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

Environmental health researchers have long used concepts like the neighborhood effect to assessing people’s exposure to environmental influences and the associated health impact. However, these are static notions that ignore people’s daily mobility at various spatial and temporal scales (e.g., daily...

Full description

Saved in:
Bibliographic Details
:
Year of Publication:2019
Language:English
Physical Description:1 electronic resource (382 p.)
Tags: Add Tag
No Tags, Be the first to tag this record!
id 993543945404498
ctrlnum (CKB)4920000000095013
(oapen)https://directory.doabooks.org/handle/20.500.12854/49631
(EXLCZ)994920000000095013
collection bib_alma
record_format marc
spelling Kwan, Mei-Po auth
Human Mobility, Spatiotemporal Context, and Environmental Health: Recent Advances in Approaches and Methods
Human Mobility, Spatiotemporal Context, and Environmental Health
MDPI - Multidisciplinary Digital Publishing Institute 2019
1 electronic resource (382 p.)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Environmental health researchers have long used concepts like the neighborhood effect to assessing people’s exposure to environmental influences and the associated health impact. However, these are static notions that ignore people’s daily mobility at various spatial and temporal scales (e.g., daily travel, migratory movements, and movements over the life course) and the influence of neighborhood contexts outside their residential neighborhoods. Recent studies 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 been developed. This Special Issue aims to showcase studies that use new approaches, methods, and data to examine the role of human mobility and non-residential contexts on human health behaviors and outcomes. It includes 21 articles that cover a wide range of topics, including individual exposure to air pollution, exposure and access to green spaces, spatial access to healthcare services, environmental influences on physical activity, food environmental and diet behavior, exposure to noise and its impact on mental health, and broader methodological issues such as the uncertain geographic context problem (UGCoP) and the neighborhood effect averaging problem (NEAP). This collection will be a valuable reference for scholars and students interested in recent advances in the concepts and methods in environmental health and health geography.
English
the elderly
regression analysis
walking event
green space
missing data
crop residue burning
correlation analysis
imputation
physical environment
crowdedness
Guangzhou
mobile phone data
GPS trace
noise pollution
mental disorders
Beijing
urban leisure
environmental exposure
environmental context cube
subway stations
air pollution exposure
long-distance walking
car ownership
multilevel model
CHAS
ecological momentary assessment
cycling for transportation
cognitive aging
3SFCA
interannual and seasonal variations
well-being experience
personal projects
spatial spread
E2SFCA
activity space
catchment areas
structural equation modeling
transport modes
greenspace exposure
health
train stations
human mobility
quantile regression
the neighborhood effect averaging problem (NEAP)
emissions estimation
taxi GPS trajectories
real-time traffic
primary healthcare
rail travel
spatial accessibility
commuting route
GPS
urban planning
environmental health
Brazil
EMA
geographical accessibility
big data
dynamic assessment
obesity
healthcare accessibility
population demand
the uncertain geographic context problem (UGCoP)
geographic impedance
collective leisure activity
multimodal network
GIS
2009 influenza A(H1N1) pandemic
UGCoP
environmental exposures
spatial data
the uncertain geographic context problem
Singapore
built environment
adults
time-weighted exposure
geographic imputation
Public Participatory GIS (PPGIS)
access probability
life-course perspectives
China
walking
active travel
foodscape exposure
car use
food environment
fuel consumption
ageing
Healthcare services
road traffic accidents
space-time kernel density estimation
multilevel Bayesian model
environmental context exposure index
spatial autocorrelation
PM concentrations
physical activity
bike paths
3-03921-183-8
language English
format eBook
author Kwan, Mei-Po
spellingShingle Kwan, Mei-Po
Human Mobility, Spatiotemporal Context, and Environmental Health: Recent Advances in Approaches and Methods
author_facet Kwan, Mei-Po
author_variant m p k mpk
author_sort Kwan, Mei-Po
title Human Mobility, Spatiotemporal Context, and Environmental Health: Recent Advances in Approaches and Methods
title_full Human Mobility, Spatiotemporal Context, and Environmental Health: Recent Advances in Approaches and Methods
title_fullStr Human Mobility, Spatiotemporal Context, and Environmental Health: Recent Advances in Approaches and Methods
title_full_unstemmed Human Mobility, Spatiotemporal Context, and Environmental Health: Recent Advances in Approaches and Methods
title_auth Human Mobility, Spatiotemporal Context, and Environmental Health: Recent Advances in Approaches and Methods
title_alt Human Mobility, Spatiotemporal Context, and Environmental Health
title_new Human Mobility, Spatiotemporal Context, and Environmental Health: Recent Advances in Approaches and Methods
title_sort human mobility, spatiotemporal context, and environmental health: recent advances in approaches and methods
publisher MDPI - Multidisciplinary Digital Publishing Institute
publishDate 2019
physical 1 electronic resource (382 p.)
isbn 3-03921-184-6
3-03921-183-8
illustrated Not Illustrated
work_keys_str_mv AT kwanmeipo humanmobilityspatiotemporalcontextandenvironmentalhealthrecentadvancesinapproachesandmethods
AT kwanmeipo humanmobilityspatiotemporalcontextandenvironmentalhealth
status_str n
ids_txt_mv (CKB)4920000000095013
(oapen)https://directory.doabooks.org/handle/20.500.12854/49631
(EXLCZ)994920000000095013
carrierType_str_mv cr
is_hierarchy_title Human Mobility, Spatiotemporal Context, and Environmental Health: Recent Advances in Approaches and Methods
_version_ 1796651477066842112
fullrecord <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>05858nam-a2201441z--4500</leader><controlfield tag="001">993543945404498</controlfield><controlfield tag="005">20231214133544.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr|mn|---annan</controlfield><controlfield tag="008">202102s2019 xx |||||o ||| 0|eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">3-03921-184-6</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(CKB)4920000000095013</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(oapen)https://directory.doabooks.org/handle/20.500.12854/49631</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EXLCZ)994920000000095013</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kwan, Mei-Po</subfield><subfield code="4">auth</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Human Mobility, Spatiotemporal Context, and Environmental Health: Recent Advances in Approaches and Methods</subfield></datafield><datafield tag="246" ind1=" " ind2=" "><subfield code="a">Human Mobility, Spatiotemporal Context, and Environmental Health</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="b">MDPI - Multidisciplinary Digital Publishing Institute</subfield><subfield code="c">2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 electronic resource (382 p.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Environmental health researchers have long used concepts like the neighborhood effect to assessing people’s exposure to environmental influences and the associated health impact. However, these are static notions that ignore people’s daily mobility at various spatial and temporal scales (e.g., daily travel, migratory movements, and movements over the life course) and the influence of neighborhood contexts outside their residential neighborhoods. Recent studies 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 been developed. This Special Issue aims to showcase studies that use new approaches, methods, and data to examine the role of human mobility and non-residential contexts on human health behaviors and outcomes. It includes 21 articles that cover a wide range of topics, including individual exposure to air pollution, exposure and access to green spaces, spatial access to healthcare services, environmental influences on physical activity, food environmental and diet behavior, exposure to noise and its impact on mental health, and broader methodological issues such as the uncertain geographic context problem (UGCoP) and the neighborhood effect averaging problem (NEAP). This collection will be a valuable reference for scholars and students interested in recent advances in the concepts and methods in environmental health and health geography.