Travel-related exposure to air pollution and its socio-environmental inequalities: Evidence from a week-long GPS-based travel diary dataset

Guo, Wenbo; Chai, Yanwei; Kwan, Mei‐Po

doi:10.1016/b978-0-12-815822-7.00013-3

Cited by 5 publications

(3 citation statements)

References 58 publications

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“…Aggregate measures of mobility are necessary when working with large mobility datasets because aggregation serves as a privacy preserving operation when combined with suitable minimum inclusion criteria (e.g., here we only kept ADA regions with a minimum of 100 devices for subsequent analysis). Individual level studies are often combined with detailed individual level survey information which provide rich datasets for analysis of socio-economic factors associated with mobility at the individual level ( Guo, Chai, & Kwan, 2020 ; Helbich et al, 2016 ; Kwan, 1999 ; Long & Reuschke, 2021 ; Schwanen, Kwan, & Ren, 2008 ). Here we do not have access to detailed individual level data, and thus focus on aggregate patterns, with a large sample of the population.…”

Section: Discussionmentioning

confidence: 99%

Associations between mobility and socio-economic indicators vary across the timeline of the Covid-19 pandemic

Long

Ren

2022

Computers, Environment and Urban Systems

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Covid-19 interventions are greatly affecting patterns of human mobility. Changes in mobility during Covid-19 have differed across socio-economic gradients during the first wave. We use fine-scale network mobility data in Ontario, Canada to study the association between three different mobility measures and four socio-economic indicators throughout the first and second wave of Covid-19 (January to December 2020). We find strong associations between mobility and the socio-economic indicators and that relationships between mobility and other socio-economic indicators vary over time. We further demonstrate that understanding how mobility has changed in response to Covid-19 varies considerably depending on how mobility is measured. Our findings have important implications for understanding how mobility data should be used to study interventions across space and time. Our results support that Covid-19 non-pharmaceutical interventions have resulted in geographically disparate responses to mobility and quantifying mobility changes at fine geographical scales is crucial to understanding the impacts of Covid-19.

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Section: Discussionmentioning

confidence: 99%

Associations between mobility and socio-economic indicators vary across the timeline of the Covid-19 pandemic

Long

Ren

2022

Computers, Environment and Urban Systems

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“…Aggregate measures of mobility are necessary when working with large mobility datasets because aggregation serves as a privacy preserving operation when combined with suitable minimum inclusion criteria (e.g., here we only kept ADA regions with a minimum of 100 devices for subsequent analysis). Individual level studies are often combined with detailed individual level survey information which provide rich datasets for analysis of socio-economic factors associated with mobility at the individual level [32][33][34][35][36] . Here we do not have access to detailed individual level data, and thus focus on aggregate patterns, with a large approximately 25% sample of the population.…”

Section: Discussionmentioning

confidence: 99%

Associations between mobility and socio-economic indicators vary across the timeline of the Covid-19 pandemic

Long¹,

Ren²

2021

Preprint

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Non-pharmaceutical interventions are being used globally to limit the spread of Covid-19, which are in turn affecting individual mobility patterns. Mobility measures were found to be strongly associated with regional socio-economic indicators during the first wave of the pandemic. Here, we use network mobility data from an ~3.5 million person sample of individuals in Ontario, Canada to study the association between three different individual-mobility measures and four socio-economic indicators throughout the first and second wave of Covid-19 (January to December 2020). We demonstrate that understanding how mobility behaviours have changed in response to Covid-19 varies considerably depending on how mobility is measured. We find a strong positive association between different mobility levels and the economic deprivation index, which demonstrates that inequities in the changes to mobility across economic gradients observed during the initial lockdown have persisted into the later stages of the pandemic. However, the associations between mobility and other socio-economic indicators vary over time. We capture a strong day-of-week pattern of association between socio-economic indicators and mobility levels. Our findings have important implications for understanding if and how mobility data should be used to study the impact of non-pharmaceutical interventions on the socio-economic conditions across geographical space, and over time. Our results support that Covid-19 non-pharmaceutical interventions have resulted in geographically disparate responses to mobility behaviour, and quantifying mobility changes at fine geographical scales is crucial to understanding the impacts of Covid-19 on local populations.

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“…Beyond measurement of air quality, well-being and health have drawn increasing attention. The health impact of changes in travel behavior, health inequalities, and social justice can be assessed within the activity-based platform [128]. With the help of geospatial data acquisition technologies like GPS, behavioral information with health data can be integrated into the development of an activity-based model to provide policies that affect the balance of transport and well-being.…”

Section: Abm Transferability To Other Non-transport Domainmentioning

confidence: 99%

Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability

Tajaddini¹,

Rose²,

Kockelman³

et al. 2021

Models and Technologies for Smart, Sustainable and Safe Transportation Systems

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Over 30 years have passed since activity-based travel demand models (ABMs) emerged to overcome the limitations of the preceding models which have dominated the field for over 50 years. Activity-based models are valuable tools for transportation planning and analysis, detailing the tour and mode-restricted nature of the household and individual travel choices. Nevertheless, no single approach has emerged as a dominant method, and research continues to improve ABM features to make them more accurate, robust, and practical. This paper describes the state of art and practice, including the ongoing ABM research covering both demand and supply considerations. Despite the substantial developments, ABM's abilities in reflecting behavioral realism are still limited. Possible solutions to address this issue include increasing the inaccuracy of the primary data, improved integrity of ABMs across days of the week, and tackling the uncertainty via integrating demand and supply. Opportunities exist to test, the feasibility of spatial transferability of ABMs to new geographical contexts along with expanding the applicability of ABMs in transportation policy-making.

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Travel-related exposure to air pollution and its socio-environmental inequalities: Evidence from a week-long GPS-based travel diary dataset

Cited by 5 publications

References 58 publications

Associations between mobility and socio-economic indicators vary across the timeline of the Covid-19 pandemic

Associations between mobility and socio-economic indicators vary across the timeline of the Covid-19 pandemic

Associations between mobility and socio-economic indicators vary across the timeline of the Covid-19 pandemic

Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability

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