fuel consumption, hot emissions, and cold start emissions are estimated based on activity type, i.e., MA, SA with engine-on and SA with engine-off. In the case study, fuel consumption and emissions of a single vehicle and a road network are estimated and visualized with GPS data. The estimation accuracy of the proposed approach is 88.6%. We also analyze the types of activities that produced fuel consumption on each road segment to explore the patterns and mechanisms of fuel consumption in the study area. The results not only show the effectiveness of the proposed approaches in estimating fuel consumption/emissions but also indicate their advantages for uncovering the relationships between fuel consumption and vehicles’ activities in road networks. Full article
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Open AccessArticle Cycling for Transportation in Sao Paulo City: Associations with Bike Paths, Train and Subway Stations
Int. J. Environ. Res. Public Health 2018, 15(4), 562; doi:10.3390/ijerph15040562
Received: 8 December 2017 / Revised: 28 February 2018 / Accepted: 2 March 2018 / Published: 21 March 2018
PDF Full-text (1108 KB) | HTML Full-text | XML Full-text
Abstract
Cities that support cycling for transportation reap many public health benefits. However, the prevalence of this mode of transportation is low in Latin American countries and the association with facilities such as bike paths and train/subway stations have not been clarified. We conducted
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Cities that support cycling for transportation reap many public health benefits. However, the prevalence of this mode of transportation is low in Latin American countries and the association with facilities such as bike paths and train/subway stations have not been clarified. We conducted a cross-sectional analysis of the relationship between bike paths, train/subway stations and cycling for transportation in adults from the city of Sao Paulo. We used data from the Sao Paulo Health Survey (n = 3145). Cycling for transportation was evaluated by a questionnaire and bike paths and train/subway stations were geocoded using the geographic coordinates of the adults’ residential addresses in 1500-m buffers. We used multilevel logistic regression, taking account of clustering by census tract and households. The prevalence of cycling for transportation was low (5.1%), and was more prevalent in males, singles, those active in leisure time, and in people with bicycle ownership in their family. Cycling for transportation was associated with bike paths up to a distance of 500 m from residences (OR (Odds Ratio) = 2.54, 95% CI (Confidence interval) 1.16–5.54) and with the presence of train/subway stations for distances >500 m from residences (OR = 2.07, 95% CI 1.10–3.86). These results are important to support policies to improve cycling for transportation in megacities such as Sao Paulo. Full article
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Open AccessArticle Using Individual GPS Trajectories to Explore Foodscape Exposure: A Case Study in Beijing Metropolitan Area
Int. J. Environ. Res. Public Health 2018, 15(3), 405; doi:10.3390/ijerph15030405
Received: 13 January 2018 / Revised: 24 February 2018 / Accepted: 25 February 2018 / Published: 27 February 2018
PDF Full-text (10376 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
With the growing interest in studying the characteristics of people’s access to the food environment and its influence upon individual health, there has been a focus on assessing individual food exposure based on GPS trajectories. However, existing studies have largely focused on the
[...] Read more.
With the growing interest in studying the characteristics of people’s access to the food environment and its influence upon individual health, there has been a focus on assessing individual food exposure based on GPS trajectories. However, existing studies have largely focused on the overall activity space using short-period trajectories, which ignores the complexity of human movements and the heterogeneity of the spaces that are experienced by the individual over daily life schedules. In this study, we propose a novel framework to extract the exposure areas consisting of the localized activity spaces around daily life centers and non-motorized commuting routes from long-term GPS trajectories. The newly proposed framework is individual-specific and can incorporate the internal heterogeneity of individual activities (spatial extent, stay duration, and timing) in different places as well as the dynamics of the context. A pilot study of the GeoLife dataset suggests that there are significant variations in the magnitude as well as the composition of the food environment in different parts of the individual exposure area, and residential environment is not representative of the overall foodscape exposure. Full article
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Open AccessArticle The Uncertain Geographic Context Problem in the Analysis of the Relationships between Obesity and the Built Environment in Guangzhou
Int. J. Environ. Res. Public Health 2018, 15(2), 308; doi:10.3390/ijerph15020308
Received: 28 December 2017 / Revised: 3 February 2018 / Accepted: 5 February 2018 / Published: 10 February 2018
Cited by 2 | PDF Full-text (2487 KB) | HTML Full-text | XML Full-text
Abstract
Traditionally, static units of analysis such as administrative units are used when studying obesity. However, using these fixed contextual units ignores environmental influences experienced by individuals in areas beyond their residential neighborhood and may render the results unreliable. This problem has been articulated
[...] Read more.
Traditionally, static units of analysis such as administrative units are used when studying obesity. However, using these fixed contextual units ignores environmental influences experienced by individuals in areas beyond their residential neighborhood and may render the results unreliable. This problem has been articulated as the uncertain geographic context problem (UGCoP). This study investigates the UGCoP through exploring the relationships between the built environment and obesity based on individuals’ activity space. First, a survey was conducted to collect individuals’ daily activity and weight information in Guangzhou in January 2016. Then, the data were used to calculate and compare the values of several built environment variables based on seven activity space delineations, including home buffers, workplace buffers (WPB), fitness place buffers (FPB), the standard deviational ellipse at two standard deviations (SDE2), the weighted standard deviational ellipse at two standard deviations (WSDE2), the minimum convex polygon (MCP), and road network buffers (RNB). Lastly, we conducted comparative analysis and regression analysis based on different activity space measures. The results indicate that significant differences exist between variables obtained with different activity space delineations. Further, regression analyses show that the activity space delineations used in the analysis have a significant influence on the results concerning the relationships between the built environment and obesity. The study sheds light on the UGCoP in analyzing the relationships between obesity and the built environment. Full article
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