2022 International Conference on Computers, Information Processing and Advanced Education (CIPAE),
Год журнала:
2022,
Номер
unknown, С. 426 - 431
Опубликована: Авг. 1, 2022
Sales
data
analysis
and
forecasting
is
of
great
importance.
In
the
retail
industry,
retailers'
historical
sales
can
help
retailers
with
purchasing,
planning
marketing
decisions.
real
life,
there
an
interactive
relationship
between
neighboring
retailers.
Though
large
number
studies
have
been
conducted
to
analyze
forecast
sales,
few
scholars
considered
impact
spatial
correlation
geographical
locations
on
volume.
Therefore,
it
practical
theoretical
importance
study
heterogeneity
patterns
among
this
paper,
we
propose
a
statistics-based
framework
for
retailer
sales.
First,
investigate
pattern
volume
using
given
region
then
measure
global
Moran
index.
Second,
measured
local
based
aggregation
map
LISA.
The
results
show
that
are
significant
correlations
at
same
time,
in
some
regions.
The
importance
of
small
urban
green
areas
has
increased
in
the
context
rapid
urbanization
and
densification
tissue.
analysis
these
through
remote
sensing
been
limited
due
to
low
spatial
resolution
freely
available
satellite
images.
We
propose
a
timeseries
on
3
m
Planet
images,
using
GEOBIA
vegetation
indices,
with
aim
extracting
assessing
quality
two
different
climatic
biogeographical
regions
–
temperate
(Bucharest,
Romania)
mediterranean
(Athens,
Greece).
Our
results
have
shown
high
accuracy
(over
91%)
regarding
extraction
both
cities,
across
all
analysed
showed
consistency
location
for
around
55%
identified
surfaces
throughout
entire
period.
indices
registered
higher
values
region,
characteristics
planning
cities.
For
same
reasons,
increase
density
quality,
as
result
distance
from
city
centre
decrease
built-up
is
more
obvious
Athens.
proposed
method
provides
valuable
insights
distribution
at
level
can
represent
ground
basis
many
analyses,
currently
by
poor
resolution.
2022 International Conference on Computers, Information Processing and Advanced Education (CIPAE),
Год журнала:
2022,
Номер
unknown, С. 426 - 431
Опубликована: Авг. 1, 2022
Sales
data
analysis
and
forecasting
is
of
great
importance.
In
the
retail
industry,
retailers'
historical
sales
can
help
retailers
with
purchasing,
planning
marketing
decisions.
real
life,
there
an
interactive
relationship
between
neighboring
retailers.
Though
large
number
studies
have
been
conducted
to
analyze
forecast
sales,
few
scholars
considered
impact
spatial
correlation
geographical
locations
on
volume.
Therefore,
it
practical
theoretical
importance
study
heterogeneity
patterns
among
this
paper,
we
propose
a
statistics-based
framework
for
retailer
sales.
First,
investigate
pattern
volume
using
given
region
then
measure
global
Moran
index.
Second,
measured
local
based
aggregation
map
LISA.
The
results
show
that
are
significant
correlations
at
same
time,
in
some
regions.
