Supply and demand analysis of urban thermal environments regulation services from an accessibility perspective: A coupled thermal risk and green space cooling assessment model
Urban Climate,
Год журнала:
2025,
Номер
60, С. 102356 - 102356
Опубликована: Март 1, 2025
Язык: Английский
How to Consider Human Footprints to Assess Human Disturbance: Evidence from Urban Agglomeration in the Yellow River Basin
Land,
Год журнала:
2024,
Номер
13(12), С. 2163 - 2163
Опубликована: Дек. 12, 2024
Natural
processes
are
substantially
impacted
by
human
activity,
and
assessing
activity
has
significant
ramifications
for
regional
ecological
conservation.
The
study
developed
an
extended
footprint
(HF)
assessment
model
based
on
the
theory
of
effects
pressures
to
evaluate
disturbances
in
urban
agglomerations
Yellow
River
Basin
using
data
from
2005
2020,
revealing
spatiotemporal
pattern
region.
conclusions
show
that
HF
value
agglomeration
steadily
increased
primarily
driven
mining
intensity
road
construction.
High
areas
concentrated
south-central
region,
with
a
tendency
spread
outward.
Medium
mainly
distributed
eastern
part
area,
spatial
distribution
increases
year
year,
extending
outward
center
area.
moderately
low
mostly
found
mountainous
northwest.
Among
Basin,
Central
Plains
UA
Shandong
Peninsula
most
heavily
affected
disturbance.
instructive
high-quality
development
Basin.
Язык: Английский
Optimisation Model for Spatialisation of Population Based on Human Footprint Index Correction
Dongfeng Ren,
Xin Qiu,
Chun Dong
и другие.
ISPRS International Journal of Geo-Information,
Год журнала:
2024,
Номер
13(12), С. 429 - 429
Опубликована: Ноя. 29, 2024
The
availability
of
high-precision
population
distribution
data
is
crucial
for
urban
planning
and
the
optimal
allocation
resources.
To
address
limitations
random
forest
model
in
addressing
spatial
heterogeneity
during
spatialisation
potential
features
to
be
lost
or
distorted
between
scale
changes,
which
can
result
excessive
error,
this
study
proposes
an
optimised
based
on
modification
Human
Footprint
Index
(HFI).
A
hierarchical
feature
coding
method
used
reduce
cross-scale
errors.
(HFI)
was
then
constructed
by
selecting
a
total
seven
characteristic
factors
five
areas,
namely,
electricity,
land
use
intensity,
built
environment,
transport
accessibility,
level
economic
development,
corrects
predictions.
resulting
dataset
Suzhou
demonstrates
following:
(1)
R2
HFI-corrected
reaches
92.8%,
with
accuracy
92.3%
medium-density
significantly
outperforming
single
(81.6%)
WorldPop
(69.3%)
overall
accuracy;
(2)
Pearson
correlation
coefficient
0.96,
higher
than
that
(0.94)
RFPop
(0.91),
further
validating
model’s
(3)
reduces
errors,
improving
percentage
points.
Язык: Английский