Computational Urban Science,
Journal Year:
2024,
Volume and Issue:
4(1)
Published: Dec. 16, 2024
Abstract
Outdoor
jogging
is
increasingly
recognized
as
a
crucial
component
of
urban
active
transport
strategies
aimed
at
improving
public
health.
Despite
growing
research
on
the
influence
both
natural
and
built
environmental
factors
outdoor
jogging,
less
known
about
relative
importance
these
factors.
Moreover,
spatial
heterogeneity
effects
remain
unclear.
Failing
to
consider
varying
regarding
impact
intensity
scale
results
in
inefficient
planning
policies
promoting
transport.
This
study
addresses
gaps
by
analyzing
crowdsourced
trajectory
data
Shenzhen
using
computational
framework
that
combines
Random
Forest
Variable
Importance
(RF-VI)
Multi-Scale
Geographically
Weighted
Regression
(MGWR).
The
analysis
identifies
hierarchical
impacts
twelve
key
determinants
across
different
scales.
Results
reveal
are
most
contributing
while
density-related
environment
contribute
least.
Additionally,
vary
scale,
direction,
intensity,
with
seven
variables
exerting
global
five
showing
localized
effects.
Notably,
central
suburban
areas
display
considerable
influences.
findings
inform
integrating
green
infrastructure,
mitigating
over-dense
development,
enhancing
pedestrian-accessible
road
networks
promote
jogging.
These
insights
advocate
for
context-sensitive
balances
environments
foster
healthier
mobility.
ISPRS International Journal of Geo-Information,
Journal Year:
2025,
Volume and Issue:
14(2), P. 80 - 80
Published: Feb. 13, 2025
Outdoor
jogging
plays
a
critical
role
in
active
mobility
and
transport-related
physical
activity
(TPA),
contributing
to
both
urban
health
sustainability.
While
existing
studies
have
primarily
focused
on
participation
volumes
through
survey
data,
they
often
overlook
the
real-time
dynamics
that
shape
experiences.
This
study
seeks
provide
data-driven
analysis
of
volume
speed,
exploring
how
environmental
factors
influence
these
behaviors.
Utilizing
dataset
over
1000
crowd-sourced
trajectories
Shenzhen,
we
spatially
linked
road-section-level
units
map
distribution
average
speed.
By
depicting
bivariate
behavioral
characteristics,
identified
spatial
patterns
behavior,
elucidating
variations
A
random
forest
regression
model
was
validated
employed
capture
nonlinear
relationships
assess
differential
impacts
various
The
results
reveal
distinct
across
city,
where
is
shaped
by
mixed
interplay
natural,
visual,
built
environment
factors,
while
speed
influenced
visual
factors.
Additionally,
highlights
effects,
particularly
identifying
threshold
beyond
which
incremental
improvements
diminishing
returns
These
findings
clarify
roles
influencing
offering
insights
into
mobility.
Ultimately,
this
provides
data-informed
implications
for
planners
seeking
create
environments
support
TPA
promote
lifestyles.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(16), P. 3056 - 3056
Published: Aug. 20, 2024
In
the
post-pandemic
era,
outdoor
jogging
has
become
an
increasingly
popular
form
of
exercise
due
to
growing
emphasis
on
health.
It
is
essential
comprehensively
analyze
factors
influencing
spatial
distribution
activities
and
propose
planning
strategies
with
practical
guidance.
Using
multi-source
geospatial
big
data
multiple
models,
this
study
constructs
a
comprehensive
analytical
framework
examine
association
between
environmental
variables
frequency
in
Guangzhou.
Firstly,
trajectory
were
collected
from
fitness
app,
potential
selected
based
perspectives
built
environment,
street
perception,
natural
environment.
For
example,
using
street-view
imagery,
objective
elements
such
as
greenery
subjective
safety
perception
extracted
human-centric
perspective.
Secondly,
included
three
models:
backward
stepwise
regression,
optimal
parameters-based
geographical
detector,
geographically
weighted
regression
(GWR)
model.
These
models
served,
screen
significant
variables,
identify
synergistic
effects
among
quantify
heterogeneity
effects,
respectively.
Finally,
area
was
clustered
results
GWR
model
urban
clear
positions
significance.
The
indicated
following:
(1)
Factors
related
environment
significantly
influence
distribution.
(2)
Public
sports
facilities,
level
greenery,
identified
key
activities,
representing
aspects
service
(3)
Specifically,
each
factor
displayed
variation.
instance,
facilities
positively
correlated
city
center.
(4)
Lastly,
divided
into
four
clusters,
different
local
associative
characteristics
activities.
zonal
recommendations
have
implications
for
planners
policymakers
aiming
create
jogging-friendly
environments.
Transactions in GIS,
Journal Year:
2025,
Volume and Issue:
29(2)
Published: March 2, 2025
ABSTRACT
The
factor
flow
network
of
the
rural
areal
system
(RAS)
provides
an
important
perspective
for
understanding
its
internal
synergistic
development.
Traditional
synergy
assessment
is
usually
based
on
space
places,
a
concept
focusing
size
(capacity),
rather
than
flows,
connections
(flows).
In
this
study,
we
developed
multilayer
model
describing
flows
factors
such
as
people,
land,
and
industry
in
RAS
using
concept,
big
data,
complex
theory,
then
quantitatively
characterized
three
metrics
(balance,
order,
coupling)
used
to
assess
development
Pearl
River
Delta.
Our
findings
showed
that
local
urban–rural
across
boundaries
administrative
districts
generated
high‐level
RAS,
promoting
integration
areas
into
Therefore,
strengthening
through
regional
coordinated
great
significance
RAS.
Urban Science,
Journal Year:
2025,
Volume and Issue:
9(4), P. 111 - 111
Published: April 4, 2025
Growing
concerns
about
the
negative
impacts
of
high-density
built
environments
on
residents’
physical
and
mental
health
have
made
optimizing
recreational
walking
networks
in
green
spaces
a
crucial
issue
for
improving
urban
public
service
efficiency.
While
previous
studies
largely
focused
static
accessibility
measures,
these
methods
cannot
capture
actual
human
behaviors
temporal
variations
space
usage.
Our
research
introduces
novel
social
network
analysis
methodology
using
GPS
trajectory
data
from
Shanghai’s
Inner
Ring
Area
to
construct
compare
during
workdays
rest
days,
revealing
dynamic
spatiotemporal
patterns
that
traditional
miss.
Key
findings
include:
(1)
At
node
level,
different
sizes
play
differentiated
roles
network,
with
large-scale
serving
as
destination
hubs
while
pocket
function
critical
connecting
points;
(2)
regional
workday
show
more
dispersed
spatial
distribution
higher
modularity,
day
form
clusters
central
area;
(3)
overall
demonstrate
density
diversity,
reflecting
expanded
activity
range
diverse
preferences.
Green
management
should
focus
value
networks.
These
provide
theoretical
methodological
support
transitioning
“static
equity”
“dynamic
justice”
system
planning,
contributing
development
inclusive
resilient