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">English</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">the elderly</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">regression analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">walking event</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">green space</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">missing data</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">crop residue burning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">correlation analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">imputation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">physical environment</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">crowdedness</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Guangzhou</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">mobile phone data</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">GPS trace</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">noise pollution</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">mental disorders</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Beijing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">urban leisure</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">environmental exposure</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">environmental context cube</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">subway stations</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">air pollution exposure</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">long-distance walking</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">car ownership</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multilevel model</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">CHAS</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">ecological momentary assessment</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cycling for transportation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cognitive aging</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">3SFCA</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">interannual and seasonal variations</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">well-being experience</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">personal projects</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">spatial spread</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">E2SFCA</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">activity space</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">catchment areas</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">structural equation modeling</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">transport modes</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">greenspace exposure</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">health</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">train stations</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">human mobility</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">quantile regression</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">the neighborhood effect averaging problem (NEAP)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">emissions estimation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">taxi GPS trajectories</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">real-time traffic</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">primary healthcare</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">rail travel</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">spatial accessibility</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">commuting route</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">GPS</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">urban planning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">environmental health</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Brazil</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">EMA</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">geographical accessibility</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">big data</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">dynamic assessment</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">obesity</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">healthcare accessibility</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">population demand</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">the uncertain geographic context problem (UGCoP)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">geographic impedance</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">collective leisure activity</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multimodal network</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">GIS</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">2009 influenza A(H1N1) pandemic</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">UGCoP</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">environmental exposures</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">spatial data</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">the uncertain geographic context problem</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Singapore</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">built environment</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">adults</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">time-weighted exposure</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">geographic imputation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Public Participatory GIS (PPGIS)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">access probability</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">life-course perspectives</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">China</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">walking</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">active travel</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">foodscape exposure</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">car use</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">food environment</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">fuel consumption</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">ageing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Healthcare services</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">road traffic accidents</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">space-time kernel density estimation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multilevel Bayesian model</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">environmental context exposure index</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">spatial autocorrelation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">PM concentrations</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">physical activity</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">bike paths</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-03921-183-8</subfield></datafield><datafield tag="906" ind1=" " ind2=" "><subfield code="a">BOOK</subfield></datafield><datafield tag="ADM" ind1=" " ind2=" "><subfield code="b">2024-03-06 05:13:36 Europe/Vienna</subfield><subfield code="f">system</subfield><subfield code="c">marc21</subfield><subfield code="a">2019-11-10 04:18:40 Europe/Vienna</subfield><subfield code="g">false</subfield></datafield><datafield tag="AVE" ind1=" " ind2=" "><subfield code="i">DOAB Directory of Open Access Books</subfield><subfield code="P">DOAB Directory of Open Access Books</subfield><subfield code="x">https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&amp;portfolio_pid=5337546220004498&amp;Force_direct=true</subfield><subfield code="Z">5337546220004498</subfield><subfield code="b">Available</subfield><subfield code="8">5337546220004498</subfield></datafield></record></collection>