Land,
Journal Year:
2024,
Volume and Issue:
13(9), P. 1496 - 1496
Published: Sept. 14, 2024
Accurately
identifying
the
expansion
characteristics
and
driving
mechanisms
at
different
development
stages
of
urban
agglomerations
is
crucial
for
their
coordinated
development.
Using
Central
Yunnan
Urban
Agglomeration
as
a
case
study,
we
employ
data
fusion
approach
to
fuse
nighttime
light
with
LandScan
utilize
U-net
neural
network
systematically
analyze
agglomeration.
The
results
indicate
that,
from
2008
2013,
was
in
an
initial
stage,
primarily
driven
by
economic
levels
population
size.
From
2013
2018,
agglomeration
entered
accelerated
mainly
industrial
structure
transformation
effect.
2018
2023,
experienced
steady
upgrading
government
support
primary
forces.
Furthermore,
found
over
time,
influence
size
forces
gradually
weakened,
while
impact
significantly
increased.
Through
multi-source
analysis
developmental
stages,
comprehensively
revealed
trajectory
provided
valuable
insights
future
planning
policymaking.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Oct. 18, 2024
High-quality
development
paths
in
important
cities
are
blurry
and
lacking.
In
order
to
explore
the
engine
for
Chengdu
high-quality
development,
driving
forces
obstacles
recognition
has
emerged
as
a
pivotal
technological
solution.
Using
Sichuan
province
of
China
research
area
quantitative
data
from
2010
2019,
this
study
used
content
mining
recognize
urban
(UHQD)
variables,
calculated
variables'
weights
by
entropy
weight
method,
explored
UHQD
technique
preference
similarity
ideal
solution
(TOPSIS)
method.
The
main
findings
are:
(1)
there
36
variables;
(2)
overall
level
soars
2017
only
with
two
negative
growth
rates
2011,
2015;
(3)
There
3
key
force
paths:
improving
green
volume
industrial
wastewater
discharged,
comprehensively
utilised
ratio
solid
wastes,
harmless
treatment
rate
domestic
garbage;
stressing
open
total
import
export/GDP,
actual
use
foreign
capital,
number
tourists/total
tourists;
intensifying
shared
funds
residents
under
basic
provision
protection.
(4)
clearing
can
also
realize
development:
innovative
R&D
internal
outlay,
patent
authorisations,
state
high-level
tech
enterprises;
optimizing
coordinated
proportion
tertiary
industry;
promoting
pension
insurance.
According
these
findings,
suggestions
put
forward
promote
perspective
policy
implementation.
Land,
Journal Year:
2024,
Volume and Issue:
13(9), P. 1496 - 1496
Published: Sept. 14, 2024
Accurately
identifying
the
expansion
characteristics
and
driving
mechanisms
at
different
development
stages
of
urban
agglomerations
is
crucial
for
their
coordinated
development.
Using
Central
Yunnan
Urban
Agglomeration
as
a
case
study,
we
employ
data
fusion
approach
to
fuse
nighttime
light
with
LandScan
utilize
U-net
neural
network
systematically
analyze
agglomeration.
The
results
indicate
that,
from
2008
2013,
was
in
an
initial
stage,
primarily
driven
by
economic
levels
population
size.
From
2013
2018,
agglomeration
entered
accelerated
mainly
industrial
structure
transformation
effect.
2018
2023,
experienced
steady
upgrading
government
support
primary
forces.
Furthermore,
found
over
time,
influence
size
forces
gradually
weakened,
while
impact
significantly
increased.
Through
multi-source
analysis
developmental
stages,
comprehensively
revealed
trajectory
provided
valuable
insights
future
planning
policymaking.
